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
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
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
Trailing-edge flap is traditionally used to improve the takeoff and landing aerodynamic performance of aircraft.In order to improve flight efficiency during takeoff,cruise and landing states,the flexible variable camb...Trailing-edge flap is traditionally used to improve the takeoff and landing aerodynamic performance of aircraft.In order to improve flight efficiency during takeoff,cruise and landing states,the flexible variable camber trailing-edge flap is introduced,capable of changing its shape smoothly from 50% flap chord to the rear of the flap.Using a numerical simulation method for the case of the GA（W）-2 airfoil,the multi-objective optimization of the overlap,gap,deflection angle,and bending angle of the flap under takeoff and landing configurations is studied.The optimization results show that under takeoff configuration,the variable camber trailing-edge flap can increase lift coefficient by about 8% and lift-to-drag ratio by about 7% compared with the traditional flap at a takeoff angle of 8°.Under landing configuration,the flap can improve the lift coefficient at a stall angle of attack about 1.3%.Under cruise state,the flap helps to improve the lift-todrag ratio over a wide range of lift coefficients,and the maximum increment is about 30%.Finally,a corrugated structure–eccentric beam combination bending mechanism is introduced in this paper to bend the flap by rotating the eccentric beam.展开更多
The seamless trailing edge morphing flap is investigated using a high-fidelity steady-state aerodynamic shape optimization to determine its optimum configuration for different flight conditions,including climb,cruise,...The seamless trailing edge morphing flap is investigated using a high-fidelity steady-state aerodynamic shape optimization to determine its optimum configuration for different flight conditions,including climb,cruise,and gliding descent.A comparative study is also conducted between a wing equipped with morphing flap and a wing with conventional hinged flap.The optimization is performed by specifying a certain objective function and the flight performance goal for each flight condition.Increasing the climb rate,extending the flight range and endurance in cruise,and decreasing the descend rate,are the flight performance goals covered in this study.Various optimum configurations were found for the morphing wing by determining the optimum morphing flap deflection for each flight condition,based on its objective function,each of which performed better than that of the baseline wing.It was shown that by using optimum configuration for the morphing wing in climb condition,the required power could be reduced by up to 3.8%and climb rate increases by 6.13%.The comparative study also revealed that the morphing wing enhances aerodynamic efficiency by up to 17.8%and extends the laminar flow.Finally,the optimum configuration for the gliding descent brought about a 43%reduction in the descent rate.展开更多
A surface mesh movement algorithm,combining surface mesh mapping with Delaunay graph mapping,is proposed for surface mesh movement involving complex intersections,like wing/pylon intersections.First,surface mesh mappi...A surface mesh movement algorithm,combining surface mesh mapping with Delaunay graph mapping,is proposed for surface mesh movement involving complex intersections,like wing/pylon intersections.First,surface mesh mapping is adopted for the movement of intersecting lines along the spanwise direction and the wing surface mesh,and then Delaunay graph mapping is utilized for the deformation of the pylon surface mesh,guaranteeing consistent and smooth surface meshes.Furthermore,the corresponding surface sensitivity procedure is implemented for accurate and efficient calculation of the surface sensitivities.The proposed surface mesh movement algorithm and the surface sensitivity procedure are integrated into a discrete adjoint-based optimization framework to optimize the nacelle position on the DLR-F6 wing-body-nacelle-pylon configuration for drag minimization.The results demonstrate that the strong shock on the initial pylon surface is nearly eliminated and the optimal nacelle position can be obtained within less than ten iterations.展开更多
For aerodynamic shape optimization, the approximation management framework （AMF） method is used to organize and manage the variable-fidelity models. The method can take full advantage of the low-fidelity, cheaper mo...For aerodynamic shape optimization, the approximation management framework （AMF） method is used to organize and manage the variable-fidelity models. The method can take full advantage of the low-fidelity, cheaper models to concentrate the main workload on the low-fidelity models in optimization iterative procedure. Furthermore, it can take high-fidelity, more expensive models to monitor the procedure to make the method globally convergent to a solution of high-fidelity problem. Finally, zero order variable-fidelity aerodynamic optimization management framework and search algorithm are demonstrated on an airfoil optimization of UAV with a flying wing. Compared to the original shape, the aerodynamic performance of the optimal shape is improved. The results show the method has good feasibility and applicability.展开更多
