energies

Article Scavenging Ports’ Optimal Design of a Two-Stroke Small Aeroengine Based on the Benson/Bradham Model

Yuan Qiao, Xucheng Duan, Kaisheng Huang * , Yizhou Song and Jianan Qian

State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China; [email protected] (Y.Q.); [email protected] (X.D.); [email protected] (Y.S.); [email protected] (J.Q.) * Correspondence: [email protected]; Tel.: +86-138-0123-7852



Received: 27 September 2018; Accepted: 11 October 2018; Published: 12 October 2018


Abstract: The two-stroke engine is a common power source for small and medium-sized unmanned aerial vehicles (UAV), which has wide civil and military applications. To improve the engine performance, we chose a prototype two-stroke small areoengine, and optimized the geometric parameters of the scavenging ports by performing one-dimensional (1D) and three-dimensional (3D) computational fluid dynamics (CFD) coupling simulations. The prototype engine is tested on a dynamometer to measure in-cylinder pressure curves, as a reference for subsequent simulations. A GT Power simulation model is established and validated against experimental data to provide initial conditions and boundary conditions for the subsequent AVL FIRE simulations. Four parameters are considered as optimal design factors in this research: Tilt angle of the central scavenging port, tilt angle of lateral scavenging ports, slip angle of lateral scavenging ports, and width ratio of the central scavenging port. An evaluation objective function based on the Benson/Bradham model is selected as the optimization goal. Two different operating conditions, including the take-off and cruise of the UAV are considered. The results include: (1) Orthogonal experiments are analyzed, and the significance of parameters are discussed; (2) the best factors combination is concluded, followed by simulation verification; (3) results before and after optimization are compared in details, including specific scavenging indexes (delivery ratio, trapping efficiency, scavenging efficiency, etc.), conventional performance indicators, and the sectional views of gas composition distribution inside the cylinder.

Keywords: two-stroke engine; scavenging ports; optimal design; Benson/Bradham model

1. Introduction In the context of the information age of the 21st century, unmanned aerial vehicle (UAV) technology has gradually matured with the rapid development of microelectronics technology and communication technology. Compared with manned aircrafts, UAVs are small in size, light in weight, undemanding in runway requirements, low in manufacturing and use cost, and can also reduce casualties on the battlefield. Therefore, UAVs are widespread in both military and civilian applications. Besides, small and medium-sized UAVs (maximum take-off weights of 10 to 1000 kg) are particularly popular because of good overall performance and usage convenience [1]. Pressured by increasingly stringent emission regulations, crankcase scavenged two-stroke engines are scarcely applied in the automotive industry [2]. However, benefitting from its significant advantages in simple structure, few accessories, and high power density, the two-stroke engine is universally selected as the power source of small and medium-sized UAVs. In Europe and the United States, several companies specialize

Energies 2018, 11, 2739; doi:10.3390/en11102739 www.mdpi.com/journal/energies Energies 2018, 11, 2739 2 of 26 in producing two-stroke light-duty engines for small and medium-sized UAVs, such as 3 W, Gobler Hirth, and Limbach in Germany, Wilksch in the UK, Zanzottera in Italy, and XRDi and DeltaHawk in the US [3]. Different from conventional four-stroke engines equipped with sophisticated valve timing systems, two-stroke engines generally employ the opening and closing of piston-motion-controlled scavenging ports as the way of pumping and exhausting. The comparatively simple scavenging system shows its superiority in engine design and manufacturing, but significant drawbacks also emerge in volumetric efficiency and in-cylinder gas flow control [4,5]. The potential of two-stroke engines has become more and more subject to increasing research work trying to optimize the gas motion during the scavenging process. The gas motion is notably dependent on the specific design of scavenging ports. Therefore, the latter is consequently selected as the research focus of this paper. A major problem in constructing the two-stroke engine model is to describe the corresponding scavenging process. Since the first two-stroke engine at the end of 19th century by Sir Dugald Clerk, various different scavenging systems have been designed and pertinent scavenging models have been successively developed and optimized by tracking studies to draw conclusions about the quality of scavenging process. Merker and Gerstle summarized a tremendous number of scavenging models and proposed a comprehensive evaluation of existing models [6]. They classified existing models into three categories. Precision, mathematical expenditure, the structure of the formula, and the difficulty of empirical parameter calibration were all taken into account to obtain the assessment results. Ma et al. applied a relatively simple perfect displacement model and perfect mixing model in the simulation modeling method and experimental investigation on the uniflow scavenging system of an opposed-piston folded-cranktrain diesel engine [7]. Based on the single-zone Benson model, Brynych et al. proposed a way of generalizing the scavenging curves to lower the time cost of three-dimensional (3D) computational fluid dynamics (CFD) simulations and accelerated the procedure of engine optimization substantially [8]. Mattarelli et al. chose main scavenging coefficients (charging efficiency, scavenging efficiency, trapping efficiency, delivery ratio, etc.) as port design criteria for two-stroke loop scavenged engines and performed a series of multi-cycle 3D CFD simulations to validate the optimum configuration obtained from the previous parametric analysis [9]. The final results demonstrated the excellent efficiency of the scavenging process. Similarly, to fulfill the potential of boosted uniflow scavenged direct injection gasoline (BUSDIG), disparate layout schemes of scavenging ports were designed and corresponding parameters were varied to investigate their impacts on the scavenging performance and the in-cylinder gas motion [10–12]. In the current studies, burgeoning one-dimensional (1D)/3D simulation technologies conspire to play an important role in providing researchers with essential and powerful tools for rapid prototyping, massive data processing, and visualization analysis [13]. Regarding the optimization of the scavenging systems of two-stroke engines, simulation techniques also show promising potential and have yielded many fruitful research achievements [14–16]. Cui et al. conducted a detailed CFD analysis of the scavenging process in a steady-state scavenging flow test and adopted the proper orthogonal decomposition (POD) method in search of the cause of precession phenomenon found in the high swirl model [17]. Xie et al. performed the numerical simulation of an Opposed-Piston 2-Stroke Diesel Engine (OP2S) and compared the results of in-cylinder gas motion with those of a uniflow-scavenged two-stroke engine using CFD engine models [18]. Zang et al. investigated the scavenging process of Linear Internal Combustion Engine-Linear Generator Integrated System (LICELGIS) and implemented a steady-state verification test to further explore the motion characteristics of LICELGIS [19]. Mattarelli et al. reported a CFD study on a two-stroke Opposed Piston High Speed Direct Injection Diesel Engine (OPHSDI) and analyzed the influence of the offset between the crankshafts by performing 3D CFD simulations [20]. Li et al. investigated the influences of intake ports and pent-roof structures on the flow characteristics of a poppet-valved two-stroke gasoline engine to minimize the short-circuiting of the intake charge [21]. He et al. emphasized the significant effects of exhaust back pressure, porting timing, and intake port layout on the scavenging quality and trapped air mass in cylinder Energies 2018, 11, 2739 3 of 26 by transient CFD simulation, including blow-down and scavenging [22]. Zhou et al. performed the thermodynamic simulation of a self-balanced opposed-piston folded-cranktrain two-stroke engine for UAVs to improve engine performance indicators, including power density, fuel compatibility, fuel economy, and durability in a harsh environment [23]. The scavenging process is the key for in-cylinder gas motion and the mixing of fresh air and injected fuel, which has remarkable influence on the in-cylinder combustion process and, eventually, on the engine performance. However, relevant two-stroke engine scavenging system optimization schemes proposed in previous studies were scarcely targeted from the perspective of scavenging models. Instead of considering comparatively sophisticated scavenging models, such as the Dang/Wallace model and Benson/Bradham model, previous researchers usually employed conventional scavenging evaluation indexes (charging efficiency, scavenging efficiency, trapping efficiency, deliver ratio, etc.) as the evaluation criteria of scavenging ports’ optimal design [6]. For the deeper investigation of the scavenging process, a two-stroke small aeroengine is selected as the prototype. Afterwards, the geometric structure of the prototype engine is parameterized. An objective function based on the more comprehensive Benson/Bradham model is established in this paper. To guarantee the consistency and compatibility between 1D and 3D boundary conditions, a 1D/3D coupling numerical simulation architecture is set up and validated against the prototype experimental data. Commonly, 3D CFD simulation is a time-consuming process on the condition that all the possible combinations of geometric parameters need to be traversed. By varying the parameters, the number of investigated cases amounted to 1352 along with dramatically increasing the time expenditure [9]. To minimize the time cost of determining the most effective geometric parameters configuration, the orthogonal experimental design (OED) method is employed to reduce the 3D CFD simulation amount. Additionally, optimization results are validated by performing dedicated 3D CFD simulations. The objective function value, scavenging indexes (delivery ratio, trapping efficiency, scavenging efficiency, etc.), conventional performance indexes, including the indicated mean effective pressure (IMEP) and indicated specific fuel consumption (ISFC), and the sectional views of gas composition distribution inside the cylinder are all taken into consideration when comparing results before and after the optimization.

2. Scavenging System Modeling

2.1. Scavenging Model Selection Quantitative evaluation indexes are needed to describe the quality of the scavenging process of a two-stroke engine. Corresponding mathematical models abstracted from an actual scavenging process can serve for this purpose and reflect how much fresh charge can be delivered and trapped in the cylinder through specific model parameters [24,25]. To establish the scavenging model, several mass parameters are defined as follows: mi is the mass of delivered fresh charge through all scavenging ports; m f C is the mass of delivered fresh charge retained in the cylinder; mleak is the mass of delivered fresh charge leaking out of the cylinder (i.e., mi is the sum of the m f C and mleak, the latter is the so-called ”short circuit loss part”); mbg is the mass of residual burned gas retained in the cylinder; mall is the mass of all trapped cylinder charge (i.e., mall is the sum of the m f C and mbg); and m0 is equal to the product of the cylinder volume and ambient density and is the so-called “reference mass” [26]. Based on these definitions, some conventional scavenging indexes can be introduced: The delivery ratio:

mi mass of delivered fresh charge through all scavenging ports λS = = (1) m0 cylinder volume × ambient density

The scavenging efficiency:

m f C m f C mass of delivered fresh charge retained in cylinder ηS = = = (2) mall m f C + mbg mass of all trapped cylinder charge Energies 2018, 11, 2739 4 of 26

The trapping efficiency:

m f C mass of delivered fresh charge retained in cylinder ηT = = (3) mi mass of delivered fresh charge through all scavenging ports

The charging efficiency:

m f C mass of delivered fresh charge retained in cylinder ηC = = (4) m0 cylinder volume × ambient density

