基于代理模型的高效全局气动优化设计方法研究进展

基于高可信度计算流体力学的数值优化设计方法，在提高飞行器气动与综合性能方面正发挥着越来越重要的作用。基于代理模型的优化算法（SBO），由于能够实现高效全局优化，逐渐成为了气动优化设计领域的研究热点之一。近20年来，代理优化算法研究已取得了长足进步，多种先进的新型代理模型被提出，优化理论和算法也不断完善和发展。以飞行器精细化气动优化设计为背景，综述了基于代理模型的高效全局气动优化设计方法研究进展。首先，介绍了基于变可信度代理模型的气动优化设计方法、结合代理模型和伴随方法的气动优化设计方法以及基于非生物进化的并行气动优化设计方法的研究现状和最新进展。然后，针对飞行器气动优化设计学科领域的前沿问题，介绍了基于代理模型的多目标气动优化设计方法、混合反设计/优化设计方法、稳健气动优化设计方法的研究进展，以及基于代理模型的多学科优化设计方法的研究进展。文献综述表明，代理优化算法在设计效率、全局性以及鲁棒性等方面性能优良，已经发展到可以解决100维（100个设计变量）以内的气动优化设计问题，具有良好的工程应用前景。最后，探讨了基于代理模型的高效全局气动优化设计在理论、方法及飞行器设计应用方面所面临的问题和挑战，给出了未来研究方向的建议。     

基于神经网络的翼型优化设计方法研究

针对气动外形优化设计中,气动特性计算可信度要求与巨大计算量之间的矛盾,采用一种基于神经网络构建适用于气动外形优化设计的气动特性计算模型的计算方法.同时,以神经网络近似模型来代替原有的流场数值计算气动分析程序,结合基于遗传算法建立的气动外形优化搜索方法,建立了一种新的翼型优化设计方法.实际翼型优化设计算例表明该方法有效减少了计算量,提高了工作效率,可以获得具有高可信度的设计结果.     

基于伴随理论的大型客机气动优化设计研究进展

大型民用客机的气动设计具有巡航马赫数高、部件间流动干扰复杂、外形精细化修形难度大等特点,完全依靠人工试凑法进行气动设计工作量巨大。基于高可信度数值模拟的优化设计技术可以对该类问题进行自动寻优设计,给设计师提供有力参考,因此在大型民用客机气动设计中发挥着越来越显著的作用。首先,以大型民用客机为背景,总结了民机气动设计中的关键科学问题,指出了梯度优化设计方法在民用客机不同优化设计问题中的适用性,并进一步对梯度求解中的伴随方法理论进行了详细介绍。然后,分析了基于伴随理论的气动优化设计方法以及气动结构多学科优化设计方法在民用客机设计中的研究进展,其中气动优化设计方面着重突出了复杂全机多部件气动外形优化设计方法以及飞机/发动机一体化优化设计方法。最后,对基于伴随理论的气动优化设计方法在大型民机中的应用进展进行了提炼,对未来发展趋势进行了展望与建议。     

Multi-infill strategy for kriging models used in variable fidelity optimization

In this paper, a computationally efficient optimization method for aerodynamic design has been developed. The low-fidelity model and the multi-infill strategy are utilized in this approach. Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach. Take the advantages of low-fidelity model and the multi-infill strategy, and no initial sample for the high-fidelity model is needed. This approach is applied to an airfoil design case and a high-dimensional wing design case. It saves a large number of high-fidelity function evaluations for initial model construction. What’s more, faster reduction of an aerodynamic function is achieved, when compared to ordinary kriging using the multi-infill strategy and variable-fidelity model using single infill criterion. The results indicate that the developed approach has a promising application to efficient aerodynamic design when high-fidelity analyses are involved.     

混合生物生长自适应搜索遗传算法在形状优化中的应用

利用自适应搜索遗传算法和生物生长算法的特点,提出一种新的优化方法—混合生物生长自适应搜索遗传算法。该算法即可充分利用前两种算法的优点,又可弥补二者的不足。为了验证该算法的合理性和正确性,对经典算例三杆桁架结构进行了优化,并将新算法进一步应用于具有复杂结构的三维离心叶轮优化设计中,结果表明混合算法较遗传算法收敛速度快,且可得到形状优化最优解。     

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.     

