Wing Structure of the Next-Generation Civil Tiltrotor: from Concept to Preliminary Design

aerospace

Article Wing Structure of the Next-Generation Civil Tiltrotor: From Concept to Preliminary Design

Marika Belardo 1,*, Aniello Daniele Marano 2 , Jacopo Beretta 3, Gianluca Diodati 1, Mario Graziano 4, Mariacarmela Capasso 4, Pierpaolo Ariola 5, Salvatore Orlando 5, Francesco Di Caprio 1 , Nicola Paletta 3 and Luigi Di Palma 1

1 Italian Aerospace Research Centre (CIRA), 81043 Capua, Italy; [email protected] (G.D.); [email protected] (F.D.C.); [email protected] (L.D.P.) 2 Department of Industrial Engineering, University of Naples Federico II, 80125 Napoli, Italy; [email protected] 3 IBK-Innovation GmbH, 21129 Hamburg, Germany; [email protected] (J.B.); [email protected] (N.P.) 4 Step Sud Mare Srl, 80038 Pomigliano d’Arco, Italy; [email protected] (M.G.); [email protected] (M.C.) 5 Magnaghi Group S.p.A, 80146 Napoli, Italy; [email protected] (P.A.); [email protected] (S.O.) * Correspondence: [email protected]

Abstract: The main objective of this paper is to describe a methodology to be applied in the pre-

liminary design of a tiltrotor wing based on previously developed conceptual design methods. The reference vehicle is the Next-Generation Civil Tiltrotor Technology Demonstrator (NGCTR-TD)

Citation: Belardo, M.; Marano, A.D.; developed by Leonardo Helicopters within the Clean Sky research program framework. In a pre- Beretta, J.; Diodati, G.; Graziano, M.; vious work by the authors, based on the specific requirements (i.e., dynamics, strength, buckling, Capasso, M.; Ariola, P.; Orlando, S.; functional), the first iteration of design was aimed at finding a wing structure with a minimized Di Caprio, F.; Paletta, N.; et al. Wing structural weight but at the same time strong and stiff enough to comply with sizing loads and Structure of the Next-Generation aeroelastic stability in the flight envelope. Now, the outcome from the first design loop is used to Civil Tiltrotor: From Concept to build a global Finite Element Model (FEM), to be used for a multi-objective optimization performed Preliminary Design. Aerospace 2021, 8, by using a commercial software environment. In other words, the design strategy, aimed at finding 102. https://doi.org/10.3390/ a first optimal solution in terms of the thickness of composite components, is based on a two-level aerospace8040102 optimization. The first-level optimization is performed with engineering models (non-FEA-based), and the second-level optimization, discussed in this paper, within an FEA environment. The latter is Academic Editor: Christian Breitsamter shown to provide satisfactory results in terms of overall wing weight, and a zonal optimization of the composite parts, which is the starting point of an engineered model and a detailed FEM (beyond the

Received: 2 March 2021 scope of the present work), which will also take into account manufacturing, assembly, installation, Accepted: 29 March 2021 accessibility and maintenance constraints. Published: 2 April 2021 Keywords: civil tiltrotor; wing; design; aeroelasticity; flutter; multi-objective optimization Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Tiltrotors are a class of vehicle that combines the advantages of aircraft (high cruise speed) and helicopters (Vertical Take-Off and Landing (VTOL) ability). Their commercial appeal is becoming increasingly important, with applications among various users, Copyright: © 2021 by the authors. such as business, search and rescue, and medical. Within this framework, Horizon 2020 Licensee MDPI, Basel, Switzerland. (H2020) Clean Sky 2 FRC IADP NextGenCTR will be dedicated to the design, construction This article is an open access article and flying of an innovative Next-Generation Civil Tiltrotor Technology Demonstrator distributed under the terms and (NGCTR-TD) [1], the configuration of which will go beyond current architectures for this conditions of the Creative Commons type of aircraft. NGCTR-TD demonstration activities, led by Leonardo Helicopters Di- Attribution (CC BY) license (https:// vision (LHD), will aim to validate its innovative architecture, technologies/systems and creativecommons.org/licenses/by/ operational concepts. 4.0/).

Aerospace 2021, 8, 102. https://doi.org/10.3390/aerospace8040102 https://www.mdpi.com/journal/aerospace Aerospace 2021, 8, 102 2 of 16

The NGCTR-TD (https://www.cleansky.eu/fast-rotorcraft-iadp (accessed on 1 April 2021)) is characterized by a 12 m long wing, a V-Tail, pitch and yaw lift surfaces’ command configuration, and a fixed-engine installation with a split gearbox to provide the proprotor tilting mechanism. The T-WING consortium is working on the composite wing of the NGCTR-TD, planned to be flying in 2023. The consortium, led by the Italian Aerospace Research Centre (CIRA), is composed of industrial partners Magnaghi Aeronautica and Salver (IT) and SSM (IT), small and medium-sized enterprises (SMEs) OMI (IT) and IBK Innovation (DE), and the University of Naples Federico II (IT). The final aim of the project is to design and qualify the structure of the wing and moveable surfaces, encompassing from design, manufacturing and ground tests up to flight, in compliance with the technical specification set by the Work Area Leader Leonardo Helicopters. The wing will be properly instrumented during the flight, with a set of sensors (strain gages and accelerometers) with the aim of validating load evaluation methods, structural monitoring for flight safety and aeroelastic characteristics. The main innovations pertaining to the wing structure are the highly integrated composite concept and the presence of two moveable surfaces: one is an external flaperon and the other is a so-called morphing surface, which has the function of reducing the wing area exposed to the propeller flow in helicopter mode. All these features lead to a wing structure which is quite compact, since the largest moveable surface spans almost half of the wing chord. For such a wing, an iterative and novel optimization process has been necessary, even if the architectural layout of the structure is set by LHD (e.g., spar and ribs locations). The optimization process was thus aimed at finding the optimal composite thickness distribution (at minimum weight). In the present case of the NGCTR-TD wing, the preliminary structural design strategy is based on a two-level optimization. The first-level optimization, presented in a previous work by the authors [1], is based on engineering models, used to perform a multi-objective optimization by means of genetic algorithms, also taking into account the aeroelastic stability. The aim of the first-level optimization is to have, at the very beginning of the project, i.e., at concept design level, a safe structural configuration, which is free from flutter at minimum mass. The second-level optimization is based on a 2D-1D elements Finite Element Model (FEM), built based on the selected first-level structural optimization [2]. The composite parts (skin and spars) of this structural configuration are then subjected to optimization by means of commercial FEM software (OptiStruct®, ver. 2017.2, Altair Engineering Inc., Troy, MI, USA). In order to speed up the optimization process and to evaluate different design choices, the equivalent laminate theory is applied to model the composite properties of the finite elements, also giving an advantage in the computational effort. The second-level optimization is the main object of the present work.