Considering that throttle opening of a small areoengine is relatively wide under medium and high load operation conditions, the intake pressure is approximately equal to the ambient pressure, which makes mall nearly the same as m0. Coincidentally, the “reference mass” in simulation analysis is normally defined as the mass of all trapped cylinder charge (i.e., mall), which makes ηS equal to ηC. Consequently, we make no distinction between the scavenging efficiency and the charging efficiency in the following content in case of over parameterization [27]:

ηS ≈ ηC = λS × ηT (5)

Energies 2017, 10, x FOR PEER REVIEW 4 of 28 Scavenging efficiency, ηS, is of vital importance toward the gas exchange quality of the scavenging 𝑚 mass of delivered fresh charge retained in cylinder process because ηS indicates 𝜂 to = what= extent the residual burned gases in the(3) cylinder have been 𝑚 mass of delivered fresh charge through all scavenging ports replaced with fresh mixtureThe charging after efficiency: the end of the scavenging process [28]. The higher the ηS value, the more sufficient the fresh charge𝑚 intake mass andof delivered the fresh greater charge retained the improvements in cylinder of power performance 𝜂 = = (4) 𝑚 cylinder volume × ambient density enhancement. In addition, the trapping efficiency, ηT, plays a pivotal role in reflecting what fraction Considering that throttle opening of a small areoengine is relatively wide under medium and of the fresh charge suppliedhigh load operation to theconditions, cylinder the intake is pressure retained is approximately in the equal cylinder. to the ambient This pressure, attribute is important especially for a port fuelwhich makes injection 𝑚 nearly (PFI) the engine,same as 𝑚 because. Coincidentally, the the short “reference circuit mass”loss in simulation of fresh mixture can lead analysis is normally defined as the mass of all trapped cylinder charge (i.e., 𝑚), which makes 𝜂 to a significant deteriorationequal to 𝜂 . inConsequently, fuel economy we make no and distinction emission between characteristics. the scavenging efficiency In and terms the of the relationship charging efficiency in the following content in case of over parameterization [27]: between these scavenging evaluation indexes, the higher the delivery ratio, λS, the more mass flow 𝜂 ≈𝜂 =𝜆 ×𝜂 (5) entering the cylinder, which means both m and m increase. Under that circumstance, the decrease Scavenging efficiency, 𝜂 , is fof C vital importanceleak toward the gas exchange quality of the of ηT and the increasescavenging of ηS processwillhappen because 𝜂 simultaneously.indicates to what extent the The residual trade-off burned gases relationship in the cylinder between ηT and ηS have been replaced with fresh mixture after the end of the scavenging process [28]. The higher the makes trouble for balancing𝜂 value, the powermore sufficient performance the fresh charge andintake fueland the consumption greater the improvements in the of power optimization process of performance enhancement. In addition, the trapping efficiency, 𝜂, plays a pivotal role in reflecting the two-stroke enginewhat scavenging fraction of the fresh system. charge supplied That to the is cyli preciselynder is retained the in the reason cylinder. This why attribute appropriate is scavenging models are needed forimportant integrative especially for optimal a port fuel design.injection (PFI) engine, because the short circuit loss of fresh mixture can lead to a significant deterioration in fuel economy and emission characteristics. In terms

The applicationof ofthe relationship the simplest between these single-zone scavenging eval single-phaseuation indexes, the higher model the delivery is the ratio, most 𝜆, extensive among previous studies [29].the Themore mass single-zone flow entering the concept cylinder, which denotes means both that 𝑚 the and cylinder𝑚 increase. volume Under that is occupied by only circumstance, the decrease of 𝜂 and the increase of 𝜂 will happen simultaneously. The trade-off one zone throughoutrelationship the scavenging between 𝜂 and process 𝜂 makes while trouble thefor balancing single-phase power performance concept and meansfuel the scavenging consumption in the optimization process of the two-stroke engine scavenging system. That is process is not dividedprecisely into the distinct reason why phases.appropriate scavenging This category models are needed is classified for integrative into optimal two design. ideal models: Perfect The application of the simplest single-zone single-phase model is the most extensive among mixing model and perfectprevious studies displacement [29]. The single-zone model. concept denote Thes that former the cylinder is volume based is occupied on the by only assumption that fresh charge introduced intoone zone the throughout cylinder the scavenging per unit process time while is the instantly single-phase mixedconcept means with the scavenging residuals. On the contrary, process is not divided into distinct phases. This category is classified into two ideal models: Perfect the latter is based onmixing assumptions, model and perfect includingdisplacement model. no mixingThe former is between based on the freshassumption charge that fresh and burned gas and charge introduced into the cylinder per unit time is instantly mixed with residuals. On the contrary, no direct loss of freshthe latter charge is based on into assumptions, the exhaust including noport mixing between [6]. Figure fresh charge1 andpresents burned gas anand no illustration of these two concepts. direct loss of fresh charge into the exhaust port [6]. Figure 1 presents an illustration of these two concepts.

(a) (b)

Figure 1. (a) Perfect mixing model; (b) perfect displacement model. Figure 1. (a) Perfect mixing model; (b) perfect displacement model. Energies 2018, 11, 2739 5 of 26

Corresponding equations are given as follows [30]:

−λS ηS = 1 − e for the perfect mixing model (6) ( λS for λS ≤ 1 ηS = for the perfect displacement model (7) 1 for λS > 1 Because these two ideal models are inherently deficient in accurate prediction about the trend behavior of two-stroke engines, detailed investigations of the scavenging process are improbable [31]. For further improvements, the single-zone Benson model was proposed and regarded as a combination of the above-mentioned two ideal models [32]. By integrating them, the single-zone Benson model assumes that the whole scavenging process can be divided into two phases: The perfect displacement model acts in the first phase and the perfect mixing model acts in the second phase [6]. A critical variable, the “displacement ratio”, is defined as follows:

Vbg x = (8) Vall where Vbg is the instant burned gas volume in the cylinder, and Vall is the whole cylinder volume. After some rearrangements of the above-mentioned models, scavenging parameters can be given [31]: ( λS for λS ≤ x ηS = x−λ (9) 1 − (1 − x)e S for λS > x

( 0 for λS ≤ x β = x−λ (10) 1 − (1 − x)e S for λS > x where β is the mass fraction of fresh charge in exhaust gas and represents the exhaust gas purity. The lower the β value, the less fresh charge leaking out of the cylinder. It can be concluded that x is the two-phase demarcation point. The larger the x value, the closer the scavenging process is to the perfect displacement model, and vice versa, the closer to the perfect mixing model. Therefore, the larger the x value, the better the scavenging consequence. In practical applications, x needs to be determined by experimental data fitting. Based on the existing single-zone Benson model, the Benson/Bradham model considers the short circuit loss of fresh charge and broadens into a multi-zone multi-phase scavenging model. The phase/zone concept of the Benson/Bradham model is shown in Figure2. By additionally introducing a key parameter, the short-circuiting fraction, y, while following the pre-established two-phase assumption, the Benson/Bradham model significantly reduces the insufficiency of the scavenging models above. The explicit functional relation of scavenging parameters can be determined by: ( x (1 − y)λS for λS ≤ η = 1−y (11) S x−(1−y)λS x 1 − (1 − x)e for λS > 1−y

( x y for λS ≤ β = 1−y (12) x−(1−y)λS x 1 − (1 − x)(1 − y)e for λS > 1−y It can be derived that the Benson/Bradham model will be reduced to a single-zone Benson model when y is 0. With an appropriate parameter variation of the displacement ratio, x, and short-circuiting fraction, y, every scavenging system can be approximated [32]. The closer x is to 1 and the closer y is to 0, the better the quality of the whole scavenging process, and eventually the better working performance of the two-stroke engine. Figure3 displays a set of different parameter configuration variation curves as a demonstration of the Benson/Bradham model equations. Energies 2018, 11, 2739 6 of 26

Compared with general scavenging process indicators, the Benson/Bradham model provides a more comprehensiveEnergies 2017, 10, x FOR method PEER REVIEW for the scavenging process and subsequent scavenging system6 optimalof 28 design.Energies However, 2017, 10, x littleFOR PEER attention REVIEWhas been paid to this significant aspect concerning the application6 of 28 of a moreof a precisemore precise Benson/Bradham Benson/Bradham model model in in the the optimizing optimizing process. Therefore, Therefore, the the key key parameters, parameters, of a more precise Benson/Bradham model in the optimizing process. Therefore, the key parameters, 𝑥 and 𝑦, of the Benson/Bradham model are employed to construct an objective function and serve x and𝑥y and, of the𝑦, of Benson/Bradham the Benson/Bradham model model are are employed employed to to construct construct an an objective objective functionfunction and serve serve as a as a port optimal design criteria for the scavenging system of the two-stroke small areoengine. port optimalas a port designoptimal criteriadesign criteria for the for scavenging the scavengi systemng system of the of the two-stroke two-stroke small small areoengine. areoengine.

(a) (b) (a) (b) Figure 2. Phase/zone concept of the Benson/Bradham model: (a) Phase I; (b) Phase II. FigureFigure 2. Phase/zone 2. Phase/zone concept concept of of the the Benson/BradhamBenson/Bradham model: model: (a) (Phasea) Phase I; (b I;) Phase (b) Phase II. II.

FigureFigure 3. Scavenging 3. Scavenging efficiency-delivery efficiency-delivery ratio ratio relationship relationshipof of thethe Benson/Bradham model. model. Figure 3. Scavenging efficiency-delivery ratio relationship of the Benson/Bradham model. 2.2. 1D GT Power Simulation Model 2.2. 1D GT Power Simulation Model 2.2. 1D GT Power Simulation Model AppropriateAppropriate boundary boundary conditions conditions and and initial initial conditions conditions areare indispensable prerequisites prerequisites for for3D 3D Appropriate boundary conditions and initial conditions are indispensable prerequisites for 3D CFD simulation.CFD simulation. Some Some physical physical quantities quantities within within them th (e.g.,em (e.g., initial initial fluid fluid state state in different in different regions regions inside CFD simulation. Some physical quantities within them (e.g., initial fluid state in different regions the engine)inside arethe difficultengine) toare be difficult directly to measuredbe directly by measured experiment. by experiment. To acquire To the acquire needed the parameters, needed inside the engine) are difficult to be directly measured by experiment. To acquire the needed a 1D simulationparameters, model a 1D simulation of the prototype model of engine the prot isotype established engine is with established GT Power. with GT Power. parameters, a 1D simulation model of the prototype engine is established with GT Power. FigureFigure4 displays 4 displays the specific the specific architecture architecture of of the the 1D 1D GT GT Power Power simulationsimulation model. model. A Atwo-stroke two-stroke Figure 4 displays the specific architecture of the 1D GT Power simulation model. A two-stroke small areoengine for UAVs is selected as the research focus in this paper. This spark-ignition smallsmall areoengine areoengine for for UAVs UAVs is is selected selected asas thethe researchresearch focus focus in in this this paper. paper. This This spark-ignition spark-ignition prototype engine is a single point injection (SPI) gasoline engine and employs a two-cylinder prototypeprototype engine engine is ais singlea single point point injection injection (SPI)(SPI) gasoline engine engine and and employs employs a two-cylinder a two-cylinder horizontally-opposed arrangement (i.e., the boxer) scheme. Moreover, it is a crankcase scavenged horizontally-opposedhorizontally-opposed arrangement arrangement (i.e., (i.e., thethe boxer)boxer) scheme. Moreover, Moreover, it is it a is crankcase a crankcase scavenged scavenged engine and the gas flow admitted to the crankcase is controlled by reed valves. Two percent synthetic engineengine and theand gasthe gas flow flow admitted admitted to to the the crankcase crankcase is controlled controlled by by reed reed valves. valves. Two Two percent percent synthetic synthetic oil with 93 octane petrol is used as engine fuel. It is important to mention that exhaust emissions is oil with 93 octane petrol is used as engine fuel. It is important to mention that exhaust emissions is oil withnot 93 selected octane as petrol the research is used focus as engine in this fuel.paper Itbecause is important military to UAVs mention equipped that with exhaust this prototype emissions is not selected as the research focus in this paper because military UAVs equipped with this prototype not selectedengine asgenerally the research have no focus stringent in this requirements paper because on pollution military emissions. UAVs equipped Relatively with simple this straight prototype engineengine generally generally have have no no stringent stringent requirements requirements onon pollution emissions. emissions. Relatively Relatively simple simple straight straight

