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.     

Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Multi-fidelity Data Fusion(MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion(...     

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.     

Efficient multi-response adaptive sampling algorithm for construction of variable-fidelity aerodynamic tables

Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-response adaptive sampling algorithm for simultaneous construction of multiple surrogate models in a time-efficient and accurate manner. The new algorithm uses the Jackknife cross-validation variance and a minimum distance metric to construct a sampling criterion function. A weighted sum of the function is used to consider the characteristics of multiple surrogate models. The proposed algorithm demonstrates good performance on total 22 numerical problems in comparison with three existing adaptive sampling algorithms. The numerical problems include several two-dimensional and six-dimensional functions which are combined into singleresponse and multi-response systems. Application of the proposed algorithm for construction of aerodynamic tables for 2 D airfoil is demonstrated. Scaling-based variable-fidelity modeling is implemented to enhance the accuracy of surrogate modeling. The algorithm succeeds in constructing a system of three highly nonlinear aerodynamic response surfaces within a reasonable amount of time while preserving high accuracy of approximation.     

面向多级加筋壳的高效变保真度代理模型

多级加筋壳结构作为一种新颖的航空航天薄壁结构,具有轻质、高承载的优势。由于其加筋结构复杂,导致基于高保真度模型的多级加筋壳后屈曲分析耗时较长,提高其后屈曲分析及优化效率对于多级加筋壳快速设计具有重要的意义。变保真度模型在复杂工程问题的设计与优化过程中得到了广泛的应用,其通过桥函数连接高保真度模型和低保真度模型,具有预测精度高和计算成本低的优点。首先建立了多级加筋壳结构的高保真度模型和低保真度模型,并基于高斯过程回归构建了多级加筋壳结构的变保真度模型,然后基于变保真度模型的最大均方根误差方法开展自适应加点。结果表明,在达到同样的较高预测精度水平时,基于提出方法构建的变保真度模型比直接采用高保真度模型构建的代理模型节约了60%的计算成本,表现出优异的效率优势。同时,还探讨了不同类型低保真度模型对于多级加筋壳结构变保真度模型预测精度的影响。分析结果表明,对于多级加筋壳承载力评估这类典型的后屈曲问题,建立可捕捉后屈曲特性的低保真度模型能有效提升变保真度模型的精度。     

一种高效高精度的气动弹性结构优化方法

气动弹性结构优化技术主要包括约束求解和优化算法两个方面的内容。针对常用的基于低阶面元法的静气动弹性分析方法计算效率高但精度低的特点,建立了一种高效高精度的基于高阶面元法的静气动弹性分析方法。针对当前气动弹性结构优化技术使用单一优化算法导致搜索精度低、收敛速度慢等特点,将遗传算法和分形算法进行结合,发展了一种遗传/分形混合算法。针对气动弹性结构优化计算时间长、设备要求高等特点,引入了Kriging代理模型方法来加快优化速度,减少时间和设备的耗费。最后以某大展弦比客机机翼为算例,采用基于高阶面元法的静气动弹性分析方法求解约束响应样本,用Kriging代理模型方法对约束响应进行模型构建和预测,并将Kriging代理模型和遗传/分形混合优化算法进行结合,构建了一种高效高精度的静气动弹性结构优化方法。优化分析结果表明,Kriging代理模型在静气动弹性响应预测上具有很高的精度,平均误差均在5%以下,副翼效率预测的平均误差甚至低于1%;遗传/分形混合算法相比于单一的遗传算法具有更快的收敛速度和更强的全局寻优能力。     

On the generation of flight dynamics aerodynamic tables by computational fluid dynamics

An approach for the generation of aerodynamic tables using computational fluid dynamics is discussed. For aircraft flight dynamics, forces and moments are often tabulated in multi-dimensional look-up tables, requiring a large number of calculations to fill the tables. A method to efficiently reduce the number of high-fidelity analyses is reviewed. The method uses a kriging-based surrogate model. Low-fidelity (computationally cheap) estimates are augmented with higher fidelity data. Data fusion combines the two datasets into one single database. The approach can also handle changes in aircraft geometry. Once constructed, the look-up tables can be used in real-time to fly the aircraft through the database. To demonstrate the capabilities of the framework presented, five test cases are considered. These include a transonic cruiser concept design, an unconventional configuration, two passenger jet aircraft, and a jet trainer aircraft. Investigations into the areas of flight handling qualities, stability and control characteristics and manoeuvring aircraft are made. To assess the accuracy of the simulations, numerical results are also compared with wind tunnel and flight test data.     

An efficient aerodynamic shape optimization of blended wing body UAV using multi-fidelity models

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.     

一种高效的CFD/CSD耦合飞行器多学科优化设计方法

建立了一种基于CFD/CSD (computational fluid dynamics/computational structure dynamics)耦合的多学科优化设计方法.该方法采用结构有限元方程进行结构特性分析,同时采用数值求解N-S方程方法获得气动载荷,保证了设计结果的可信性,并采用基于代理模型的优化设计方法解决了采用高精度模型后计算量太大的问题.采用RBF(径向基函数)插值方法进行流固耦合界面数据插值,保证了传递过程中的能量守恒.利用建立的优化设计方法对一典型的翼身组合体进行了气动和结构优化设计,算例表明在满足约束条件的前提下优化后阻力和质量分别减小了5.0%和4.2%,证明了该方法的有效性.      