2. Previous Similar Works A remarkable example of a design optimization framework for a tiltrotor wing is about the SUAV TRS4 composite wing [3], in which the mathematical optimization process, based on a gradient-based method and a Variation Asymptotic Beam Sectional (VABS) analysis, was performed by implementing numerical analysis modules, in a single MATLAB® (ver. R2017b, MathWorks, Natick, MA, USA) environment. Another literature example of tiltrotor wing structural optimization is presented in [4], in which the use case was the Bell XV-15 Tiltrotor, manufactured by the US Bell Aircraft Corporation (Buffalo, NY, USA), predecessor of Bell Helicopter Textron. In this work, the enhancement of the aeroelastic stability is selected as an objective in the upper-level optimization, in which the Response Surface Method (RSM) is selected. On the other hand, lower-level optimization seeks to determine the local detailed cross-sectional parameters, such as the ply orientation angles and ply thickness, which are relevant to the wing structural properties obtained at the upper level. To avoid manufacturing difﬁculties, only a few discrete ply orientation angles and an integral number of plies are considered as Aerospace 2021, 8, 102 3 of 16

constraints. A genetic algorithm is selected as the optimizer at the lower level. Finally, a Korea Aerospace Research Institute (KARI) in-house aeroelastic analysis is carried out in order to predict flutter stability. In the present work, the geometrical constraints of the wing architecture are dictated by LHD; thus, the design variables taken into account for the optimization of the wing-box are mainly the thicknesses/layups of the composite material. The composite material is an already certified material owned by one partner of the T-WING consortium. The aeroelastic problem considered in the optimization is related to the flutter, whereas the whirl flutter is treated as an ex post verification by LHD. The sizing loads are supplied by LHD as well. The aeroelastic behavior is influenced not only by the wing stiffness itself, but also by the local stiffness of the junctions, in this case the junction between the wing and the nacelles. This behavior was already found by the authors in a previous work on a joined fixed-wing configuration [5,6]. The first-phase optimization of the NGCTR-TD wing has already been performed by the authors and briefly recalled in this paper.

3. Requirements and Design Strategy For the NGCTR-TD, the requirements derived from airworthiness specifications are drawn from EASA (European Aviation Safety Agency) large airplanes (CS-25) and large rotorcrafts (CS-29), for the particular nature of the vehicle. In addition to that, tailored requirements are necessary, since not all the airplane and rotorcraft requirements encompass all the possible scenarios which characterize the tiltrotor. Regarding the structural architecture, as mentioned before, LHD has fixed a number of technical and functional requirements, which have as an outcome a prescribed position of the spars, ribs and moveable surfaces’ geometry. As an example, the fuel capacity of the vehicle is a crucial aspect, which dictates the space available for the fuel bladders (fuel is stored in bladders to cope with crashworthiness requirements), whereas the remaining systems (hydraulics, electrical and avionics equipment, the control surface actuators and the Inter-Connecting Drive Shaft, ICDS) are hosted in the wing-box according to segregation requirements. Given the architecture, the wing structural design, in terms of skin and spar web thicknesses and stringers and spar cap areas, has to cope with strength and buckling requirements, at the lowest possible weight, and more predominantly, with stiffness requirements. Stiffness requirements are of paramount importance when designing the wing of a tiltrotor, making the task quite challen ging also in view of other requirements such as the weight and the accessibility (numerous and wide access holes and removable panels, which tend to reduce torsional stiffness). Stiffness requirements are expressed in terms of limitations on the frequency values of the airframe elastic modes. In particular, for the NGCTR-TD wing, airframe modes that involve significant movement of the hub center in the rotor disc plane directions shall have frequencies falling outside “forbidden bands”, and in order to avoid whirl flutter stability issues, further limits (minimum values) are imposed on some elastic modes’ frequencies, such as torsion modes [7–11]. There is also a requirement on the lowest elastic airframe mode frequency, which shall not be lower than a minimum value, in order to avoid coupling with aeromechanical modes—this is quite demanding when dealing with big masses at the wing tip (nacelles, engine and propellers). Based on the abovementioned requirements, the design strategy in the preliminary phase is composed of two main phases/levels as shown in Figure1. Aerospace 2021, 8, 102 4 of 16 Aerospace 2021, 8, x 4 of 16

FigureFigure 1.1.Design Design strategystrategy ﬂowchart.flowchart.

