Propeller design is a highly intricate and interdisciplinary task that necessitates careful trade-offs between radiated noise levels and aerodynamic efficiency.To achieve efficient trade-off designs,an enhanced on-the...Propeller design is a highly intricate and interdisciplinary task that necessitates careful trade-offs between radiated noise levels and aerodynamic efficiency.To achieve efficient trade-off designs,an enhanced on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is developed,which maintains the fidelity of the Navier-Stokes solution for unsteady flow and of the moving-medium Ffowcs Williams-Hawkings(FW-H)formulation for capturing tonal noise.Furthermore,this on-the-fly approach enables a unified architecture for discreteadjoint sensitivity analysis encompassing both aerodynamics and aeroacoustics,facilitating effective multi-objective weighted optimizations.Subsequently,this proposed methodology is applied to perform trade-off optimizations between aerodynamics and aeroacoustics for a propeller by employing varying weighting factors to comprehend their influence on optimal configurations.The results demonstrate a positive correlation between efficiency and noise sensitivities,and thus indicate an inherent synchronicity where pursing noise reduction through purely aeroacoustic optimization inevitably entails sacrificing aerodynamic efficiency.However,by effectively incorporating appropriate weighting factors(recommended to range from 0.25 to 0.5)into the multi-objective function combined with both aerodynamics and aeroacoustics,it becomes feasible to achieve efficiency enhancement and noise reduction simultaneously.Key findings show that reducing blade planform size and equipping“rotated-S”shaped airfoil profiles in the tip region can effectively restrain noise levels while maintaining aerodynamic performance.展开更多
The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicle...The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicles,reducing aerodynamic drag remains a critical strategy for improving energy efficiency and lowering emissions.This study investigates the influence of key geometric parameters on the aerodynamic drag of vehicles.A parametric vehicle model was developed,and computational fluid dynamics(CFD)simulations were conducted to analyse variations in the drag coefficient(C_(d))and pressure distribution across different design configurations.The results reveal that the optimal aerodynamic performance—characterized by a minimized drag coefficient—is achieved with the following parameter settings:engine hood angle(α)of 15°,windshield angle(β)of 25°,rear window angle(γ)of 40°,rear upwards tail lift angle(θ)of 10°,ground clearance(d)of 100 mm,and side edge angle(s)of 5°.These findings offer valuable guidance for the aerodynamic optimization of vehicle body design and contribute to strategies aimed at energy conservation and emission reduction in the automotive sector.展开更多
A train body's cross-sectional shape has a significant impact on aerodynamic drag and operational safety in high-speed trains(HSTs).This study extracts five design variables from a real-world HST body:height,width...A train body's cross-sectional shape has a significant impact on aerodynamic drag and operational safety in high-speed trains(HSTs).This study extracts five design variables from a real-world HST body:height,width,side arc radius,arc radius at the connection between the side and the roof,and arc radius at the connection between the side and the train's bottom.The cross-validated Kriging surrogate model and the genetic algorithm are used to perform two types of aerodynamic optimization,with the cross-sectional area as a constraint.Cross-sectional shapes are optimized in both windless and windy conditions.Numerical results indicate that in a windless environment,the aerodynamic drag coefficient of the whole train is reduced by 2.4%;in a windy condition,the aerodynamic drag coefficient of the entire vehicle is reduced by 2.4%,and the aerodynamic lateral force of the leading car is reduced by 37.8%.These suggest that a flat and wide shape helps to reduce not only overall aerodynamic drag in a windless environment but also aerodynamic load in a windy environment,which can be accomplished by reducing the area of the side wall and top region,lowering the train body's height,increasing its width,and lowering the radius of the side and top arcs.展开更多
Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experi...Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.展开更多
The high-speed reentry vehicle operates across a broad range of speeds and spatial domains,where optimal aerodynamic shapes for different speeds are contradictory.This makes it challenging for a single-Mach optimizati...The high-speed reentry vehicle operates across a broad range of speeds and spatial domains,where optimal aerodynamic shapes for different speeds are contradictory.This makes it challenging for a single-Mach optimization design to meet aerodynamic performance requirements throughout the vehicle’s flight envelope.Additionally,the strong coupling between aerodynamics and control adds complexity,as fluctuations in aerodynamic parameters due to speed variations complicate control system design.To address these challenges,this study proposes an aerodynamic/control coupling optimization design approach.This method,based on aerodynamic optimization principles,incorporates active control technology,treating aerodynamic layout and control system design as primary components during the conceptual design phase.By integrating the design and evaluation of aerodynamics and control,the approach aims to reduce design iterations and enhance overall flight performance.The comprehensive design of the rotary reentry vehicle,using this optimization strategy,effectively balances performance at supersonic and hypersonic speeds.The results show that the integrated design model meets aerodynamic and control performance requirements over a broader range of Mach numbers,preventing performance degradation due to deviations from the design Mach number,and providing a practical solution for high-speed reentry vehicle design.展开更多
