energies

Article Approaches for Modelling the Physical Behavior of Technical on the Example of Wind Turbines

Ralf Stetter

Department of , Ravensburg-Weingarten University (RWU), 88250 Weingarten, Germany; [email protected]

 Received: 18 March 2020; Accepted: 16 April 2020; Published: 22 April 2020 

Abstract: Models of technical systems are an essential means in design and product-development processes. A large share of technical systems, or at least subsystems, are directly or indirectly connected with the generation or transformation of energies. In design science, elaborated modelling approaches were developed for different levels of product concretization, for instance, requirement models and models, which support innovation and new product-development processes, as well as for energy-generating or -transforming systems. However, on one product-concretization level, the abstract level that describes the physical behavior, research is less mature, and an overview of the approaches, their respective advantages, and the connection possibilities between them and other modelling forms is difficult to achieve. This paper proposes a novel discussion structure based on modelling perspectives and digital-engineering frameworks. In this structure, current approaches are described and illustrated on the basis of an example of a technical , a wind turbine. The approaches were compared, and their specific advantages were elaborated. It is a central conclusion that all perspectives could contribute to holistic product modelling. Consequently, combination and integration possibilities were discussed as well. Another contribution is the derivation of future research directions in this field; these were derived both from the identification of “white spots” and the most promising modelling approaches.

Keywords: abstract physics; behavior modelling; wind turbines; ; design engineering

1. Introduction Today, the general consensus is that the early stages of product development are essential [1–3]. Changes in advanced stages can be avoided through systematic and structured procedure schemes, and through the application of efficient methods and tools that aim at raising the knowledge of the system at the beginning of development to a higher level [4]. However, a widely accepted methodology with computational support for these stages, which is applied in industrial practice, does not yet exist [1]. Consequently, design and control engineers are challenged with undocumented considerations and decisions, incompatible modelling languages, unclear and incomplete interfaces, redundant work, and non-value-adding data-transformation activities. This leads, on the one hand, to product-development processes that lack efficiency, and, on the other hand, to suboptimal solutions for technical systems to be developed. Today, the early stages of requirement management [5,6] and function modelling [7,8] are well-researched, but this research that is concerning the modelling possibilities on an intermediate level is much less mature, which links these two description modes to concrete product geometry, material, and structure. A general need for a description of abstract physics was identified in 1965 by Atkin [9]. Research presented in this paper is based on the hypothesis that an efficient and effective approach for modelling the physical behavior of technical

Energies 2020, 13, 2087; doi:10.3390/en13082087 www.mdpi.com/journal/energies Energies 2020, 13, 2087 2 of 27 systems on an abstract level could help engineers to synthesize innovative, reliable, and safe solutions. The respective research question is formulated in the next subsection.

1.1. Research-Question Development The general consensus in design science is that the description levels of function, behavior, and structure can be distinguished [10]. As stated above, powerful methods and tools are available today that allow a holistic digital description on the functional [7] and structural level. However, on the intermediate level of behavior, no holistic digital approach has arrived in industrial practice. It is very important for engineers to be familiar with the behavior and physical cause–effect thinking, and to know properties and possible applications of physical phenomena since the central functions of most technical systems are physically realized [11]. During the last few decades, several models, methods, and tools were proposed that address this important level. Some of these concentrate on the rather qualitative exploration and synthesis of chains of physical phenomena, others on quantitative simulations of behavior (analytical, logical, statistical, and numerical), and others on interfaces between function carriers. Relatively few research activities are concentrated on a combination of the perspectives and the integration of industrial companies in digital processes, and the results have not yet been applied in industrial practice. This leads to the central research question of this paper: what the current status is of modelling approaches and engineering frameworks that allow to capture physical-phenomenon chains, behavior simulation, control design, and interfaces between function carriers, as well as to connect those to product and process structures, and to integrate them in digital product-development processes.

1.2. Scientfic Contribution The unique contribution of this paper from a scientific point of view is the collection, evaluation, and comparison of ontologies and modelling techniques that enable improved and integrated analysis, and synthesis on the layer that describes the abstract physical realization of functions of a technical system. All modelling perspectives and engineering frameworks are explained on the basis of a common example, a wind turbine. The aim was not the development of a conclusive answer on in which manner with which models the abstract physical behavior of technical systems should be modelled for enabling efficient and effective system-development processes, and efficient, effective, safe, and reliable production and operation of these technical systems. The unique contribution is in-depth analysis of languages, modelling approaches, and frameworks, and identification of their specific merits and limitations. This analysis led to an overview of existing approaches, and resulted in a summary of perspectives and important aspects. Analysis results explain integration necessities and approaches, and consequently present a structure and promising areas for future research.

1.3. Paper Structure The paper is structured as follows: Section2 provides a background on abstract physics. Section3 concentrates on different modelling perspectives on this level of product concretization. The integration of these perspectives in an engineering framework are the topic of Section4. Section5 formulates desirable future research activities and concludes the paper.

2. Abstract Physics—Background For many decades, design science has developed abstract product models and a distinction of product models on the basis of their level of abstraction (compare with Ehrlenspiel and Meerkamm [11], Lindemann [12], Pahl et al. [13], Ponn and Lindemann [14]); today, a four-level distinction is generally agreed upon (sometimes the most abstract level, requirements, is not explicitly included, but its existence is not challenged). The four-level distinction is illustrated in Figure1. Energies 2020, 13, 2087 3 of 27

Energies 2020, 13, x FOR PEER REVIEW 3 of 28

Requirement level: • characteristics to be achieved

Functional level: abstract • functions realize functional requirements

Abstract physical level: • which physical phenomena realize the functions? • how and to what extend are physical phenomena acting?

(Macro- and micro-) geometry, material, concrete concrete product structure 88 89 FigureFigure 1. Four-level 1. Four-level distinction distinction of of product product concretization concretization..

90 The requirements, i.e., the characteristics of the product needed to fulfil the demands of all The91 requirements,stakeholders (most i.e., prominently the characteristics, the customers) of form the the product top level. needed It is often toemphasized fulfil the that demands an of all stakeholders92 occupation (most prominently,with requirements the is inevitable customers) throughout form any the product top level.-development It is process often emphasized[14]; a that an occupation93 thorough with clarification requirements and negotiation is inevitable of the requirements throughout in anythe early product-development stages is one of the mainprocess [14]; 94 cornerstones of product-development success [3]. More concrete is the functional level, because it is a thorough clarification and negotiation of the requirements in the early stages is one of the main 95 analyzed and synthesized on this level of which functions in what structure are able to realize the cornerstones96 functional of product-development requirements. Both the requirement success [level3]. and More the function concrete level is were the integrated functional in the level, last because it is analyzed97 few and years synthesized in digital-engineering on this frameworks level of [ which6,8]. The actual functions (macro- in and what micro- structure) geometry, material are able, to realize 98 and product structure are described on the most concrete level. Additionally, concrete control the functional requirements. Both the requirement level and the function level were integrated in 99 concepts, fault-detection systems, reliability, and safety considerations primarily concern this level. the last100 few The years gap inbetween digital-engineering this very concrete level frameworks and the rather [ 6abstract,8]. The functional actual level (macro- can be filled and with micro-) an geometry, material,101 andintermediate product structurelevel that consists are described of physical onphenomena the most or effects concrete and their level. combinations Additionally, that realize concrete control concepts,102 fault-detectionthe functions; the network systems, of these reliability, physical phenomena and safety leads considerations to the physical behavior primarily of the technical concern this level. 103 system. Another aspect, especially in the field of process and chemical engineering, is chemical effects The gap104 betweenthat can thisalso be very essential concrete for technical level systems and the (especially rather matter abstract-oriented functional technical systems level) canbut are be filled with an intermediate105 not in level the focus that of consists this paper of. Analysis physical of the phenomena current literature or e ledffects to the and conclusion their combinations that increasing that realize the functions;106 scientific the network interest can of analyze, these physical reflect, understand phenomena, and support leads toanalysis the physicaland synthesis behavior of physical of- the technical 107 effect chains and networks that may realize required functions in the domain of physics and physical system.108 Another behavior. aspect, This analysis especially also leads in the to the field insight of processthat more andthan chemicalone perspective engineering, is required in is order chemical effects that can109 also to be allow essential a holisticfor model technical of abstract systems physics. Figure (especially 2 illustrates matter-oriented the possible perspectives. technical systems) but are not in the focus of this paper. Analysis of the current literature led to the conclusion that increasing scientific interest can analyze, reflect, understand, and support analysis and synthesis of physical-effect chains and networks that may realize required functions in the domain of physics and physical behavior. This analysis also leads to the insight that more than one perspective is required in order to allow a holistic model of abstract physics. Figure2 illustrates the possible perspectives. Energies 2020, 13, x FOR PEER REVIEW 4 of 28

phenomenon oriented requirements physical effects, effect chains, effect networks

functions control oriented perspectives logic oriented actor – plant – sensor; of reliability state space abstract and safety representation analyses abstract physics physics behavior oriented geometry and material; linear and non-linear equations, interface oriented concrete product differential equations, numerical contact and connector approach structure behavior models (C&C2 – Albers et al. 2014) 110

111 FigureFigure 2. Abstract-physics 2. Abstract-physics perspectives perspectives..

112 In the literature, five different perspectives of abstract physics can be identified: a phenomenon- In113 the oriented literature, perspective five that di containsfferent physical perspectives effects, chains, of and abstract networks physics; a behavior can-oriented be identified: a phenomenon-oriented114 perspective that contains perspective linear and that nonlinear contains equations, physical differential e ffequationsects,, chains, and numerical and networks; a behavior-oriented115 behavior models perspective; an interface that-oriented contains perspective linear that and describes nonlinear physical equations, contacts and di connectorsfferential; equations, 116 a logic-oriented perspective, primarily used for reliability and safety analyses; and a control-oriented 117 perspective, employing actor–plant sensor models and state-space representations. An explanation 118 and discussion of abstract-physical-modelling approaches that are based on these perspectives is the 119 content of the next section.

120 3. Modelling Perspectives of Abstract Physics 121 For analysis and synthesis of abstract physics, five perspectives can be found in the literature 122 and clearly distinguished: phenomenon-, a behavior-, logic-, control-, and interface-oriented 123 perspectives.

124 3.1. Phenomenon-Oriented Perspective 125 Several years ago, design science proposed the consideration of abstract physics in the form of 126 the physical effects (elementary demarcated physical phenomena) and effect chains or networks that 127 are composed of the elementary physical phenomena. In this context, physical effects are understood 128 as elementary definable physical phenomena that can be described on the basis of conservation laws 129 (mass, energy, linear momentum, and angular momentum-conservation law) and equilibrium laws 130 (force and moment equilibrium) by means of their relationships with each other [11]. The occupation 131 with physical effects is recommended for fostering a better and deeper understanding of technical 132 systems and for developing innovative solutions that use different physical principles [11]. Engineers 133 need to be familiar with the main physical cause-and-effect chains of the product under development. 134 Engineers should know the application possibilities of physical effects, and their limitations. An 135 additional positive effect of this occupation with the physical background is that a solution search 136 with physical effects can dissolve mental blocks [11]. Stetter and Niedermeier [15] analyzed 137 innovative breakthrough products and concluded that such products in most cases rely on different 138 physical principles. In the same direction, Wulf [16] formulated the insight that a discursive, 139 hierarchical search for solutions that includes the level functions and physical structure probably 140 leads to good design. 141 Analysis and synthesis of physical structures using physical effects can be explained using the 142 example of a wind turbine. A very important subfunction is “change speed”, which means to increase 143 the angular velocity from the rotor shaft to the generator because generators with a higher angular 144 velocity can be smaller and more efficient. Two possible physical effects to realize this subfunction 145 are shown in the lower part of Figure 3.