Energies 2018, 11, 2739 7 of 26 Energies 2017, 10, x FOR PEER REVIEW 7 of 28 exhaustexhaust pipes pipes are are employed employed on on the the test test bed bed and and co correspondingrresponding parts parts are are constructed constructed in in the the GT GT Power Power simulationsimulation model model for for the the sake sake of of simplicity. simplicity. Ther Therefore,efore, the the workload workload during during the the modelling modelling process process cancan be be reduced reduced while while the the obtained obtained simulation simulation results results will will not not be be significantly significantly affected. affected. The The prototype prototype engineengine specifications specifications are are listed listed in in Table Table 11..

FigureFigure 4. 4. GTGT Power Power simulation simulation model model architecture. architecture. Table 1. Prototype engine specifications. Table 1. Prototype engine specifications. Parameters Value Unit Parameters Value Unit Total displacement 498 cm3 Total displacement 498 cm3 Bore 75 mm BoreStroke 75 56 mm mm ConnectingStroke rod56 110 mm mm ConnectingCompress ratiorod 110 10.6 mm - CompressRated power ratio 10.6 32 kW - Rated speed 6500 r/min RatedRated power torque 32 47 N kW·m NumberRated of scavenging speed ports 6500 3 r/min - NumberRated of torque exhaust ports 47 1 N·m - ◦ NumberScavenging of scavenging port opening ports 1103 CA - Exhaust port opening 94 ◦CA Number of exhaust ports 1 - Scavenging port closing 250 ◦CA ScavengingExhaust port port closingopening 110 266 °CA◦CA IgnitionExhaust timing port (high/partial opening load) 347/352 94 °CA◦CA ScavengingAmbient temperatureport closing 250 298 °CA K ExhaustAmbient port pressure closing 2661.013 °CA bar Intake pressure 1.105 bar Ignition Exhausttiming (high/partial back pressure load) 347/352 1.013 °CA bar AmbientAssumed temperature air fuel ratio 29813.24 K - AmbientEquivalence pressure ratio 1.013 1.11 bar - Intake pressure 1.105 bar To guarantee the accuracy,Exhaust corresponding back pressure configuration 1.013 parameters bar of the GT Power simulation model are precisely calibrated.Assumed In-cylinder air fuel pressure ratio is an important 13.24 working - performance index and its variation curve can be accuratelyEquivalence measured ratio on the test bed. Therefore, 1.11 in-cylinder - pressure is selected as the calibration target and thus we adjust the parameter settings accordingly to make sure that the simulationTo guarantee curve matchesthe accuracy, well withcorresponding the measured configuration curve. As describedparameters below, of the the GT most Power quintessential simulation modeloperating are conditionsprecisely calibrated. of the UAV In-cylinder equipped pressure with the prototypeis an important engine working are the highperformance load take-off index (speed and its6000 variation r/min, throttlecurve can opening be accurately 100%), andmeasured the partial on th loade test cruise bed. (speed Therefore, 4500 r/min,in-cylinder throttle pressure opening is selected40%). As as a the consequence, calibration wetarget carry and out thus the we prototype adjust the test parameter under these settings two accordingly operating conditions to make sure and thatmeasure the simulation the in-cylinder curve curves matches on the well test with bed. Basedthe measured on the measured curve. As in-cylinder described curves, below, the the pressure most quintessentialdiagrams can beoperating obtained conditions and applied of the for UAV GT Power equipped model with calibration. the prototype As shown engine in are Figure the high5, the load GT take-offPower simulation(speed 6000 model r/min, is validatedthrottle opening against 100%), experimental and the data partial in terms load ofcruise the in-cylinder(speed 4500 pressure r/min, throttleduring opening the complete 40%). engine As a consequence, cycle and the we comparative carry out the results prototype display test a perfectunder these matching. two operating Based on conditionsthe GT Power and simulation measure the model, in-cylinder tremendous curves characteristic on the test bed. parameters Based on (e.g., the the measured back pressure in-cylinder of the curves,exhaust the ports, pressure the intake diagrams pressure can of be the obtained scavenging an ports,d applied the massfor GT flow Power into and model out ofcalibration. the crankcase, As shown in Figure 5, the GT Power simulation model is validated against experimental data in terms of the in-cylinder pressure during the complete engine cycle and the comparative results display a perfect matching. Based on the GT Power simulation model, tremendous characteristic parameters

Energies 2017, 10, x FOR PEER REVIEW 8 of 28 Energies 20182017,, 1110,, 2739x FOR PEER REVIEW 88 of of 2628

(e.g.,(e.g., the the back back pressure pressure of of the the exhaust exhaust ports, ports, the the intake intake pressure pressure of of the the scavenging scavenging ports, ports, the the mass mass flowandflow intothe into and initial and out out temperatures of of the the crankcase, crankcase, of the and cylinder,and the the initial initial the exhausttemperatures temperatures duct, andof of the the cylinder, cylinder, combustion the the exhaustchamber exhaust duct, duct, wall) and and can thebethe combustion exported combustion for chamber subsequentchamber wall) wall) 3D can can CFD be be expo simulations. exportedrted for for subsequent subsequent 3D 3D CFD CFD simulations. simulations.

(a()a ) (b()b )

FigureFigure 5. 5. 5.ComparisonComparison Comparison ofof in-cylinderof in-cylinder in-cylinder pressure pressure pressure curves curv curv betweeneses between between the experiment the the experiment experiment and GT and Power and GT GT simulation. Power Power simulation.(simulation.a) High load (a )( aHigh operating) High load load operating condition; operating condition; (b condition;) partial ( loadb ()b partial) operatingpartial load load condition.operating operating condition. condition. 2.3. 3D AVL FIRE Simulation Model 2.3.2.3. 3D 3D AVL AVL FIRE FIRE Simulation Simulation Model Model Important engine parts of the scavenging system (e.g., piston, cylinder, and cylinder head) are ImportantImportant engine engine parts parts of of the the scavenging scavenging system system (e.g., (e.g., piston, piston, cylinder, cylinder, and and cylinder cylinder head) head) are are 3D-scanned to build the fundamental computer aided design (CAD) model. On the basis of massive 3D-scanned3D-scanned to to build build the the fundamental fundamental computer computer ai aidedded design design (CAD) (CAD) model. model. On On the the basis basis of of massive massive point cloud data, corresponding CAD models are reconstructed. Figure6 shows an example of the pointpoint cloud cloud data, data, corresponding corresponding CAD CAD models models are are reco reconstructed.nstructed. Figure Figure 6 6shows shows an an example example of of the the comparison of the engine cylinder between the reconstruction CAD result and the actual part. comparisoncomparison of of the the engine engine cylinder cylinder between between the the re reconstructionconstruction CAD CAD result result and and the the actual actual part. part.

(a()a ) (b()b )

FigureFigure 6. 6.Comparison Comparison of of the the engine engine cylinder. cylinder. (a )( aReconstruction)) Reconstruction Reconstruction computer computer computer aided aided aided design design design (CAD) (CAD) (CAD) result; result; result; (b(()b actual)) actualactual part. part.part. By parameterizing the main design considerations of the scavenging system, the whole fluid ByBy parameterizing parameterizing the the main main design design consideratio considerationsns of of the the scavenging scavenging system, system, the the whole whole fluid fluid domain model is established in a CATIA environment. It has to be mentioned that some geometric domaindomain model model is isestablished established in in a aCATIA CATIA environment. environment. It Ithas has to to be be mentioned mentioned that that some some geometric geometric characteristics are simplified for the sake of the perfect compatibility of the whole model with 3D mesh characteristicscharacteristics are are simplified simplified for for the the sake sake of of the the perf perfectect compatibility compatibility of of the the whole whole model model with with 3D 3D mesh mesh generation. Subsequently, the meshing process is implemented using AVL FIRE software. The whole generation.generation. Subsequently, Subsequently, the the meshing meshing process process is isimplemented implemented using using AVL AVL FIRE FIRE software. software. The The whole whole model is divided into the static (scavenging ports, exhaust ports) and the moving part (the combustion modelmodel is isdivided divided into into the the static static (scavenging (scavenging po ports,rts, exhaust exhaust ports) ports) and and the the moving moving part part (the (the chamber, crankcase). Fame Hexa Meshing (FEM) and Fame Engine Plus (FEP) methods are respectively combustioncombustion chamber, chamber, crankcase). crankcase). Fame Fame Hexa Hexa Meshing Meshing (FEM) (FEM) and and Fame Fame Engine Engine Plus Plus (FEP) (FEP) methods methods applied to generate the static and the transient meshes. Before assembling these two meshes, special areare respectively respectively applied applied to to generate generate the the static static an and dthe the transient transient meshes. meshes. Before Before assembling assembling these these two two “arbitrary interface” needs to be defined to realize the precise connectivity between them.

Energies 2017, 10, x FOR PEER REVIEW 9 of 28 Energies 2018, 11, 2739 9 of 26 meshes, special “arbitrary interface” needs to be defined to realize the precise connectivity between them. Figure7 shows the simplified fluid domain model of the scavenging system in a CATIA Figure 7 shows the simplified fluid domain model of the scavenging system in a CATIA environment and the corresponding CFD calculation model in an AVL FIRE environment, where part environment and the corresponding CFD calculation model in an AVL FIRE environment, where part A is the central scavenging port, part B and B are the lateral scavenging ports, part C is the crankcase, A is the central scavenging port, part B11 and B2 are the lateral scavenging ports, part C is the crankcase, partpart D is D the is combustionthe combustion chamber, chamber, and and part part EE isis the exhaust port. port.