In the past two decades,the world’s unmanned aerial vehicle(UAV)industry has developed rapidly.Various kinds of UAVs have been used in military and civilian fields.Based on the characteristics of UAVs and the develop...In the past two decades,the world’s unmanned aerial vehicle(UAV)industry has developed rapidly.Various kinds of UAVs have been used in military and civilian fields.Based on the characteristics of UAVs and the development of aerodynamics,this article analyzes the development of aerodynamic optimization design and dynamic numerical simulation technology,then lists engineering applications.Both aerodynamic optimization design and dynamic numerical simulation have greatly shortened the UAV design period and reduced the research and design cost.These two methods gradually replace traditional methods such as wind tunnel test.展开更多
The advanced optimization method named as adaptive range differential evolution(ARDE)is developed.The optimization performance of ARDE is demonstrated using a typical mathematical test and compared with the standard g...The advanced optimization method named as adaptive range differential evolution(ARDE)is developed.The optimization performance of ARDE is demonstrated using a typical mathematical test and compared with the standard genetic algorithm and differential evolution.Combined with parallel ARDE,surface modeling method and Navier-Stokes solution,a new automatic aerodynamic optimization method is presented.A low aspect ratio transonic turbine stage is optimized for the maximization of the isentropic efficiency with forty-one design variables in total.The coarse-grained parallel strategy is applied to accelerate the design process using 15 CPUs.The isentropic efficiency of the optimum design is 1.6%higher than that of the reference design.The aerodynamic performance of the optimal design is much better than that of the reference design.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
The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is time-consuming.In this study,a new multi-model ensemble optimizati...The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is time-consuming.In this study,a new multi-model ensemble optimization algorithm is proposed to tackle such an expensive optimization problem.The multi-variable and multi-objective optimization are conducted with a new flexible multi-objective infill criterion.In addition,the search direction is determined by the multi-model ensemble assisted evolutionary algorithm and the feature extraction by the principal component analysis is used to reduce the dimension of optimization variables.First,the proposed algorithm and other two optimization algorithms which prevail in fan optimizations were compared by using test functions.With the same number of objective function evaluations,the proposed algorithm shows a fast convergency rate on finding the optimal objective function values.Then,this algorithm was used to optimize the rotor and stator blades of a large axial fan,with the efficiencies as the objectives at three flow rates,the high,the design and the low flow rate.Forty-two variables were included in the optimization process.The results show that compared with the prototype fan,the total pressure efficiencies of the optimized fan at the high,the design and the low flow rate were increased by 3.35%,3.07%and 2.89%,respectively,after CFD simulations for 500 fan candidates with the constraint for the design pressure.The optimization results validate the effectiveness and feasibility of the proposed algorithm.展开更多
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,...Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.展开更多
High-fidelity aerodynamic optimization of compressors is afflicted by the"curse of dimensionality",which limits its engineering applications.This paper proposes a new multi-degrees-of-freedom(MDOF)surface pa...High-fidelity aerodynamic optimization of compressors is afflicted by the"curse of dimensionality",which limits its engineering applications.This paper proposes a new multi-degrees-of-freedom(MDOF)surface parameterization method that combines the characteristics of conventional surface parameterization methods,low-dimensionality and surface smoothness,with the advantages of design flexibility and ease of construction.The proposed method is applied to the high-fidelity aerodynamic optimization of Rotor37.An optimized solution is obtained within 111 h by combining a phased optimization strategy based on the idea of modal optimization.To explore a better way of setting the control variables of the blade body,two methods of varying the control points of the suction and pressure surfaces,independent change and synchronous change,are compared.Synchronous change has better flexibility,and under the condition of satisfying the constraints,it increases the efficiency at the design point by 2.2%and the surge margin by 0.5%.This demonstrates the effectiveness of the proposed method in the high-fidelity aerodynamic optimization of compressors.It also provides technical support to solve the"curse of dimensionality"problem.展开更多
Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization p...Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization problems Inspired from the natural evolution history that different periods with certain environments have different criteria for the evaluations of individuals’ fitness, a hierarchical fidelity model is introduced to reach high optimization efficiency The shape of an NACA0012 based airfoil is optimized in maximizing the lift coefficient under a given transonic flow condition Optimized results are presented and compared with the single model results and traditional GA展开更多
Nowadays,the adjoint method has become a popular approach in the optimization of turbomachinery to further improve its aerodynamic performance.However,design variables in these adjoint optimization applications are ge...Nowadays,the adjoint method has become a popular approach in the optimization of turbomachinery to further improve its aerodynamic performance.However,design variables in these adjoint optimization applications are generally not direct design parameters of blade(such as wedge angles or maximum thickness),making the geometric variation by adjoint optimization can hardly be re-extracted as the change of each design parameter.By giving considerations to the G1 continuity constraint of adjoint method on its parameterization method,this manuscript shows how to apply a parameterization method in 3D blade design process into adjoint optimization.Nearly all design parameters can therefore be treated as design variables in the adjoint method and then participate in the sensitivity-based optimization.Finally,a fitted Rotor 67 blade is optimized and the adiabatic efficiency is significantly improved by nearly 0.91%.展开更多
Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil...Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.展开更多
The significance of flow optimization utilizing the lattice Boltzmann(LB)method becomes obvious regarding its advantages as a novel flow field solution method compared to the other conventional computational fluid dyn...The significance of flow optimization utilizing the lattice Boltzmann(LB)method becomes obvious regarding its advantages as a novel flow field solution method compared to the other conventional computational fluid dynamics techniques.These unique characteristics of the LB method form the main idea of its application to optimization problems.In this research,for the first time,both continuous and discrete adjoint equations were extracted based on the LB method using a general procedure with low implementation cost.The proposed approach could be performed similarly for any optimization problem with the corresponding cost function and design variables vector.Moreover,this approach was not limited to flow fields and could be employed for steady as well as unsteady flows.Initially,the continuous and discrete adjoint LB equations and the cost function gradient vector were derived mathematically in detail using the continuous and discrete LB equations in space and time,respectively.Meanwhile,new adjoint concepts in lattice space were introduced.Finally,the analytical evaluation of the adjoint distribution functions and the cost function gradients was carried out.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
This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation mo...This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.展开更多
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.
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.
文摘Trailing-edge flap is traditionally used to improve the takeoff and landing aerodynamic performance of aircraft.In order to improve flight efficiency during takeoff,cruise and landing states,the flexible variable camber trailing-edge flap is introduced,capable of changing its shape smoothly from 50% flap chord to the rear of the flap.Using a numerical simulation method for the case of the GA（W）-2 airfoil,the multi-objective optimization of the overlap,gap,deflection angle,and bending angle of the flap under takeoff and landing configurations is studied.The optimization results show that under takeoff configuration,the variable camber trailing-edge flap can increase lift coefficient by about 8% and lift-to-drag ratio by about 7% compared with the traditional flap at a takeoff angle of 8°.Under landing configuration,the flap can improve the lift coefficient at a stall angle of attack about 1.3%.Under cruise state,the flap helps to improve the lift-todrag ratio over a wide range of lift coefficients,and the maximum increment is about 30%.Finally,a corrugated structure–eccentric beam combination bending mechanism is introduced in this paper to bend the flap by rotating the eccentric beam.