Energies 2020, 13, 2087 4 of 27 and numerical behavior models; an interface-oriented perspective that describes physical contacts and connectors; a logic-oriented perspective, primarily used for reliability and safety analyses; and a control-oriented perspective, employing actor–plant sensor models and state-space representations. An explanation and discussion of abstract-physical-modelling approaches that are based on these perspectives is the content of the next section.

3. Modelling Perspectives of Abstract Physics For analysis and synthesis of abstract physics, five perspectives can be found in the literature and clearly distinguished: phenomenon-, a behavior-, logic-, control-, and interface-oriented perspectives.

3.1. Phenomenon-Oriented Perspective Several years ago, design science proposed the consideration of abstract physics in the form of the physical effects (elementary demarcated physical phenomena) and effect chains or networks that are composed of the elementary physical phenomena. In this context, physical effects are understood as elementary definable physical phenomena that can be described on the basis of conservation laws (mass, energy, linear momentum, and angular momentum-conservation law) and equilibrium laws (force and moment equilibrium) by means of their relationships with each other [11]. The occupation with physical effects is recommended for fostering a better and deeper understanding of technical systems and for developing innovative solutions that use different physical principles [11]. Engineers need to be familiar with the main physical cause-and-effect chains of the product under development. Engineers should know the application possibilities of physical effects, and their limitations. An additional positive effect of this occupation with the physical background is that a solution search with physical effects can dissolve mental blocks [11]. Stetter and Niedermeier [15] analyzed innovative breakthrough products and concluded that such products in most cases rely on different physical principles. In the same direction, Wulf [16] formulated the insight that a discursive, hierarchical search for solutions that includes the level functions and physical structure probably leads to good design. Analysis and synthesis of physical structures using physical effects can be explained using the example of a wind turbine. A very important subfunction is “change speed”, which means to increase the angular velocity from the rotor shaft to the generator because generators with a higher angular velocity can be smaller and more efficient. Two possible physical effects to realize this subfunction are shown in the lower part of Figure3. Energies 2020, 13, x FOR PEER REVIEW 5 of 28

wind transform change transform electrical energy energy speed energy energy

possible solutions with physical effects law of the lever hydrostatic pressure

A2 F, v A1 s F 2 M, ω 1 F r 2 s1 possible possible realization: realization: hydraulic pump and pair of spur gears hydraulic motor 146

Figure147 3. FunctionFigure 3. Function structure structure of wind of wind turbine turbine and and possible physical physical effects e forffects function for function“change speed” “change. speed”.

148 Both of the presented physical effects can lead to an increase of speed. Despite the fact that nearly Both of the presented physical effects can lead to an increase of speed. Despite the fact that nearly 149 all wind turbines rely on the law of the lever with spur or planetary gears, solutions with hydraulic all wind150 turbinespumps and rely motors on the or hybrid law oftransmissions the lever are with also spurbeing investigated or planetary [17,18 gears,]. Obviously solutions, the choice with hydraulic 151 of the optimal physical effect and the synthesis of the physical structure strongly depend on the 152 specific requirements of the given application case. 153 Nearly a decade ago, analysis of physical effects was expanded in order to include uncertainties 154 in the form of disturbances [19]. It is possible to define robustness ratios of the physical effects on the 155 basis of known disturbances, and one may evaluate and compare different solution possibilities of a 156 subfunction concerning their respective robustness. In the area of computational design synthesis 157 (CDS), Helms [20] realized an integration of phenomenon-oriented abstract physics in the form of 158 physical effects by creating a method to automate the transfer of formerly paper-based engineering 159 knowledge about physical effects into a computable representation. 160 Lang [4] developed the idea to reduce technical systems to such an extent that physical effects 161 were as isolated as possible and could be empirically analyzed under clearly defined conditions. He 162 found that analysis and understanding of physical effects can be a decisive factor for principal 163 investigations. According to Lang [4], these investigations can be empirical analyses in the early 164 phases of a product-development process that are intended to quantitatively describe physical 165 phenomena. It is a central characteristic of such principal investigations that real processes are 166 reduced to isolated physical principles or effects. On the same level of abstraction is the contribution 167 of Wagner [21], who described a synthesis process including physical effects for the realization of 168 function integration in technical systems. 169 For all complex technical systems, more than one physical effect is needed, and the physical 170 effects need to be combined to physical-effect chains or networks (a network, in contrast to a chain, 171 allows, among others, feedback loops that are possible in physical systems). A physical-effect chain 172 describing the blade pitch-angle-adjustment system of a wind turbine is shown in Figure 4 173 (phenomenon-oriented perspective).

Energies 2020, 13, 2087 5 of 27 pumps and motors or hybrid transmissions are also being investigated [17,18]. Obviously, the choice of the optimal physical effect and the synthesis of the physical structure strongly depend on the specific requirements of the given application case. Nearly a decade ago, analysis of physical effects was expanded in order to include uncertainties in the form of disturbances [19]. It is possible to define robustness ratios of the physical effects on the basis of known disturbances, and one may evaluate and compare different solution possibilities of a subfunction concerning their respective robustness. In the area of computational design synthesis (CDS), Helms [20] realized an integration of phenomenon-oriented abstract physics in the form of physical effects by creating a method to automate the transfer of formerly paper-based engineering knowledge about physical effects into a computable representation. Lang [4] developed the idea to reduce technical systems to such an extent that physical effects were as isolated as possible and could be empirically analyzed under clearly defined conditions. He found that analysis and understanding of physical effects can be a decisive factor for principal investigations. According to Lang [4], these investigations can be empirical analyses in the early phases of a product-development process that are intended to quantitatively describe physical phenomena. It is a central characteristic of such principal investigations that real processes are reduced to isolated physical principles or effects. On the same level of abstraction is the contribution of Wagner [21], who described a synthesis process including physical effects for the realization of function integration in technical systems. For all complex technical systems, more than one physical effect is needed, and the physical effects need to be combined to physical-effect chains or networks (a network, in contrast to a chain, allows, among others, feedback loops that are possible in physical systems). A physical-effect chain describing the blade pitch-angle-adjustment system of a wind turbine is shown in Figure4

(phenomenon-orientedEnergies 2020, 13, x FOR perspective). PEER REVIEW 6 of 28

rotation of pitch change blades demand rotation force at contact expressed as of line of spur electrical shaft gears energy on induction, lever, lever, motor Biot-Savart- CRB* CRB* law

(*) CRB: cohesion of rigid bodies 174 175 Figure 4.FigurePhysical-e 4. Physicalffect-effect chain chain forfor electrical electrical-blade-blade pitch- pitch-angleangle adjustment adjustment.. 176 Input is an intended change of the blade pitch angle that is expressed in the form of electrical Input177 isenergy an intendedon the blade change pitch-adjustment of the blademotor. The pitch physical angle effects that of isinduction expressed and the in Biot the–Savart form of electrical energy178 on thelaw bladein this motor pitch-adjustment lead to the rotation motor. of a short The shaft physical that holds e aff smallects ofspur induction gear. The physical and the effects Biot–Savart law in this179 motor of leadthe lever to theand rotationthe cohesion of of a shortrigid bodies shaft (only that one holds rigid a body small can spur be at gear.a certain The position physical at a effects of the 180 certain time, and two rigid bodies do not permeate each other) lead to contact force at the contact line lever181 and thewith cohesion a second, ofmuch rigid larger bodies spur (onlygear. This one spur rigid gear body is connected can be atto athe certain blades and position leads to at a a certain time, and two182 rigidrotational bodies movement do not of permeate the blades eacharound other) the longitudinal lead to contactaxis—a change force of at the the pitch contact angle. Again line with, a second, much183 larger the spur physical gear. effects This of spur the lever gear and is connectedthe cohesion toof rigid the bladesbodies are and employed. leads to Such a rotational chains and movement of 184 networks can be transformed into an initial description of the behavioral perspective (Figure 5), the blades185 aroundwhich includes the longitudinal a mathematical axis—a description change, and ofmay the be pitch used for angle. simulation Again, and the optimization physical effects of the lever186 and thepurposes. cohesion of rigid bodies are employed. Such chains and networks can be transformed into an initial description of the behavioral perspective (Figure5), which includes a mathematical description, andEelectrical may be used for simulation and optimization purposes.

induction, Biot-Savart- M = (η ∙ U ∙ I)/ω ** law shaft

lever, CRB* Fcontact = Mshaft / llever 1 **

(*) CRB: cohesion of rigid lever, bodies CRB* Mblade = Fcontact ∙ llever 2** (**) simplified, approximate 187 calculation 188 Figure 5. Physical-effect chain with simplified mathematical description.

189 This kind of model leads to the next section, which discusses the behavior-oriented perspective. 190 To conclude, modelling approaches that are based on the phenomenon-oriented perspective are 191 demarcated physical phenomena that are combined to affect chains or networks. The main advantage 192 of their application is supporting a deeper understanding of the technical system, and the potential for 193 breakthrough innovation if the main physical effects of a technical system are altered.

194 3.2. Behavior-Oriented Perspective 195 Abstract physical behavior is also the main emphasis of the intermediate level of function– 196 behavior–structure (FBS) ontology [10], which is essentially a design ontology that can describe 197 designed artefacts. FBS consists of three fundamental constructs, function (F), behavior (B), and 198 structure (S). The construct function represents the objective of the artefact (for what the artefact is). The 199 construct behavior describes the attributes of the artefact, which are either derived from its structure Energies 2020, 13, x FOR PEER REVIEW 6 of 28

rotation of pitch change blades demand rotation force at contact expressed as of line of spur electrical shaft gears energy on induction, lever, lever, motor Biot-Savart- CRB* CRB* law

(*) CRB: cohesion of rigid bodies 174 175 Figure 4. Physical-effect chain for electrical-blade pitch-angle adjustment.

176 Input is an intended change of the blade pitch angle that is expressed in the form of electrical 177 energy on the blade pitch-adjustment motor. The physical effects of induction and the Biot–Savart 178 law in this motor lead to the rotation of a short shaft that holds a small spur gear. The physical effects 179 of the lever and the cohesion of rigid bodies (only one rigid body can be at a certain position at a 180 certain time, and two rigid bodies do not permeate each other) lead to contact force at the contact line 181 with a second, much larger spur gear. This spur gear is connected to the blades and leads to a 182 rotational movement of the blades around the longitudinal axis—a change of the pitch angle. Again, 183 the physical effects of the lever and the cohesion of rigid bodies are employed. Such chains and Energies1842020 , 13network, 2087 s can be transformed into an initial description of the behavioral perspective (Figure 5), 6 of 27 185 which includes a mathematical description, and may be used for simulation and optimization 186 purposes.