(a) (b)

FigureFigure 7. Scavenging7. Scavenging system system model.model. ( (aa)) CAD CAD model; model; (b () bCFD) CFD calculation calculation model. model.

AccurateAccurate boundary boundary conditions conditions and and initialinitial conditions are are fundamental fundamental prerequisites prerequisites for 3D for CFD 3D CFD simulationsimulation and and these these can can be be provided provided by by 1D 1D GTGT PowerPower simulation results. results. Main Main specifications specifications and and conditioncondition parameters parameters are are listed listed in in Table Table2. It2. isIt crucialis crucial that that the the relevant relevant parameter parameter configuration configuration in AVL AVL FIRE should match with that in the GT Power model to guarantee the reliability of results FIRE should match with that in the GT Power model to guarantee the reliability of results obtained obtained from the simulation. For example, the ambient conditions set in AVL FIRE are exactly the from the simulation. For example, the ambient conditions set in AVL FIRE are exactly the same as same as those in GT Power for consistency. those in GT Power for consistency. Table 2. Main specifications and condition parameters in AVL FIRE. Table 2. Main specifications and condition parameters in AVL FIRE. Condition Parameters High Load Partial Load ConditionEngine speed Parameters (rpm) 6000 High Load 4500 Partial Load Engine speedFuel (rpm) 2% synthetic 6000oil with 93 octane petrol 4500 AssumedFuel air fuel ratio 2% synthetic 13.24 oil with 93 octane petrol AssumedEquivalence air fuel ratio ratio 1.11 13.24 AmbientEquivalence temperature ratio (K) 298 1.11 AmbientAmbient temperature pressure (bar) (K) 1.013 298 AmbientIntake pressure pressure (bar) (bar) 1.105 1.013 ExhaustIntake back pressure pressure (bar) (bar) 1.013 1.105 Exhaust back pressure (bar) 1.013 Ignition timing (°CA) 347 352 Ignition timing (◦CA) 347 352 Boundary temperature of the combustion Boundary temperature of the combustion chamber wall (K)528 528497 497 Boundary temperaturechamber wall of the (K) crankcase (K) 330 312 BoundaryBoundary temperature temperature of of the the crankcase cylinder (K) (K) 330 504 312 480 BoundaryBoundary temperature temperature of of the the exhaust cylinder duct (K) (K) 504 476 480 460 BoundaryInitial temperaturetemperature ofof thethe crankcaseexhaust duct (K) (K) 476 364 460 358 InitialInitial temperature pressure of theof the crankcase crankcase (bar) (K) 364 1.105 358 0.984 InitialInitial temperaturepressure of the of thecrankcase cylinder (bar) (K) 1.105 1719 0.984 1476 InitialInitial temperature pressure of theof the cylinder cylinder (bar) (K) 1719 4.922 1476 3.017 Initial temperature of the exhaust duct (bar) 1.013 1.013

Initial pressure of the exhaust duct (K) 734 582

Regarding the solver settings in AVL FIRE, the species transport module and combustion module are activated. The standard transport model (STM) and extended coherent flame model (ECFM) are Energies 2017, 10, x FOR PEER REVIEW 10 of 28

Initial pressure of the cylinder (bar) 4.922 3.017 Initial temperature of the exhaust duct (bar) 1.013 1.013 Initial pressure of the exhaust duct (K) 734 582

EnergiesRegarding2018, 11, 2739 the solver settings in AVL FIRE, the species transport module and combustion10 of 26 module are activated. The standard transport model (STM) and extended coherent flame model (ECFM) are selected. Both the boundary conditions and initial conditions are derived from the 1D GT Powerselected. simulation Both the results. boundary Fluid conditions properties and are initialgiven by conditions experimental are derived measurements. from the The 1D extrapolate GT Power methodsimulation is used results. for Fluidthe calculation properties of are boundary given by valu experimentales and the measurements. least squared Thefit method extrapolate is employed method tois usedcalculate for thederivatives. calculation Severa of boundaryl variable valueslimits (e.g., and themaximu leastm/minimum squared fit methodtemperature, is employed minimum to density,calculate minimum derivatives. turbulence Several kine variabletic energy limits (TKE), (e.g., maximum/minimumand maximum velocity temperature, magnitude) minimumare set to eliminatedensity, minimum abnormal turbulenceresults. Because kinetic the energy cell number (TKE), of and the maximum full-scale CFD velocity model magnitude) amounts to are 710,519 set to ateliminate the top abnormaldead center results. (TDC) Because and 914,533 the cell at number the bottom of the dead full-scale center CFD (BDC), model a cell amounts quality to check 710,519 and at cellthe topface dead adjustment center (TDC)function and is 914,533 applied at for the mesh bottom smoothing dead center and (BDC),fine tuning a cell during quality the check simulation and cell process.face adjustment The hybrid function wall is treatment applied for (HWT) mesh option smoothing is used and for fine wall tuning treatment during and the simulationthe standard process. wall Thefunction hybrid (SWF) wall is treatment selected (HWT)as the optionheat transfer is used wall for wall model. treatment Momentum, and the continuity, standard wall and function energy (SWF)equations is selected are all ascons theidered. heat transfer Besides, wall the model.k-ζ-f model Momentum, is applied continuity, to capture and turbulence. energy equations The semi- are ζ implicitall considered. method Besides, for pressure the k- linked-f model equations is applied (SIMPLE) to capture algorithm turbulence. is adopted The semi-implicit with a two-stage method pressurefor pressure correction. linked equations Differencing (SIMPLE) schemes algorithm of continuity, is adopted energy, with aand two-stage momentum pressure equations correction. are centralDifferencing differencing, schemes ofupwind continuity, differencing, energy, and and momentum MINMOD equations relaxed, are respectively. central differencing, Corresponding upwind under-relaxationdifferencing, and factors MINMOD and relaxed,convergence respectively. criteria of Corresponding these equations under-relaxation are also determined. factors andThe gradientconvergence stabilized criteria biconjugate of these equations (GSTB) method are also is sele determined.cted as the Thelinear gradient solver type stabilized for the biconjugate solution of (GSTB)main equations. method is selected as the linear solver type for the solution of main equations. The time before before exhaust exhaust port port opening opening is is selected selected as as the the start start of ofsimulation simulation calculation, calculation, because because the combustionthe combustion process process has ended has ended by that by time that timeand th ande temperature the temperature and composition and composition distribution distribution in the cylinderin the cylinder remain remainrelatively relatively stable. It stable. can be Itseen can from be seen Table from 1 that Table the1 that exhaust the exhaustport opening port opening is 94 °CA. is ◦ ◦ ◦ Therefore,94 CA. Therefore, the simulation the simulation calculation calculation ranges from ranges 90 from°CA 90to 450CA °CA to 450 to completeCA to complete a two-stroke a two-stroke engine cycle.engine Model cycle. configurations Model configurations of the AVL of theFIRE AVL simulation FIRE simulation are similarly are adjusted similarly to adjusted match well to match with wellcorresponding with corresponding measured measureddata. Figu data.re 8 presents Figure8 presents the comparison the comparison of in-cylinder of in-cylinder pressure pressure curves betweencurves between the experiment the experiment and AVL and FIRE AVL simulation. FIRE simulation. As shown As shown in Figure in Figure 8, the8, thereliability reliability of the of the3D AVL3D AVL FIRE FIRE simulation simulation model model has has been been verified, verified, which which lays lays a asolid solid foundation foundation for for the follow-up optimal design.

(a) (b)

Figure 8.8.Comparison Comparison of of in-cylinder in-cylinder pressure pressure curves curves between between the experiment the experiment and AVL and FIRE AVL simulation. FIRE (simulation.a) High load (a) operating High load condition; operating (b condition;) partial load (b) operatingpartial load condition. operating condition.

3. Orthogonal Experiment Design 3.1. Optimal Design Determinants The essence of scavenging system optimization is the ports’ optimal design. It is mainly composed of three aspects: The shapes and size of ports affect the resistance loss; the position of ports determines the opening and closing time (i.e., the timing phase of scavenging process); the orientation of ports determines the flowing direction of fresh charge and eventually has significant impacts on the in-cylinder flow field. From the literature review, the first two have been thoroughly studied using 1D simulation platforms, such as GT Power and AVL Boost [8,16]. To make full use of the powerful Energies 2017, 10, x FOR PEER REVIEW 11 of 28

3. Orthogonal Experiment Design

3.1. Optimal Design Determinants The essence of scavenging system optimization is the ports’ optimal design. It is mainly composed of three aspects: The shapes and size of ports affect the resistance loss; the position of ports determines the opening and closing time (i.e., the timing phase of scavenging process); the orientation of ports determines the flowing direction of fresh charge and eventually has significant impacts on the in-cylinder flow field. From the literature review, the first two have been thoroughly studied Energies 2018, 11, 2739 11 of 26 using 1D simulation platforms, such as GT Power and AVL Boost [8,16]. To make full use of the powerful visualization tools provided by AVL FIRE, the orientation of ports is parameterized and selectedvisualization as the toolsmain provided research byobject. AVL FIRE, the orientation of ports is parameterized and selected as the mainThe research prototype object. engine scavenging system consists of three scavenging ports and one exhaust port as shownThe in prototype Figure 9. engine For one scavenging thing, the system central consists scavenging of three port scavenging has a clear portstilt angle. and oneConsequently, exhaust port freshas shown charge in through Figure9 the. For central one thing, scavenging the central port scavengingis led to the portupper has part a clear of the tilt cylinder angle. Consequently,to sweep out thefresh burned charge gas. through For another, the central the scavenging combination port of is ledtwo to lateral the upper scavenging part of the ports cylinder displays to sweep mirror out symmetry.the burned Each gas. of For the another, two has the a slip combination angle and of is two biased lateral toward scavenging the central ports scavenging displays mirror port. symmetry.Besides, theEach lateral of the scavenging two has a ports slip angle also have and isa tilt biased angle. toward On account the central of these scavenging angles mentioned port. Besides, above, the swirl lateral andscavenging tumble are ports generated also have in a the tilt gas angle. exchange On account process. of these Afterwards, angles mentioned they are broken above, into swirl small-scale and tumble turbulence,are generated which in thecontributes gas exchange significantly process. to Afterwards, air-fuel mixing they and are flame broken propagation. into small-scale Moreover, turbulence, the undersidewhich contributes of the exhaust significantly port is tospecifically air-fuel mixing designed and to flame be tilted propagation. for clearing Moreover, the burned the underside gas out of of thethe cylinder. exhaust port is specifically designed to be tilted for clearing the burned gas out of the cylinder.

