Research on multi-fidelity aerodynamic optimization methods

 Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance. In this paper, using co-Kriging method, an efficient multifidelity 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.     

Stationary flow fields prediction of variable physical domain based on proper orthogonal decomposition and kriging surrogate model

In this paper a new flow field prediction method which is independent of the governing equations, is developed to predict stationary flow fields of variable physical domain. Predicted flow fields come from linear superposition of selected basis modes generated by proper orthogonal decomposition(POD). Instead of traditional projection methods, kriging surrogate model is used to calculate the superposition coefficients through building approximate function relationships between profile geometry parameters of physical domain and these coefficients. In this context,the problem which troubles the traditional POD-projection method due to viscosity and compressibility has been avoided in the whole process. Moreover, there are no constraints for the inner product form, so two forms of simple ones are applied to improving computational efficiency and cope with variable physical domain problem. An iterative algorithm is developed to determine how many basis modes ranking front should be used in the prediction. Testing results prove the feasibility of this new method for subsonic flow field, but also prove that it is not proper for transonic flow field because of the poor predicted shock waves.     

涡轮叶片的气动-热-结构多学科设计优化研究

涡轮叶片设计是典型的多学科问题,在保证结果精度的同时必须重视计算效率.通过控制样本的数量和质量,近似模型能够在保证一定精度的前提下,简化多学科优化过程中的数据管理,并提高优化效率.通过分析涡轮转子叶片的气动-热-结构三个学科的设计过程和数据传递关系,充分利用各学科现有的分析工具,建立了涡轮叶片的气动-热-结构多学科优化设计框架.对某涡轮转子叶片分别使用Kriging近似模型和精确分析方法进行优化对比,可以看出两者的结果误差约为1.86%,而效率提高了将近46%,表明采用近似方法的优化结果在工程上是可用的,而且在计算效率更占优势.      

Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids

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 infill-sampling 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.     

Optimization of aerodynamic efficiency for twist morphing MAV wing

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 successive increase in its lift generation. Therefore, further CL/CDmaxoptimization 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/CDresponses 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/CDmaxmagnitude 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 CDgeneration which in turn improves its overall CL/CDmaxperformance.     

恶劣天气条件下航路网络修复优化

针对恶劣天气条件下可用空域资源不足导致的航班大面积延误问题,基于复杂网络修复理论和交通流分配理论,借鉴交通网络设计思想提出了一种航路网络修复优化策略。首先,建立了航路网络修复场景,基于气象信息生成了恶劣天气飞行受限区。然后,建立了上层模型以修复成本最低为目标函数、下层模型为多约束交通流分配模型的双层规划修复模型,应用改进粒子群算法对模型整体进行求解,结合K最短路径算法对下层模型进行求解。最后,提出局部和全局两类指标对航路网络修复效果进行评估。基于典型航路网络,以两类基础修复策略为对比方法,同时对比了实际运行结果,研究了不同修复策略的修复效果和适用性。仿真结果表明：航路网络修复优化策略既能弥补原有拓扑结构修复策略的结构受限不足,又能解决拓扑结构调整修复策略带来的巨额协调费用问题,能够保证在对正常运行航班干扰最小的同时,以最小的修复成本使所有受影响的航班都恢复正常运行,对于减缓航路拥堵和航班延误有极大的意义。     

Variable-fidelity optimization with design space reduction

Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity (LF) model provides physics-based approximation of the high-fidelity (HF) model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.     