Eelectrical

induction, Biot-Savart- M = (η ∙ U ∙ I)/ω ** law shaft

lever, CRB* Fcontact = Mshaft / llever 1 **

(*) CRB: cohesion of rigid lever, bodies CRB* Mblade = Fcontact ∙ llever 2** (**) simplified, approximate 187 calculation 188 Figure 5. Physical-effect chain with simplified mathematical description. Figure 5. Physical-effect chain with simplified mathematical description. 189 This kind of model leads to the next section, which discusses the behavior-oriented perspective. This190 kindTo conclude, of model mode leadslling toapproaches the next that section, are based which on the discusses phenomenon the-oriented behavior-oriented perspective are perspective. To conclude,191 demarcated modelling physical approaches phenomena that that are arecombined based to a onffectthe chains phenomenon-oriented or networks. The main advantage perspective are 192 of their application is supporting a deeper understanding of the technical system, and the potential for demarcated193 breakthrough physical phenomena innovation if the that main are physical combined effects of to a atechnicalffect chains system orare networks.altered. The main advantage of their application is supporting a deeper understanding of the technical system, and the potential for 194 3.2. Behavior-Oriented Perspective breakthrough innovation if the main physical effects of a technical system are altered. 195 Abstract physical behavior is also the main emphasis of the intermediate level of function– 3.2. Behavior-Oriented196 behavior–structure Perspective (FBS) ontology [10], which is essentially a design ontology that can describe 197 designed artefacts. FBS consists of three fundamental constructs, function (F), behavior (B), and Abstract198 structure physical (S). The construct behavior function is represents also the the objective main of emphasis the artefact (for of what the the artefact intermediate is). The level of 199 construct behavior describes the attributes of the artefact, which are either derived from its structure function–behavior–structure (FBS) ontology [10], which is essentially a design ontology that can describe designed artefacts. FBS consists of three fundamental constructs, function (F), behavior (B), and structure (S). The construct function represents the objective of the artefact (for what the artefact is). The construct behavior describes the attributes of the artefact, which are either derived from its structure (what the artefact does) or expected behavior (what the artefact should do). The definition of this construct in the literature does not include notions such as operation, process, or physical effects; the explanations and examples clearly indicate that this kind of concept could also be appropriate to describe phenomena on this abstraction level. The last construct structure stands for the components of a technical system and their relationships (of what the artefact consists); this construct clearly corresponds to the concrete realization of the technical system. Generally, the intended level of abstraction can be understood as a level describing the physical behavior of a technical system, i.e., its intended or actual performance for realizing certain functions. Many physical phenomena can be described by means of analytic equations (linear and nonlinear equations); however, abstraction and simplification from actual physical behavior are usually necessary. For instance, the circumferential speed of the rotor of a wind turbine can be derived from the number of revolutions in a certain time interval using the following equation.

vc = 2 π r nr (1) · · · where vc stands for circumferential speed, r for rotor radius, and nr for the number of revolutions in a certain time interval. In an engineering context, frequently differential or partial differential equations are used, such as the governing differential equation of motion for the transverse vibration of the tower of a wind turbine [22,23]. ! ∂2η ∂2 ∂2η ρA = EI + f (y, t), (2) ∂t2 ∂y2 ∂y2 where ρ stands for air density, A for cross-sectional area of the tower, η is the transverse displacement coordinate, y the vertical coordinate measured from the tower base, E the Young’s modulus of the tower, I the second moment of area of the tower, and f is a function describing the wave and wind load. In current engineering practice, numerical modelling and analysis techniques are very often employed. Energies 2020, 13, 2087 7 of 27

Current examples concern the simulation of wind turbines with hydraulic transmission systems [24], of wind-turbine-blade vibration [25], of the drive train of a wind turbine [26], and of the aeroelastic behavior of wind-turbine blades [27]. In this research area, Chu et al. [28] performed comparative analysis of appropriate identification methods concerning the mechanical dynamics of wind turbines. In the area of research concerning the planning and operation of power systems, Li et al. [29] proposed a planning model and a new objective function for maximizing the accommodation of renewable energy, and provided an extensive literature review in this research area. Needless to say, due to the enormous importance of wind turbines in the scope of renewable-energy generation, numerous further research activities could be identified. Besides the large number of research directions, powerful possibilities for behavior modelling already exist; prominent examples are finite-element analysis (FEA) and analysis of multibody systems (MBS). Usually, these possibilities require concrete geometrical and physical parameters. Topology optimization systems [30] also incorporate certain kinds of physical-behavior modelling to synthesize optimized structures. Important examples for behavior modelling are also or MATLAB/ (The MathWorks, Natick, Massachusetts, MA, USA); several commercial products enable behavioral descriptions of technical systems that are based on Modelica (e.g., (Dassault Systèmes, Vélizy-Villacoublay, France), (Siemens PLM Software, Plano, Texas, USA, MapleSim (Maplesoft, Waterloo, Ontario, Canada), Wolfram SystemModeler (Wolfram Research, Champaign, Illinois, USA), Modelon (Modelon AB, Lund, Sweden) and SimulationX (ESI ITI GmbH, Dresden, Germany). These tools allow the development of complex simulation models (compare with, e.g., Fritzson [31]). Despite their convincing performance and versatility, these simulation systems do not provide holistic links to the requirements and functions of a technical system, and require concrete geometrical and physical parameters. An advanced solution for the integration between graph-based languages and behavior modelling in the form of continuous mechanics was proposed by Vogel [32]; main emphasis was also on concrete geometry and advanced analyses of this geometry. An initial modelling approach for effect chains on the behavior level was proposed by Chamas and Paetzold [1]. In the field of computational-design synthesis (CDS), approaches to connect requirements, functions, structural parameters, and behavioral-performance evaluation have already been proposed. These approaches are based on bond-graph-based simulation models for performance evaluations on the behavioral level [33]; they are discussed in detail in Section 4.2. We can be concluded that a behavior-oriented perspective on abstract physics that allows analytical and numerical simulation and optimization plays an enormous role in research and industrial practices. This perspective can also be connected to the early approach towards describing abstract physics by Atkin [9].

3.3. Interface-Oriented Perspective On a similar level of abstraction, the research group of Albers proposed the contact and connector approach (C&C2–A). This approach allows analysis of interfaces and physical structures within technical systems [34,35]. An example, for the ball contact in the main bearing of a wind turbine, is shown in Figure6. Energies 2020, 13, x FOR PEER REVIEW 8 of 28

246 In the field of computational-design synthesis (CDS), approaches to connect requirements, 247 functions, structural parameters, and behavioral-performance evaluation have already been 248 proposed. These approaches are based on bond-graph-based simulation models for performance 249 evaluations on the behavioral level [33]; they are discussed in detail in Section 4.2. 250 We can be concluded that a behavior-oriented perspective on abstract physics that allows 251 analytical and numerical simulation and optimization plays an enormous role in research and 252 industrial practices. This perspective can also be connected to the early approach towards describing 253 abstract physics by Atkin [9].

254 3.3. Interface-Oriented Perspective 255 On a similar level of abstraction, the research group of Albers proposed the contact and 256 connector approach (C&C²–A). This approach allows analysis of interfaces and physical structures Energies 2020, 13, 2087 8 of 27 257 within technical systems [34,35]. An example, for the ball contact in the main bearing of a wind 258 turbine, is shown in Figure 6.

function: transfer load Connector C1 WSP 1 CSS WSP2 Connector C2 {bearing ball} {ball/oil} {oil} {oil/ring} {bearing ring} External Force Force Force External effects: transfer transmission transfer effects: Energy • Mechanical Internal Internal Internal • Mechanical • Electrical properties: properties: properties: • Electrical • Thermal • Size, • Volume, • Size, • Thermal • Chemical • Shape, • Position, • Shape, • Chemical • Tolerance, • Strength, • Tolerance, • Friction, • Stiffness, • Friction, bearing • … • … • … ball C1 WSP1 oil CSS WSP: Working Surface Pair WSP2 C2 CSS: Channel and Support Structures bearing ring 259 Figure260 6. ExampleFigure 6. for Example contact for c andontact connector and connector (C&C (C&C22)) model model based based on ball on contact ball contactof main bearing of main. bearing.

In261 C&C 2 models,In C&C² models, the eff ectthe effect network network is is composed composed of of the the following following fundamental fundamental modelling modelling 262 elements [34]: elements263[ 34]:• working surface pairs (WSP) that stand for interfaces between physical structures; 264 • channel and support structures (CSS) that permanently or occasionally represent interacting working265 surfacephysical pairs structures (WSP) of solid that bodies, stand liquids, for interfaces gases, or fields between; and physical structures; • channel266 • andconnectors support (C) structures, which model (CSS) elements that that permanently denote the effect oroccasionally and state properties represent of the interacting • 267 environment that is relevant for the function of a system. physical268 structuresFigure 6 depicts of solid force bodies, flow (described liquids, as energy gases, because or fields;, in many and flow-oriented function models, connectors269 only (C), matter, which energy model, and information elements flows that are denote distinguished) the effect from and the state bearing properties ball through of a the layer environment • that270 is relevantof oil or grease for the to the function bearing ring. of a For system. all entities in this chain, internal properties can be listed, as 271 well as external effects for the connectors to the environment. In this approach, an effect network is Figure272 6 describeddepicts forcethat stores, flow transforms (described, and asexchanges energy inputs because, and outputs in many that flow-orientedmay be energy, materia functionl, models, 273 and information flows. However, the inclusion and description of demarcated physical phenomena only matter,274 are energy, not explicitly and information addressed. The flowsdistinctive are advantage distinguished) of this perspective from the is that bearing it allows ball detailed through a layer of oil or275 grease analyses to the of effect bearing surfaces ring. and Forinteractions all entities that are innot this included chain, in the internal other perspectives. properties can be listed, as well as external effects for the connectors to the environment. In this approach, an effect network is 276 3.4. Logic-Oriented Perspective described that stores, transforms, and exchanges inputs and outputs that may be energy, material, and information flows. However, the inclusion and description of demarcated physical phenomena are not explicitly addressed. The distinctive advantage of this perspective is that it allows detailed analyses of effect surfaces and interactions that are not included in the other perspectives.

3.4. Logic-Oriented Perspective Due to the complexity of modern technical systems, it is a major challenge to ensure their reliability. In order to estimate the reliability of a complex system, an accurate reliability model of the system needs to be developed [36]. Gausemeier et al. [37] emphasized that the information provided by the abstract physical solution is useful for supporting procedures for analyzing the reliability of systems to be developed. Complex technical systems require reliability-analysis methods and tools that can be applied in the early engineering phase of conceptual design in order to enable the detection of failures at an early stage, and to avoid time-consuming and costly iteration loops [38]. Models used in safety analysis (compare with [39,40]) are, in general, similar to the ones applied for reliability analysis. Very often, the models for these two areas describe logical relationships because nearly all systems dispose of redundant elements, of which the influence on safety and reliability needs to be explored in a logical manner. The reliability of wind turbines is a major issue because maintenance and replacement activities are time-consuming and can even be dangerous. The logical-modelling perspective is explained using a rather simple system, a redundant wind sensor. The common modelling approach is a logical fault tree that represents a superordinate system with its subsystems. This tree can connect the fault possibilities of the subsystems or components to the fault possibility of the superordinate system. A fault tree with main connection elements AND and OR is shown in Figure7 (left). Energies 2020, 13, x FOR PEER REVIEW 9 of 28

277 Due to the complexity of modern technical systems, it is a major challenge to ensure their 278 reliability. In order to estimate the reliability of a complex system, an accurate reliability model of the 279 system needs to be developed [36]. Gausemeier et al. [37] emphasized that the information provided 280 by the abstract physical solution is useful for supporting procedures for analyzing the reliability of 281 systems to be developed. Complex technical systems require reliability-analysis methods and tools 282 that can be applied in the early engineering phase of conceptual design in order to enable the 283 detection of failures at an early stage, and to avoid time-consuming and costly iteration loops [38]. 284 Models used in safety analysis (compare with [39,40]) are, in general, similar to the ones applied for 285 reliability analysis. Very often, the models for these two areas describe logical relationships because 286 nearly all systems dispose of redundant elements, of which the influence on safety and reliability 287 needs to be explored in a logical manner. The reliability of wind turbines is a major issue because 288 maintenance and replacement activities are time-consuming and can even be dangerous. The logical- 289 modelling perspective is explained using a rather simple system, a redundant wind sensor. The 290 common modelling approach is a logical fault tree that represents a superordinate system with its Energies2912020 , 13subsystems, 2087 . This tree can connect the fault possibilities of the subsystems or components to the fault 9 of 27 292 possibility of the superordinate system. A fault tree with main connection elements AND and OR is 293 shown in Figure 7 (left).