FigureFigure 9. 9. Ports’Ports’ arrangement arrangement of of the the prot prototypeotype engine engine scavenging scavenging system. system.

FourFour parameters parameters are are considered considered and and are are optimal optimal design design determinants: determinants: Tilt Tilt angle angle of of the the central central scavengingscavenging port, port, 𝛼αC; ;tilt tilt angle angle of of the the lateral lateral scavenging scavenging ports, ports, 𝛼α;l ;slip slip angle angle of of the the lateral lateral scavenging scavenging ports,ports, 𝛽βl;; and thethe widthwidth ratio ratio of of the the central central scavenging scavenging port, port,γC. The𝛾 . firstThe threefirst parametersthree parameters are defined are definedas shown as inshown Figure in 10 Figure. The width10. The ratio widt ofh the ratio central of the scavenging central scavenging port, γC, describes port, 𝛾 the, describes proportion the of proportionthe mass flow of the entering mass flow the cylinder entering through the cylinder the central through scavenging the central port scaven and isging given port by and the followingis given byequation the following [28]: equation [28]: L γ = C (13) C 𝐿 + 𝛾 = LC 2 L l (13) 𝐿 +2𝐿 where LC is the width of the central scavenging port, and Ll is the width of the single lateral scavenging whereport. Based𝐿 is onthe the width geometric of the parameters central scavenging of the prototype port, and engine 𝐿 is scavenging the width system, of the ansingle appropriate lateral scavenginggeometric parameterport. Based table on the with geometric four factors parameters and five of levels the prototype is given in engine Table3 :scavenging system, an appropriate geometric parameter table with four factors and five levels is given in Table 3: Table 3. Geometric parameter configuration.

◦ ◦ ◦ Level αC ( ) αl ( ) βl ( ) γC LC (mm) Ll (mm) 1 45 −10 10 14.29% 12 36 2 50 0 20 19.05% 16 34 3 55 10 30 23.81% 20 32 4 60 20 40 28.57% 24 30 5 65 30 50 33.33% 28 28 Energies 2017, 10, x FOR PEER REVIEW 12 of 28

Table 3. Geometric parameter configuration.

Level 𝜶𝑪 (°) 𝜶𝒍 (°) 𝜷𝒍 (°) 𝜸𝑪 𝑳𝑪 (mm) 𝑳𝒍 (mm) 1 45 −10 10 14.29% 12 36 2 50 0 20 19.05% 16 34 3 55 10 30 23.81% 20 32 4 60 20 40 28.57% 24 30 Energies 2018, 11, 2739 12 of 26 5 65 30 50 33.33% 28 28

Figure 10. DefinitionsDefinitions of importantimportant geometric parameters.parameters.

3.2. Evaluation Objective Function 3.2. Evaluation Objective Function Because each investigated factor of the four has five levels, 54 or 625 CFD simulation cases Because each investigated factor of the four has five levels, 54 or 625 CFD simulation cases are arerequired required for fortraversing traversing all allthe the possible possible combinat combinationsions of of geometric geometric parameters. Considering thethe enormousenormous timetime expenditure,expenditure, thethe OEDOED methodmethod isis adoptedadopted toto reducereduce thethe numbernumber ofof cases to accelerate thethe wholewhole simulationsimulation procedure.procedure. Regardless of thethe interaction effect betweenbetween thesethese investigatedinvestigated 6 factors, the L25 (56 ) orthogonal table is employed and four parameters mentioned-above are taken as factors, the L25 (5 ) orthogonal table is employed and four parameters mentioned-above are taken as independentindependent variables.variables. As As a result,a result, a remarkable a remarkable reduction reduction takes placetakes in place the total in numberthe total of number simulation of cases,simulation and only cases, 25 casesand only need 25 to becases executed need asto shownbe executed in Table as4 ,shown which meansin Table the 4, corresponding which means time the costcorresponding is acceptable. time In thecost well-designed is acceptable. orthogonal In the we table,ll-designed the frequency orthogonal of occurrences table, the of frequency each level of eachoccurrences factor is of the each same level for of the each sake factor of balance. is the same On the for basisthe sake of the of OEDbalance. results, On the the basis estimated of the mean OED valuesresults, of the investigated estimated factors mean can values be used of toinvestigated select the optimal factors combinationcan be used of independentto select the variables. optimal Tocombination evaluate the of influenceindependent of each variables. independent To evaluate variable theupon influence the dependent of each independent variable, the variable significance upon levelthe dependent of each factor variable, can be the derived significance from the level range of ea analysisch factor and can variance be derived analysis. from the range analysis and varianceFor the analysis. subsequent results analysis, an evaluation objective function based on the Benson/BradhamFor the subsequent scavenging results model isanalysis, selected asan the evaluation dependent objective variable for function the designed based orthogonal on the − tableBenson/Bradham as well as the scavenging optimization model goal. is selected As shown as the above,dependent the variable crucial η forS theλS designedrelationship orthogonal of the Benson/Bradham scavenging model satisfies Equation (11) and it can be derived that the closer x is to table as well as the optimization goal. As shown above, the crucial 𝜂 −𝜆 relationship of the 1Benson/Bradham and the closer y isscavenging to 0, the better model the satisfies scavenging Equation quality. (11) Byand the it can AVL be FIRE derived simulation, that the thecloser desired 𝑥 is − ηtoS 1 andλS relation the closer curve 𝑦 ofis eachto 0, case the canbetter be obtainedthe scavenging and the quality. corresponding By the coefficients,AVL FIRE simulation,x and y, can the be ascertained by curve fitting. Therefore, the sum of squares of the deviations of x and y from ideal values is employed to construct the elementary evaluation objective function:

2 2 ηBB = (1 − x) + y (14) Energies 2018, 11, 2739 13 of 26

Additionally, the scavenging quality under different operating conditions will also be different. To establish the integrated objective function, the most quintessential operating conditions of the UAV equipped with the prototype engine, including the high load take-off (speed 6000 r/min, throttle opening 100%) and the partial load cruise (speed 4500 r/min, throttle opening 40%), are considered simultaneously: ηBB,c = ηBB,h + ηBB,p (15) where ηBB,h is the evaluation objective function for the high load take-off, ηBB,p is the evaluation objective function for the partial load cruise, and ηBB,c is the combination of the two above. Besides, conventional performance indicators, IMEP and ISFC, are employed as constraint conditions to exclude those cases failing to meet the basic power performance and fuel economy requirements. ( IMEP ≥ 5.3 bar for the high load s.t : (16) IMEP ≥ 3.0 bar for the partial load

( ISFC ≤ 260 g/(kW·h) for the high load s.t : (17) ISFC ≤ 310 g/(kW·h) for the partial load

Table 4. Designed orthogonal table.

◦ ◦ ◦ No. αC ( ) αl ( ) βl ( ) γC 1 45 −10 10 14.29% 2 45 0 20 19.05% 3 45 10 30 23.81% 4 45 20 40 28.57% 5 45 30 50 33.33% 6 50 −10 20 23.81% 7 50 0 30 28.57% 8 50 10 40 33.33% 9 50 20 50 14.29% 10 50 30 10 19.05% 11 55 −10 30 33.33% 12 55 0 40 14.29% 13 55 10 50 19.05% 14 55 20 10 23.81% 15 55 30 20 28.57% 16 60 −10 40 19.05% 17 60 0 50 23.81% 18 60 10 10 28.57% 19 60 20 20 33.33% 20 60 30 30 14.29% 21 65 −10 50 28.57% 22 65 0 10 33.33% 23 65 10 20 14.29% 24 65 20 30 19.05% 25 65 30 40 23.81%

4. Simulation Results Analysis

4.1. OED Results Analysis A total of 25 simulation cases are sequentially implemented by AVL FIRE. Table5 displays the overall simulation results; subscripts h and p refer to parameters for the high load and the partial load, respectively. Energies 2018, 11, 2739 14 of 26

Table 5. Overall simulation results.

No. λS,h ηT,h ηS,h xh yh ηBB,h λS,p ηT,p ηS,p xp yp ηBB,p ηBB,c 1 1.028 0.770 0.791 0.764 0.103 0.066 0.776 0.861 0.668 0.658 0.046 0.119 0.185 2 0.995 0.805 0.801 0.777 0.089 0.058 0.769 0.886 0.682 0.673 0.036 0.108 0.166 3 0.917 0.856 0.785 0.767 0.063 0.058 0.764 0.895 0.684 0.670 0.025 0.110 0.168 4 0.830 0.912 0.757 0.752 0.047 0.064 0.731 0.924 0.675 0.675 0.019 0.106 0.170 5 0.783 0.922 0.722 0.718 0.027 0.080 0.699 0.929 0.650 0.657 0.012 0.118 0.198 6 0.882 0.877 0.773 0.781 0.089 0.056 0.731 0.898 0.657 0.671 0.037 0.110 0.165 7 0.861 0.894 0.769 0.771 0.070 0.057 0.758 0.894 0.678 0.668 0.028 0.111 0.168 8 0.754 0.942 0.711 0.756 0.047 0.062 0.651 0.957 0.623 0.676 0.020 0.105 0.167 9 0.767 0.941 0.722 0.740 0.041 0.069 0.734 0.907 0.666 0.668 0.021 0.111 0.180 10 1.207 0.657 0.793 0.743 0.073 0.071 0.845 0.833 0.704 0.663 0.020 0.114 0.185 11 0.834 0.901 0.752 0.771 0.076 0.058 0.699 0.919 0.643 0.656 0.032 0.119 0.178 12 0.884 0.874 0.773 0.769 0.078 0.059 0.681 0.923 0.628 0.671 0.037 0.110 0.169 13 0.743 0.953 0.708 0.742 0.044 0.069 0.664 0.945 0.628 0.663 0.026 0.114 0.183 14 1.082 0.743 0.804 0.760 0.075 0.063 0.830 0.856 0.710 0.674 0.020 0.107 0.170 15 0.996 0.796 0.793 0.759 0.052 0.061 0.791 0.890 0.704 0.676 0.015 0.105 0.166 16 0.783 0.936 0.733 0.792 0.081 0.050 0.651 0.938 0.611 0.664 0.038 0.114 0.164 17 0.727 0.963 0.700 0.738 0.085 0.076 0.634 0.954 0.605 0.656 0.026 0.119 0.195 18 1.015 0.796 0.809 0.780 0.077 0.054 0.813 0.866 0.703 0.663 0.023 0.114 0.168 19 0.992 0.809 0.802 0.771 0.057 0.056 0.807 0.905 0.731 0.691 0.019 0.096 0.152 20 0.914 0.845 0.773 0.760 0.075 0.063 0.852 0.850 0.724 0.685 0.022 0.100 0.163 21 0.719 0.953 0.685 0.720 0.056 0.082 0.641 0.959 0.615 0.656 0.028 0.119 0.201 22 0.958 0.829 0.794 0.779 0.082 0.056 0.788 0.871 0.687 0.650 0.028 0.123 0.179 23 1.032 0.785 0.811 0.783 0.082 0.054 0.833 0.848 0.706 0.669 0.030 0.110 0.164 24 0.949 0.836 0.794 0.775 0.058 0.054 0.794 0.891 0.708 0.680 0.022 0.103 0.157 25 0.869 0.896 0.778 0.761 0.035 0.058 0.751 0.934 0.702 0.698 0.016 0.091 0.151