基于工程环境的气动多目标优化设计平台研究

工程环境中,飞机气动力设计面临在多个目标和多种约束条件下寻找最优值,需在较短时限内完成设计优化,并保证最终方案可靠。基于高性能计算环境,采用现代计算流体力学(CFD)数值模拟技术和优化技术等构建了面向实际工程的飞行器气动多目标优化设计平台:采用基于非均匀有理B样条(NURBS)方法的自由曲面变形技术,实现对工程复杂气动外形的参数化表达;采用网格变形技术,实现优化过程中计算网格的自动更新;采用基于有限体积方法和多块结构网格的雷诺平均Navier-Stokes(RANS)方程并行解算器进行气动力求解;采用基于精英保留策略的非支配排序的多目标遗传算法(NSGA-II)进行多目标全局优化求解;采用非线性单纯形算法进行局部优化求解,优化过程中,通过人工调整优化种群,引入人工经验,构建"人在回路"的设计流程。以某翼型/机翼气动力优化设计为例对该平台技术进行验证:多目标优化设计可得到清晰的Pareto前沿解分布;优化后的翼型/机翼在满足各项约束的前提下,具备更高的综合气动性能。结果表明:所发展的气动多目标优化设计平台具有很好的工程适用性。     

风扇转子的气动优化设计研究

风扇转子气动优化设计是结合CFD、遗传算法和风扇特性的跨学科自动设计方法,这种设计较少依赖经验,能实现多目标和多设计参数组合的优化设计。先研究了四个关键技术：具有耗时少、计算较准确的叶片通道流场数值计算方法;具有良好寻优能力的数值最优化方法;三维叶片造型的参数化方法;以气动性能为目标的多目标函数构建方法。再将上述四个关键技术模块整合,构建简易的气动优化设计软件。三维叶片优化算例证明了该软件的设计能力,其设计结果较满意、设计效率较高、设计周期短。     

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.     

基于代理模型的大型民机机翼气动优化设计

先进的气动优化设计思想与方法,对于提升大型民机气动与综合性能具有至关重要的意义。探讨了大型民机超临界机翼气动优化设计的基本准则和要点,并结合代理优化算法,提出了一种面向工程应用的多轮次高效全局气动优化设计方法。首先,通过一系列解析函数/翼型/机翼优化测试算例进行了验证。其次,将所提出的方法与人工修型相结合,开展了针对宽体客机超临界机翼的两轮气动优化设计,使其气动性能得到显著改善。最后,采用不同的雷诺平均Navier-Stokes（RANS）方程求解器对安装优化机翼的全机巡航构型进行了典型状态的气动性能综合评估。研究结果表明,所提出的代理优化算法具有很高的优化效率、较强的约束处理和全局优化能力;将所发展的基于代理模型的多轮次气动优化设计方法与人工修型相结合,能够获得满足设计要求的气动外形,验证了该方法在大型民机超临界机翼气动设计中的有效性和工程实用性。     

基于自适应代理模型的气动优化方法

气动优化设计中,为了减少优化系统的计算周期,提高搜索效率,引入结构简单、计算量较小的代理模型,而运用有效的插值和选样方法(自适应选样)可以大大减少建立代理模型的时间。因此本文提出了一种基于自适应代理模型的气动优化方法。首先对自适应代理模型进行研究,建立了 Kriging 自适应代理模型和支持向量回归自适应代理模型,这两种自适应代理模型在相同样本点情况下比一般代理模型拥有更高的预测能力,然后将这其应用到翼型优化设计中,取得了良好的优化效果,从而表明这两种自适应代理模型不仅简单实用,而且明显提高了气动分析的计算效率。     

基于CST参数化的翼型优化遗传算法研究

用外形建模的CST参数化方法,构建翼型几何可直接利用其后缘角、前缘半径等几何特征,其控制参数更能反映翼型特有的气动敏感性,有助于遗传算法搜索寻优。基于这一参数化方法,结合遗传算法,构造了用于翼型优化的设计方法。算法中,CST控制参数作为设计变量,采用二进制编码,并通过引入精英策略,提高了遗传算法的收敛性能。算法适应度评估涉及的流场求解则采用了基于Jameson有限体积法的Euler方程解算程序。先以NACA0012翼型为例,以其某一已知的表面压力分布为目标,进行了遗传算法的重构运算,给出了重构的翼型几何外形,验证了方法。在此基础上,进行了带约束的跨音速翼型优化设计,给出了升力系数极大化和阻力系数极小化等设计算例,展示出翼型优化设计的效果。     

A study of morphing aircraft on morphing rules along trajectory

Morphing aircraft can meet requirements of multi-mission during the whole flight due to changing the aerodynamic shape, so it is necessary to study its morphing rules along the trajectory. However, trajectory planning considering morphing variables requires a huge number of expensive CFD computations due to the morphing in view of aerodynamic performance. Under the given missions and trajectory, to alleviate computational cost and improve trajectory-planning efficiency for morphing aircraft, an offline optimization method is proposed based on Multi-Fidelity Kriging (MFK) modeling. The angle of attack, Mach number, sweep angle and axial position of the morphing wing are defined as variables for generating training data for building the MFK models, in which many inviscid aerodynamic solutions are used as low-fidelity data, while the less high-fidelity data are obtained by solving viscous flow. Then the built MFK models of the lift, drag and pressure centre at the different angles of attack and Mach numbers are used to predict the aerodynamic performance of the morphing aircraft, which keeps the optimal sweep angle and axial position of the wing during trajectory planning. Hence, the morphing rules can be correspondingly acquired along the trajectory, as well as keep the aircraft with the best aerodynamic performance during the whole task. The trajectory planning of a morphing aircraft was performed with the optimal aerodynamic performance based on the MFK models, built by only using 240 low-fidelity data and 110 high-fidelity data. The results indicate that a complex trajectory can take advantage of morphing rules in keeping good aerodynamic performance, and the proposed method is more efficient than trajectory optimization by reducing 86% of the computing time.     