Partial fault tree of Minimum cut sets a wind turbine representation

System Failure System Failure

OR AND

OR OR AND AND AND AND

SEN: Wind sensor; SEN TRA SEN TRA SEN SEN TRA SEN SEN TRA TRA TRA TRA: Transmission 1 1 2 2 1 2 1 2 1 2 1 2 294 295 Figure 7. ExampleFigure 7. Example of partial of partial fault fault tree tree of of wind wind turbine turbine and and a minim a minimalal cut-set representation cut-set representation..

296 In the literature, numerous possibilities for the generation and analysis of fault trees can be In297 the literature,found [36]. An numerous initial analysis possibilities step is the generation for the of a generationminimal cut set and, shown analysis in Figure 7 of (middle). fault trees can be found298 [36 ]. AnHere, initial the fault analysis tree was converted step is the into generationminimal-cut-set of representation. a minimal cutCut sets set, can shown be understood in Figure as 7 (middle). Here,299 the faultunique tree combinations was converted of subsystem into minimal-cut-set or component failures representation. that can cause Cut the sets failure can of be the understood as 300 superordinate system [41]. A minimal cut set is achieved if, when any basic event is removed from unique combinations of subsystem or component failures that can cause the failure of the superordinate 301 the set, the remaining events are collectively no longer a cut set [42]. The scope of the given example Commented [M1]: The reference order is wrong. Reference system302 [41 ].is the A minimalwind sensors cut (SEN) set themselves is achieved and all if, elements when that any are necessary basic event for transmission is removed (TRA) from from the set, the 43 is missing. remaining303 eventsthe sensor are to collectivelythe central control no unit longer that control a cuts setthe angle [42]. of The the wind scope turbine. of the For given correct exampleoperation is the wind 304 of the superordinate system, that is, the wind-information subsystem of the wind turbine, it is Commented [RS2R1]: Corrected – the citation “Bruch C:” sensors (SEN) themselves and all elements that are necessary for transmission (TRA) from the sensor 305 required that at least one subsystem (wind sensor with its transmission system) is working properly, was missing” and needed to be inserted after 45 to the306 central and control that, within unit this that system controls, both wind the sensor angle and of transmission the wind are turbine. functional. For Such correct interaction operations of of the superordinate307 system system, element thats can is,be sho thewn wind-information in a logical fault tree and subsystem then analyzed of in the multiple wind ways turbine,, e.g., for it is required 308 reliability modelling [43] and redundancy analysis [44,45]. that at least one subsystem (wind sensor with its transmission system) is working properly, and that, 309 A similar intention is followed by research aiming at exploring undesired functions of principle within310 this system,solutions, which both may wind be fault sensor causes and [46]. transmissionCurrent research concerns are functional. the stochastic Such [45] or interactions data-driven of system elements311 can[36 be] generation shown inof reliability a logical models fault because tree and current then systems analyzed include in hardware multiple and ways, software e.g., with for reliability 312 complex interactions that are becoming increasingly difficult to model. Further research activities modelling [43] and redundancy analysis [44,45]. 313 explore the reliability of interdependent networks with cascading failures [47], load-sharing failure- A314 similar based intention reliability isanalysis followed for parallel by research systems [48 aiming], and frameworks at exploring for analyzing undesired and modelling functions the of principle solutions,315 whichfailure behavior may beof cognitive fault products causes in [46 the]. conceptual Current design research phase [49]. concerns the stochastic [45] or data-driven [36] generation of reliability models because current systems include hardware and software with complex interactions that are becoming increasingly difficult to model. Further research activities explore the reliability of interdependent networks with cascading failures [47], load-sharing failure-based reliability analysis for parallel systems [48], and frameworks for analyzing and modelling the failure behavior of cognitive products in the conceptual design phase [49]. To conclude, the logical perspective offers distinct advantages and a unique view on the technical system, not only on which physical phenomena the system should perform, but also which it will perform in the case of unintended behavior of subsystems and components.

3.5. Control-Oriented Perspective Today, nearly all technical systems dispose of control systems that are, in general, based on some sensing of the systems and some corrective activity. In control engineering, it was found advantageous to describe theses control elements; commonly, a system perspective is employed that distinguishes actuators, the process itself, sensors, and controller(s). In recent times, additional entities were added that allowed fault accommodation: a fault-diagnosis block, analytic redundancy (based on mathematical models of the process, the actuator(s), and/or sensor(s)), and elements that allow the redesign of the controller in the case of a fault. This kind of modern control-system model is shown using the example of a wind turbine in Figure8; compare with [41,50]. Energies 2020, 13, x FOR PEER REVIEW 10 of 28

316 To conclude, the logical perspective offers distinct advantages and a unique view on the 317 technical system, not only on which physical phenomena the system should perform, but also which 318 it will perform in the case of unintended behavior of subsystems and components.

319 3.5. Control-Oriented Perspective 320 Today, nearly all technical systems dispose of control systems that are, in general, based on some 321 sensing of the systems and some corrective activity. In control engineering, it was found 322 advantageous to describe theses control elements; commonly, a system perspective is employed that 323 distinguishes actuators, the process itself, sensors, and controller(s). In recent times, additional 324 entities were added that allowed fault accommodation: a fault-diagnosis block, analytic redundancy Energies3252020 , 13(based, 2087 on mathematical models of the process, the actuator(s), and/or sensor(s)), and elements that 10 of 27 326 allow the redesign of the controller in the case of a fault. This kind of modern control-system model 327 is shown using the example of a wind turbine in Figure 8; compare with [41,50]. Example wind turbine Information concerning faults

Controller Fault re-design diagnosis

Analytic redundancy Faults and Unknown Inputs Process The central process of transforming wind energy to electrical yrel u u* y energy Controller A P S E.g. the wind speed Sensors sensor Actuators E.g. the blade angle adjustment system 328 Figure329 8. Control-orientedFigure 8. Control-oriented perspective perspective on on a a technical technical system system (example: (example: wind turbine) wind with turbine) modern with modern 330 control system. control system. 331 On the basis of understanding the system, system behavior is frequently modeled using state- On332the space basis representation, of understanding which isthe the system,mathematical system model behavior of a technical isfrequently system represented modeled as a set using of state-space representation,333 input, which output, isand the state mathematical variables usuallymodel related by of first a technical-order differential system equations. represented The values as of a set of input, 334 so-called state variables evolve over time. They serve as variables on the axes of the state space, and output,335 and the state state variables is represented usually as a vector related in this by space. first-order Three general differential kinds of state equations.-space descriptions The values can of so-called state336 variables be distinguished: evolve over continuous time. time, They discrete serve time as, and variables discrete event. on the axes of the state space, and the state337 is represented In continuous as a vector time, the in state this of space.a system Threecan, for generalinstance, be kinds described of state-spacewith the following descriptions set of can be 338 equations. distinguished: continuous time, discrete time, and discrete event. xt()= Atxt () () + Btut () () ++ Bt () f Ww , In continuous time, the state of a system can, for instance,a be11 described with the(3) following set of equations. . yt()= Ct () xt () ++ Cfs f Ww22 (4) x(t) = A(t)x(t) + B(t)u(t) + B(t) fa + W1w1, (3) 339 where x is the state vector, is its time derivative dx/dt, y is the measurement vector, u is the input 340 vector, fa denotes the actuator faulty(t )vector, = C (fst denotes)x(t) + theC sensorfs + faultW wvector, w1,2 stand for disturbance (4) 𝑥𝑥̇ f 2 2 vectors, A is the state matrix, B is the input matrix, C is the output matrix, Cf is the output matrix for 341 . where342x is thesensor state faults, vector, and Wx is1,2 denote its time disturbance derivative-distributiondx/dt, y matrices.is the measurement For a wind turbine vector,, a stateu isvector the input vector, fa denotes the actuator fault vector, fs denotes the sensor fault vector, w1,2 stand for disturbance vectors, A is the state matrix, B is the input matrix, C is the output matrix, Cf is the output matrix for sensor faults, and W1,2 denote disturbance-distribution matrices. For a wind turbine, a state vector based on a reduced-order model (compare with [51]) may be formulated for certain kinds of generators as

T T 3 x := (x1, x2, x3) = (ω, udc, β) R , (5) ∈ where ω stands for the angular velocity of the generator (this is usually proportional to the angular velocity of the wind turbine), udc for DC-link voltage, and β for the pitch angle. Control input vector u may consist of reference signals, such as reference blade pitch angle βref. Actuator faults, such as a jammed-blade pitch-angle-adjustment system, are expressed in actuator fault vector fa, and sensor faults (such as a wind-speed sensor deviation caused, e.g., by friction) are expressed in sensor-fault vector fs. When transferred to discrete time (often done for simulation purposes using certain discretization techniques such as Euler discretization), the following set of equations form the system description.

x(k+1) = Akxk + Bkuk + Bk fa,k + W1w1,k, (6)

yk = Ckxk + C f ,k fs,k + W2w2,k. (7) Energies 2020, 13, x FOR PEER REVIEW 11 of 28

343 based on a reduced-order model (compare with [51]) may be formulated for certain kinds of 344 generators as

TT3 x:=()() xxx123 ,, =ωβ , udc , ∈ , (5)

345 where ω stands for the angular velocity of the generator (this is usually proportional to the angular 346 velocity of the wind turbine), udc for DC-link voltage, and β for the pitch angle. Control input vector 347 u may consist of reference signals, such as reference blade pitch angle βref. Actuator faults, such as a 348 jammed-blade pitch-angle-adjustment system, are expressed in actuator fault vector fa, and sensor 349 faults (such as a wind-speed sensor deviation caused, e.g., by friction) are expressed in sensor-fault Energies 2020350, 13,vector 2087 fs. When transferred to discrete time (often done for simulation purposes using certain 11 of 27 351 discretization techniques such as Euler discretization), the following set of equations form the system 352 description.

For many systems, such as productionx(k+ 1) =++ systems, Ax kk Bu kk it is B kak sensible f, + Ww 1 1, to k, model not instances(6 of) time, but events such as the arrival of a preproduction part at a manufacturing station. An exemplary system y=++ Cx C f Ww . description for a discrete-event system wouldk be k k f, k sk , 2 2,k (7) 353 For many systems, such as production systems, it is sensible to model not instances of time, but events such as the arrival of a preproduction part at a manufacturing station. An exemplary system 354 x(n+1) = Anxn + Bnun + Bn fa,n, (8) 355 description for a discrete-event system would be =++ yx(nn+ 1)= C Axn nnxn + BuC nnf ,n f Bs, nann fn,., (8) Commented (9) [M3]: Please, revise yellow equations. Please, In recent years, another aspect was added to the control perspective, the inclusion of remaininguse MathType to add them. yn= Cx n n + C f,, n f sn n. (9) useful life (RUL). A knowledge of RUL of certain (redundant) system elements allows control algorithmsCommented [RS4R3]: Added as MathType 356 In recent years, another aspect was added to the control perspective, the inclusion of remaining that decrease357 useful the load life (RUL). on these A knowledge elements, of andRUL of thus certain prolong (redundant) the operationsystem elements time allows of the control superordinate system.358 In orderalgorithms to do that so, andecrease additional the load stepon these can elements be added, and tothus the prolong well-known the operation steps time of of diagnostics, the as shown in359 Figure superordinate9. system. In order to do so, an additional step can be added to the well-known steps of 360 diagnostics, as shown in Figure 9.