This study discusses a machine learning‑driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non‑conventional,like Bionica box‑wing,aircraft configurat...This study discusses a machine learning‑driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non‑conventional,like Bionica box‑wing,aircraft configurations.The approach leverages advanced parameterization techniques,such as class and shape transformation(CST)and Bezier curves,to reduce design complexity while preserving flexibility.Computational fluid dynamics(CFD)simulations are performed to generate a comprehensive dataset,which is used to train an extreme gradient boosting(XGBoost)model for predicting aerodynamic performance.The optimization process,using the non‑dominated sorting genetic algorithm(NSGA‑Ⅱ),results in a 12.3%reduction in drag for the CRM wing and an 18%improvement in the lift‑to‑drag ratio for the Bionica box‑wing.These findings validate the efficacy of machine learning based method in aerodynamic optimization,demonstrating significant efficiency gains across both configurations.展开更多
An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective...An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network(Bicycle-GAN),whose performance is compared with that of the conditional variational autoencoder(cVAE)based parameterization method in terms of parsimony,flawlessness,intuitiveness,and physicality.In all four aspects,the Bicycle-GAN-based parameterization method is superior to the cVAEbased parameterization method.Combined with multifidelity Gaussian process regression(MFGPR)surrogate model and a Bayesian optimization algorithm,a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow,which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness.The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thin-airfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation.For both supersonic conditions,the CFD simulation costs are reduced by>20%compared with those of the cVAE-based optimization,and better optimization results are obtained through the Bicycle-GAN model.The optimization results for this supersonic flow point to a sharper leading edge,a smaller camber and thickness with a flatter lower surface,and a maximum thickness at 50%chord length.The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics,surrogate model prediction accuracy and optimization efficiency.展开更多
The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise character...The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise characteristics of a commercial high-speed hair dryer through Computational Fluid Dynamics(CFD)analysis.The velocity field,streamline patterns,and vector distribution within the primary flow path and internal cavity were systematically examined.Results indicate that strong interactions between the wake flow generated by the guide vanes and the straight baffle in the rear flow path induce vortex structures near the outlet,which are primarily responsible for highfrequency noise.To address this,the guide vanes and rear flow path geometry were redesigned and optimized for improved acoustic and aerodynamic performance.Underrated operating conditions(28 V,20,000 rpm),the optimized configuration achieves a noise reduction of more than 2.2 dB while increasing outlet wind speed by over 9%.Moreover,the noise suppression effect becomes more pronounced at lower rotational speeds.展开更多
The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint ...The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...展开更多
To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the ...To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.展开更多
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a...Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.展开更多
A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation,with assistance of lower-fidelity and cheaper simulation(s).However...A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation,with assistance of lower-fidelity and cheaper simulation(s).However,most existing works only incorporate‘‘two"levels of fidelity,and thus efficiency improvement is very limited.In order to reduce the number of high-fidelity simulations as many as possible,there is a strong need to extend it to three or more fidelities.This article proposes a novel variable-fidelity optimization approach with application to aerodynamic design.Its key ingredient is the theory and algorithm of a Multi-level Hierarchical Kriging(MHK),which is referred to as a surrogate model that can incorporate simulation data with arbitrary levels of fidelity.The high-fidelity model is defined as a CFD simulation using a fine grid and the lower-fidelity models are defined as the same CFD model but with coarser grids,which are determined through a grid convergence study.First,sampling shapes are selected for each level of fidelity via technique of Design of Experiments(DoE).Then,CFD simulations are conducted and the output data of varying fidelity is used to build initial MHK models for objective(e.g.C_D)and constraint(e.g.C_L,C_m)functions.Next,new samples are selected through infillsampling criteria and the surrogate models are repetitively updated until a global optimum is found.The proposed method is validated by analytical test cases and applied to aerodynamic shape optimization of a NACA0012 airfoil and an ONERA M6 wing in transonic flows.The results confirm that the proposed method can significantly improve the optimization efficiency and apparently outperforms the existing single-fidelity or two-level-fidelity method.展开更多
In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number...In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition（POD） in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.展开更多
Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multi-fidelity surrogate model is constructe...Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multi-fidelity surrogate model is constructed based on two independent high and low fidelity samples.Co-Kriging method can use a greater quantity of low-fidelity information to enhance the accuracy of a surrogate of the high-fidelity model by modeling the correlation between high and low fidelity model,thus computational cost of building surrogate model can be greatly reduced.A wing-body problem is taken as an example to compare characteristics of co-Kriging multi-fidelity(CKMF)model with traditional Kriging based multi-fidelity(KMF)model.A sampling convergence of the CKMF model and the KMF model is conducted,and an appropriate sampling design is selected through the sampling convergence analysis.The results indicate that CKMF model has higher approximation accuracy with the same high-fidelity samples,and converges at less high-fidelity samples.A wing-body drag reduction optimization design using genetic algorithm is implemented.Satisfying design results are obtained,which validate the feasibility of CKMF model in engineering design.展开更多
An optimization strategy is proposed to deal with the aerodynamic/stealthy/structural multidisciplinary design optimization(MDO)issue of unmanned combat air vehicle(UCAV).In applying the strategy,the MDO process is di...An optimization strategy is proposed to deal with the aerodynamic/stealthy/structural multidisciplinary design optimization(MDO)issue of unmanned combat air vehicle(UCAV).In applying the strategy,the MDO process is divided into two levels,i.e.system level optimization and subsystem level optimization.The system level optimization is to achieve optimized system objective(or multi-objective)through the adjustment of global external configuration design variables.The subsystem level optimization consists of the aerodynamic/stealthy integrated design and the structural optimization.The aerodynamic/stealthy integrated design aims at achieving the minimum aerodynamic drag coefficient under the constraint of stealthy requirement through the adjustment of local external configuration design variables.The structural optimization is to minimize the structural weight by adjusting the dimefisions of structural components.A flowchart to implement this strategy is presented.The MDO for a flying-wing configuration of UCAV is employed to illustrate the detailed process of the optimization.The results indicate that the overall process of the surrogate-based two-level optimization strategy can be implemented automatically,and quite reasonable results are obtained.展开更多
Twist morphing （TM） is a practical control technique in micro air vehicle （MAV） flight. However, TM wing has a lower aerodynamic efficiency （CL/CD） compared to membrane and rigid wing. This is due to massive dra...Twist morphing （TM） is a practical control technique in micro air vehicle （MAV） flight. However, TM wing has a lower aerodynamic efficiency （CL/CD） compared to membrane and rigid wing. This is due to massive drag penalty created on TM wing, which had overwhelmed the succes- sive increase in its lift generation. Therefore, further CL/CDmax optimization on TM wing is needed to obtain the optimal condition for the morphing wing configuration. In this paper, two-way fluid- structure interaction （FSI） simulation and wind tunnel testing method are used to solve and study the basic wing aerodynamic performance over （non-optimal） TM, membrane and rigid wings. Then, a multifidelity data metamodel based design optimization （MBDO） process is adopted based on the Ansys-DesignXplorer frameworks. In the adaptive MBDO process, Kriging metamodel is used to construct the final multifidelity CL/CD responses by utilizing 23 multi-fidelity sample points from the FSI simulation and experimental data. The optimization results show that the optimal TM wing configuration is able to produce better CL/CDmax magnitude by at least 2% than the non-optimal TM wings. The flow structure formation reveals that low TV strength on the optimal TM wing induces low CD generation which in turn improves its overall CL/CDmax performance.展开更多
This paper presents a new non-linear formulation of the classical Vortex Lattice Method(VLM)approach for calculating the aerodynamic properties of lifting surfaces.The method accounts for the effects of viscosity,and ...This paper presents a new non-linear formulation of the classical Vortex Lattice Method(VLM)approach for calculating the aerodynamic properties of lifting surfaces.The method accounts for the effects of viscosity,and due to its low computational cost,it represents a very good tool to perform rapid and accurate wing design and optimization procedures.The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections,according to strip theory,and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface,calculated with a fully three-dimensional vortex lifting law.The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment,as well as in predicting the wing drag.The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.展开更多