基金the Hydra Technologies team in Mexicothe CREATEUTILI Program for their financial support。
文摘The seamless trailing edge morphing flap is investigated using a high-fidelity steady-state aerodynamic shape optimization to determine its optimum configuration for different flight conditions,including climb,cruise,and gliding descent.A comparative study is also conducted between a wing equipped with morphing flap and a wing with conventional hinged flap.The optimization is performed by specifying a certain objective function and the flight performance goal for each flight condition.Increasing the climb rate,extending the flight range and endurance in cruise,and decreasing the descend rate,are the flight performance goals covered in this study.Various optimum configurations were found for the morphing wing by determining the optimum morphing flap deflection for each flight condition,based on its objective function,each of which performed better than that of the baseline wing.It was shown that by using optimum configuration for the morphing wing in climb condition,the required power could be reduced by up to 3.8%and climb rate increases by 6.13%.The comparative study also revealed that the morphing wing enhances aerodynamic efficiency by up to 17.8%and extends the laminar flow.Finally,the optimum configuration for the gliding descent brought about a 43%reduction in the descent rate.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘A surface mesh movement algorithm,combining surface mesh mapping with Delaunay graph mapping,is proposed for surface mesh movement involving complex intersections,like wing/pylon intersections.First,surface mesh mapping is adopted for the movement of intersecting lines along the spanwise direction and the wing surface mesh,and then Delaunay graph mapping is utilized for the deformation of the pylon surface mesh,guaranteeing consistent and smooth surface meshes.Furthermore,the corresponding surface sensitivity procedure is implemented for accurate and efficient calculation of the surface sensitivities.The proposed surface mesh movement algorithm and the surface sensitivity procedure are integrated into a discrete adjoint-based optimization framework to optimize the nacelle position on the DLR-F6 wing-body-nacelle-pylon configuration for drag minimization.The results demonstrate that the strong shock on the initial pylon surface is nearly eliminated and the optimal nacelle position can be obtained within less than ten iterations.
基金Project supported by the National Natural Science Foundation of China (No.10502043)
文摘For aerodynamic shape optimization, the approximation management framework （AMF） method is used to organize and manage the variable-fidelity models. The method can take full advantage of the low-fidelity, cheaper models to concentrate the main workload on the low-fidelity models in optimization iterative procedure. Furthermore, it can take high-fidelity, more expensive models to monitor the procedure to make the method globally convergent to a solution of high-fidelity problem. Finally, zero order variable-fidelity aerodynamic optimization management framework and search algorithm are demonstrated on an airfoil optimization of UAV with a flying wing. Compared to the original shape, the aerodynamic performance of the optimal shape is improved. The results show the method has good feasibility and applicability.
文摘In the past two decades,the world’s unmanned aerial vehicle(UAV)industry has developed rapidly.Various kinds of UAVs have been used in military and civilian fields.Based on the characteristics of UAVs and the development of aerodynamics,this article analyzes the development of aerodynamic optimization design and dynamic numerical simulation technology,then lists engineering applications.Both aerodynamic optimization design and dynamic numerical simulation have greatly shortened the UAV design period and reduced the research and design cost.These two methods gradually replace traditional methods such as wind tunnel test.
基金This project is supported by Advanced Propulsion Technologies Demonstration Program of Commission of Science Technology and Industry for National Defense of China(No.APTD-0602-04).
文摘The advanced optimization method named as adaptive range differential evolution(ARDE)is developed.The optimization performance of ARDE is demonstrated using a typical mathematical test and compared with the standard genetic algorithm and differential evolution.Combined with parallel ARDE,surface modeling method and Navier-Stokes solution,a new automatic aerodynamic optimization method is presented.A low aspect ratio transonic turbine stage is optimized for the maximization of the isentropic efficiency with forty-one design variables in total.The coarse-grained parallel strategy is applied to accelerate the design process using 15 CPUs.The isentropic efficiency of the optimum design is 1.6%higher than that of the reference design.The aerodynamic performance of the optimal design is much better than that of the reference design.
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.
基金support of National Science and Technology Major Project(2017-11-0007-0021)。
文摘The constrained multi-objective multi-variable optimization of fans usually needs a great deal of computational fluid dynamics(CFD)calculations and is time-consuming.In this study,a new multi-model ensemble optimization algorithm is proposed to tackle such an expensive optimization problem.The multi-variable and multi-objective optimization are conducted with a new flexible multi-objective infill criterion.In addition,the search direction is determined by the multi-model ensemble assisted evolutionary algorithm and the feature extraction by the principal component analysis is used to reduce the dimension of optimization variables.First,the proposed algorithm and other two optimization algorithms which prevail in fan optimizations were compared by using test functions.With the same number of objective function evaluations,the proposed algorithm shows a fast convergency rate on finding the optimal objective function values.Then,this algorithm was used to optimize the rotor and stator blades of a large axial fan,with the efficiencies as the objectives at three flow rates,the high,the design and the low flow rate.Forty-two variables were included in the optimization process.The results show that compared with the prototype fan,the total pressure efficiencies of the optimized fan at the high,the design and the low flow rate were increased by 3.35%,3.07%and 2.89%,respectively,after CFD simulations for 500 fan candidates with the constraint for the design pressure.The optimization results validate the effectiveness and feasibility of the proposed algorithm.