DIAGNOSTICS PROGNOSTICS

Fault Fault Fault Remaining detection isolation identification useful life Detecting Determining Estimating the prediction and reporting which nature and Identifying the an abnormal component extent of the lead time to condition is failing fault failure

361

362 Figure 9.FigurePrognostic 9. Prognostic step step added added to diagnostic diagnostic steps steps..

363 Steps in the diagnostics phase in Figure 9 are carried out in the fault-diagnosis block in Figure 8. Steps364 in theFault diagnostics diagnosis is phasea three-step in Figure process9: aredetecting carried an abnormal out in the condition, fault-diagnosis continuing blockwith the in Figure8. Fault diagnosis365 determination is a three-step of the failing process: component(s) detecting, and concluding an abnormal with an estimation condition, of the continuing nature and with the determination366 extent of the of the failing fault. The component(s), central objective of and the prognostics concluding phase with in Figure anestimation 9 is the prognosis of theof the nature and extent of the fault. The central objective of the prognostics phase in Figure9 is the prognosis of the phenomenonEnergies of physical 2020, 13, x FOR degradation. PEER REVIEW Typical degradation behavior (compare with12 of [41 28 ]) is shown in Figure367 10.phenomenon of physical degradation. Typical degradation behavior (compare with [41]) is shown in 368 Figure 10.

Estimated Time To Failure = Remaining Useful Life (RUL)

Failure Threshold

Defective Non-defective Phase Phase Degradation Indicator Degradation

Current Time Time

e.g. sensor readings from a vibration sensor in the wind turbine gondola 369

370 FigureFigure 10. Typical 10. Typical physical-degradation physical-degradation behavior behavior.. 371 Obviously, typical degradation behavior as shown in Figure 10 can also be understood as a 372 physical-behavior model because, during degradation in a defective phase, physical behavior 373 changes. Ultimately, the prognosis step aims at the prediction of the health of a product (compare 374 with [52,53]). For this prediction, it is possible to process information describing prior use, current 375 state, and past, present, and future environmental conditions. The enormous importance of 376 degradation processes has led to numerous research efforts. These efforts aim at predicting the 377 remaining useful life (RUL) or, synonymously, the estimated time to failure (ETTF), and at 378 developing approaches that determine if technical systems exhibit clear indicators for degradation. 379 In this research, clear distinction is proposed between a fault (a deviation of a property from the usual 380 condition [54] without serious consequences, such as system shut-down) and a failure (a catastrophic 381 event [50]). In many cases, it is possible to derive failure thresholds from a system’s functional 382 requirements [6] or from models of product functions [55]. 383 A further research area relying on the control-oriented perspective concerns the fault tolerance 384 of technical systems. In complex and networked technical systems, errors can never be completely 385 avoided, so the lack of fault tolerance can lead to enormous losses, so the development of effective 386 methods to increase fault tolerance is an essential and challenging research topic [56]. In the 387 application field of turbines, several research groups aim at increasing fault tolerance. For instance, 388 Habibi et al. [57] proposed an adaptive fault-tolerant neural-based control design considering 389 unknown wind speeds. 390 The control-oriented perspective offers insights and possibilities that other perspective cannot. 391 The control-oriented perspective enables and supports the control of technical systems itself, stability 392 analyses, controller design, the accommodation of faults, and many similar tasks. Current expansions 393 of this perspective also include changed physical behavior caused by degradation; obviously, this 394 topic is connected with the reliability and safety aspects of the logical perspective, but integrated 395 modelling approaches are still to be developed. The control-oriented perspective is gaining 396 importance in the age of ubiquitous computing—even relatively small subsystems can sense their 397 environment, adapt their behavior, communicate with other subsystems and, through this, contribute 398 to system-spanning planning and control endeavors, as well as systemwide fault detection and 399 identification. The inclusion of RUL aspects allows planning and control activities that prolong the Energies 2020, 13, 2087 12 of 27

Obviously, typical degradation behavior as shown in Figure 10 can also be understood as a physical-behavior model because, during degradation in a defective phase, physical behavior changes. Ultimately, the prognosis step aims at the prediction of the health of a product (compare with [52,53]). For this prediction, it is possible to process information describing prior use, current state, and past, present, and future environmental conditions. The enormous importance of degradation processes has led to numerous research efforts. These efforts aim at predicting the remaining useful life (RUL) or, synonymously, the estimated time to failure (ETTF), and at developing approaches that determine if technical systems exhibit clear indicators for degradation. In this research, clear distinction is proposed between a fault (a deviation of a property from the usual condition [54] without serious consequences, such as system shut-down) and a failure (a catastrophic event [50]). In many cases, it is possible to derive failure thresholds from a system’s functional requirements [6] or from models of product functions [55]. A further research area relying on the control-oriented perspective concerns the fault tolerance of technical systems. In complex and networked technical systems, errors can never be completely avoided, so the lack of fault tolerance can lead to enormous losses, so the development of effective methods to increase fault tolerance is an essential and challenging research topic [56]. In the application field of turbines, several research groups aim at increasing fault tolerance. For instance, Habibi et al. [57] proposed an adaptive fault-tolerant neural-based control design considering unknown wind speeds. The control-oriented perspective offers insights and possibilities that other perspective cannot. The control-oriented perspective enables and supports the control of technical systems itself, stability analyses, controller design, the accommodation of faults, and many similar tasks. Current expansions of this perspective also include changed physical behavior caused by degradation; obviously, this topic is connected with the reliability and safety aspects of the logical perspective, but integrated modelling approaches are still to be developed. The control-oriented perspective is gaining importance in the age of ubiquitous computing—even relatively small subsystems can sense their environment, adapt their behavior, communicate with other subsystems and, through this, contribute to system-spanning planning and control endeavors, as well as systemwide fault detection and identification. The inclusion of RUL aspects allows planning and control activities that prolong the system functionality of a supersystem, e.g., if it is possible to reduce the load on already degrading subsystems.

3.6. Summary Concluding Section3, which investigated five di fferent perspectives of abstract physics, all of the perspectives have their unique merits and offer information content that is not present in the models of other perspectives. The perspectives are compared in Table1.

Table 1. Overview of modelling perspectives.

Perspective Characterization Specific Merit Describes demarcated physical Supports deeper understanding Phenomenon-oriented perspective phenomena and breakthrough innovations Quantitatively describes behavior Supports simulation and Behavior-oriented perspective based on analytical and numerical optimization mathematics Describes how surfaces and Supports detailed analyses of Interface-oriented perspective support structures realize effect surfaces and interactions technical function Describes logical relationships that Supports analyses of Logic-oriented perspective determine reliability and safety unintended-behavior possibilities Supports development of complex Describes potential for and Control-oriented perspective control systems including activities of a control system fault-tolerant control Energies 2020, 13, 2087 13 of 27

A holistic-model-based engineering framework needs to include elements of all five perspectives, and existing approaches that aim at integrating the models may lead to this framework; these are discussed in the subsequent section.

4. Integration of Perspectives Holistic development processes of technical systems require the introduction of high-level integrated design methods, and early design phases, such as conceptual design and system modelling, play important roles [58]. Consequently, the integration of several perspectives of abstract physics is desirable. Several research activities address this challenge, and a conscious selection is investigated in this section.

4.1. Expansion of Integrated-Function-Modelling Framework The main focus of the integrated-function-modelling (IFM) framework is on more abstract-level function [7]. Holistic functional analysis of systems or subsystems is enabled through this framework; an example from a wind turbine is the subsystem that adjusts the pitch angle of the blades, and it is shown in Figure 11. Energies 2020, 13, x FOR PEER REVIEW 14 of 28

Use Case: adjust pitch angle of blades

actual wind actual wind velocity velocity initial state

P1 P1 process transform measured electrical electrical wind energy at energy at measured velocity controller controller wind velocity state

P2 P2 process transfer electrical electrical electrical energy at energy at energy at motor motor motor state P3 P3 process transform mechanical mechanical mechanical energy at energy at spur energy at spur gears gears spur gears state P4 P4 transform pitch pitch movement of movement of blades blades state wind velocity pitch electrical pair of spur sensor controller motor gears energy signal P1: P2: P3: P4: Actors Operands transform transfer transform transform wind velocity pitch electrical pair of spur sensor controller motor gears energy signal wind velocity x x sensor x pitch

x x controller Actors X electrical x x motor x

pair of x spur gears x (x: affecting; o: being affected) x x x energy

421 x signal Operands Figure422 11. FunctionFigure 11. modelFunction ofmodel blade of blade pitch-angle pitch-angle adjustment adjustment (modified (modified integrated integrated-function-modelling-function-modelling 423 (IFM) framework). (IFM) framework). 424 In the state view (upper-left corner of Figure 11), it can be seen that actual wind velocity is In425 the statetransformed view to (upper-left measured wind corner velocity of Figurethat could 11 be), itprocessed can be by seen the pitch that controller actual wind. This velocity is transformed426 controller to measured transfer winds electrical velocity energy that to the could electrical be processedmotor for blade by pitch the- pitchangle adjustment controller. if the This controller 427 controller determines that wind speed makes a change of the pitch angle desirable. The electrical transfers428 electricalmotor transform energys electrical to the to electrical mechanical motor energy at for a set blade of spur pitch-angle gears (motor and adjustment set of spur gears if the controller determines429 thatare present wind speedat each blade). makes The a changepair of spur of thegears pitch transform angle the desirable. incoming mechanical The electrical energy (rather motor transforms electrical430 to mechanicallow torque, but energyhigher speed) at a to set mechanical of spur energy gears that (motor can change and the set blade of spurpitch angle gears (rather are high present at each 431 torque, but lower speed). Other elements of the IFM are the interaction view (lower-left corner Figure blade). The pair of spur gears transform the incoming mechanical energy (rather low torque, but 432 11), the actor view (lower-right corner Figure 11), and the process-flow view (upper-right corner higher433 speed) Figure to mechanical11). It is obvious energy that the connection that can of change functions the and blade flows of pitch operand angle energy (rather and signal high are torque, but 434 clarified in this model. This framework already considers effects that can be applied in order to realize 435 functions [7], i.e., the IFM includes the so-called effect view (Figure 12). Energies 2020, 13, 2087 14 of 27 lower speed). Other elements of the IFM are the interaction view (lower-left corner Figure 11), the actor view (lower-right corner Figure 11), and the process-flow view (upper-right corner Figure 11). It is obvious that the connection of functions and flows of operand energy and signal are clarified in this model. This framework already considers effects that can be applied in order to realize functions [7], i.e., the IFM includes the so-called effect view (Figure 12). Energies 2020, 13, x FOR PEER REVIEW 15 of 28

Process Blade Energy EFFECT flow angle State view view VIEW 5° electrical E1, E2 E1, E2 10° mechanical