Experiment design method is a key to construct a highly reliable surrogate model for numerical optimization in large-scale project. Within the method, the experimental design criterion directly affects the accuracy of...Experiment design method is a key to construct a highly reliable surrogate model for numerical optimization in large-scale project. Within the method, the experimental design criterion directly affects the accuracy of the surrogate model and the optimization efficient. According to the shortcomings of the traditional experimental design, an improved adaptive sampling method is proposed in this paper. The surrogate model is firstly constructed by basic sparse samples. Then the supplementary sampling position is detected according to the specified criteria, which introduces the energy function and curvature sampling criteria based on radial basis function （RBF） network. Sampling detection criteria considers both the uniformity of sample distribution and the description of hypersurface curvature so as to significantly improve the prediction accuracy of the surrogate model with much less samples. For the surrogate model constructed with sparse samples, the sample uniformity is an important factor to the interpolation accuracy in the initial stage of adaptive sam- pling and surrogate model training. Along with the improvement of uniformity, the curvature description of objective function surface gradually becomes more important. In consideration of these issues, crowdness enhance function and root mean square error （RMSE） feedback function are introduced in C criterion expression. Thus, a new sampling method called RMSE and crowd- ness enhance （RCE） adaptive sampling is established. The validity of RCE adaptive sampling method is studied through typical test function firstly and then the airfoil/wing aerodynamic opti- mization design problem, which has high-dimensional design space. The results show that RCE adaptive sampling method not only reduces the requirement for the number of samples, but also effectively improves the prediction accuracy of the surrogate model, which has a broad prospects for applications.展开更多
To improve the safety of trains running in an undesirable wind environment,a novel louver-type wind barrier is proposed and further studied in this research using a scaled wind tunnel simulation with 1:40 scale models...To improve the safety of trains running in an undesirable wind environment,a novel louver-type wind barrier is proposed and further studied in this research using a scaled wind tunnel simulation with 1:40 scale models.Based on the aerodynamic performance of the train-bridge system,the parameters of the louver-type wind barrier are optimized.Compared to the case without a wind barrier,it is apparent that the wind barrier improves the running safety of trains,since the maximum reduction of the moment coefficient of the train reaches 58%using the louver-type wind barrier,larger than that achieved with conventional wind barriers(fence-type and grid-type).A louver-type wind barrier has more blade layers,and the rotation angle of the adjustable blade of the louver-type wind barrier is 90–180°(which induces the flow towards the deck surface),which is more favorable for the aerodynamic performance of the train.Comparing the 60°,90°and 120°wind fairings of the louver-type wind barrier blade,the blunt fairing is disadvantageous to the operational safety of the train.展开更多
The high aerodynamic noise induced by automotive air conditioning systems has important effects on the ride comfort, and the centrifugal fan is the largest noise source in these systems. It is very important to reduce...The high aerodynamic noise induced by automotive air conditioning systems has important effects on the ride comfort, and the centrifugal fan is the largest noise source in these systems. It is very important to reduce the aerodynamic noise generated by the centrifugal fan. The flow field and the sound field on the whole centrifugal fan configuration have been carried out using the computational fluid dynamics. Simulation results show that the sound pressure level near the outlet of the centrifugal fan is too high. Based on the relationship between flow characteristics and the aerodynamic noise, four parameters of the centrifugal fan, i.e., impeller blade＇s outlet angle 0, volute tongue＇s gap t, collector inclination angle fl, and rotating speed n, were selected as design variables and optimized using response surface methodology. While keeping the function of flow rate unchanged, the peak noise level is reduced by 8 dB or 10.8%. The noise level is satisfactorily reduced.展开更多
基金supported by the National Science and Technology Major Project,China(No.Y2019-I-0018-0017)the National Natural Science Foundation of China(No.11602200)+1 种基金Hunan Innovative Province Construction Special Fund,China(No.2021GK1020)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China。
文摘Propeller design is a highly intricate and interdisciplinary task that necessitates careful trade-offs between radiated noise levels and aerodynamic efficiency.To achieve efficient trade-off designs,an enhanced on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is developed,which maintains the fidelity of the Navier-Stokes solution for unsteady flow and of the moving-medium Ffowcs Williams-Hawkings(FW-H)formulation for capturing tonal noise.Furthermore,this on-the-fly approach enables a unified architecture for discreteadjoint sensitivity analysis encompassing both aerodynamics and aeroacoustics,facilitating effective multi-objective weighted optimizations.Subsequently,this proposed methodology is applied to perform trade-off optimizations between aerodynamics and aeroacoustics for a propeller by employing varying weighting factors to comprehend their influence on optimal configurations.The results demonstrate a positive correlation between efficiency and noise sensitivities,and thus indicate an inherent synchronicity where pursing noise reduction through purely aeroacoustic optimization inevitably entails sacrificing aerodynamic efficiency.However,by effectively incorporating appropriate weighting factors(recommended to range from 0.25 to 0.5)into the multi-objective function combined with both aerodynamics and aeroacoustics,it becomes feasible to achieve efficiency enhancement and noise reduction simultaneously.Key findings show that reducing blade planform size and equipping“rotated-S”shaped airfoil profiles in the tip region can effectively restrain noise levels while maintaining aerodynamic performance.