TheThe firstfirst level level consists consists in in a a multi-objective multi-objective optimization optimization (M-OO), (M-OO), looped looped with with aeroelas- aeroe- ticlastic analyses, analyses,performed performed with with MSC MSC Nastran Nastran (ver. (ver. 2013.1, 2013.1, MSC MSC Software, Software, Newport Newport Beach, Beach, CA, USA).CA, USA). It mainly It mainly consists consists of Matlab of Matlab in-house in-house codes codes (based (based on classical on classical shear flowshear formulas flow for- inmulas closed in thin-walledclosed thin-walled sections sections and panel and buckling panel buckling formulas), formulas), which allow which the allow performance the per- offormance optimization of optimization runs in a very runs short in a very time sh (comparedort time (compared with Finite with Element Finite Analysis) Element Anal- with anysis) acceptable with an degreeacceptable of fidelity.degree of The fidelity. process The aim process is to find aim a is set to offind feasible a set web/skinof feasible thicknessesweb/skin thicknesses and spar capand areas spar compliantcap areas comp withliant the strength with the and strength structural and structural dynamics dy- re- quirements,namics requirements, with the lowestwith the possible lowest possible structural structural mass. In mass. this manner,In this manner, a first compositea first com- wingposite structure, wing structure, able to withstandable to withstand preliminary preliminary sizing loads, sizing and loads, free and from free flutter from within flutter thewithin flight the envelope, flight envelope, is obtained is obtained [11]. Starting [11]. fromStarting the solutionfrom the identified solution inidentified the first-level in the optimization,first-level optimization, the models the are models updated are to allowupdated calculation to allow ofcalculation updatedloads of updated and to loads compute and againto compute flutter again speed. flutter speed. TheThe secondsecond levellevel consistsconsists inin aa FiniteFinite Element-basedElement-based multi-objectivemulti-objective optimizationoptimization withinwithin anan AltairAltair OptiStructOptiStruct environment.environment. InIn particular,particular,the theoptimization optimizationis isperformed performedfor for thethe compositecomposite elementselements (skins(skins andand spars),spars), inin orderorder toto findfind thethe bestbest solution—in solution—in termsterms ofof thickness—whichthickness—which minimizesminimizes thethe structuralstructural weightweight andand isis compliantcompliant withwith strengthstrength andand bucklingbuckling requirementsrequirements andand mainlymainly withwith thethe stiffnessstiffness (flexural(flexural andand torsional),torsional), whichwhich isis aa designdesign driverdriver forfor aa tiltrotortiltrotor wing.wing. TheThe FiniteFinite ElementElement modelmodel ofof thethe wingwing isis mainlymainly builtbuilt withwith 2D 2D and and 1D 1D elements elements (composite (composite 2D 2D elements elements modeled modeled with with equivalent equivalent shells shells based based on theon classicalthe classical lamination lamination theory). theory). The The optimization optimization constraints constraints are noare buckling no buckling up to up a pre-to a definedpre-defined percentage percentage of limit of loadlimit (noload buckling (no buckling up to 80%up to× 80%LL for× LL the for skin; the upskin; to 130%up to ×130%LL for× LL the for spars), the spars), positive positive margins margins of safety of safety at ultimate at ultimate load, andload, minimum and minimum flexural flexural (in-plane (in- andplane out-of-plane) and out-of-plane) and torsional and torsional stiffness. stiffness. The algorithm The algorithm is a gradient is a gradient descent optimization.descent opti- Themization. design The variables design ofvariables this optimization of this optimization process are process the thicknesses are the thicknesses of the upper of the and up- lower skins and of the spars, all made of carbon fiber-reinforced plastic. The outcome is a per and lower skins and of the spars, all made of carbon fiber-reinforced plastic. The out- zonal optimization along the wingspan. The optimized FEM will be used to compute new come is a zonal optimization along the wingspan. The optimized FEM will be used to stiffness distributions, then to update the structural aeroelastic stick beam model and to compute new stiffness distributions, then to update the structural aeroelastic stick beam repeat analyses. Moreover, this zonal optimization is the starting point of a detailed FEM, model and to repeat analyses. Moreover, this zonal optimization is the starting point of a which will also take into account manufacturing, assembly, installation, accessibility and detailed FEM, which will also take into account manufacturing, assembly, installation, maintenance constraints, and which is beyond the scope of the present work. accessibility and maintenance constraints, and which is beyond the scope of the present work.

Aerospace 2021, 8, 102 5 of 16

3.1. Materials and Methods The aim of the optimization is to find the best solution from a set of feasible solutions to a problem. In structural optimization, the kind of optimization applied in the present work, typical problems are sizing optimization, shape optimization and topology optimization. As already reported in the previous section, a two-phase/level structural optimization method was needed for this application. The first one makes use of 1D beam theory to achieve the best wing-box thickness distributions able to cope with different load conditions encountered in-flight by the aircraft and respecting flutter stability; the second one is a refinement based on the use of a more accurate models, based on finite elements. The first-level optimization phase rapidly spans the variable space, through an evolutionary genetic algorithm, in order to highlight the variable space zone with best performances (in terms of stress, buckling and stiffness properties). The second-level optimization makes use of a more efficient but local optimization algorithm, to identify the best wing- box candidate satisfying, from an FE model point of view, the requirements (only the structure thickness is varied during the optimization process) starting from the good starting solutions highlighted during the first phase.

3.1.1. Genetic Algorithm Multi-Objective Optimization Approach (First Phase) A Multi-Objective Genetic Algorithm optimization was used to optimize the wing- box structure [12]. The Genetic Algorithm is a stochastic algorithm that simulates the mechanism of natural evolution by applying the principle of survival in order to produce and obtain improved approximations to a solution. Individuals are encoded as strings, chromosomes: Chromosome values (the genotype) are uniquely mapped to the design variables (96 wing-box thicknesses in our problem, the wing-box phenotype). From the phenotype it is possible to assess the performance of the wing-box represented by a certain chromosome by calculating the mass and the performance of the wing-box—its fitness values. Once the individuals have been assigned a fitness value, they can be chosen from the population, with a probability according to their relative fitness. The best individuals have more chances to transmit their genetic heritage to future generations. Then they are recombined (by choosing parts from their chromosome, and thus from their wing-box thickness distribution) to produce the next generation. The Genetic Algorithm selects a number of individuals inside the current population, called “parents”, and uses them to create the individuals of the next generation called “offspring” or “children”. The performance of individuals in a population tends to increase generation by generation, and the Genetic Algorithm (GA) is terminated when one or more pre-fixed criteria are satisfied. During evolution guided by GAs, non-dominated optimal solutions that identify a Pareto front [13,14] can be recognized, and a solution which best fits the requirements can be chosen by performing an a posteriori trade-off between non-dominated solutions. The optimization objectives (wing mass, strength and buckling) together with the stress and stiffness properties of the wing are calculated through an in-house code, written in Matlab language, according to bending and shear stress analysis of the wing from engineering manuals [15]. Input data for the program include wing geometrical and airfoil shapes, wing-box architecture (positions of the cross-sectional structural elements), wingspan loads, material data, and initial guess values for skin thickness and spar cap areas. Output data include internal stresses, wing structural mass estimation, and stiffness properties of the wing at selected span locations. The output stresses are used to calculate strength and buckling Margins of Safety (MS). A solution is considered feasible if all strength and buckling MS are ≥0.

3.1.2. Aeroelastic Model The ﬁrst level optimization activity, among the other outputs, gives the wingspan beam stiffness and structural mass distribution, which is used to build the dynamic part of the aeroelastic model, Figure2. Aerospace 2021, 8, x 6 of 16

3.1.2. Aeroelastic Model

Aerospace 2021, 8, 102 The first level optimization activity, among the other outputs, 6gives of 16 the wingspan beam stiffness and structural mass distribution, which is used to build the dynamic part of the aeroelastic model, Figure 2.