Actor Interaction view E1: transform energy E2: change angle view

436

437 Figure 12.FigureEffect 12. viewEffect view in IFM in IFM for for the the subfunctionsubfunction “adjust “adjust blade- blade-pitchpitch angle”. angle”. 438 The effects in the view shown in Figure 12 are defined as representations of physiochemical The439 effectseffects in or theprinciples view that shown are necessary in Figure for enabling 12 are or defined supporting as the representations execution of transformation of physiochemical effects440 or principlesand/or interaction that are processes necessary [7]. In a forlarge enabling-scale study orinvestigating supporting the industrial the execution applicability of of transformation the and/or441 interaction IFM framework processes, contents [7]. of In the a large-scaleeffect view (physiochemical study investigating effects; effects the related industrial to different applicability of 442 processes) were considered useful by more than half of the participants [7]. The distinctive quality of the IFM443 framework, the IFM is the contents connection of of the several effect views view (four (physiochemical functional views ande oneffects; physical effects view) related and the to different processes)444 wereestablished considered industrial useful acceptance by. It more is obvious than that half the present of the effect participants view is not satisfactory [7]. The distinctiveto fulfill quality of the445 IFM ismodelling the connection necessities ofidentified several in viewsSection (four3. However, functional the link viewsto a holistic and functional one physical structure view) and the 446 makes a connection of future modelling approaches of abstract physics to the IFM highly desirable. established industrial acceptance. It is obvious that the present effect view is not satisfactory to fulfill modelling447 necessities4.2. Integration identified in Computational in Section-Design3 Synthesis. However, Framework the link to a holistic functional structure makes a connection448 of futureFor decades modelling, many industrial approaches companies ofabstract have pursue physicsd the goal to theof automat IFM highlying their desirable. design 449 processes to accelerate them, reduce design costs, and allow individual user designs. In many cases, 4.2. Integration450 a high in Computational-Designlevel of automation can already Synthesis be realized, Framework especially for routine design and for modular 451 products, which may be automatically configured according to specific requirements (compare with For452 decades,[59]). In spite many of existing industrial applications, companies computational have support pursued for the the abstract goal initial of automatingphases of design their design processes453 toand accelerate product-development them, reduce processes design is rare costs, [60].and A major allow research individual direction user can be designs. summarized In many cases, 454 under the notion of knowledge-based engineering (KBE); the main intention is the automation and a high455 level s oftreamlini automationng of routine/repetitive can already design be realized, tasks mainly especially occurring in for the routine later design design stages and [61]; for modular products,456 whichusually may, the be main automatically physical phenomena configured are already according determined to specific in these requirementsphases, and the (comparemerit for with [59]). In spite457 of existingperspectives applications, concerning abstract computational physics is relatively support small. for theAnother abstract main research initial phasesdirection ofis design and 458 computational-design synthesis (CDS), main intention of which being the support of the early stages product-development459 of design processes processes, and large is rare and often [60]. unstructured A major research solutions are direction explored can[61]. CDS be summarized is researched under the notion460 of knowledge-basedsince several years (a engineering good overview (KBE); of earlier the approaches main intention can be found is the in automation [62]). A far-reaching and streamlining of routine461 /repetitiveapproach in design the area tasksof CDS mainlywas reported occurring by Wölkl inand the Shea later [63], who design developed stages a computational [61]; usually, the main 462 product model in system modelling language (SysML, based on unified modelling language) for physical463 phenomena conceptual aredesign already, which determinedalso included abstract in these physics. phases, For behavior and the modelling merit for, four perspectives of concerning abstract464 physics SysML is can, relatively in general, small. be employed: Another themain activity, research sequence, direction state-machine is computational-design, and use-case diagrams. synthesis (CDS),465 main These intention diagrams of, which together being with other the supportimportant diagrams of the early of SysML stages (compare of design with [ processes,63]), are shown and large and 466 in Figure 13. often unstructured solutions are explored [61]. CDS is researched since several years (a good overview of earlier approaches can be found in [62]). A far-reaching approach in the area of CDS was reported by Wölkl and Shea [63], who developed a computational product model in system modelling language (SysML, based on unified modelling language) for conceptual design, which also included abstract physics. For behavior modelling, four diagrams of SysML can, in general, be employed: the activity, sequence, state-machine, and use-case diagrams. These diagrams, together with other important diagrams of SysML (compare with [63]), are shown in Figure 13. Energies 2020, 13, 2087 15 of 27

Energies 2020, 13, x FOR PEER REVIEW 16 of 28

Energies 2020, 13, x FOR PEER REVIEW SysML 16 of 28 Diagrams

SysML Behavior RequirementDiagrams Structure Diagrams Diagrams

Behavior Requirement Structure Diagrams Diagram Diagrams Activity Sequence State Machine Diagram* Diagram* Diagram* Diagram*

modelsActivity modelsSequence Statemodels Machine modelsUse Case workflowsDiagram*of interactionsDiagram* transitionsDiagram* userDiagram* stepwise in a time between states interactions modelsactivities modelssequence models models of interactions transitions user stepwise in a time between states interactions activities sequence Block Definition Internal Block Package Diagram Diagram Diagram * generally appropriate for Block Definition Internal Block Package behavior modelling Parametric Diagram Diagram Diagram Diagram 467 * generally appropriate for behavior modelling Parametric 468 Figure 13. PossibilitiesFigure 13. Possibilities for behavior for behavior modelling modelling in in the the system system modellingDiagram modelling language language (SysML). (SysML). 467 469 Wölkl and Shea [63] emphasized the general applicability of SysML for conceptual design stages 468 Figure 13. Possibilities for behavior modelling in the system modelling language (SysML). Wölkl470 andincluding Shea abstract [63] emphasized physics. The main the emphasis general was applicability on non-geometric of SysML information. for conceptualFurther research design stages 471 activities in this field showed possibilities to include a phenomenon-oriented perspective (compare including469 abstractWölkl physics. and Shea The[63] emphasize main emphasisd the general was applicability on non-geometric of SysML for conceptual information. design stages Further research 472 with [20]). Helms [20] was able to develop a scheme that allows a systematic, automatic search for activities470 inincluding this field abstract showed physics. possibilities The main emphasis to include was ona non phenomenon-oriented-geometric information. Further perspective research (compare 473 appropriate physical effects (Figure 14). with [47120]). Helmsactivities [ i20n this] was field able show toed possibilities develop ato scheme include a thatphenomenon allows-oriented a systematic, perspective automatic (compare search for 472 with [20]). Helms [20] was able to develop a scheme that allows a systematic, automatic search for appropriate physical effects (Figure 14). 473 appropriateAssignment physical of abstraction effects (Figure 14). ports to physical effects Search for List of functions physical effects AssignmentList of ofabstraction state with ports to physicalvariables effects abstraction ports Search for List of functions physical effects List of state with 2 Assignmentvariables of abstraction ports Output: List of List of physical Physical effect abstraction ports suitable physical effects with search to physical effect abstraction ports 2 effects Assignment of Output: List of List of physical Physical effect 3 abstraction ports suitable physical effects with search to physical effect Physical effect abstraction ports Input: Function 1 effects catalog 3 Physical effect Input: Function 1 catalog Example: abstract physics of a wind turbine

1 e.g. transform energy 2 e.g. abstraction port transformer (TF) Example: abstract physics of a wind turbine 3 effects with abstraction port transformer (TF) e.g. law of the lever e.g. abstraction port transformer (TF) 474 1 e.g. transform energy 2 3 effects with abstraction port transformer (TF) e.g. law of the lever 475 Figure 14. Scheme for automatic search for appropriate physical effects. 474 476 The scheme shown in Figure 14 is based on so-called abstraction ports. These ports were 475 Figure 14. Scheme for automatic search for appropriate physical effects. 477 proposedFigure because 14. theScheme currentfor lack automatic of reuse of search design forknowledge appropriate is, among physical others e, ffcausedects. by the 476 The scheme shown in Figure 14 is based on so-called abstraction ports. These ports were The477 schemeproposed shown because in Figurethe current 14 lacisk based of reuse on of so-called design knowledge abstraction is, among ports. others These, caused ports by the were proposed because the current lack of reuse of design knowledge is, among others, caused by the heterogeneity of numerous knowledge sources and calls for the development of means that makes it possible to systematically express and classify physical effects at a uniform layer of abstraction [20]. This means could bridge the gap between function and behavior, and would enable standardization. The concept of abstraction ports provides uniform classification, thus allowing for the search of physical-effect catalogs. Current research activities still integrate some aspects of abstract physics and physical behavior [64]; these activities were successfully applied for the creation of multiple solution alternatives of hybrid-car concepts [65]. Current work also concerns network-based analysis of transition graphs; a novel approach was proposed for analyzing grammar rules by means of combining graph representation Energies 2020, 13, 2087 16 of 27

Energies 2020, 13, x FOR PEER REVIEW 17 of 28

(transition478 graph)heterogeneity of generated of numerous designs knowledge and sources rule and applications calls for the development with network-analysis of means that makes algorithms it and interactive479 visualizationspossible to systematically [66]. Currently,express and classify research physical is beingeffects at carried a uniform out layer that of abstraction aims at [ estimating20]. the impact480 of designThis means automation could bridge [60 the,61 gap,67 between,68]. function and behavior, and would enable standardization. 481 The concept of abstraction ports provides uniform classification, thus allowing for the search of In482 the CDSphysical field,-effect a catalogs. rich body Current for research design activities automation still integrate that some is also aspects in theof abstract early physics stages and can be found. Most proposed483 physical approaches behavior [64 include]; these activities some were aspects successfully of physical applied behavior;for the creation in-depth of multiple investigation solution of the possible484 perspectives alternatives of on hybrid this- levelcar concepts is frequently [65]. Current not work the mainalso concerns emphasis. network Further-based analysis research of is needed 485 transition graphs; a novel approach was proposed for analyzing grammar rules by means of for implementing486 combining developed graph representation approaches (transition that graph) aim of at generated supporting designs engineers and rule applications to consciously with choose appropriate487 network physical-analysis effects, algorithms and and to designinteractive e ffvisualizationsect chains. [66]. The Current designly, research of e isff ectbeing networks carried that are connected488 toout other that aims models at estimating in product-development the impact of design automation processes [60,61 also,67,68 needs]. further scientific emphasis. 489 In the CDS field, a rich body for design automation that is also in the early stages can be found. 4.3. Integration490 Most in proposed Frameworks approaches Based include on Graph-Based some aspects of Design physical Languages behavior; in-depth investigation of the 491 possible perspectives on this level is frequently not the main emphasis. Further research is needed In492 recent for years, implementing an engineering developed approaches framework that based aim at onsupporting graph-based engineers design to consciously languages choose (GBDL) was 493 appropriate physical effects, and to design effect chains. The design of effect networks that are developed494 inconnected close co-operation to other models with in product industrial-development companies. processes This also needs engineering further scientific framework emphasis addresses. the challenge that current product development processes are characterized by a multiplicity of different domains;495 the4.3. consequence Integration in Frameworks are immense Based on eGffraphorts-Based for D exchangingesign Languages data and information between these domains.496 CurrentIn recent research years, activitiesan engineering were framework able to based show on graph that- itbased is possible design languages to use (GBDL) a common was language 497 developed in close co-operation with industrial companies. This engineering framework addresses to describe498 thethe productchallenge that and current processes product throughout development theprocesses product are characterized lifecycle, forby a instance, multiplicity a of graph-based design499 language different based domains; on thethe consequence unified modelling are immense language efforts for (UML). exchanging Graph-based data and information design languages for engineering500 between design these anddomains. product Current development research activities were were firstable to proposed show that it by is Rudolphpossible to use [69 a]. Examples 501 common language to describe the product and processes throughout the product lifecycle, for of the502 application instance,of a graph graph-based-based design languages language based include on the powerunified m polesodelling [ 70language], satellite (UML). systems Graph- [71], gear systems503 for suburbanbased design trains languages [6], for quadcopters engineering design and multicoptersand product development [55], balanced were first two-wheel proposed by scooters [72], automotive504 dashboardsRudolph [69]. [Examples73], exhaust of the systems application [32 of], graph car bodies-based languages [74], and include automated power poles guided [70] vehicles, [41]. 505 satellite systems [71], gear systems for suburban trains [6], quadcopters and multicopters [55], The application506 balanced of this two kind-wheel ofscooters rule-based [72], automotive systems dashboards leads to [73 a] central, exhaust datasystems model [32], car in bodies the form [74], of a design graph507 (compiled and auto frommated the guided UML vehicles model [41]. -The compare application with of this [75 kind]) with of rule interfaces-based systems to leads alldomains to a in the product508 lifecycle central (Figure 15 in). the form of a design graph (compiled from the UML model - compare with [75]) 509 with interfaces to all domains in the product lifecycle (Figure 15).