Robust design of NLF airfoils

 A robust optimization design approach of natural laminar airfoils is developed in this paper. First, the non-uniform rational B-splines (NURBS) free form deformation method based on NURBS basis function is introduced to the airfoil parameterization. Second, aerodynamic characteristics are evaluated by solving Navier-Stokes equations, and the γ-Re_θt transition model coupling with shear-stress transport (SST) turbulent model is introduced to simulate boundary layer transition. A numerical simulation of transition flow around NLF0416 airfoil is conducted to test the code. The comparison between numerical simulation results and wind tunnel test data approves the validity and applicability of the present transition model. Third, the optimization system is set up, which uses the separated particle swarm optimization (SPSO) as search algorithm and combines the Kriging models as surrogate model during optimization. The system is applied to carry out robust design about the uncertainty of lift coefficient and Mach number for NASA NLF-0115 airfoil. The data of optimized airfoil aerodynamic characteristics indicates that the optimized airfoil can maintain laminar flow stably in an uncertain range and has a wider range of low drag.     

基于气动数值模拟的翼型反设计方法

将计算流体动力学（CFD）与反设计技术相结合,通过数值求解欧拉方程,对翼型绕流流场作出数值模拟,再用几何和流动控制方程,反复迭代求得满足给定流场的翼型。以NACA0012为初始翼型,RAE-2822为目标翼型,选取两种工况,都取得了满意的结果。     

基于代理模型方法的翼型优化设计

提出了基于代理模型的两步优化方法,用于翼型在黏性流场中气动外形的优化设计.第一步优化使用基于代理模型的遗传算法(GA)获得全局最优解的大致范围.以本征正交分解(POD)方法作为第一步优化中气动力计算的代理模型方法,降低遗传算法的计算量,并对其采样解的生成方法进行改进,提高了计算精度;第二步优化使用基于NavierSto...     

锥导乘波构型设计、优化与分析

乘波构型是高超声速飞行器高升阻比气动布局设计的参考外形之一,设计中需要综合考虑升阻比、容积率和容积等要求。在锥导乘波构型参数化设计的基础上,采用工程估算和计算流体力学相结合的方法,通过正交试验设计分析了不同参数对目标影响的敏感性,合理选择设计参数优化区间,应用改进的多目标遗传算法对乘波构型进行了优化设计,针对优化外形开展了气动性能的数值模拟研究,并在高超声速炮风洞中完成了缩比模型的验证性实验。结果表明:优化设计外形具有良好的升阻比,且在一定攻角范围内升阻比较高,数值模拟和实验分析基本吻合。研究结果可为高超声速滑翔式飞行器的设计提供参考。     

基于分层策略的翼型多参量气动优化设计研究

与基于梯度的优化方法相比,遗传算法因其极强的鲁棒性、随机搜索及优化结果全局性等特点在工程优化中得到越来越广泛的应用。为提高优化设计的效率,改进了传统的遗传算法,采用并行分层策略基因遗传算法开展了翼型多参量气动优化设计研究,包括翼型和多段翼型的基因编码、外形参数化,以及动网格技术。结果表明,并行分层策略在得到较优气动优化结果的同时,极大地缩短了优化时间,提高了计算效率,具有广阔的工程应用前景。     

基于代理模型全局优化的自适应参数化方法

对于翼型气动隐身设计问题,设计变量的配置对设计结果影响很大,而简单地增加设计变量不能保证得到理想的结果。提出一种适用于代理模型全局优化的自适应参数化方法：利用全局敏感性分析方法——基本效应法,得到设计空间关于目标函数的敏感区域信息,并以此为根据增加设计变量;利用节点插入算法将低维样本在高维空间内进行重构,避免了重新取样的工作量。相对于传统固定设计空间维度方法,自适应参数化方法在设计空间的敏感区域扩展维度,能够更加精准地描述外形并反映目标的变化趋势。通过飞翼布局翼型的气动隐身优化算例,证实自适应参数化方法可以大幅提高优化设计质量和效率。     

基于卷积神经网络和多项式混沌方法的翼型鲁棒性优化

在常规的翼型优化设计方法中,设计点处最优翼型的气动性能会在非设计点处有所恶化,因此有必要对翼型鲁棒性优化方法进行研究。提出一种基于卷积神经网络和多项式混沌方法的翼型鲁棒性设计方法,首先搭建基于卷积神经网络的气动力预测模型;其次采用多项式混沌方法对马赫数和攻角进行不确定度量化,构建翼型鲁棒性气动优化设计系统;最后对以 RAE2822 翼型为基准翼型的气动优化设计问题进行优化设计验证。结果表明：本文提出的翼型鲁棒性设计方法可行,优化后翼型的气动性能和鲁棒性气动优化设计效率在较宽的设计范围内都有所提升。