Take the high load take-off operating condition as an example, the scatterplots of 25 cases are shown in Figure 11. With respect to the delivery ratio, λS,h, and scavenging efficiency, ηS,h, Figure 11a shows that a positive correlation exists between λS,h and ηS,h, which is in accordance with previously discussed analysis. In terms of teh delivery ratio, λS,h, and trapping efficiency, ηT,h, Figure 11b shows that a linearly drop can be observed in ηT,h with the increasing λS,h. Intuitively, we can corroborate that the trade-off relationship between the fuel economy indicator, ηT, and the power performance indicator, λS, indeed exists for the prototype engine scavenging system, which makes it difficult to improveEnergies 2017 power, 10, x FOR performance PEER REVIEW while keeping fuel consumption low. 15 of 28

(a) (b)

FigureFigure 11.11. The high load results.results. (a)) ScavengingScavenging efficiency;efficiency; ((bb)) trappingtrapping efficiency.efficiency.

TakeFigure the 12 high presents load take-offthe detailed operating comparison condition result ass anbetween example, the thehigh scatterplots load and the of 25partial cases load. are shownAs shown in Figure in Figure 11. With 12a–c, respect all the to thescavenging delivery indexes, ratio, λS ,h𝜂, and, 𝜂, scavenging and 𝜆, generally efficiency, exhibitηS,h, Figurea positive 11a correlation when comparing the two operating conditions. For instance, the higher 𝜂,, the higher 𝜂,. However, the linearity is not high, which indicates the same geometric parameter configuration will vary in scavenging performance under different operating conditions. With regards to the evaluation objective function, Figure 12d shows that there exists no obvious relationship between

𝜂, and 𝜂, . Therefore, it is indeed necessary to consider multiple operating conditions to establish comprehensive optimal design criteria.

(a) (b)

(c) (d)

Figure 12. Comparison between the high load results and the partial load results. (a) Scavenging efficiency; (b) trapping efficiency; (c) delivery ratio; (d) evaluation objective function.

Energies 2017, 10, x FOR PEER REVIEW 15 of 28

Energies 2018, 11, 2739 15 of 26

shows that a positive correlation exists between λS,h and ηS,h, which is in accordance with previously discussed analysis. In terms of teh delivery ratio, λS,h, and trapping efficiency, ηT,h, Figure 11b shows that a linearly drop can be observed in η with the increasing λ . Intuitively, we can corroborate (a) T,h S,h (b) that the trade-off relationship between the fuel economy indicator, ηT, and the power performance indicator, λS,Figure indeed 11. exists The high for theload prototype results. (a) engineScavenging scavenging efficiency; system, (b) trapping which efficiency. makes it difficult to improve power performance while keeping fuel consumption low. FigureFigure 1212 presentspresents thethe detaileddetailed comparisoncomparison resultsresults betweenbetween thethe highhigh loadload andand thethe partialpartial load.load. As shown in Figure 12a–c, all the scavenging indexes, 𝜂 , 𝜂 , and 𝜆 , generally exhibit a positive As shown in Figure 12a–c, all the scavenging indexes, ηS, ηT, and λS, generally exhibit a positive correlation when comparing the two operating conditions. For instance, the higher 𝜂,, the higher correlation when comparing the two operating conditions. For instance, the higher ηT,h, the higher ηT,p. However,𝜂,. However, the linearity the linearity is not is high, not high, which which indicates indicates the same the same geometric geometric parameter parameter configuration configuration will varywill invary scavenging in scavenging performance performance under differentunder different operating operating conditions. conditions. With regards With to regards the evaluation to the evaluation objective function, Figure 12d shows that there exists no obvious relationship between objective function, Figure 12d shows that there exists no obvious relationship between ηBB,h and ηBB,p. Therefore,𝜂, and it𝜂 is, indeed. Therefore, necessary it tois considerindeed necessary multiple operating to consider conditions multiple to establishoperating comprehensive conditions to optimalestablish design comprehensive criteria. optimal design criteria.

(a) (b)

(c) (d)

FigureFigure 12.12. ComparisonComparison betweenbetween thethe highhigh loadload resultsresults andand thethe partialpartial loadloadresults. results. ((aa)) ScavengingScavenging efficiency;efficiency; ((bb)) trapping trapping efficiency; efficiency; ( c(c)) delivery delivery ratio; ratio; ( d(d)) evaluation evaluation objective objective function. function.

The desired ηS,h − λS,h relation curve of one of the 25 cases is shown in Figure 13a. The root-mean- square error of the corresponding curve fitting is in the order of 10−4, indicating that the fitting curve matches well with the simulation result. In addition, the satisfying fitting result proves that the whole scavenging process of the prototype engine can be precisely described by the Benson/Bradham scavenging model. As for the coefficients, xh and yh, Figure 13b shows that there exists no clear relationship in the scatterplot, which means these two key parameters change independently. It is Energies 2017, 10, x FOR PEER REVIEW 16 of 28

The desired 𝜂, −𝜆, relation curve of one of the 25 cases is shown in Figure 13a. The root-mean- square error of the corresponding curve fitting is in the order of 10−4, indicating that the fitting curve matches well with the simulation result. In addition, the satisfying fitting result proves that the whole Energiesscavenging2018, 11 process, 2739 of the prototype engine can be precisely described by the Benson/Bradham16 of 26 scavenging model. As for the coefficients, 𝑥 and 𝑦, Figure 13b shows that there exists no clear relationship in the scatterplot, which means these two key parameters change independently. It is worthyworthy ofof specialspecial mentionmention thatthat thethe partialpartial loadload conditioncondition isis veryvery similar,similar, thusthus relatedrelated discussionsdiscussions areare notnot repeated.repeated.

(a) (b)

FigureFigure 13.13.The The highhigh loadload results.results. ((aa)) ComparisonComparison betweenbetween thethe simulationsimulation resultresult andand thethe fittingfitting curve;curve;

(b(b)) scatterplot scatterplot of of coefficients, coefficients,x h𝑥and andyh .𝑦.

AsAs mentionedmentioned above,above, η𝜂BB,,c is is determined determined as as the the dependent dependent variable variable for for the the designed designed orthogonal orthogonal tabletable asas well well as as the the optimization optimization goal. goal. To To make make the the indicating indicating coefficients, coefficients,x and 𝑥 yand, approach 𝑦, approach as close as asclose possible as possible to the to ideal the values,ideal values, respectively, respectively,ηBB,c needs 𝜂, to needs be minimized: to be minimized:

𝑀𝑖𝑛 (𝜂,)=𝑓(𝛼,𝛼,𝛽,𝛾) (18) Min (ηBB,c) = f (αC, αl, βl, γC) (18) Based on the above OED results, the mean values of the different levels of each factor are calculatedBased as on shown the above in Table OED 6. results,It can be the concluded mean values that the of theeffect different order of levels the various of each levels factor of arethe calculated as shown in Table6. It can be concluded that the effect order of the various levels of the four four investigated factors on 𝜂, is: A1 > A2 = A3 > A5 > A4, B1 > B 2> B5 > B3 > B4, C5 > C1 > C3 > C4 > C2, D4 investigated factors on ηBB,c is: A1 > A2 = A3 > A5 > A4,B1 > B 2> B5 > B3 > B4,C5 > C1 > C3 > C4 > C2, = D5 > D1 > D2 > D3. Therefore, the best factors combination is determined as A4B4C2D3, namely 𝛼 = D60°,4 = D𝛼5 >= 20°, D1 > 𝛽 D 2 => 20°, D3. and Therefore, 𝛾 = 23.81%. the best factors combination is determined as A4B4C2D3, namely ◦ ◦ ◦ αC = 60 , αl = 20 , βl = 20 , and γC = 23.81%. Table 6. Mean values calculation results. Table 6. Mean values calculation results. Level A (𝜶𝑪) B (𝜶𝒍) C (𝜷𝒍) D (𝜸𝑪) Level1 A (α 0.1774C) B (α0.1787l) C (βl)0.1776 D (γC0.1723) 12 0.1774 0.1733 0.17870.1754 0.17760.1626 0.17230.1710 23 0.1733 0.1733 0.17540.1701 0.16260.1667 0.17100.1696 34 0.1733 0.1684 0.17010.1656 0.16670.1640 0.16960.1746 45 0.1684 0.1701 0.16560.1724 0.16400.1913 0.17460.1746 5 0.1701 0.1724 0.1913 0.1746 Range analysis is conducted with the results shown in Table 7. The effect order of the investigatedRange analysis factors is conductedon 𝜂, is: with 𝛽 the > results𝛼 > shown𝛼 > 𝛾 in ; Table obviously7. The effectthe slip order angle of the of investigated the lateral factorsscavenging on ηBB ports,,c is: β𝛽l >, isα lthe> α mostC > γ significantC; obviously factor the slip among angle the of four. the lateral scavenging ports, βl, is the most significant factor among the four.

Table 7. Range analysis results.

Factors Range

αC 0.0090 αl 0.0131

βl 0.0287 γC 0.0050 Energies 2018, 11, 2739 17 of 26

Table8 displays the variance analysis results. The effect order of the investigated factors on ηBB,c is: βl > αl > αC > γC; which is exactly the same as the range analysis. The significance level for the variance analysis is set as α = 0.05. It can be observed from Table8 that the p-value of βl is 0.00131 < 0.01, which means βl has the most significant influence on ηBB,c; the p-value of αl is 0.0485 < 0.05, which means αl has a comparatively significant influence on ηBB,c; neither αC and γC are statistically significant in terms of their influence on ηBB,c.

Table 8. Variance analysis results.