Design-Graph (central data model)

Geometry BOM* Abstract physics Functions Requirements representation Digital Digital *) bill of materials 510 511 Figure 15.FigureCentral 15. Central data data model model and and didifferentfferent domains domains in product in product lifecycle. lifecycle.

A main advantage of the application of graph-based design languages is the resulting inherent consistency of the generated graph because all different domains receive their information from this data model. Computer software that is able to compile a graph-based design language is the so-called Design Compiler 43 [76]. This design compiler was developed by IILS GmbH, Trochtelfingen, Germany, in co-operation with the University of Stuttgart, and it has the unique capability to synthesize detailed and complex geometry models from UML diagrams. From the design graph, the central data model, different kinds of models can be generated, such as requirement (e.g., conventional lists or ReqIf models (ReqIF is an XML file format for the documentation and exchange of requirements), function (e.g., modelled in the IFM, Section 4.1), simplified-abstract-physics (compare with [77]), and geometry Energies 2020, 13, x FOR PEER REVIEW 18 of 28

512 A main advantage of the application of graph-based design languages is the resulting inherent 513 consistency of the generated graph because all different domains receive their information from this 514 data model. Computer software that is able to compile a graph-based design language is the so-called 515 Design Compiler 43 [76]. This design compiler was developed by IILS GmbH, Trochtelfingen, 516 Germany, in co-operation with the University of Stuttgart, and it has the unique capability to 517 synthesize detailed and complex geometry models from UML diagrams. From the design graph, the Energies 2020518 , 13central, 2087 data model, different kinds of models can be generated, such as requirement (e.g., 17 of 27 519 conventional lists or ReqIf models (ReqIF is an XML file format for the documentation and exchange 520 of requirements), function (e.g., modelled in the IFM, Section 4.1), simplified-abstract-physics models,521 and (compare a “bill ofwith materials” [77]), and geometry (BOM). models The, digitaland a “bill product-development of materials” (BOM). The digital process product on- the basis of 522 development process on the basis of graph-based design languages with UML is sketched in Figure graph-based523 16 design (compare languages with [55] and with [78]) UML. is sketched in Figure 16 (compare with [55] and [78]).

Digital design process

Definition & programming Compilation & execution Partial (Sequence Design Design Vocabulary Structure Results ontologies of) Rules compiler graph

abstract UML class UML activity UML classes geometry diagram diagram

abstract CAD physics Product attributes FEM abstract operations control subclasses CAP

… Office tools

abstract etc. digital factory -independent data model Project-specific data model

Project-specific requirements and optimization criteria 524

525 Figure 16.FigureDigital 16. Digital design design process process based based on graph graph-based-based design design languages languages..

The526 digital designThe digital process design process based based on graph-basedon graph-based design languages languages starts with starts so- withcalled partial so-called partial 527 ontologies that serve as the basis for the creation of a language usually describing a family of possible ontologies528 thatproducts. serve A asn ontology the basis is a forformal the description creation of of the a concepts language and usuallyrelationships describing that exist for a a family certain of possible products.529 Anarea ontology of knowledge is aformal or application description, and it defines of the the concepts basic terms, and concept relationshipss, and relations that comprising exist for a certain area of530 knowledge the vocabulary or application, of a domain and [79]; it for defines example the, the basic partial terms, ontology concepts, of abstract andgeometry relations defines comprising the the 531 basic geometrical terms (such as cylinder), concepts (such as intersection), and relations (such as vocabulary of a domain [79]; for example, the partial ontology of abstract geometry defines the basic 532 positioned at the border) in the domain of industrial-system development. Partial ontologies need to geometrical533 termscapture (such the engineering as cylinder), knowledge concepts of the involved (suchas domains; intersection), terms, concepts and, relationsand relations (such that are as positioned at the border)534 not in defined the domain in a partial of ontology industrial-system cannot be used development. in the system language Partial, and ontologies cannot be expressed need to in capture the 535 the resulting model perspectives and models of the technical system. Many research works do not engineering knowledge of the involved domains; terms, concepts, and relations that are not defined in 536 explicitly include the investigation of ontologies; however, implicitly, they are part of any system a partial537 ontology modelling cannot endeavor be and used any in modelling the system perspective. language, Research and activities cannot are also be expressedconnected that in aim the resulting model538 perspectives at metamodels, and models i.e., abstract of themodels technical that describe system. the organization Many research and structure works of dothe product not explicitly and include the investigation539 process of models ontologies; to be created however, (compare implicitly, [80]). they are part of any system modelling endeavor 540 On the basis of partial ontologies, responsible engineers are able to program the vocabulary and any541 modelling (expressed perspective. in UML classes Researchfor product entities activities such as are product also connectedcomponents), structure that aim (expressed at metamodels, in i.e., abstract542 models UML that class describe diagrams— thetheorganization structure of product and structureentities), and of rules the product(expressed andas UML process activity models to be created (compare [80]). On the basis of partial ontologies, responsible engineers are able to program the vocabulary (expressed in UML classes for product entities such as product components), structure (expressed in UML class diagrams—the structure of product entities), and rules (expressed as UML activity diagrams–model transformations) for a whole product family or spectrum. The resulting project independent-data model is then objected to project-specific requirements and optimization criteria, and an automated compilation and execution process is carried out. The result of the compilation is a design graph; all kinds of domain-specific proprietary-data formats can be created from this design graph. First, scientific attempts to integrate abstract physics into digital product design using design languages are currently investigated [77]. An existing design language for functional modelling was extended to include abstract physics; the resulting class diagram is shown in Figure 17 (compare with [77]). Energies 2020, 13, x FOR PEER REVIEW 19 of 28

543 diagrams–model transformations) for a whole product family or spectrum. The resulting project 544 independent-data model is then objected to project-specific requirements and optimization criteria, 545 and an automated compilation and execution process is carried out. The result of the compilation is 546 a design graph; all kinds of domain-specific proprietary-data formats can be created from this design 547 graph. First, scientific attempts to integrate abstract physics into digital product design using design Energies5482020 , 13languages, 2087 are currently investigated [77]. An existing design language for functional modelling was 18 of 27 549 extended to include abstract physics; the resulting class diagram is shown in Figure 17 (compare with 550 [77]).

Requirement SystemUnderConsideration 1 id: Integer 2 text: String operator: String value: String Unit: String useCase[1..*] type: String useCase[1..*] 5 level: Integer responsible: String UseCase State 3 id: Integer sub text: String id: Integer text: String inputState[1] outputState[1] useCase[1..*] state[1..*] Effect Network ProcessOfUseCase[1..*] effectNetwork actor[1..*] ProcessOfUseCase previousProcess[1..*] Actor id: Integer 4 text: String nextProcess[1..*] effect[1..*] 6 sub Effect 7 affecting[1..*] Paffecting[1..*] supporting[1..*] Psupporting[1..*]

Stakeholder stakeholder[1..*] Environment environment[1..*] TechnicalSystem environment[1..*] technicalSystem[1..*]

Example: abstract physics of a wind turbine 1 e.g. energy production

2 e.g. wind turbine 3 e.g. transform energy 4 e.g. change speed

5 e.g. input speed 6 e.g. pair of spur gears 7 e.g. law of the lever 551 552 Figure 17. Possible class diagram of integrated design language. Figure 17. Possible class diagram of integrated design language. 553 So far, this expansion was explained using one example, the steering system of a balanced two- So554 far, wheel this expansionscooter. The wasexpansion explained includes usingthe integration one example, of three theaspects steering of abstract system physics of a balanced two-wheel555 scooter.(phenomenon The-, behavior expansion-, and interface includes-oriented) the in integrationa digital engineering of three framework aspects. The presented of abstract physics 556 framework potentially has the ability to compile numerous instances of information formulated in (phenomenon-,557 UML behavior-,, and to synthesize and interface-oriented)a design graph with interconnected in a digital geometrical, engineering physical framework., and structural The presented framework558 information. potentially A hasfurther the strength ability is the to compileintegration numerousin the product instances life cycle. Still, of a information comparison with formulated in UML,559 and toperspectives synthesize explained a design in Section graph 3 makes with it obvious interconnected that further research geometrical, is needed physical, for integrating and structural 560 all necessary perspectives and aspects concerning the modelling of abstract physics. Additionally, information.561 some A furtheraspects concerning strength the is vertical the integration integration in in cyber the–physical product systems life cycle.and their Still, development a comparison with perspectives562 are explained not yet covered; in Section these are3 investigatedmakes it obvious in the subsequent that further section. research is needed for integrating all necessary perspectives and aspects concerning the modelling of abstract physics. Additionally, some 563 4.4. Integration in Frameworks for Cyber–Physical-System Development aspects concerning the vertical integration in cyber–physical systems and their development are not 564 The integration of data, information, and models is the main emphasis of research activities yet covered;565 concerning these are the investigated development inof cyber the subsequent–physical systems section. (CPSs). A cyber–physical system can be 566 defined as a physical system in which operations are monitored, co-ordinated, controlled, and 4.4. Integration567 integrated in Frameworks by a communication for Cyber–Physical-System and computation system Development [81]; they may be understood as 568 mechatronic systems with both horizontal multidomain integration within one organization and The integration of data, information, and models is the main emphasis of research activities concerning the development of cyber–physical systems (CPSs). A cyber–physical system can be defined as a physical system in which operations are monitored, co-ordinated, controlled, and integrated by a communication and computation system [81]; they may be understood as mechatronic systems with both horizontal multidomain integration within one organization and vertical integration between subsystem suppliers and the suppliers of complete systems (compare with [58]). A CPS produces physical and virtual processes, and their integration in all phases of a system-development process and consistent system modelling are major challenges (Figure 18); (compare with [58]). Energies 2020, 13, x FOR PEER REVIEW 20 of 28

569 vertical integration between subsystem suppliers and the suppliers of complete systems (compare Energies5702020 , 13with, 2087 [58]). A CPS produces physical and virtual processes, and their integration in all phases of a 19 of 27 571 system-development process and consistent system modelling are major challenges (Figure 18); 572 compare with [58]).

Integration of Virtual Physical virtual and processes processes physical processes (computations)

Conceptual design

Clarifying requirements

Generating solution concepts

Evaluating CPS solution concepts PHYSICAL SYSTEMS PHYSICAL - Selecting and deciding temporal dimension System modelling

Model modularization

Model interfaces

Model integration PROCESSES OFCYBER INTEGRATION OFDISCIPLINES IN DEVELOPMENT INTEGRATION mode of representation 573 574 Figure 18. Integration of disciplines during an early design process of cyber–physical systems Figure 18. Integration of disciplines during an early design process of cyber–physical systems (CPSs). 575 (CPSs).