Figure 2. Aeroelastic model. Figure 2. Aeroelastic model.

The model takesThe into model account—concerning takes into account—concerning structural dynamics—all structural dynamics—all the parts of the the parts of the vehicle, not onlyvehicle, the wing not andonly moveable the wing surfacesand moveable (represented surfaces as (represented beam-like models). as beam-like The models). The remaining tiltrotorremaining parts were tiltrotor supplied parts aswere Nastran supplied super-elements as Nastran super-elements by LHD (e.g., fuselage, by LHD (e.g., fuselage, nacelles, verticalnacelles, tail and vertical junction tail between and junction the wing between and the the fuselage). wing and The the kinematicsfuselage). The of kinematics of the moveable surfaces’the moveable actuation surfaces’ chains actuation are simulated chains byare means simulated of connection by means elementsof connection elements of suitable stiffness.of suitable The stiffness. mass model The mass consists model in lumpedconsists in masses lumped (structural masses (structural and non- and non-struc- structural) alongtural) the wingspan.along the Thewingspan. wing structural The wing masses structural are an masses output are of thean first-leveloutput of the first-level structural designstructural optimization, design whereas optimization, non-structural whereas masses non-structural encompass mass fuel,es fuel encompass system, fuel, fuel sys- ICDS, hydraulicstem, system, ICDS, electricalhydraulics cables, system, actuators. electrical The cables, aerodynamic actuators. model The aerodynamic has been model has realized accordingbeen to realized the Doublet according Lattice to Method the Doublet [16,17 Lattice]. This Method aerodynamic [16,17]. method, This aerodynamic based method, on the linearizedbased potential on the flow linearized aerodynamic potential theory, flow is aerody the extensionnamic theory, to unsteady is the flowsextension of to unsteady the simple andflows steady of Vortex the simple Lattice and Method. steady Aerodynamic Vortex Lattice flat Method. panels Aerodynamic are on the wing flat and panels are on the tail, whereas perpendicularwing and tail, flat whereas panels perpendicular simulate fuselage flat andpanels nacelles, simulate by fuselage creating crosses,and nacelles, by creat- in order to bettering represent, crosses, in in order addition to better to the represent, vertical aerodynamic in addition contribution, to the vertical the aerodynamic lateral contribu- aerodynamic loadstion, inducedthe lateral by aerodynamic the volumetric loads shape induced of the abovementioned by the volumetric components. shape of the abovemen- Infinite Plates Spines [18,19] assure the matching between dynamics and aerodynamics. tioned components. Infinite Plates Spines [18,19] assure the matching between dynamics 3.1.3. Size Optimizationand aerodynamics. with FEM (Second Phase)

ALTAIR Hyperworks3.1.3. Size Optimization OptiStruct [ 20with] has FEM the (Second capability Phase) of performing size optimization, the type of optimization suitable for the problem in hand. In size optimization, the shape of the structureALTAIR is known Hyperworks and the propertiesOptiStruct of[20] structural has the capability elements of such perfor as shellming size optimiza- thickness, beamtion, cross-sectional the type of properties,optimization spring suitable stiffness, for the and problem mass are in modified hand. In to size solve optimization, the the optimizationshape problem. of the Instructure the present is known work, and the thicknessesthe properties of 2Dof FEMstructural elements elements are such as shell used as optimizationthickness, variables beam tocross-sectional search for an properties, optimized structure:spring stiffness, thickness and variation mass are modified to during optimizationsolve the is taken optimization by OptiStruct problem. as continuous. In the presen Eacht work, size variablethe thicknesses is defined of 2D by FEM elements using a DESVARare (DESign used as VARiables) optimization bulk variables data entry. to Thesearch DESVAR for an cardsoptimized are related structure: to size thickness varia- properties in thetion model during using optimization a DVPREL1 is bulk taken data by entry OptiStru (thesect cardsas continuous. teach the solver Each howsize variable is de- to link the designfined variables—one by using a DESVAR or more—to (DESign FEM properties VARiables) used bulk by thedata FE entry. solver). The Differ- DESVAR cards are ent responses canrelated be set to in size OptiStruct properties as in design the model goals, using as shown a DVPREL1 in Table bulk1 . Buckling data entry factor (these cards teach and static stress/strainthe solver are how of to interest link the for design the present variables— method.one Thereor more—to are many FEM different properties used by the methods or algorithmsFE solver). that Different can be used responses to optimize can be a set structure. in OptiStruct In OptiStruct, as design algorithms goals, as shown in Table based on the gradient1. Buckling method factor for and size static (and stress/strain shape) optimization are of interest are implemented.for the present Formethod. There are this class of methods,many different the quality methods of the startingor algorithms design that is a verycan be important used to componentoptimize a structure. of In Op- the process: ThetiStruct, probability algorithms of finding based at leaston the one gradient optimal me localthod representative for size (and of shape) the best optimization are possible solutionimplemented. is higher when For the this distance class of between methods, the the initial quality design of parameters the starting and design the is a very im- global optimumportant is kept component as small as of possible the process: based onThe engineering probability judgement of finding orat previousleast one optimal local global optimizationsrepresentative [21]. of the best possible solution is higher when the distance between the initial

Aerospace 2021, 8, 102 7 of 16

Table 1. Optistruct design goals and functionalities.

Mass Volume Center of Gravity Moment of Inertia Static Compliance Static Displacement Natural Frequency Buckling Factor Static Stress, Strain, Forces Frequency Response Static Composite Stress, Strain, Frequency Response Stress, Displacement, Velocity, Failure Index Strain, Forces Acceleration Weighted Compliance Weighted Frequency Combined Compliance Index Function Temperature

As gradient search methods are iterative procedures, it is necessary to instruct the optimizer on how long to search by imposing the maximum number of iterations. Further, it is necessary to specify how ﬁne the search must be: If the difference between two consecutive proposals is less than a convergence tolerance, acceptable from a design perspective, the optimizer concludes the job. Before starting the optimization, OptiStruct performs a design sensitivity analysis of the structural responses (with respect to the design variables) to maximize the efﬁciency of the optimization process [22].