Factors Type III Sum of Squares DOF Mean Square F P

A(αC) 0.00236 4 0.000059 1.079 0.32755 B(αl) 0.00050 4 0.000125 6.287 0.04845 C(βl) 0.00291 4 0.000727 13.303 0.00131 D(γC) 0.00010 4 0.000025 0.454 0.76752 Error 0.00044 8 0.000055 - - Total 0.00418 24 - - -

Generally, multi-objective programming can be described as follows [33]: (
T gi(n) ≥ 0, i ∈ I Min f (n) = f1(n), f2(n),..., fp(n) s.t : (19) hj(n) = 0, j ∈ E

Considering all possible solutions, if solution n1 is no worse than n2 in all objectives and solution n1 is strictly better than n2 in at least one objective, then n1 dominates n2. Given a set of solutions, the non-dominated solution set is a set of all the solutions that are not dominated by any member of the solution set. The non-dominated set of the entire feasible decision space is the so-called Pareto-optimal set. The boundary defined by the set of all points mapped from the Pareto-optimal set is called the Pareto front. A Pareto front is more intuitive to display the simulation results of a multi-objective optimization engineering problem [33]. Based on the simulation results given by Table5, the set of non-dominated solutions can be obtained and corresponding 3D Pareto front diagrams are generated. Both operating conditions are taken into consideration. In Figures 14 and 15 the trapping efficiency, delivery ratio, and scavenging efficiency are selected as three objectives. In Figures 16 and 17, the short-circuiting fraction, displacement ratio, and evaluation objective function are considered as three objectives. These present findings can provide more comprehensive guidance for the designers ofEnergies the next 2017, generation 10, x FOR PEER of UAVs.REVIEW 18 of 28

FigureFigure 14.14. 3D3D ParetoPareto frontfront resultsresults ofof thethe highhigh loadload operatingoperatingcondition. condition.

Figure 15. 3D Pareto front results of the partial load operating condition.

Figure 16. 3D Pareto front results of the high load operating condition.

EnergiesEnergies 20172017,, 1010,, xx FORFOR PEERPEER REVIEWREVIEW 1818 ofof 2828

Energies 2018, 11, 2739 18 of 26 FigureFigure 14.14. 3D3D ParetoPareto frontfront resultsresults ofof thethe highhigh loadload operatingoperating condition.condition.

FigureFigure 15.15. 3D3D Pareto Pareto front front resultsresults ofof thethe partialpartial loadload operatingoperating condition.condition.

Energies 2017, 10, x FORFigureFigure PEER REVIEW 16.16. 3D3D Pareto Pareto front front resultsresults ofof thethe highhigh loadload operatingoperating condition.condition. 19 of 28

Figure 17. 3D3D Pareto Pareto front results of the partial load operating condition.

4.2. Verification Verification of the Best Factors Combination

Based on the OED results analysis, A44B4CC22DD33 isis selected selected as as the the optimal optimal parameter parameter configuration. configuration.

The expected value of η𝜂BB,,c is is 0.149 0.149 with witha a 95%95% confidenceconfidence intervalinterval (0.1456,(0.1456, 0.1524).0.1524). For verifying the best factors combination, thethe correspondingcorresponding 3D 3D CFD CFD model model is is established established and and AVL AVL FIRE FIRE simulation simulation is performed.is performed. The The comparison comparison results results between between the prototype the prototype engine and engine optimal and design optimal are presenteddesign are in presented in Table 9. Apart from the scavenging parameters and evaluation objective functions discussed before, the conventional power performance and fuel economy indicators, IMEP and ISFC, are taken into consideration as well. Because mechanical loss (pumping loss, throttling loss, friction loss, etc.) is not involved in AVL FIRE simulation, indicated indexes are analyzed rather than effective indexes.

Table 9. Comparison results between the prototype engine and optimal design.

Prototype Engine Optimal Design Parameter High Load Partial Load High Load Partial Load

𝜆 0.889 0.730 0.979 0.785 𝜂 0.882 0.931 0.861 0.917 𝜂 0.784 0.680 0.843 0.720 𝑥 0.778 0.671 0.782 0.686 𝑦 0.071 0.030 0.057 0.018 𝜂 0.054 0.109 0.051 0.099 𝜂, 0.163 0.150 IMEP (bar) 5.641 3.310 5.840 3.463 ISFC [g/(kW·h)] 251.4 306.7 253.2 310.4

Compared with the prototype engine, both 𝜆 and 𝜂 in the optimal design results are improved, yet 𝜂 declines to some extent. With regard to the fitting coefficients, 𝑥 and 𝑦, of the Benson/Bradham model, both of the two are closer to ideal values, respectively (i.e., 𝑥 is closer to 1 and 𝑦 is closer to 0 after optimization). As a consequence, the combined evaluation objective function, 𝜂,, descends with an increasing 𝑥 and a decreasing 𝑦. The whole scavenging process approaches more closely to the ideal perfect displacement model after optimization, which means the expected refinement has been received. For one thing, the comparatively notable increase of 𝜂, with the slight decrease of 𝜂, indicates that the scavenging quality of the optimal design is superior to that of the prototype engine. For another, the evaluation objective function, 𝜂,, based on the Benson/Bradham model is proven to reflect the scavenging quality. As for conventional performance indexes, there are also improvements in IMEP, with a 3.4% increase under the high load and a 4.6% increase under the partial load, respectively. Additionally, ISFC shows a 0.7% increase under the high load and a 1.2% increase under the partial load. Because

𝜆 and 𝜂 go up while 𝜂 declines slightly after optimization, more fresh charge is delivered into

Energies 2018, 11, 2739 19 of 26

Table9. Apart from the scavenging parameters and evaluation objective functions discussed before, the conventional power performance and fuel economy indicators, IMEP and ISFC, are taken into consideration as well. Because mechanical loss (pumping loss, throttling loss, friction loss, etc.) is not involved in AVL FIRE simulation, indicated indexes are analyzed rather than effective indexes.

Table 9. Comparison results between the prototype engine and optimal design.

Prototype Engine Optimal Design Parameter High Load Partial Load High Load Partial Load

λS 0.889 0.730 0.979 0.785 ηT 0.882 0.931 0.861 0.917 ηS 0.784 0.680 0.843 0.720 x 0.778 0.671 0.782 0.686 y 0.071 0.030 0.057 0.018 ηBB 0.054 0.109 0.051 0.099 ηBB,c 0.163 0.150 IMEP (bar) 5.641 3.310 5.840 3.463 ISFC [g/(kW·h)] 251.4 306.7 253.2 310.4

Compared with the prototype engine, both λS and ηS in the optimal design results are improved, yet ηT declines to some extent. With regard to the fitting coefficients, x and y, of the Benson/Bradham model, both of the two are closer to ideal values, respectively (i.e., x is closer to 1 and y is closer to 0 after optimization). As a consequence, the combined evaluation objective function, ηBB,c, descends with an increasing x and a decreasing y. The whole scavenging process approaches more closely to the ideal perfect displacement model after optimization, which means the expected refinement has been received. For one thing, the comparatively notable increase of ηS, with the slight decrease of ηT, indicates that the scavenging quality of the optimal design is superior to that of the prototype engine. For another, the evaluation objective function, ηBB,c, based on the Benson/Bradham model is proven to reflect the scavenging quality. As for conventional performance indexes, there are also improvements in IMEP, with a 3.4% increase under the high load and a 4.6% increase under the partial load, respectively. Additionally, ISFC shows a 0.7% increase under the high load and a 1.2% increase under the partial load. Because λS and ηS go up while ηT declines slightly after optimization, more fresh charge is delivered into the cylinder and retained there after the end of the scavenging process, and the short circuit loss also grows accordingly. Therefore, the enhancement of IMEP caused by more fresh charge trapped in the cylinder and the improvement of ISFC due to more fresh charge leaking out of the cylinder takes place simultaneously. Taken together, the optimal design shows superior progress in the power performance while marginally deteriorating the fuel economy, which makes it still acceptable overall. However, targeted improvement in the power performance and fuel economy cannot be achieved with ηBB,c as the optimization goal since there is no direct relationship between the coefficients of the Benson/Bradham model and engine performance indexes. In addition, when ηBB,c is selected as the evaluation objective function, different operating conditions are considered simultaneously, but the weights of the concerned operating conditions are not easy to identify (we make no distinction between the high load and partial load in this paper), which also brings some deficiency into this optimizing method.

4.3. 3D Flow Field Visualization With the powerful visualization tools provided by AVL FIRE simulation, various sectional views of the gas composition distribution inside the cylinder are accessible. As shown in Figure 18a, the X section passes through the cylinder axis and is exactly the symmetry plane of the cylinder. As shown in Figure 18b, the Z section is normal to the cylinder axis and passes through the center of four ports located on the cylinder wall. Energies 2017, 10, x FOR PEER REVIEW 20 of 28

the cylinder and retained there after the end of the scavenging process, and the short circuit loss also grows accordingly. Therefore, the enhancement of IMEP caused by more fresh charge trapped in the cylinder and the improvement of ISFC due to more fresh charge leaking out of the cylinder takes place simultaneously. Taken together, the optimal design shows superior progress in the power performance while marginally deteriorating the fuel economy, which makes it still acceptable overall. However, targeted improvement in the power performance and fuel economy cannot be

achieved with 𝜂, as the optimization goal since there is no direct relationship between the coefficients of the Benson/Bradham model and engine performance indexes. In addition, when 𝜂, is selected as the evaluation objective function, different operating conditions are considered simultaneously, but the weights of the concerned operating conditions are not easy to identify (we make no distinction between the high load and partial load in this paper), which also brings some deficiency into this optimizing method.

4.3. 3D Flow Field Visualization With the powerful visualization tools provided by AVL FIRE simulation, various sectional views of the gas composition distribution inside the cylinder are accessible. As shown in Figure 18a, the X section passes through the cylinder axis and is exactly the symmetry plane of the cylinder. As shown Energiesin Figure2018 18b,, 11, 2739 the Z section is normal to the cylinder axis and passes through the center of four20 ports of 26 located on the cylinder wall.

(a) (b)

FigureFigure 18.18. ((aa)) XX section;section; ((bb)) ZZ section.section.