The576 effectiveThe and effective effi cientand efficient development development of of CPSsCPSs requires requires the application the application of abstract of integrated abstract integrated design577 methods design and methods models and that models allow that engineers allow engineers to simultaneously to simultaneously consider consider all all aspects aspectsof of a a multidomain 578 multidomain system. The physical behavior of a CPS is controlled by virtual processes; this system.579 Thecircumstance physical has behavior to be considered of a CPS both during is controlled conceptual bydesign virtual and system processes; modelling this (Figure circumstance 18). has to be considered580 Several bothtools, models during, and conceptual methods are proposed design for and addressing system this modelling issue, e.g., the (Figure use of UML 18). and Several tools, models,581 and SysML methods (compare areproposed with [82–84 for]). Frequently, addressing Modelica this issue, is applied e.g., for the behavior use of modelling UML and because SysML (compare 582 models created in Modelica can be transferred into mathematical models that allow detailed with [58382– 84]).simulations Frequently, [58]. Modelica Modelica is a isfreely applied available, for object behavior-oriented modelling modelling language because that models was created in Modelica584 canspecifically be transferred developed into for mathematicalthe modelling of heterogeneous models that systems allow, detailedand was applied simulations in many [cases58]. Modelica is a freely585 available, for the object-orientedconnection of virtual modelling and physical language processes [85 that,86]. was specifically developed for the modelling 586 Wind turbines can also be understood as part of a CPS because they frequently do not work of heterogeneous systems, and was applied in many cases for the connection of virtual and physical 587 alone, but in clusters called wind farms [58]. The data gathered by all wind turbines that are elements processes588 [85of,86 a wind]. farm are processed by applying wind-turbine supervisory control and data acquisition Wind589 turbines(SCADA); this can arrangement also be understood is shown in Figure as part19. of a CPS because they frequently do not work alone, but in clusters called wind farms [58]. The data gathered by all wind turbines that are elements of a wind farm are processed by applying wind-turbine supervisory control and data acquisition (SCADA); this arrangement is shown in Figure 19. Energies 2020, 13, x FOR PEER REVIEW 21 of 28

Supervisory Control and Data Acquisition (SCADA) – condition monitoring system for a cyber-physical system WINDFARM

590

591 Figure 19. Condition monitoring for a cyber–physical system (CPS). Figure 19. Condition monitoring for a cyber–physical system (CPS). 592 Large amounts of data are analyzed in a remote engineering center in order to to monitor the 593 physical behavior and to generate information concerning the condition of all wind turbines of the 594 wind farm and their components such as gear systems, generators, or converters. Hehenberger et al. 595 [58] described a prediction system for this kind of CPS that used SCADA data and applied an 596 adaptive network-based fuzzy inference system (ANFIS). This system can predict a failure of gear 597 systems (Section 3.5). A model for strategic maintenance scheduling of wind farms was proposed by 598 Mazidi et al. [87]; this model allows preventive maintenance scheduling for this kind of CPS. These 599 authors also provided an extensive literature review concerning the design and maintenance of 600 offshore wind turbines. 601 CPSs are more or less only an intermediate step to cloud-based systems, which add the aspect 602 of global integration and participate in the Internet of Things (IoT) [58]. The interaction of all technical 603 systems with the system that produces them, and with some superordinate monitoring, planning 604 control, and diagnosis system, as well as with co-operating systems and subsystems, plays an 605 important role in the development of a future systems; models of system development need to 606 consider and depict all these interaction processes because, in the future, nearly all physical processes 607 in technical systems will be influenced by these interaction processes. The approaches realized in 608 frameworks for cyber–physical-system development enable horizontal and vertical integration, and 609 can be extended to a global cloud-based scale. Consequently, holistic modelling of abstract physics 610 needs to include these aspects that are crucial for the development of future technical systems.

611 4.5. Summary 612 Detailed analysis of four consciously chosen integration approaches leads to the insights that all 613 discussed integration approaches have specific merits, and that holistic integration needs to 614 incorporate aspects and model integration solutions of all integration approaches. It is also obvious 615 that models that are to be integrated are rich enough—they contain all important static and dynamic 616 local and distributed information concerning the abstract and concrete physical behavior—for 617 sensible integration. The integration approaches are compared in Table 2.

Energies 2020, 13, 2087 20 of 27

Large amounts of data are analyzed in a remote engineering center in order to to monitor the physical behavior and to generate information concerning the condition of all wind turbines of the wind farm and their components such as gear systems, generators, or converters. Hehenberger et al. [58] described a prediction system for this kind of CPS that used SCADA data and applied an adaptive network-based fuzzy inference system (ANFIS). This system can predict a failure of gear systems (Section 3.5). A model for strategic maintenance scheduling of wind farms was proposed by Mazidi et al. [87]; this model allows preventive maintenance scheduling for this kind of CPS. These authors also provided an extensive literature review concerning the design and maintenance of offshore wind turbines. CPSs are more or less only an intermediate step to cloud-based systems, which add the aspect of global integration and participate in the Internet of Things (IoT) [58]. The interaction of all technical systems with the system that produces them, and with some superordinate monitoring, planning control, and diagnosis system, as well as with co-operating systems and subsystems, plays an important role in the development of a future systems; models of system development need to consider and depict all these interaction processes because, in the future, nearly all physical processes in technical systems will be influenced by these interaction processes. The approaches realized in frameworks for cyber–physical-system development enable horizontal and vertical integration, and can be extended to a global cloud-based scale. Consequently, holistic modelling of abstract physics needs to include these aspects that are crucial for the development of future technical systems.

4.5. Summary Detailed analysis of four consciously chosen integration approaches leads to the insights that all discussed integration approaches have specific merits, and that holistic integration needs to incorporate aspects and model integration solutions of all integration approaches. It is also obvious that models that are to be integrated are rich enough—they contain all important static and dynamic local and distributed information concerning the abstract and concrete physical behavior—for sensible integration. The integration approaches are compared in Table2.

Table 2. Overview of integration approaches.

Integration Approach Characterization Specific Merit Expansion of Integrates physical phenomena in Allows linking to concise function integrated-function-modelling multi-view framework for modelling framework (IFM) framework function representation Integrates physical phenomena Integration in Allows linking to large framework and behavior descriptions in computational-design-synthesis for design automation, even in design framework for abstract, (CDS) framework early phases early phases of design Integrates physical phenomena Allows linking to an engineering Integration in frameworks based and behavior descriptions in a framework with the ability to on graph-based design languages framework spanning the whole compile numerous instances of (GBDL) product life cycle information including geometry Integrates behavior descriptions in Integration in frameworks for Allows linking to an engineering a framework, including locally cyber–physical system (CPS) framework that includes vertical and organizationally distributed development integration processes and cloud solutions

The central conclusions of this and the previous section are summarized in the next section, and recommendations for further research are given. Energies 2020, 13, 2087 21 of 27

5. Conclusions and Recommendations for Further Research Today, several possibilities to model abstract and concrete physical behavior are available, and several approaches to integrate these models with themselves and other, more abstract or concrete models are proposed. However, no common methodology that describes the holistic modelling of abstract physics is present. Detailed analysis in the two previous sections may explain why this methodology has not yet been developed. This may be because a multitude of perspectives, functions, and dependences need to be present in the models of such a methodology, and model-integration approaches need to be domain- and system-spanning, dynamical, and able to handle multi-perspective models. Both for the models and their integration, numerous promising approaches, models, modelling languages, algorithms, and integration schemes are available. Scientific challenges in the next years are the combination, adaptation, and integration of these elements. The goal is a methodology that allows the interconnected representation of the level of abstract physics that is as complete and conclusive as the representation methodology already present on the concrete level. On this level, a combination of systems and models like computer-aided design (CAD), computer-aided engineering (CAE), product-data management (PDM), product-life-cycle management (PLM) and enterprise-resource planning (ERP) already allows an extremely rich, interconnected, and dynamic representation of technical systems. This paper collected several insights concerning the necessities and possibilities of future models and systems that can represent abstract physics of technical systems. Central insights are:

The phenomenon-oriented perspective was proposed by design science and has its unique merits • but is usually weakly represented in integrated modelling approaches. Initial approaches for the digital modelling of this perspective were proposed in the last few years; however, it remains a promising field for further research. For the behavior perspective, a multitude of modelling possibilities are available, but only some • are integrated in existing engineering frameworks. Additionally, abstract representations of the behavior perspective are sparse and rarely employed; in this area, further scientific activities are desirable. Research concerning the interface-oriented perspective is relatively new; the connection with • other modelling attempts and the integration in engineering frameworks is a field for future scientific activities. The logic-oriented perspective is relatively well-researched and frequently applied in engineering • practice for reliability and safety analyses; links to other system-modelling possibilities are usually still rather weak. Numerous modelling possibilities concerning the control-oriented perspective were developed • and successfully applied in the last few decades; similar to the logic-oriented perspective, links to other system-modelling possibilities are usually still rather weak. Strong links to the requirements concerning a technical system to the functional domain • and structural domain are highly desirable in order to avoid repetitive work, inconsistencies, and fault possibilities. Engineering frameworks that connect several views in the functional domain may allow • an expansion towards a more concrete modelling of abstract physics; further research in this area is desirable. In research activities in the area of computational design synthesis, several perspectives concerning • the physical behavior of future technical systems are already incorporated. Engineering frameworks based on graph-based design languages may allow an expansion • towards the more concrete modelling of abstract physics; further scientific activities concerning this expansion have large potential. Energies 2020, 13, 2087 22 of 27

Ontologies and metamodels play a central role in all modelling endeavors; frequently, their • investigation is not explicitly addressed, but it is mandatory for the development of future holistic and integrated modelling possibilities in the field of abstract physics. As most future complex technical systems are cyber–physical or even cloud-based, the vertical • and horizontal integration of information, models, and processes in this field is inevitable; future research has to address this need.

The modelling perspectives of abstract physics and the integration approaches were explained on the basis of wind turbines. Wind turbines, and especially wind-turbine farms, are complex, distributed technical systems that include aspects of mechanics, hydraulics, , electronics, and software. Requirements concerning reliability and safety are enormous, and the number of operation hours is extremely large. Consequently, it is admissible to assume that the presented analysis and conclusions are valuable for other technical systems as well as other energy-generation and -transformation systems (solar systems, power plants, etc.), but also transportation systems (cars, trucks, trains, ships, aircrafts, etc.) and production systems. Obviously, this paper does not give a conclusive answer on how the abstract physical behavior of technical systems should be modelled in order to enable efficient and effective system-development processes, and the efficient, effective, safe, and reliable production and operation of these technical systems. All languages, modelling approaches, and frameworks have their specific merits, but also their undeniable limitations. This paper gave an overview of existing perspectives and important aspects and explained integration necessities and approaches. It presented structure and promising areas for future research. The creation of holistic and integrated modelling approaches might be the outcome of these research activities. These approaches may lead to more robust and efficient development processes for technical systems and subsystems for energy generation and transformation, and other systems.

Funding: This research was partly funded in the scope of the Digital Product Life. Cycle (ZaFH) project (information under: https://dip.reutlingen-university.de/), which is supported by a grant from the European Regional Development Fund and the Ministry of Science, Research, and the Arts of Baden-Württemberg, Germany (information under: https://efre-bw.de/). Conflicts of Interest: The author declares no conflict of interest.

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