汽车水平外形参数对气动阻力影响的仿真分析

针对现有的汽车气动性能优化研究大多集中于纵向外形参数上,缺乏对水平外形参数研究的问题,选取水平外形参数中车尾收缩角和后风窗收缩角作为气动优化研究对象.利用数值仿真软件建立车体模型并进行仿真,求解获得水平外形参数的变化对气动阻力的影响规律.将水平参数的变化与对应的纵向外形参数的减阻效果进行对比分析.结果表明：水平外形参数的变化引起尾流结构显著变化,且与对应的纵向外形参数相比其减阻效果更好.因此,水平外形参数对汽车气动性能优化具有积极影响.     

高空远程滑翔UUV数值仿真研究

研究滑翔翼优化气动特性,高空远程滑翔UUV是一种集无人空中飞行器技术和水下航行器技术于一体的新概念远程UUV,为了能获取各种气动参数,应用计箅流体力学的数值仿真方法对高空远程滑翔UUV的相关气动特性进行了研究.针对高空远程滑翔UUV的气动外形,通过求解自适应笛卡尔网格欧拉方程,用高空远程滑翔UUV的三维绕流流场进行了数...     

面向汽车外形空气动力学优化的代理模型方法

针对代理模型在汽车外形气动优化上的适应性研究较少的现状,运用不同数量样本点构建径向基函数(Radial Basis Function,RBF)模型、多项式模型和Kriging模型等3种常用代理模型.对比发现,在样本点相同的情况下,RBF模型的精度最高,最优解更好.在样本点增加的基础上,多项式和Kriging模型的精度提高,但计算量也大幅增加;多项式最优解更接近RBF模型的最优解,而Kriging模型最优解仍不理想.综合评估可知RBF模型更适用于汽车外气动优化.     

平流层飞艇外形的设计优化

平流层飞艇是依靠浮力升空的飞行器.飞艇外形对于平流层飞艇的设计至关重要,为了获得能够满足动力结构和重量等各个学科要求的最优艇形,将综合设计优化技术引入到飞艇外形设计中,提出了适用于优化的飞艇外形生成曲线,分析了与外形有关的气动、结构和重量等因素,建立了飞艇气动阻力、表面积和最小环向应力的模型,构造了复合目标函数,并针对某飞艇外形进行了优化设计.仿真结果证明,利用蒙特卡罗算法优化设计后的艇形优于传统艇形.     

水下自航行器外形及水动力性能优化

研究水下自航行器(AUV) 外形及水动力性能优化的问题,为使得AUV具有较小航行阻力的同时拥有较大承载能力,需要不断进行AUV模型重建以及水动力结果分析,人工完成将会耗时很长,Isight多学科优化设计平台搭载常用的优化算法—NSGA-II遗传算法,整合Solidworks、Gambit、Fluent三大集成模块实现数据交换以进行AUV外形的建模、仿真并完成设计过程的自动化和智能的设计探索,确定最佳设计参数;仿真结果表明,最终优化后的AUV不仅减小了航行阻力并且拥有更大的承载能力;因此采用多学科优化软件Isight能够有效提高AUV外形及水动力性能优化的准确性和效率,提升其整体水动力性能。     

基于FLUENT的新型跨介质航行器气动水动特性研究

针对目前水下航行器和空中航行器难以以单一外形同时满足水空两种航行环境的特点,提出一种通过改变机体外形实现水空介质跨越的新型航行器.应用FLUENT对航行器的气动水动特性进行数值仿真,得到了航行器水中、空中航行的阻力、升力曲线.对结果的分析发现,通过改变外形能够满足航行器水中、空中的航行和机动要求,新型航行器的流体受弹体扰动较大,升阻特性有一定减弱,水空跨介质航行器的外形还有较大的优化空间.     

结构优化设计软件Tosca初探

一、前言 Tosca是标准的无参数结构优化系统,可以对具有任意载荷情况的有限元模型进行拓扑和外形优化.在优化过程中,可以直接使用已经存在的有限元模型.Tosca进行结构优化的每一迭代过程均在外部求解器中进行求解,通过采用众多业界认可的标准求解器从而保证了计算结果的高质量.     