基金funded by the“Hundred Outstanding Talents”Support Program of Jining University,a provincial-level key project in the field of natural sciences,grant number 2023ZYRC23Jining Key R&D Program(Soft Science)Project,No.2024JNZC010Shandong Province Key Research and Development Program(Technology-Based Small and Medium-sized Enterprises Innovation Capability Improvement)Project No.2025TSGCCZZB0679.
文摘The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicles,reducing aerodynamic drag remains a critical strategy for improving energy efficiency and lowering emissions.This study investigates the influence of key geometric parameters on the aerodynamic drag of vehicles.A parametric vehicle model was developed,and computational fluid dynamics(CFD)simulations were conducted to analyse variations in the drag coefficient(C_(d))and pressure distribution across different design configurations.The results reveal that the optimal aerodynamic performance—characterized by a minimized drag coefficient—is achieved with the following parameter settings:engine hood angle(α)of 15°,windshield angle(β)of 25°,rear window angle(γ)of 40°,rear upwards tail lift angle(θ)of 10°,ground clearance(d)of 100 mm,and side edge angle(s)of 5°.These findings offer valuable guidance for the aerodynamic optimization of vehicle body design and contribute to strategies aimed at energy conservation and emission reduction in the automotive sector.
基金Project(RE-KRIS/FF67/020)supported by the King Mongkut's Institute of Technology Ladkrabang(Fundamental Fund by National Science Research and Innovation Fund(NSRF)),Thailand。
文摘A train body's cross-sectional shape has a significant impact on aerodynamic drag and operational safety in high-speed trains(HSTs).This study extracts five design variables from a real-world HST body:height,width,side arc radius,arc radius at the connection between the side and the roof,and arc radius at the connection between the side and the train's bottom.The cross-validated Kriging surrogate model and the genetic algorithm are used to perform two types of aerodynamic optimization,with the cross-sectional area as a constraint.Cross-sectional shapes are optimized in both windless and windy conditions.Numerical results indicate that in a windless environment,the aerodynamic drag coefficient of the whole train is reduced by 2.4%;in a windy condition,the aerodynamic drag coefficient of the entire vehicle is reduced by 2.4%,and the aerodynamic lateral force of the leading car is reduced by 37.8%.These suggest that a flat and wide shape helps to reduce not only overall aerodynamic drag in a windless environment but also aerodynamic load in a windy environment,which can be accomplished by reducing the area of the side wall and top region,lowering the train body's height,increasing its width,and lowering the radius of the side and top arcs.
基金supported by the National Natural Science Foundation of China(U20A20306,62276191)the Fundamental Research Funds for the Central Universities(HUST2023JYCXJJ011).
文摘Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.
基金supported by the National Natural Science Foundation of China(Grant Nos.52192633,92371201,11872293,and 92152301)the Natural Science Foundation of Shaanxi Province(Grant No.2022JC-03).
文摘The high-speed reentry vehicle operates across a broad range of speeds and spatial domains,where optimal aerodynamic shapes for different speeds are contradictory.This makes it challenging for a single-Mach optimization design to meet aerodynamic performance requirements throughout the vehicle’s flight envelope.Additionally,the strong coupling between aerodynamics and control adds complexity,as fluctuations in aerodynamic parameters due to speed variations complicate control system design.To address these challenges,this study proposes an aerodynamic/control coupling optimization design approach.This method,based on aerodynamic optimization principles,incorporates active control technology,treating aerodynamic layout and control system design as primary components during the conceptual design phase.By integrating the design and evaluation of aerodynamics and control,the approach aims to reduce design iterations and enhance overall flight performance.The comprehensive design of the rotary reentry vehicle,using this optimization strategy,effectively balances performance at supersonic and hypersonic speeds.The results show that the integrated design model meets aerodynamic and control performance requirements over a broader range of Mach numbers,preventing performance degradation due to deviations from the design Mach number,and providing a practical solution for high-speed reentry vehicle design.