基于Kriging模型插值的孔位修正策略

飞机装配时的孔位精度直接影响其最终装配质量,而待制孔零件易发生变形和整体偏移,因此一般通过基准孔信息对待制孔位进行补偿。提出一种基于Kriging插值的孔位修正方法,通过基准孔建立孔位理论坐标与实际偏差值的Kriging模型,进而预测待制孔的孔位偏差,并给出每个位置的预测标准误差,以指导基准孔的增添和布置。最后通过有限元和数值实验分别验证了零件在极限变形和整体平移旋转情况下该方法的有效性,实现了孔位估计误差小于0.3 mm。     

涡轮转子径向变形多目标协同稳健性优化

考虑到涡轮转子径向变形对涡轮叶尖径向间隙以及篦齿径向封严间隙的影响,提出涡轮转子径向变形多目标协同稳健性优化方法。利用基于Kriging模型的分布式协同响应面法(DCRSM)分别建立涡轮转子和涡轮篦齿的参数与径向变形间的响应面模型,并求解单目标下的稳健最优解。采用理想点法建立涡轮转子和篦齿径向变形多目标协同稳健性优化模型,并进行多目标协同稳健性优化求解。优化结果显示:提出的多目标协同稳健性优化方法与单目标稳健性优化方法相比涡轮转子和篦齿径向变形量的标准差分别降低了2.6%和4.9%。提出的方法为涡轮转子参数设定提供一定的参考。      

Filters for manufacturability in design optimization of variable stiffness composites

In the design optimization of variable stiffness composites, manufacturing constraints imposed by the automated fiber placement technology must be considered. In the present paper, two filters are proposed to address this issue, and they are incorporated into the Shepard interpolation-based design optimization framework developed in our previous studies. The fiber angle arrangement of a composite is represented by a continuous function that interpolates fiber angles at scattered design points. Two filters are appointed for each design point to deal with two typical manufacturing constraints, i.e., fiber curvature and gap/overlap. At each design point, the sensitivity is first filtered in a rectangular region around this point, and by this means the fiber curvature is controlled; then in another rectangular region around this design point, the filtered sensitivities are averaged, and the result is used to update the corresponding design variable. Several numerical examples are investigated, and the results show that the proposed method is effective.     

Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC) approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization(CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP). Then, the distributed cost function is ...     

An optimization method for metamorphic mechanisms based on multidisciplinary design optimization

The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design variables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for metamorphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization（MDO）. Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collaborative optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierarchical scheme with global optimizer and configuration optimizer loops. The method is demonstrated by optimizing a planar five-bar metamorphic mechanism which has two configurations,and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.     

基于替代模型的高超声速前体/进气道一体化优化

采用基于替代模型的渐进优化策略对二维高超声速前体/进气道进行一体化设计优化,采用拉丁超立方试验设计法选择样本点,采用二维粘性CFD方法计算进气道流场来建立样本数据库,综合运用了多项式响应面、Kriging模型、BP神经网络和径向基神经网络等替代模型.相对于基准构型,前体/进气道的优化构型在设计态时提高了流量捕获与来流压缩能力,提高了总压恢复性能,同时减小了阻力系数,综合性能提高了5.3%;在非设计态时优化构型的综合性能也有不同程度的改善.      

Parameter optimization of controllable local degree of freedom for reducing vibration of flexible manipulator

 Parameter optimization of the controllable local degree of freedom is studied for reducing vibration of the flexible manipulator at the lowest possible cost. The controllable local degrees of freedom are suggested and introduced to the topological structure of the flexible manipulator, and used as an effective way to alleviate vibration through dynamic coupling. Parameters introduced by the controllable local degrees of freedom are analyzed and their influences on vibration reduction are investigated. A strategy to optimize these parameters is put forward and the corresponding optimization method is suggested based on Particle Swarm Optimization (PSO). Simulations are conducted and results of case studies confirm that the proposed optimization method is effective in reducing vibration of the flexible manipulator at the lowest possible cost.     