基于遗传算法的高速列车头型多目标优化

高速列车的气动阻力与列车的外形,特别是头部外形有着密切的关系.为了改善列车气动性能降低列车运行的气动阻力,建立高速列车的三维参数化模型,以高速列车头部所受的阻力和升力为优化目标,通过FLUENT软件与Isight软件多学科优化联合仿真分析方法,利用Sculptor软件对车头部分网格自动变形,基于计算流体力学,实现对高速列车流线型头型进行减阻的多目标自动优化设计.优化完成后,得到影响优化目标阻力和升力的关键设计变量,并对优化设计变量和优化目标之间的非线性相关性进行分析.通过对比原始流线型列车气动性能发现,列车头部的长度对阻力的影响比较大,列车头部的高度能够对列车所受到的升力产生较大的影响.     

面向分级设计优化的飞行器参数化建模方法

针对飞行器气动隐身外形综合设计优化问题,提出合适的面向分级设计优化流程,建立适应该流程的渐进分层参数化建模方法;用基于敏度分析的参数影响程度分析方法筛选复杂设计变量;采用多学科设计优化（Multidisplinary Design Optimization,MDO）理论和差分进化算法进行飞行器气动隐身外形的综合设计优化.将该方法用于某飞行器外形设计优化,结果表明：该方法合理可行,可为飞行器外形多学科设计优化提供一定参考.     

高空滑翔UUV气动参数估算与气动特性分析

研究水下航行器,针对无人水下航行器无法同时大幅度提高航速和航程的现状,为优化无人水下航行器气动特性,增强系统的稳定性,提出了高空滑翔无人水下航行器(UUV)总体气动布局进行设计.对气动特性进行分析,根据飞机和导弹气动参数的估算方法,通过类比的方式,对升力系数、阻力系数、俯仰力矩系数、航向静导数、动导数和操纵导数等主要气...     

Robust aerodynamic shape design based on an adaptive stochastic optimization framework

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method.     

A surrogate-model based multidisciplinary shape optimization method with application to a 2D subsonic airfoil

A surrogate-model based shape optimization method is presented and applied to the case of the multidisciplinary shape optimization of a 2D NACA subsonic airfoil. The cost function is designed so that both the far-field radiated noise and the aerodynamic forces are controlled. The surrogate model is based on the Kriging optimal interpolation technique. In order to increase the efficiency of the method, a dynamic Kriging method is developed, which can be interpreted as an Adaptive Mesh Refinement method in the shape optimization parameters.     

基于近似模型的整车气动外形优化

以类车体DrivAer的气动阻力因数为优化目标,选取影响其气动性能的5个形状参数作为设计变量,通过引入网格变形、试验设计(Design of Experiment,DOE)及近似模型等技术搭建自动仿真优化平台,探索气动性能最佳的参数匹配方案.优化后的DrivAer气动阻力因数降低4.5%,表明近似模型方法能够较好地取代实际仿真过程进行寻优.分析DOE的结果,发现影响气动阻力因数和气动升力因数的主要参数分别为行李箱高度与离去角,而多参数变化时的交互效应也会影响整车的气动性能.     

基于近似模型的车身气动外形优化

以英国汽车工业研究协会(Motor Industry Research Association,MIRA)阶背模型为基本模型,用参数化建模方法建立其纵向对称面的二维模型.运用优化拉丁超立方方法对每组参数化方案生成600组样本点;将MATLAB与Gambit结合,自动快速生成其网格模型;用FLUENT计算每个样本点的气动阻力.建立径向基神经网络(Radial Basis Function Neural Network,RBFNN)近似模型,以阻力最小为优化目标,采用多岛遗传算法优化外形参数;对优化后的结果进行数值模拟,结果表明阻力减少31.9%.三维验证结果表明:二维优化结果不能完全代表三维结果,直接进行三维优化设计的效果更好.     