文摘This study discusses a machine learning‑driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non‑conventional,like Bionica box‑wing,aircraft configurations.The approach leverages advanced parameterization techniques,such as class and shape transformation(CST)and Bezier curves,to reduce design complexity while preserving flexibility.Computational fluid dynamics(CFD)simulations are performed to generate a comprehensive dataset,which is used to train an extreme gradient boosting(XGBoost)model for predicting aerodynamic performance.The optimization process,using the non‑dominated sorting genetic algorithm(NSGA‑Ⅱ),results in a 12.3%reduction in drag for the CRM wing and an 18%improvement in the lift‑to‑drag ratio for the Bionica box‑wing.These findings validate the efficacy of machine learning based method in aerodynamic optimization,demonstrating significant efficiency gains across both configurations.
基金supported by the National Natural Science Founda-tion of China(Grant No 12302226)the China Postdoctoral Science Foundation(Grant No BX20230453).
文摘An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network(Bicycle-GAN),whose performance is compared with that of the conditional variational autoencoder(cVAE)based parameterization method in terms of parsimony,flawlessness,intuitiveness,and physicality.In all four aspects,the Bicycle-GAN-based parameterization method is superior to the cVAEbased parameterization method.Combined with multifidelity Gaussian process regression(MFGPR)surrogate model and a Bayesian optimization algorithm,a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow,which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness.The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thin-airfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation.For both supersonic conditions,the CFD simulation costs are reduced by>20%compared with those of the cVAE-based optimization,and better optimization results are obtained through the Bicycle-GAN model.The optimization results for this supersonic flow point to a sharper leading edge,a smaller camber and thickness with a flatter lower surface,and a maximum thickness at 50%chord length.The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics,surrogate model prediction accuracy and optimization efficiency.
基金supported by Research Project of Zhuhai City Polytechnic(Grant No.2024KYBS06)Education Research Project of Zhuhai City Polytechnic(Grant No.JY20250404).
文摘The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise characteristics of a commercial high-speed hair dryer through Computational Fluid Dynamics(CFD)analysis.The velocity field,streamline patterns,and vector distribution within the primary flow path and internal cavity were systematically examined.Results indicate that strong interactions between the wake flow generated by the guide vanes and the straight baffle in the rear flow path induce vortex structures near the outlet,which are primarily responsible for highfrequency noise.To address this,the guide vanes and rear flow path geometry were redesigned and optimized for improved acoustic and aerodynamic performance.Underrated operating conditions(28 V,20,000 rpm),the optimized configuration achieves a noise reduction of more than 2.2 dB while increasing outlet wind speed by over 9%.Moreover,the noise suppression effect becomes more pronounced at lower rotational speeds.
基金National Natural Science Foundation of China (10872093)
文摘The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...
文摘To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.
文摘Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.
基金sponsored by the National Natural Science Foundation of China(Nos.11772261 and 11972305)Aeronautical Science Foundation of China(No.2016ZA53011)Foundation of National Key Laboratory(No.JCKYS2019607005).
文摘A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation,with assistance of lower-fidelity and cheaper simulation(s).However,most existing works only incorporate‘‘two"levels of fidelity,and thus efficiency improvement is very limited.In order to reduce the number of high-fidelity simulations as many as possible,there is a strong need to extend it to three or more fidelities.This article proposes a novel variable-fidelity optimization approach with application to aerodynamic design.Its key ingredient is the theory and algorithm of a Multi-level Hierarchical Kriging(MHK),which is referred to as a surrogate model that can incorporate simulation data with arbitrary levels of fidelity.The high-fidelity model is defined as a CFD simulation using a fine grid and the lower-fidelity models are defined as the same CFD model but with coarser grids,which are determined through a grid convergence study.First,sampling shapes are selected for each level of fidelity via technique of Design of Experiments(DoE).Then,CFD simulations are conducted and the output data of varying fidelity is used to build initial MHK models for objective(e.g.C_D)and constraint(e.g.C_L,C_m)functions.Next,new samples are selected through infillsampling criteria and the surrogate models are repetitively updated until a global optimum is found.The proposed method is validated by analytical test cases and applied to aerodynamic shape optimization of a NACA0012 airfoil and an ONERA M6 wing in transonic flows.The results confirm that the proposed method can significantly improve the optimization efficiency and apparently outperforms the existing single-fidelity or two-level-fidelity method.