3.1.3.1. Structural Finite Element Model—Starting Model The Nastran FEM used for the optimization was primarily built to allow for strength and buckling veriﬁcation in the preliminary design phase. The model is a Global FEM, meaning that it also comprises the remaining subsystems of the vehicle as super-elements or concentrated masses (i.e., fuselage, wing tip nacelles, tail, systems hosted in the wing, fuel mass). Analyses are performed by activating the INERTIA RELIEF option with SUPORT at aircraft Center of Gravity (CoG). The wing FE model is shown in Figure3. It is characterized by 2D (TRIA and QUAD) elements for the wing’s upper and lower skin, stringers, spar webs and ribs; and by 1D (CROD) elements for caps. RBE3 elements have the function Aerospace 2021, 8, x 8 of 16 of transferring loads to the structure (inertial; aerodynamics). Suitable RBE2 elements simulate the structural links at the wing–fuselage junction.

FigureFigure 3.3. GlobalGlobal FiniteFinite ElementElement ModelModel (FEM).(FEM).

3.1.3.2.For Materials composite element properties, PCOMP cards and 2D orthotropic material MAT8 are used. PSHELL and MAT1 cards are for isotropic material parts. The sizing load The main wing components (i.e., spars, skins, shape ribs) are designed in carbon fi- conditions are a number of 50 conditions, which comprise aerodynamic loads, propulsion ber-reinforced plastics, while the end ribs and ribs joined to the wing–fuselage links are forces at nacelle prop-rotor location and inertia loads. designed in aluminum alloy. Tables 2 and 3 show the properties used for the optimization phases.

Table 2. Composite material properties [23].

Density Cured Ply Thickness Properties Direction Modulus RTD Value (MPa) (Kg/m3) (mm) Tension 0 E11 59,777 1520 0.31877 Compression 0 −E11 55,916 Tension 90 E22 58,053 Compression 90 −E22 55,020 Shear - G12 3930 Poisson ratio - ν12 0.056

Table 3. Aluminum alloy 7050 (room temperature values).

Direction Tensile Strength Density Yield Strength Properties Modulus (GPa) (°) (MPa) (Kg/m3) (MPa) Tension 0, 90 71 525 2750 470 Shear - 26.5 - Poisson ratio - 0.33 0.056

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3.1.3.2. Materials The main wing components (i.e., spars, skins, shape ribs) are designed in carbon ﬁber-reinforced plastics, while the end ribs and ribs joined to the wing–fuselage links are designed in aluminum alloy. Tables2 and3 show the properties used for the optimization phases.

Table 2. Composite material properties [23].

Density Cured Ply Properties Direction Modulus RTD Value (MPa) (Kg/m3) Thickness (mm)

Tension 0 E11 59,777 1520 0.31877

Compression 0 −E11 55,916

Tension 90 E22 58,053

Compression 90 −E22 55,020

Shear - G12 3930

Poisson ratio - ν12 0.056

Table 3. Aluminum alloy 7050 (room temperature values).

Direction Modulus Tensile Strength Density Yield Strength Properties (◦) (GPa) (MPa) (Kg/m3) (MPa) Tension 0, 90 71 525 2750 470 Shear - 26.5 - Poisson ratio - 0.33 0.056

3.1.3.3. Loads The loads provided by the vehicle manufacturer are in terms of accelerations and rates at vehicle CoG plus the corresponding aerodynamic pressures, balancing loads and concentrated loads such as the prop-rotor thrusts. Two sets of load cases are provided: twenty-five sizing loads conditions at International Standard Atmosphere (ISA) ambient condition, plus an additional twenty-five sizing conditions at −45◦ Outside Air Temper- ature (OAT) ambient condition (cold day). The load conditions are employed to load the wing FEM model and the remainder of the vehicle in order to perform inertial relief analysis on the whole tiltrotor (each load condition determines fully balanced vehicle conditions). Preliminary MSC Nastran static analysis was performed in order to calculate the shear/bending/torsion diagrams along the wing and to identify the most critical load conditions to be used in the optimization FE loop. Each set contains 25 load cases, encompassing ground (e.g., taxi, turning, brake roll, landing), flight maneuvers both in helicopter and aircraft modes (e.g., symmetric pull-up, symmetric push-over, yaw, rolling) and gust conditions. For the case under study, the most demanding load conditions have been proven to be LC09 (low speed pull-up maneuver in helicopter mode) and LC12 (Taxi). In the Figures4 and5, the wing internal loads diagram of the most demanding load conditions are presented. Aerospace 2021, 8, x 9 of 16

shear/bending/torsion diagrams along the wing and to identify the most critical load conditions to be used in the optimization FE loop. Each set contains 25 load cases, encompassing ground (e.g., taxi, turning, brake roll, landing), flight maneuvers both in helicopter and aircraft modes (e.g., symmetric pull-up, symmetric push-over, yaw, rolling) and gust conditions. For the case under study, the most demanding load conditions have been proven to be LC09 (low speed pull-up maneuver in helicopter mode) and LC12 (Taxi). In the Figures 4 and 5, the wing internal loads diagram of the most demanding load conditions are presented. For the first-phase optimization, all the 50 load conditions were used. For the second- phase optimization, a sub-set of the six most demanding loading conditions was used (Table 4).

Table 4. Most demanding loading conditions.

Wing Mass Condi- Load Case ID Description Nacelle Angle Airspeed (V/VD) tion 9 Symmetric Pull-up Zero Fuel 90 0.42 12 Taxi Full Fuel 95 0.00 13 Gust Condition Full Fuel 0 0.43 15 Symmetric Pull-up Full Fuel 95 0.41 Aerospace 2021, 8, 102 9 of 16 17 Symmetric Pull-up Zero Fuel 90 0.99 25 Rolling Pull-out Full Fuel 30 1.06

Figure 4. Low speed pull-up maneuver (airspeed = 0.11 VD; helicopter mode): shear, bending and torsion diagrams (COLD AerospaceFigure 2021 4., 8,Low x speed pull-up maneuver (airspeed = 0.11 VD; helicopter mode): shear, bending and torsion diagrams (COLD10 of 16 conditions). conditions).

Figure 5. Taxi: shear, bending and torsiontorsion diagrams (COLD conditions).