Interactive design of 2D car profiles with aerodynamic feedback

The design of car shapes requires a delicate balance between aesthetic and performance. While fluid simulation provides the means to evaluate the aerodynamic performance of a given shape, its computational cost hinders its usage during the early explorative phases of design, when aesthetic is decided upon. We present an interactive system to assist designers in creating aerodynamic car profiles. Our system relies on a neural surrogate model to predict fluid flow around car shapes, providing fluid visualization and shape optimization feedback to designers as soon as they sketch a car profile. Compared to prior work that focused on time-averaged fluid flows, we describe how to train our model on instantaneous, synchronized observations extracted from multiple pre-computed simulations, such that we can visualize and optimize for dynamic flow features, such as vortices. Furthermore, we architectured our model to support gradient-based shape optimization within a learned latent space of car profiles. In addition to regularizing the optimization process, this latent space and an associated encoder-decoder allows us to input and output car profiles in a bitmap form, without any explicit parameterization of the car boundary. Finally, we designed our model to support pointwise queries of fluid properties around car shapes, allowing us to adapt computational cost to application needs. As an illustration, we only query our model along streamlines for flow visualization, we query it in the vicinity of the car for drag optimization, and we query it behind the car for vortex attenuation.     

基于NURBS方法的气动外形优化设计

采用NURBS曲线曲面,对钝锥弹头和钝双锥弹体建立参数化曲面模型,取NURBS曲线控制点作为设计参数,应用高超声速面元法求解气动力特性,在给定设计约束下,采用遗传算法进行气动外形优化设计,并对优化结果进行了比较分析。结果表明,采用NURBS方法构造参数化外形,并结合优化技术可方便快速地获得所需最优外形;与应用二次曲线构造参数化外形相比,该方法对弹体形状控制更加灵活,并可局部修改弹头曲线形状。因此,基于NURBS方法发展整套的系统优化设计算法很有现实意义和应用价值。     

Variable-fidelity expected improvement method for efficient global optimization of expensive functions

The efficient global optimization method (EGO) based on kriging surrogate model and expected improvement (EI) has received much attention for optimization of high-fidelity, expensive functions. However, when the standard EI method is directly applied to a variable-fidelity optimization (VFO) introducing assistance from cheap, low-fidelity functions via hierarchical kriging (HK) or cokriging, only high-fidelity samples can be chosen to update the variable-fidelity surrogate model. The theory of infilling low-fidelity samples towards the improvement of high-fidelity function is still a blank area. This article proposes a variable-fidelity EI (VF-EI) method that can adaptively select new samples of both low and high fidelity. Based on the theory of HK model, the EI of the high-fidelity function associated with adding low- and high-fidelity sample points are analytically derived, and the resulting VF-EI is a function of both the design variables x and the fidelity level l. Through maximizing the VF-EI, both the sample location and fidelity level of next numerical evaluation are determined, which in turn drives the optimization converging to the global optimum of high-fidelity function. The proposed VF-EI is verified by six analytical test cases and demonstrated by two engineering problems, including aerodynamic shape optimizations of RAE 2822 airfoil and ONERA M6 wing. The results show that it can remarkably improve the optimization efficiency and compares favorably to the existing methods.     

Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models

Surrogate models are used to dramatically improve the design efficiency of numerical aerodynamic shape optimization, where high-fidelity, expensive computational fluid dynamics (CFD) is often employed. Traditionally, in adaptation, only one single sample point is chosen to update the surrogate model during each updating cycle, after the initial surrogate model is built. To enable the selection of multiple new samples at each updating cycle, a few parallel infilling strategies have been developed in recent years, in order to reduce the optimization wall clock time. In this article, an alternative parallel infilling strategy for surrogate-based constrained optimization is presented and demonstrated by the aerodynamic shape optimization of transonic wings. Different from existing methods in which multiple sample points are chosen by a single infill criterion, this article uses a combination of multiple infill criteria, with each criterion choosing a different sample point. Constrained drag minimizations of the ONERA-M6 and DLR-F4 wings are exercised to demonstrate the proposed method, including low-dimensional (6 design variables) and higher-dimensional problems (up to 48 design variables). The results show that, for surrogate-based optimization of transonic wings, the proposed method is more effective than the existing parallel infilling strategies, when the number of initial sample points are in the range from N _v to 8N _v (N _v here denotes the number of design variables). Each case is repeated 50 times to eliminate the effect of randomness in our results.