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.     

Simulation of the secondary air system of turbofan engines: Insights from 1D-3D modeling

Focusing on the internal flow and heat transfer analysis, a platform for the performance evaluation of the Secondary Air System (SAS) is developed. A multi-fidelity modeling technique has been developed in a turbofan engine model under different flight conditions. A turbine blade cooling model which integrates external heat transfer calculations and coolant side modeling with common components is proposed. In addition, the Computational Fluid Dynamics (CFD) method is selected to capture the complex flow field structure in the preswirl system. The validity of the SAS models is compared with publicly available data. An elaborately designed cooling system for the AGTF30 engine is analyzed through three main branches. It is found that the 1D-3D modeling technique can provide more accurate predictions of the SAS for the AGTF30 engine. The results demonstrate the versatility and flexibility of the SAS models, thereby indicating the capacity of meeting most of the demands of flow and thermal analysis of the SAS.     

基于XML语言和代理模型的翼型通用气动数据库技术

建立翼型气动数据库对翼型的选择和设计具有重要的意义。本文基于一种建立翼型通用气动数据库的思想,介绍该数据库的基本框架和基本功能;该数据库采用一种基于可扩展标记语言的通用数据格式进行存储,包含翼型几何特性数据、不同流动状态下力系数、物面压力分布等各种气动数据,并且还可提供灵活多样的数据处理功能;采用代理模型和变可信度模型技术,实现气动数据快速检索和插值功能。通过DU93-W-210翼型算例初步表明如何实现气动数据生成和快速插值,从而初步展示了所提出翼型气动数据库技术的可行性。     

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.     

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。     

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.     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.     

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.     

基于代理模型的气动外形优化

探讨了一种将CAD软件、CFD商用软件与代理模型相结合的飞机气动外形优化方法。重点介绍建立气动分析代理模型的过程,这一过程包括确定设计变量及其取值范围、生成试验设计点、建立参数化几何模型、CFD数值计算、提取气动特性和构造代理模型。通过一个简单的机翼气动外形优化算例验证了这种方法的可行性和有效性。     

基于自动核构造高斯过程的导弹气动性能预测

在导弹的初期设计阶段,通常需要对导弹的气动性能进行快速粗略评估。针对传统工程估算软件计算精度低和CFD方法计算代价大的缺陷,提出一种基于高斯过程回归（GPR）代理模型快速预测典型导弹气动性能的方案。以导弹外形参数和攻角作为模型输入,升力系数、阻力系数和力矩系数作为模型输出,对GPR模型的气动性能预测结果进行分析。首先,与其他常用代理模型的预测精度对比,GPR模型对3种系数的预测误差分别仅为0.24%、0.36%和0.36%,高于其他代理模型的预测精度。其次,考虑GPR模型核函数选择严重依赖人工先验知识的问题,采用了一种自动核构造算法,无需先验知识即可从数据中自动学习核函数。将该算法嵌入GPR框架中,与传统GPR模型比较,实验结果表明：基于该算法的GPR模型对3种系数的预测误差分别降低到0.10%、0.22%和0.17%。最后,给出导弹气动性能快速预测的应用实例,结果表明所提出的GPR模型的导弹气动性能预测方案是有效的,能够满足设计初期快速且精确的气动性能预测需求。     

基于XML的飞行仿真气动力模型存储格式

飞行仿真获取气动力/力矩的传统方法主要是将气动力模型硬编码在仿真程序进行求解。由于气动力模型和求解程序耦合,一旦修正或更换气动力模型,需要花费大量代价来重新构建仿真过程,无法满足现代飞行器仿真的需求。根据气动力模型树形结构的特点,提出了气动力模型树的概念并基于XML语言的特点设计了一种模型存储格式（MBX）来存储气动力模型树。MBX存储格式不仅提高了飞行仿真系统更换和修正气动力模型的效率,而且提高了气动力/力矩解算的通用性,使得气动力/力矩的求解能够被标准化。MBX存储格式作为气动力模型交换标准不仅能把不同部门交换气动力模型的时间从几周/人缩短至几天/人,也能加快气动力模型逼近飞机真实气动特性的进程。     

A multi-fidelity simulation method research on front variable area bypass injector of an adaptive cycle engine

Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Comp...     

吸气式空空导弹外形多学科一体化优化设计

针对采用整体式固冲发动机的吸气式空空导弹外形气动与推进耦合的推阻匹配设计难题,引入多学科优化设计方法提出了一种综合考虑气动/推进/质量/弹道的导弹外形多学科一体化优化设计技术。其中,气动性能预测采用代理模型技术,主要基于外形参数化建模、非结构网格技术和流场精细数值计算来自动构建气动数据库,据此建立了包含外形几何信息的气动预测代理模型,并对其预测精度进行了验证;推进性能预测采用推进求解模型,该模型根据固冲发动机理论建立,精度满足工程要求。对所建立的学科预测模型完成一体化集成后,以质点弹道仿真评估的战技指标为优化目标,对一款吸气式空空导弹进气道和翼面外形进行了优化设计,取得了推阻匹配的优化外形,优化后导弹动力射程提高10%。所提出的一体化优化设计技术,有助于吸气式空空导弹外形气动与推进耦合推阻匹配设计和提高导弹动力射程。     