基金supported by the National Natural Science Foundation of China (No. 11502211)
文摘In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition（POD） in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.
基金supported by the Seventh Framework Programme of China-EU Collaborative Projects
文摘Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multi-fidelity surrogate model is constructed based on two independent high and low fidelity samples.Co-Kriging method can use a greater quantity of low-fidelity information to enhance the accuracy of a surrogate of the high-fidelity model by modeling the correlation between high and low fidelity model,thus computational cost of building surrogate model can be greatly reduced.A wing-body problem is taken as an example to compare characteristics of co-Kriging multi-fidelity(CKMF)model with traditional Kriging based multi-fidelity(KMF)model.A sampling convergence of the CKMF model and the KMF model is conducted,and an appropriate sampling design is selected through the sampling convergence analysis.The results indicate that CKMF model has higher approximation accuracy with the same high-fidelity samples,and converges at less high-fidelity samples.A wing-body drag reduction optimization design using genetic algorithm is implemented.Satisfying design results are obtained,which validate the feasibility of CKMF model in engineering design.
文摘An optimization strategy is proposed to deal with the aerodynamic/stealthy/structural multidisciplinary design optimization(MDO)issue of unmanned combat air vehicle(UCAV).In applying the strategy,the MDO process is divided into two levels,i.e.system level optimization and subsystem level optimization.The system level optimization is to achieve optimized system objective(or multi-objective)through the adjustment of global external configuration design variables.The subsystem level optimization consists of the aerodynamic/stealthy integrated design and the structural optimization.The aerodynamic/stealthy integrated design aims at achieving the minimum aerodynamic drag coefficient under the constraint of stealthy requirement through the adjustment of local external configuration design variables.The structural optimization is to minimize the structural weight by adjusting the dimefisions of structural components.A flowchart to implement this strategy is presented.The MDO for a flying-wing configuration of UCAV is employed to illustrate the detailed process of the optimization.The results indicate that the overall process of the surrogate-based two-level optimization strategy can be implemented automatically,and quite reasonable results are obtained.
基金the Government of Malaysia via the sponsorship by the Ministry of Education under the IPTA Academic Training Schemethe Malaysia Ministry of Higher Education’s Fundamental Research Grant Scheme (FRGS) (No. 600-RMI/FRGS 5/3 (22/2012))
文摘Twist morphing （TM） is a practical control technique in micro air vehicle （MAV） flight. However, TM wing has a lower aerodynamic efficiency （CL/CD） compared to membrane and rigid wing. This is due to massive drag penalty created on TM wing, which had overwhelmed the succes- sive increase in its lift generation. Therefore, further CL/CDmax optimization on TM wing is needed to obtain the optimal condition for the morphing wing configuration. In this paper, two-way fluid- structure interaction （FSI） simulation and wind tunnel testing method are used to solve and study the basic wing aerodynamic performance over （non-optimal） TM, membrane and rigid wings. Then, a multifidelity data metamodel based design optimization （MBDO） process is adopted based on the Ansys-DesignXplorer frameworks. In the adaptive MBDO process, Kriging metamodel is used to construct the final multifidelity CL/CD responses by utilizing 23 multi-fidelity sample points from the FSI simulation and experimental data. The optimization results show that the optimal TM wing configuration is able to produce better CL/CDmax magnitude by at least 2% than the non-optimal TM wings. The flow structure formation reveals that low TV strength on the optimal TM wing induces low CD generation which in turn improves its overall CL/CDmax performance.
基金the Natural Sciences and Engineering Research Council of Canada(NSERC)for the funding of the Canada Research Chair in Aircraft Modeling and Simulation Technologiesthe Canada Foundation of Innovation(CFI),the Ministerèdu Développementéconomique,de l’Innovation et de l’Exportation(MDEIE)and Hydra Technologies for the acquisition of the UAS-S4 using the Leaders Opportunity Funds+2 种基金the financial support obtained in the framework of the CRIAQ MDO-505 projectthe implication of our industrial partners Bombardier Aerospace and Thales CanadaNSERC for their support
文摘This paper presents a new non-linear formulation of the classical Vortex Lattice Method(VLM)approach for calculating the aerodynamic properties of lifting surfaces.The method accounts for the effects of viscosity,and due to its low computational cost,it represents a very good tool to perform rapid and accurate wing design and optimization procedures.The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections,according to strip theory,and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface,calculated with a fully three-dimensional vortex lifting law.The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment,as well as in predicting the wing drag.The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.