150150°◦ CA,CA, 180180°◦ CA,CA, andand 210210°◦ CACA areare selectedselected asas thethe representativerepresentative crankcrank anglesangles ofof thethe wholewhole scavengingscavenging process.process. XX cross-sectionalcross-sectional viewsviews andand ZZ cross-sectionalcross-sectional viewsviews ofof bothboth thethe prototypeprototype engineengine andand thethe optimaloptimal designdesign underunder twotwo differentdifferent operatingoperating conditionsconditions areare presented.presented. TheThe colorcolor inin thesethese figuresfigures indicatesindicates thethe proportionproportion ofof freshfresh charge.charge. TheThe valuevalue ofof 11 indicatesindicates allall freshfresh chargecharge whilewhile 00 indicatesindicates allall burnedburned gas.gas. TheThe 3D3D flowflow fieldfield visualizationvisualization results results show show that: that:

(1)(1) As shownshown inin FiguresFigures 1919–22,–22, whenwhen comparingcomparing thethe scavengingscavenging processprocess ofof thethe prototypeprototype engineengine under two operating operating conditions, conditions, it it is is not not difficult difficult to to find find that that the the fresh fresh intake intake charge charge under under the thehigh high load load is less is lessthan than that thatof the of thepartial partial load load in the in initial the initial stage stage of the of scavenging the scavenging process process (150 (150°CA).◦CA). The in-cylinder The in-cylinder pressure pressure under under the high the high load load is higher is higher than than that that of the of thepartial partial load load and and the theexhaust exhaust process process takes takes a longer a longer time, time, thus thus the thein-cyl in-cylinderinder pressure pressure is still is still at a at high a high level level after after the thescavenging scavenging port port opening. opening. As a As result, a result, the thepressure pressure of the of thefresh fresh charge charge is not is notenough enough to enter to enter the thecylinder cylinder in the in the early early scavenging scavenging phase. phase. Contrast Contrastingly,ingly, the the fresh fresh charge charge intake intake under thethe highhigh loadload isis notablynotably moremore thanthan thatthat ofof thethe partialpartial loadload atat 180180◦ °CACA andand 210210 ◦°CA.CA. BecauseBecause thethe throttlethrottle isis wide open under the high load, the crankcase pressure is higher and the piston motion is faster and the crankcase scavenged process is more effective, thus more fresh charge is delivered into the cylinder. This significant difference can be perceived based on the λS simulation data as well. (2) As shown in Figures 19 and 20, when comparing the simulation results of the prototype engine and optimal design under the partial load, it can be found that at 150 ◦CA, the fresh charge intake of the prototype engine case is more than that of the optimal design case, especially around the lateral scavenging ports; at 180 ◦CA, the situation is similar to the high load simulation results, fresh charge distribution is biased toward the upper part of the cylinder with a larger “coverage area” after the optimal design; at 210 ◦CA, the optimal design results still show advantages over the prototype engine results in the scavenging quality. For the latter case, fresh charge is concentrated on the side of the central scavenging port as shown in the relevant Z cross-sectional views. Both lateral scavenging ports are inclined toward the central scavenging port. In consequence, corresponding gas motion due to the arrangement of the lateral scavenging ports hinders the fresh charge from entering the cylinder through the central scavenging port, and eventually gives rise to airflow obstruction and reduces the scavenging quality. (3) As shown in Figures 21 and 22, when comparing the simulation results of the prototype engine and optimal design under the high load, it can be concluded that their gas composition Energies 2018, 11, 2739 21 of 26

distributions in the cylinder at 150 ◦CA are similar; at 180 ◦CA, fresh charge inside the cylinder is more biased toward the upper part in the optimal design results; at 210 ◦CA, fresh charge has reached more than 75% of the area of the entire cylinder after optimization. By contrast, the proportion of fresh charge in the combustion area is relatively low in the prototype engine case, which means the burned gas has not been cleared out of the cylinder even in the final scavenging phase.

Investigated factor values before and after optimization are displayed in Table 10. Taken together, the optimal design has a larger αC and αl, which yields better scavenging effects especially for the upper part of the cylinder, while a smaller βS alleviates the conflict impact resulted from the gas motion of fresh charge entering the cylinder through the central scavenging port and the lateral scavenging ports. The combination of these two points contributes to the optimal design results with a higher scavenging efficiency.

Table 10. Investigated factor values before and after optimization.

Factors Prototype Engine Optimal Design ◦ αC ( ) 55 60 ◦ αl ( ) 0 20 ◦ βl ( ) 30 20 γC 23.81% 23.81%

Energies 2017, 10, x FOR PEER REVIEW 22 of 28

Figure 19. X cross-sectionalFigure 19. X cross-sectional views views of theof the prototype prototype en enginegine simulation simulation case and optimal case anddesign optimal design simulation case under the partial load operating condition. simulation case under the partial load operating condition.

Energies 2018, 11, 2739 22 of 26

Energies 2017, 10, x FOR PEER REVIEW 23 of 28

Figure 20. Z cross-sectionalFigure 20. Z cross-sectional views views of theof the prototype prototype en enginegine simulation simulation case and optimal case anddesign optimal design simulation case under the partial load operating condition. simulation caseEnergies under 2017, 10, x the FOR partialPEER REVIEW load operating condition. 24 of 28

Figure 21. X cross-sectionalFigure 21. X cross-sectional views views of theof the prototype prototype en enginegine simulation simulation case and optimal case anddesign optimal design simulation case under the high load operating condition. simulation case under the high load operating condition.

Energies 2018, 11, 2739 23 of 26

Energies 2017, 10, x FOR PEER REVIEW 25 of 28

Figure 22. Z cross-sectionalFigure 22. Z cross-sectional views views of theof the prototype prototype en enginegine simulation simulation case and optimal case anddesign optimal design simulation case under the high load operating condition. simulation case under the high load operating condition. 5. Discussion and Conclusions 5. Discussion and Conclusions As a common power source for widely applied small and medium-sized UAVs, the two-stroke small areoengine was selected as the research focus in this paper. Because of the particular scavenging As a commonsystem power structure, source the scavenging for widely process features applied prominently small in and the engine medium-sized power performance UAVs, and the two-stroke small areoenginefuel was economy. selected Scavenging as the models research are also focus proposed in this to describe paper. the Because corresponding of the scavenging particular scavenging system structure,process. the However, scavenging few previous process researchers features have paid prominently enough attention in to these the more engine accurate power and performance sophisticated scavenging models when optimizing the design of original ports’ arrangement. To and fuel economy.illuminate Scavenging this uncharted models area, we areselected also the proposed comprehensive to evaluation describe objective the corresponding function based scavenging process. However,on the fewBenson/Bradham previous model researchers as the optimization have paid goal enoughand performed attention the 1D/3D to coupling these more accurate simulation to verify the optimal design results. Although demonstrated by preliminary simulation and sophisticatedresults, scavenging this optimization models method suffers when from optimizing some limitations the due design to the lack of of original a practicalports’ bench arrangement. To illuminate thistest of unchartedthe redesigned area,two-stroke we small selected areoengi thene. Despite comprehensive that inadequacy, evaluationthe present research objective function findings provide the requisite criteria necessary for the appropriate design of a crankcase scavenging based on the Benson/Bradhamsystem, which plays a vital model role in asengine the performa optimizationnce improvement. goal andDetailed performed analysis leads the to the 1D/3D coupling simulation to verifyfollowing the conclusions: optimal design results. Although demonstrated by preliminary simulation results, this optimization(1) With respect method to the investigated suffers prototype from some engine limitations scavenging system, due there to the exists lack an obvious of a practical bench test of the redesignedtrade-off two-stroke relationship between small the areoengine. scavenging efficiency, Despite 𝜂, and that trapping inadequacy, efficiency, 𝜂 the, which present research findings provide the requisite criteria necessary for the appropriate design of a crankcase scavenging system, which plays a vital role in engine performance improvement. Detailed analysis leads to the following conclusions:

(1) With respect to the investigated prototype engine scavenging system, there exists an obvious trade-off relationship between the scavenging efficiency, ηS, and trapping efficiency, ηT, which makes it difficult to balance the power performance and fuel economy during the optimization process. (2) Characteristics of the actual gas exchange process can be indicated by the key parameters, x and y, and the evaluation objective function, ηBB,c, based on the Benson/Bradham model was proven to reflect the scavenging quality precisely.

(3) From the OED results analysis, it is clear that the slip angle of lateral scavenging ports, βl, has the most significant influence on ηBB,c, followed by the tilt angle of lateral scavenging ports, αl, while the tilt angle of the central scavenging port, αC, and the width ratio of the central Energies 2018, 11, 2739 24 of 26

scavenging port, γC, are not statistically significant in terms of their influence on ηBB,c. The best ◦ ◦ ◦ factors combination is αC = 60 , αl = 20 , βl = 20 , and γC = 23.81%. (4) Compared with the prototype engine, both λS and ηS in the optimal design results are improved, yet ηT declines to some extent. Taken together, the optimal design results show superior progress in power performance (IMEP) while marginally deteriorations in the fuel economy (ISFC), which makes it still acceptable overall. (5) Powerful visualization tools provided by AVL FIRE software can be employed to analyze the gas composition distribution inside the cylinder during the whole scavenging process.

Author Contributions: Conceptualization, K.H. and Y.Q.; Methodology, J.Q. and K.H.; Software, J.Q. and Y.S.; Formal Analysis, Y.Q. and X.D.; Data Curation, Y.Q. and J.Q.; Writing-Original Draft Preparation, Y.Q.; Writing-Review & Editing, K.H. and Y.Q. Funding: This research was funded by Beijing Municipal Science & Technology Commission grant number D17111000490000. Acknowledgments: The authors gratefully acknowledge the administrative and technical support from the AVL Shanghai Technology Center Co., Ltd. and IDAJ Beijing Co., Ltd. Conflicts of Interest: The authors declare no conflict of interest.

Nomenclatures

Abbreviations UAV unmanned aerial vehicle 1D one-dimensional 3D three-dimensional CFD Computational Fluid Dynamics BUSDIG Boosted Uniflow Scavenged Direct Injection Gasoline POD Proper Orthogonal Decomposition OP2S Opposed-Piston 2-Stroke Diesel Engine LICELGIS Linear Internal Combustion Engine-Linear Generator Integrated System OPHSDI Opposed Piston High Speed Direct Injection Diesel Engine OED Orthogonal Experimental Design IMEP indicated mean effective pressure ISFC indicated specific fuel consumption SPI single point injection CAD Computer Aided Design FEM Fame Hexa Meshing FEP Fame Engine Plus STM Standard Transport Model ECFM Extended Coherent Flame Model TKE turbulence kinetic energy TDC top dead center BDC bottom dead center SIMPLE Semi-Implicit Method for Pressure Linked Equations HWT Hybrid Wall Treatment SWF Standard Wall Function GSTB Gradient Stabilized Biconjugate Symbols mi mass of delivered fresh charge through all scavenging ports m f C mass of delivered fresh charge retained in cylinder mleak mass of delivered fresh charge leaking out of the cylinder mbg mass of residual burned gas retained in cylinder mall mass of all trapped cylinder charge m0 reference mass λS delivery ratio ηS scavenging efficiency Energies 2018, 11, 2739 25 of 26

ηT trapping efficiency ηC charging efficiency Vbg instant burned gas volume in the cylinder Vall the whole cylinder volume x displacement ratio y short-circuiting fraction αC tilt angle of the central scavenging port αl tilt angle of lateral scavenging ports βl slip angle of lateral scavenging ports γC width ratio of the central scavenging port LC width of the central scavenging port Ll width of single lateral scavenging port ηBB elementary evaluation objective function ηBB,h evaluation objective function for the high load ηBB,p evaluation objective function for the partial load ηBB,c combined evaluation objective function α significance level for the variance analysis

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