基金supported by the National Natural Science Foundation of China (No.11402288)
文摘Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.
基金financially supported by Civil Aircraft Special Project(Grant No.MJZ-2017-D-32(Y81H061A41)).
文摘High-fidelity aerodynamic optimization of compressors is afflicted by the"curse of dimensionality",which limits its engineering applications.This paper proposes a new multi-degrees-of-freedom(MDOF)surface parameterization method that combines the characteristics of conventional surface parameterization methods,low-dimensionality and surface smoothness,with the advantages of design flexibility and ease of construction.The proposed method is applied to the high-fidelity aerodynamic optimization of Rotor37.An optimized solution is obtained within 111 h by combining a phased optimization strategy based on the idea of modal optimization.To explore a better way of setting the control variables of the blade body,two methods of varying the control points of the suction and pressure surfaces,independent change and synchronous change,are compared.Synchronous change has better flexibility,and under the condition of satisfying the constraints,it increases the efficiency at the design point by 2.2%and the surge margin by 0.5%.This demonstrates the effectiveness of the proposed method in the high-fidelity aerodynamic optimization of compressors.It also provides technical support to solve the"curse of dimensionality"problem.
基金Start-up foundation item of the Educational Department of China for returnees
文摘Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization problems Inspired from the natural evolution history that different periods with certain environments have different criteria for the evaluations of individuals’ fitness, a hierarchical fidelity model is introduced to reach high optimization efficiency The shape of an NACA0012 based airfoil is optimized in maximizing the lift coefficient under a given transonic flow condition Optimized results are presented and compared with the single model results and traditional GA
基金supported by the National Major Science and Technology Project of China(Nos.2017-II-0006-0020 and J2019-II-0003-0023)。
文摘Nowadays,the adjoint method has become a popular approach in the optimization of turbomachinery to further improve its aerodynamic performance.However,design variables in these adjoint optimization applications are generally not direct design parameters of blade(such as wedge angles or maximum thickness),making the geometric variation by adjoint optimization can hardly be re-extracted as the change of each design parameter.By giving considerations to the G1 continuity constraint of adjoint method on its parameterization method,this manuscript shows how to apply a parameterization method in 3D blade design process into adjoint optimization.Nearly all design parameters can therefore be treated as design variables in the adjoint method and then participate in the sensitivity-based optimization.Finally,a fitted Rotor 67 blade is optimized and the adiabatic efficiency is significantly improved by nearly 0.91%.
基金supported by CITRIS and the Banatao Institute,Air Force Office of Scientific Research(Grant No.FA9550-22-1-0420)National Science Foundation(Grant No.ACI-1548562).
文摘Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.
文摘The significance of flow optimization utilizing the lattice Boltzmann(LB)method becomes obvious regarding its advantages as a novel flow field solution method compared to the other conventional computational fluid dynamics techniques.These unique characteristics of the LB method form the main idea of its application to optimization problems.In this research,for the first time,both continuous and discrete adjoint equations were extracted based on the LB method using a general procedure with low implementation cost.The proposed approach could be performed similarly for any optimization problem with the corresponding cost function and design variables vector.Moreover,this approach was not limited to flow fields and could be employed for steady as well as unsteady flows.Initially,the continuous and discrete adjoint LB equations and the cost function gradient vector were derived mathematically in detail using the continuous and discrete LB equations in space and time,respectively.Meanwhile,new adjoint concepts in lattice space were introduced.Finally,the analytical evaluation of the adjoint distribution functions and the cost function gradients was carried out.
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.
文摘This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.