超燃冲压发动机一体化设计与优化方法研究

建立了超燃冲压发动机一体化设计的优化模型，研究了超燃冲压发动机在总体性能要求与约束条件下，进气道、燃烧室（含隔离段）、尾喷管之间的性能匹配和参数优化。针对将计算流体力学（CFD）方法应用到优化设计过程中所带来的运算量巨大的问题，引入了基于PVM（并行虚拟机）的遗传算法，为包含有复杂流场计算过程的优化设计问题提出了一条易于实现并经济、可靠的解决途径。     

Whole-process design and experimental validation of landing gear lower drag stay with global/local linked driven optimization strategy

Landing gear lower drag stay is a key component which connects fuselage and landing gear and directly effects the safety and performance of aircraft takeoff and landing. To effectively design the lower drag stay and reduce the weight of landing gear, Global/local Linked Driven Optimization Strategy (GLDOS) was developed to conduct the overall process design of lower drag stay in respect of optimization thought. The whole-process optimization involves two stages of structural conceptual design and detailed design. In the structural conceptual design, the landing gear lower drag stay was globally topologically optimized by adopting multiple starting points algorithm. In the detailed design, the local size and shape of landing gear lower drag stay were globally optimized by the gradient optimization strategy. The GLDOS method adopts different optimization strategies for different optimization stages to acquire the optimum design effect. Through the experimental validation, the weight of the optimized lower dray stay with the developed GLDOS is reduced by 16.79% while keeping enough strength and stiffness, which satisfies the requirements of engineering design under the typical loading conditions. The proposed GLDOS is validated to be accurate and efficient in optimization scheme and design cycles. The efforts of this paper provide a whole-process optimization approach regarding different optimization technologies in different design phases, which is significant in reducing structural weight and enhance design precision for complex structures in aircrafts.     

Kriging模型在机翼气动外形优化中的应用

针对粒子群等随机优化算法计算量大的缺点,发展了基于Kriging模型的优化方法.采用改进的量子粒子群算法对Kriging模型的相关模型参数进行优化,以提高代理模型预测精度,并与具有双层结构的粒子群算法相结合.采用雷诺平均N-S方程流场求解器与多目标非线性适应值加权方法,对高维度多目标多约束的跨声速机翼进行了优化,设计的机翼具有理想的压力分布,降低了机翼阻力系数,并且有效控制了低头力矩和翼根弯矩,表明该方法具有较强的工程实用性.     

High-fidelity trajectory optimization for aeroassisted vehicles using variable order pseudospectral method

In this study, the problem of time-optimal reconnaissance trajectory design for the aeroassisted vehicle is considered. Different from most works reported previously, we explore the feasibility of applying a high-order aeroassisted vehicle dynamic model to plan the optimal flight trajectory such that the gap between the simulated model and the real system can be narrowed. A highly-constrained optimal control model containing six-degree-of-freedom vehicle dynamics is established. To solve the formulated high-order trajectory planning model, a pipelined optimization strategy is illustrated. This approach is based on the variable order Radau pseudospectral method, indicating that the mesh grid used for discretizing the continuous system experiences several adaption iterations. Utilization of such a strategy can potentially smooth the flight trajectory and improve the algorithm convergence ability. Numerical simulations are reported to demonstrate some key features of the optimized flight trajectory. A number of comparative studies are also provided to verify the effectiveness of the applied method as well as the high-order trajectory planning model.     

Multi-disciplinary design optimization with variable complexity modeling for a stratosphere airship

This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.     

Uncertainty analysis and design optimization of hybrid rocket motor powered vehicle for suborbital flight

In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor(HRM) powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity analysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization(DDO) and uncertainty-based design optimization(UDO) are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation(KMCS) and Kriging-based Taylor series approximation(KTSA), are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods: DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.In this paper,we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor(HRM)powered vehicle.The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified.The sensitivity analysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances.Then the differences between deterministic design optimization(DDO)and uncertainty-based design optimization(UDO)are discussed.Two newly formed uncertainty analysis methods,including the Kriging-based Monte Carlo simulation(KMCS)and Kriging-based Taylor series approximation(KTSA),are carried out using a global approximation Kriging modeling method.Based on the system design model and the results of design uncertainty analysis,the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods:DDO,KMCS and KTSA.The comparisons indicate that the two UDO methods can enhance the design reliability and robustness.The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.