3.1.3.4.For Modifications the ﬁrst-phase to optimization, the Model Loading all the 50Strategy load conditions were used. For the second- phaseThe optimization, baseline Global a sub-set FEM described of the six in most Section demanding 3.1.3.1 was loading first converted conditions into was Altair used Hypermesh(Table4). compatible format, and subsequently it was necessary to avoid the use of IN- ERTIA RELIEF, which resulted to be incompatible with buckling in Optistruct. Moreover, it was necessary to convert super-element matrices from op2 or output binary format to .pch format. The composite laminates PCOMP were converted into equivalent PSHELL. The equivalent PSHELL was chosen as the design variable, while the minimization of the mass is the design objective. The design constraints are set on buckling first eigenvalue, strains, wing tip displacements and rotations. A scheme of the process followed is shown in Figure 6.

Figure 6. Optistruct optimization process.

As regards the modification needed to avoid the use of INERTIA RELIEF, the model was constrained at the SUPORT grid (CoG), and then linear and angular accelerations (previously calculated with the use of INERTIA RELIEF) were applied to the model to take into account inertial forces for the buckling runs. Each of the loading conditions is characterized by a different position of the center of gravity, different fuel mass values, and different positions of the moveable surfaces and the tip masses. For this reason, to easily allow the program to select the appropriate mass conditions for each analyzed load case, it was necessary to define multipoint constraint equations for masses and CoG grids. Once the preparatory phase of the model is finished, the thicknesses associated with the equivalent PSHELL of the upper panel’s skin, lower

Aerospace 2021, 8, x 10 of 16

Aerospace 2021, 8, 102 10 of 16

Table 4. Most demanding loading conditions.

Load Case ID Description Wing Mass Condition Nacelle Angle Airspeed (V/VD) 9 Symmetric Pull-up Zero Fuel 90 0.42 12 Taxi Full Fuel 95 0.00 13 Gust Condition Full Fuel 0 0.43 15 Symmetric Pull-up Full Fuel 95 0.41 17 Symmetric Pull-up Zero Fuel 90 0.99 Figure 5. Taxi:25 shear, bending Rolling Pull-outand torsion diagrams Full (COLD Fuel conditions). 30 1.06

3.1.3.4.3.1.3.4. ModificationsModifications toto thethe ModelModel LoadingLoading StrategyStrategy TheThe baselinebaseline GlobalGlobal FEMFEM describeddescribed inin SectionSection 3.1.3.13.1.3.1 waswas firstfirst convertedconverted intointo AltairAltair HypermeshHypermesh compatible format, format, and and subsequently subsequently it itwas was necessary necessary to toavoid avoid the the use use of IN- of INERTIAERTIA RELIEF, RELIEF, which which resulted resulted to to be be incompatible incompatible with with buckling buckling in in Optistruct. Optistruct. Moreover, Moreover, itit waswas necessarynecessary toto convertconvert super-elementsuper-element matricesmatrices fromfrom op2op2 oror outputoutput binarybinary formatformat toto .pch.pch format.format. TheThe compositecomposite laminateslaminates PCOMPPCOMP werewere convertedconverted intointo equivalentequivalent PSHELL.PSHELL. TheThe equivalentequivalent PSHELLPSHELL waswas chosenchosen asas thethe designdesign variable,variable, whilewhile thethe minimizationminimization ofof thethe massmass isis thethe designdesign objective.objective. The design constraintsconstraints are setset onon bucklingbuckling firstfirst eigenvalue,eigenvalue, strains,strains, wingwing tiptip displacementsdisplacements andand rotations.rotations. AA schemescheme ofof thethe processprocess followedfollowed isis shownshown inin FigureFigure6 6..

FigureFigure 6.6. Optistruct optimizationoptimization process.process.

AsAs regardsregards thethe modificationmodification neededneeded toto avoidavoid thethe useuse ofof INERTIAINERTIA RELIEF,RELIEF, thethe modelmodel waswas constrainedconstrained atat thethe SUPORTSUPORT gridgrid (CoG),(CoG), andand thenthen linearlinear andand angularangular accelerationsaccelerations (previously(previously calculatedcalculated with with the the use use of of INERTIA INERTIA RELIEF) RELIEF) were were applied applied to the to modelthe model to take to intotake accountinto account inertial inertial forces forces for the for buckling the buckling runs. runs. EachEach ofof thethe loadingloading conditionsconditions isis characterizedcharacterized byby aa differentdifferent positionposition ofof thethe centercenter ofof gravity,gravity, differentdifferent fuelfuel massmass values,values, andand differentdifferent positionspositions ofof thethe moveablemoveable surfacessurfaces andand thethe tiptip masses.masses. ForFor thisthis reason,reason, toto easilyeasily allowallow thethe programprogram toto selectselect thethe appropriateappropriate massmass conditionsconditions forfor eacheach analyzedanalyzed loadload case,case, itit waswas necessarynecessary toto definedefine multipointmultipoint constraintconstraint equationsequations forfor massesmasses andand CoGCoG grids.grids. Once the preparatory phase of the model is finished,finished, thethe thicknessesthicknesses associatedassociated withwith thethe equivalentequivalent PSHELLPSHELL ofof thethe upperupper panel’spanel’s skin,skin, lowerlower panel’s skin and of the spar are selected as design variables, by imposing upper and lower limit values. Subsequently the response functions are defined: the first buckling eigenvalue, the torsional and flexural stiffness, and total structural mass. The final objective is the minimization of the mass (objective response). Different constrained optimization analyses were performed for the different design constraints.

3.1.4. Wing Stiffness Evaluation from FEM (Second Phase) The wing Finite Element model resulting from the Optistrut optimization is then used to extract the wing stiffness properties of a beam-like model equivalent to the wing FEM in order to feed the aeroelastic stick-beam model. The evaluation of the wing stiffness Aerospace 2021, 8, 102 11 of 16

properties is based on static FE calculation with unitary loads (forces/moments) applied at the tip of the wing, having as output a set of nodal displacements at a certain number of wingspan stations. The calculation of equivalent beam properties is done by means of a 3D point cloud registration method [24]. The position and the displacements of the nodes corresponding to the selected wing stations were extracted from the BDF and PCH Nastran ﬁles for the un-deformed and deformed wing (for each one of the six load conditions: force and moment along each axis). Sections displacements and rotations were then calculated as the rigid motions (three translations and three rotations) of the un-deformed section that minimize the root mean square error between the FEM deformed and rigid displaced section points. Relations between beam stiffness and section displacements can be obtained making use of the theory for Saint-Venant [24].