Double-stage Metamodel and Its Application in Aerodynamic Design Optimization

Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel (DSM) which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerody-namic analysis are investigated. In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.     

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。     

An adaptive sequential experiment design method for model validation

Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space. Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model. In this paper, an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed. The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points. To avoid the clustering of samples in the local region, an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation. Besides, the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design. Finally, six benchmark problems and an engineering case are applied to examine the performance of the proposed method. The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.     

1 种基于Kriging 近似模型的叶片罩量优化设计方法

在航空发动机叶片设计过程中,需要进行叶片罩量优化来减小多种载荷引起的弯曲应力,改善其应力状况。为了提高叶片罩量优化设计效率,根据Kri gi ng近似模型和试验采样技术,提出了1种叶片罩量优化设计方法。利用序列采样方法逐步改善近似模型预测精度,然后在近似模型上进行全局寻优。结果表明:该方法简单易用,通过构造近似模型代替真实的物理模型,降低了计算成本,提高了优化效率。优化后的叶片最大等效应力减小了12.43%,有效地减小叶片的峰值应力。     

基于近似技术的高亚声速运输机机翼气动/结构优化设计

探索基于近似技术的高亚声速运输机机翼气动/结构多学科设计优化方法,建立了基于近似技术的多学科设计优化框架。气动学科采用全速势方程加黏性修正进行翼身组合体跨声速流动的气动计算,结构学科采用有限元分析方法进行应力与变形计算。采用均匀设计法给出若干样本点,分别采用二次响应面、Kriging模型和径向基神经网络等多种近似技术,构造气动学科和结构学科的近似分析模型,并对几种近似模型精度进行了分析和比较。研究发现,Kriging模型和二次响应面具有几乎等同的较高的近似精度,神经网络的近似精度则较差,由于二次响应面计算量更小,故最终选定为机翼设计优化的近似方法。以升阻比和结构重量为目标,考虑升力、机翼面积以及应力和应变约束条件,对运输机机翼4个外形参数和4个结构参数进行多目标、多约束优化设计。优化后的机翼具有较好的气动/结构综合性能,表明本文方法是可行的。     

基于超大涡模拟的燃烧室气动性能仿真研究进展

航空发动机燃烧室涉及旋流、雾化蒸发、掺混、化学反应、湍流与火焰相互作用等多尺度强耦合物理化学过程,相关的高 精度建模和数值模拟面临极大的挑战。超大涡模拟是近些年发展的兼顾计算精度、计算效率和强鲁棒性的数值模拟新方法,具备 试验室尺度和复杂工程应用场景下湍流流动与燃烧仿真能力。针对航空发动机燃烧室相关流动与燃烧基本特征,阐述了超大涡 模拟的理论方法及特点,从旋流流动、湍流燃烧、液雾雾化、碳烟生成、燃烧不稳定等典型多物理过程,以及双旋流模型燃烧室和高 温升燃烧室气动性能集成仿真等方面介绍了超大涡模拟的研究进展,对涉及的物理机制进行了分析,为超大涡模拟在航空发动机 燃烧室中规模化工程应用提供了坚实支撑。超大涡模拟在较低的计算资源消耗下具备与传统大涡模拟相当的计算精度,是一种 经济可承受的燃烧室高精度气动性能仿真新方法。     

基于代理模型的高效全局低音爆优化设计方法

研究发展高效实用的低音爆优化设计方法,对于新一代低音爆超声速客机的研制具有重要的理论意义和应用价值。目前国内外发展的低音爆优化方法主要包括遗传算法（GA）和基于Adjoint的梯度优化。遗传算法虽然具有较强的全局优化能力,但其优化效率较低,无法很好满足实际应用的需要;而梯度优化虽然优化效率高,但易陷入局部最优。将最新发展的代理优化算法与音爆预测方法相结合,发展了一种具有全局优化能力的高效低音爆优化设计方法。首先,概述了所采用的线性音爆预测方法,并用NASA超声速圆锥体模型进行验证,表明其计算效率高、预测精度可满足飞行器初步设计的需要。其次,对所采用的代理优化（SBO）方法进行了概述,包括试验设计、代理模型建模、优化加点准则和收敛标准等。再次,运用所发展的方法开展了NASA多段圆锥体模型的低音爆优化设计算例研究,并与遗传算法和梯度优化的结果进行了比较,表明其优化效率比遗传算法提高了2个量级以上,且优化结果优于梯度方法。最后,将所发展的方法应用于AIAA音爆预测大会提供的翼身组合体外形（69°后掠三角翼）的低音爆优化设计,将远场音爆N型波峰值减少了27.4%,表明所发展的方法在复杂外形低音爆优化设计中具有很好的应用潜力。     

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.     

可应用于民机空气动力设计中的数值优化方法

目前民机成功设计的一个关键要素即在设计中有效地引入计算流体力学（CFD）的模拟方法和软件,特别是具有设计能力的方法和工具。本文概要地叙述了反设计、基于CFD低可信度和高可信度模型等数值优化方法的发展和应用于民机设计的历史和现状;简单地介绍了即将举行的空气动力优化设计计算系列研讨会;重点讨论了对可应用于民机设计的基于Navier-Stokes方程解算器的OPTIMAS的数值优化方法的要求及其构造方法,并以翼身组合体整流外形和翼身融合体（BWB）外形的算例表明其有效性,说明OPTIMAS可以成为民机日常设计的方法和工具之一。