基金co-supported by the National Natural Science Foundation of China (Nos. 11402288 and 11372254)
文摘Experiment design method is a key to construct a highly reliable surrogate model for numerical optimization in large-scale project. Within the method, the experimental design criterion directly affects the accuracy of the surrogate model and the optimization efficient. According to the shortcomings of the traditional experimental design, an improved adaptive sampling method is proposed in this paper. The surrogate model is firstly constructed by basic sparse samples. Then the supplementary sampling position is detected according to the specified criteria, which introduces the energy function and curvature sampling criteria based on radial basis function （RBF） network. Sampling detection criteria considers both the uniformity of sample distribution and the description of hypersurface curvature so as to significantly improve the prediction accuracy of the surrogate model with much less samples. For the surrogate model constructed with sparse samples, the sample uniformity is an important factor to the interpolation accuracy in the initial stage of adaptive sam- pling and surrogate model training. Along with the improvement of uniformity, the curvature description of objective function surface gradually becomes more important. In consideration of these issues, crowdness enhance function and root mean square error （RMSE） feedback function are introduced in C criterion expression. Thus, a new sampling method called RMSE and crowd- ness enhance （RCE） adaptive sampling is established. The validity of RCE adaptive sampling method is studied through typical test function firstly and then the airfoil/wing aerodynamic opti- mization design problem, which has high-dimensional design space. The results show that RCE adaptive sampling method not only reduces the requirement for the number of samples, but also effectively improves the prediction accuracy of the surrogate model, which has a broad prospects for applications.
基金Project(2017T001-G)supported by the Science and Technology Research and Development Program of China Railway CorporationProject(2017YFB1201204)supported by the National Key Research and Development Program of China+2 种基金Project(U1534206)supported by the National Natural Science Foundation of ChinaProject(2015CX006)supported by the Innovation-driven Plan in Central South University,ChinaProject(2017zzts521)supported by the Fundamental Research Funds for the Central Universities,China
文摘To improve the safety of trains running in an undesirable wind environment,a novel louver-type wind barrier is proposed and further studied in this research using a scaled wind tunnel simulation with 1:40 scale models.Based on the aerodynamic performance of the train-bridge system,the parameters of the louver-type wind barrier are optimized.Compared to the case without a wind barrier,it is apparent that the wind barrier improves the running safety of trains,since the maximum reduction of the moment coefficient of the train reaches 58%using the louver-type wind barrier,larger than that achieved with conventional wind barriers(fence-type and grid-type).A louver-type wind barrier has more blade layers,and the rotation angle of the adjustable blade of the louver-type wind barrier is 90–180°(which induces the flow towards the deck surface),which is more favorable for the aerodynamic performance of the train.Comparing the 60°,90°and 120°wind fairings of the louver-type wind barrier blade,the blunt fairing is disadvantageous to the operational safety of the train.
基金Project(50975083) supported by the National Natural Science Foundation of ChinaProject(61075001) supported by China State Key Laboratory of Advanced Design and Manufacturing for Vehicle BodyProject(201-IV-068) supported by the Fundamental Research Funds for the Central Universities,China
文摘The high aerodynamic noise induced by automotive air conditioning systems has important effects on the ride comfort, and the centrifugal fan is the largest noise source in these systems. It is very important to reduce the aerodynamic noise generated by the centrifugal fan. The flow field and the sound field on the whole centrifugal fan configuration have been carried out using the computational fluid dynamics. Simulation results show that the sound pressure level near the outlet of the centrifugal fan is too high. Based on the relationship between flow characteristics and the aerodynamic noise, four parameters of the centrifugal fan, i.e., impeller blade＇s outlet angle 0, volute tongue＇s gap t, collector inclination angle fl, and rotating speed n, were selected as design variables and optimized using response surface methodology. While keeping the function of flow rate unchanged, the peak noise level is reduced by 8 dB or 10.8%. The noise level is satisfactorily reduced.