4. Preliminary Structural Design Results 4.1. Results of the Optimization Process and Flutter Analysis in the First Phase The work already presented in [2] gives a number of solutions on the Pareto front, which can also be analyzed from a manufacturing point of view. In particular, the solution which guarantees the strength and buckling requirements at minimum mass and is feasible from a manufacturing point of view is selected for ﬂutter analyses, and if cleared (ﬂutter speed VF ≥ 1.15 times the Dive Speed, VD), it is further involved in the second-level optimization performed in this work, which is based on FE models.

4.2. Results of Finite Element Optimization (Second Phase) and Comparison with Respect to the Base Design By performing dimensional optimizations in Optistruct, it is possible to obtain a wing- box with minimum weight, and optimal strength and ﬂexural and torsional stiffness. The results obtained in terms of percentage of weight reduction with respect to the baseline model obtained in the ﬁrst-level optimization for the main combinations of interest are reported in Table5, where it is apparent that the more the objective functions considered, the less the weight reduction obtained.

Table 5. Results of Optistruct optimization in terms of percentage of delta weight with respect to the baseline obtained in the ﬁrst-phase optimization.

Optimization Delta-Weight (%) Buckling −26% Buckling −14% Stiffness (Out-of-plane ﬂexural and Torsional) Buckling −5% Strength Buckling Stiffness (In-plane and Out-of-plane ﬂexural and Torsional) −3% Strength

As an example, Figures7 and8 depict with different colors the thickness distribution of the upper and lower skin, for the buckling-optimized case, where the numbers represent the fraction with respect to the maximum thickness. Aerospace 2021, 8, x 12 of 16

Aerospace 2021, 8, x 12 of 16

As an example, Figures 7 and 8 depict with different colors the thickness distribution of theAs upper an example, and lower Figures skin, 7for and the 8 depictbuckling-optimized with different case,colors where the thickness the numbers distribution repre- Aerospace 2021, 8, 102 sentof the the upper fraction and with lower respect skin, tofor the the maximum buckling-optimized thickness. case, where the numbers 12repre- of 16 sent the fraction with respect to the maximum thickness.

Figure 7. Upper and lower skin initial thickness distribution along wingspan. Figure 7. Upper and lower skin initial thickness distribution along wingspan.

Figure 8. OptiStruct results: the different colors indicateindicate different thickness of the wing zones.zones. Aerospace 2021, 8, x 13 of 16 Figure 8. OptiStruct results: the different colors indicate different thickness of the wing zones. Figures9 9 andand 1010 depictdepict thethe strains related toto thethe samesame case (non-dimensional results).results). Figures 9 and 10 depict the strains related to the same case (non-dimensional results).

FigureFigure 9. 9.Contour Contour plot plot of of the the upper upper skin skin strains strains (non-dimensional (non-dimensional results). results).

Figure 10. Contour plot of the lower skin strains (non-dimensional results).

4.3. Wing Stiffness Evaluation Starting from the optimized configuration, the beam-like stiffness properties of the wing are evaluated (Section 3.1.4) and shown in Figure 11. The output of this calculation will be used for updating the aeroelastic model and then to perform the flutter calculation on the optimized configuration.

Aerospace 2021, 8, x 13 of 16

Aerospace 2021, 8, 102 13 of 16

Figure 9. Contour plot of the upper skin strains (non-dimensional results).

Figure 10. Contour plot of the lower skin strains (non-dimensional results). Figure 10. Contour plot of the lower skin strains (non-dimensional results). 4.3. Wing Stiffness Evaluation 4.3.Starting Wing Stiffness from theEvaluation optimized configuration, the beam-like stiffness properties of the Aerospace 2021, 8, x wing areStarting evaluated from (Section the optimized 3.1.4) and configuration, shown in Figure the beam-like 11. The output stiffness of thisproperties calculation of the14 of 16 willwing be usedare evaluated for updating (Section the aeroelastic 3.1.4) and modelshown andin Figure then to 11. perform The output the flutter of this calculation calculation onwill the be optimized used for updating configuration. the aeroelastic model and then to perform the flutter calculation on the optimized configuration.

FigureFigure 11. 11. WingWing stiffness stiffness properties and and elastic elastic axis axis position, position, to feed to feed aeroelastic aeroelastic model. model.

4.4.4.4. Flutter Flutter Results Results ComparisonComparison AA schematic schematic comparisoncomparison between between ﬂutter flutter results results related related to ﬁrst-phase to first-phase and second- and second- phasephase optimizations optimizations isis shown shown in in Table Table6. 6.

Table 6. Comparison of flutter speed (ratio of flutter speed and dive speed) between first-phase and second-phase optimization.

Case Analyzed First-Phase Optimized Wing Second-Phase Optimized Wing Full fuel case 1.52 1.67 Zero fuel case 1.48 1.63

With respect to the first-phase optimization, no changes in the flutter mechanisms have been detected. Due to the fact that the tail is the main driver of these flutter mechanisms, the only effect of the update of the wing structural properties, in line with the wing second-phase optimization, is a small increase of the flutter speed.

5. Higher Fidelity Models The results of this two-level optimization, in terms of wing-box sizing, will be transferred into higher fidelity models (detailed FEM and digital mockup) to verify compliance with all the remaining requirements, such as stress and fatigue, crashworthiness, accessibility, assembly and integration, and manufacturability. After that, the final wing configuration will be launched for manufacturing. In Figure 12, an image of the digital mockup (DMU) is reported, which takes into account manufacturing, assembly, installation, accessibility to internal systems and maintenance constraints.

Aerospace 2021, 8, 102 14 of 16

Table 6. Comparison of flutter speed (ratio of flutter speed and dive speed) between first-phase and second-phase optimization.

Case Analyzed First-Phase Optimized Wing Second-Phase Optimized Wing Full fuel case 1.52 1.67 Zero fuel case 1.48 1.63

With respect to the first-phase optimization, no changes in the flutter mechanisms have been detected. Due to the fact that the tail is the main driver of these flutter mechanisms, the only effect of the update of the wing structural properties, in line with the wing second-phase optimization, is a small increase of the flutter speed.

5. Higher Fidelity Models The results of this two-level optimization, in terms of wing-box sizing, will be transferred into higher fidelity models (detailed FEM and digital mockup) to verify compliance with all the remaining requirements, such as stress and fatigue, crashworthiness, accessibility, assembly and integration, and manufacturability. After that, the final wing Aerospace 2021, 8, x 15 of 16 configuration will be launched for manufacturing. In Figure 12, an image of the digital mockup (DMU) is reported, which takes into account manufacturing, assembly, installation, accessibility to internal systems and maintenance constraints.