A novel multi-fidelity coupled simulation method for flow systems

For the numerical simulation of flow systems with various complex components, the traditional one-dimensional (1D) network method has its comparative advantage in time consuming and the CFD method has its absolute advantage in the detailed flow capturing. The proper coupling of the advantages of different dimensional methods can strike balance well between time cost and accuracy and then significantly decrease the whole design cycle for the flow systems in modern machines. A novel multi-fidelity coupled simulation method with numerical zooming is developed for flow systems. This method focuses on the integration of one-, two-and three-dimensional codes for various components. Coupled iterative process for the different dimensional simulation cycles of sub-systems is performed until the concerned flow variables of the whole system achieve convergence. Numerical zooming is employed to update boundary data of components with different dimen-sionalities. Based on this method, a highly automatic, multi-discipline computing environment with integrated zooming is developed. The numerical results of Y-Junction and the air system of a jet engine are presented to verify the solution method. They indicate that this type of multi-fidelity simulationmethod can greatly improve the prediction capability for the flow systems.     

直升机桨叶气动外形多目标优化设计

基于自由尾迹方法建立了直升机桨叶空气动力学分析模型,应用人工神经网络方法建立代理模型,采用改进的多目标遗传算法构建了优化框架,对直升机的悬停和大速度前飞状态进行优化.以悬停效率、旋翼等效升阻比及桨叶叶素的最大阻力系数为约束,对两个飞行状态的需用功率进行优化,得到了Pareto最优解集.并以UH-60A直升机的桨叶为算例,对其外形进行优化设计,优化结果表明,提出的桨叶气动外形多目标优化框架是有效可行的.      

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.     

基于数学模型的气动力数据融合研究

气动力误差来源复杂,气动力真值通常未知,气动力误差传递数学模型的建立非常困难,因此直接将信息融合技术应用于气动力数据融合目前还不可行。迄今为止,未见气动力数据融合的公开报道。本文首次提出了基于数学模型的气动力数据融合准则和方法。首先建立反映气动数据变化规律的数学模型,然后基于气动力数学模型,根据无论计算数据、风洞实验数据、还是飞行试验数据都必须满足气动力变化规律的原则,以气动力数据满足气动力变化规律的程度为依据,建立气动力数据融合准则和融合方法。算例表明,所建立的融合准则和方法是可行的。     

扑翼飞行器动力系统建模方法

为快速评估扑翼飞行器的航时,便于针对不同扑动翼进行动力系统设计与优化,逐步减少实物验证与试飞,加快扑翼飞行器的研制,基于实验数据参数辨识的方法建立了包含直流无刷电机、电调（ESC）、锂电池和扑动机构等扑翼飞行器动力系统组件的动态模型,其中电机模型相对误差小于10%,锂电池动态模型相对误差小于6%;提出了一种基于风洞试验气动数据和功率数据的扑动轴瞬时气动载荷半经验高精度建模方法,解决了气动载荷测量较为困难的问题,模型确定系数大于0.89;集成以上模型后的扑翼飞行器仿真系统还包含扑动翼周期平均气动模型、平尾气动模型和纵向控制模型,确保仿真在动态配平状态下进行,可进行全任务剖面航时仿真,航时仿真与实际试飞结果相比误差小于3%。集成的扑翼飞行器仿真系统采用模块化建模思想,各模型参数独立可调,能进一步应用于扑翼飞行器多学科优化等研究。     

基于改进CST参数化方法和转捩模型的翼型优化设计

为提高翼型优化设计效率,增大设计空间,采用B样条基函数替代传统的形状类别函数(CST)方法中的Bezier多项式,增强了对翼型参数化表达的局部控制能力并提高了翼型局部表达精度。为了确保翼型在优化设计过程中的几何光顺特性和代理模型的准确性,采用小波分解技术提出了多分辨率翼型的局部光顺方法。采用基于本征正交分解(POD)的流场数值代理模型,并结合γ-Reθt转捩模型实现了快速准确的气动力与流动转捩预测。采用小波技术光顺的CST翼型参数化建模、POD流场数值计算代理模型以及γ-Reθt转捩模型,结合遗传算法建立了完整的翼型气动优化设计系统。针对低速层流翼型与超临界翼型进行优化设计,优化设计后的翼型升阻比分别提高了47.42%和45.85%,且对改进前后参数化建模方法的优化性能进行了对比,结果表明本文构建的翼型气动优化设计系统具备很高的优化效率。     

锥导乘波体气动代理建模方法研究

传统的气动计算方法计算繁琐、计算效率低,不适应于乘波体多学科设计优化,通过建立气动代理模型可以很好地解决气动计算精度和效率的矛盾.利用面元法进行气动估算,采集了锥导乘波体在设计点、非设计点的气动特性作为训练数据,构建了Kriging和LS-SVM代理模型,对比了两种模型对此高维问题的代理效果.结果表明,Kriging代理模型能更准确地表达锥导乘波体的气动特性,应用代理模型进行优化等工作的计算效率与传统气动计算方法相比有显著的提高.