FigureFigure 12. 12.WingWing Digital Digital Mockup. Mockup. 6. Conclusions 6. Conclusions This work is a continuation of a former work by the authors [2]. The previous and This work is a continuation of a former work by the authors [2]. The previous and the present works represent an overview of a two-level optimization of the composite the present works represent an overview of a two-level optimization of the composite wing of a tiltrotor. The main requirements underpinning the design of the wing have been wing of a tiltrotor. The main requirements underpinning the design of the wing have been presented, with a focus on the limitation on certain wing structural mode frequencies and presented,aeroelastic with clearance. a focus on The the objective limitation of on the certain present wing paper structural is the description mode frequencies of the secondand aeroelasticpart, i.e., clearance. second-level The multi-objective objective of the optimization present paper by is using the description FE models (notof the engineering second part,models i.e., second-level as done in themulti-objective former work). optimi Thezation problems by using (e.g., FE compatibility models (not issuesengineering between modelsOptistruct as done and in Nastranthe former INERTIA work). The RELIEF) problems encountered (e.g., compatibility in implementing issues between such a process Op- tistructand theand modification Nastran INERTIA needed RELIEF) to the model encoun havetered been in implementing described. The such output a process of the processand theis modification the best solution—in needed to the terms model of thickness—which have been described. minimizes The output the structuralof the process weight is the and bestis solution—in compliant with terms stiffness of thickness—which (flexural and mi torsional),nimizes the strength structural and weight buckling and requirements. is compli- antThe with validation stiffness (flexural of the numerical and torsional), methodology strength and and tools buckling could requirements. be delegated to The other valida- papers, tionusing of the the numerical preliminary methodology results obtained and tools in this could work be asdelegated a reference to other and thepapers, experimental using theresults preliminary that will results be achievedobtained inin thethis nextwork project as a reference phases. and The the acquired experimental experience results with thattiltrotor will be wing achieved design in allowed the next the project authors ph toases. reduce The theacquired computational experience optimization with tiltrotor effort wingby streamliningdesign allowed the setthe ofauthors sizing loadto reduce conditions, the computational which are quite optimization different with effort respect by to a streamliningfixed-wing the aircraft. set of Thesizing optimal load conditions solution was, which then are analyzed quite different from a flutterwith respect point of to view a fixed-wingand results aircraft. compared The optimal with previous solution outcomes, was then showing analyzed that from the optimizeda flutter point wing of structure view and results compared with previous outcomes, showing that the optimized wing structure is flutter-free in the flight envelope. The results of this two-level optimization, in terms of wing-box sizing, will be transferred into higher fidelity models (detailed FEM and digital mockup) to verify compliance with all the remaining requirements, such as stress and fatigue, crashworthiness, accessibility, assembly and integration, and manufacturability. After that, the final wing configuration will be launched for manufacturing.

Author Contributions: All authors have equally contributed to this article. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Clean Sky 2 Joint Undertaking under the European Un- ion’s Horizon 2020 research and innovation program under Grant Agreement number: 807090— FRC GAM 2018—H2020-IBA-CS2-GAMS-2017 Amendment Reference No. AMD-807090-23 [1].

Institutional Review Board Statement: Not Applicable

Aerospace 2021, 8, x 15 of 16

Figure 12. Wing Digital Mockup.

6. Conclusions This work is a continuation of a former work by the authors [2]. The previous and the present works represent an overview of a two-level optimization of the composite wing of a tiltrotor. The main requirements underpinning the design of the wing have been presented, with a focus on the limitation on certain wing structural mode frequencies and aeroelastic clearance. The objective of the present paper is the description of the second part, i.e., second-level multi-objective optimization by using FE models (not engineering models as done in the former work). The problems (e.g., compatibility issues between Op- tistruct and Nastran INERTIA RELIEF) encountered in implementing such a process and the modification needed to the model have been described. The output of the process is the best solution—in terms of thickness—which minimizes the structural weight and is compliant with stiffness (flexural and torsional), strength and buckling requirements. The validation of the numerical methodology and tools could be delegated to other papers, using the preliminary results obtained in this work as a reference and the experimental results that will be achieved in the next project phases. The acquired experience with tiltrotor wing design allowed the authors to reduce the computational optimization effort by Aerospace 2021, 8, 102 streamlining the set of sizing load conditions, which are quite different with respect15 to of 16a fixed-wing aircraft. The optimal solution was then analyzed from a flutter point of view and results compared with previous outcomes, showing that the optimized wing struc- tureis flutter-free is flutter-free in the in flight the envelope.flight envelope. The results The results of this two-levelof this two-level optimization, optimization, in terms in of termswing-box of wing-box sizing, will sizing, be transferred will be transfe into higherrred into fidelity higher models fidelity (detailed models FEM (detailed and digital FEM andmockup) digital to mockup) verify compliance to verify compliance with all the with remaining all the remaining requirements, requirements, such as stress such and as stressfatigue, and crashworthiness, fatigue, crashworthiness, accessibility, accessibi assemblylity, assembly and integration, and integration, and manufacturability. and manufacturability.After that, After the final that, wing the final configuration wing configuration will be launched will be forlaunched manufacturing. for manufacturing.

AuthorAuthor Contributions: All authors have equally contributed to this article. All All authors have read andand agreed agreed to to the the published published version version of of the the manuscript. Funding:Funding: ThisThis research was fundedfunded byby the the Clean Clean Sky Sky 2 Joint2 Joint Undertaking Undertaking under under the the European European Union’s Un- ion’sHorizon Horizon 2020 research2020 research and innovation and innovation program program under Grant under Agreement Grant Agreement number: 807090—FRCnumber: 807090— GAM FRC2018—H2020-IBA-CS2-GAMS-2017 GAM 2018—H2020-IBA-CS2-GAMS-2017 Amendment Amendment Reference No.Reference AMD-807090-23 No. AMD-807090-23 [1]. [1].

InstitutionalInstitutional Review Review Board Board Statement: Statement: NotNot Applicable Applicable. Informed Consent Statement: Not Applicable. Data Availability Statement: Not Applicable.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

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