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.     

Optimization on conventional and electric air-cycle refrigeration systems of aircraft: A short-cut method and analysis

The air-cycle refrigeration system is widely used in commercial and military aircraft, and its efficiency greatly affects aircraft performance. Nowadays, this system requires a more efficient design and optimization method. In this paper, a short-cut optimization method with high efficiency and effectiveness is introduced for both conventional and electric air-cycle refrigeration systems. Based on the system characteristics, a four-layer parameter matching algorithm is designed which avoids computational difficulty caused by simultaneous equations. Fuel penalty is chosen as the objective function of optimization; design variables are reduced based on sensitivity analysis to improve optimization efficiency. The results show that the 3-variable optimization of the conventional air-cycle refrigeration system can obtain almost the same results as the traditional 6-variable optimization in that these two optimizations can both significantly reduce the fuel penalty. However, the computer running time of the 3-variable optimization is much shorter than that of the 6-variable optimization. The optimal fuel penalty of the electric air-cycle refrigeration system is lower than that of the conventional one. This study can provide reference for optimizing the air-cycle refrigeration system of aircraft.     

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.     

基于全局气动优化方法的跨声速叶栅气动优化

提出了适用于叶栅三维气动设计优化的全局自动气动优化方法.对NASA Rotor 37转子叶栅进行了气动优化设计.利用该叶栅的试验数据校核了计算流体(CFD)程序的可靠性.以等熵效率最高为目标函数,在满足流量约束和总压比约束的条件下,完成了跨声速叶栅的气动优化设计.优化叶栅的等熵效率提高了1.66%,具有优秀的气动性能和变工况性能.优化结果表明,通过优化三维跨声速叶栅的型线和径向基迭方式,可以有效的减小跨声速叶栅的激波损失.      

基于POD-BPNN的热启动策略及其在气动代理优化中的应用

传统气动优化设计需要大量CFD 分析,而代理优化（SBO）方法能够有效降低CFD 分析次数,但该方法并没有改变单个CFD 分析时间。提出一种基于本征正交分解-反向传播神经网络（POD-BPNN）模型的热启动策略,并应用于气动代理优化。使用POD-BPNN 模型对SBO 中的初始样本建立从几何设计变量到流场数据的预测模型...     

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。     

基于遗传算法与响应面模型的压气机叶片气动优化设计

1引言由于叶轮机械内部复杂的三维流动,各种波涡结构的广泛存在,吸引了众多学者对其进行了积极的探索与研究。CFD作为可靠省时的设计手段之一,在叶轮机械气动设计的过程中显示了其卓越的性能[1~3]。June Chung和Lee K i D[4]采用基于梯度法的优化程序,以绝热效率为目标函数对NA     

一种减小再入飞行器侧向气动非线性的布局优化方法

为改善升力式再入飞行器在跨声速段出现的侧向气动特性非线性问题,发展了一种基于Kriging代理模型的自适应迭代气动布局优化方法.设计了一种常规升力式再入飞行器布局,计算了该布局在跨声速段的侧向气动力,分析了可能影响侧向气动特性的机翼布局参数.根据气动布局优化流程,计算了气动布局样本气动特性,建立了布局参数到侧向力矩系数...     

后掠翼三维鼓包串激波控制参数的影响

针对跨声速后掠翼,三维鼓包串作为一种有效的减阻方式具有结构简单、高效及鲁棒性好等优点.利用全局优化算法探索了鼓包设计参数空间的整体特性,并对鼓包长度、三维鼓包展向设计参数对鼓包减阻效果的影响进行了研究,发现鼓包顶点位置和高度对阻力系数最敏感,三维鼓包的展向设计参数则对阻力系数不敏感,而鼓包长度和鼓包相对展长越长越有利于减阻.在此基础上开展了小后掠角自然层流机翼加3种不同类型鼓包串的优化研究,通过优化结果发现,增加优化后的三维鼓包串,可将小后掠角自然层流机翼阻力发散马赫数向后推移,并且鼓包平均长度和控制区越大,效果越好.三维鼓包串具有良好的局部控制特性,可用于局部较强激波的抑制.三维鼓包串对常规后掠翼波阻具有良好的控制效果,同时能够抑制激波诱导的机翼后缘气流分离.      

A surface parametric control and global optimization method for axial flow compressor blades

An aerodynamic optimization method for axial flow compressor blades available for engineering is developed in this paper. Bezier surface is adopted as parameterization method to control the suction surface of the blades, which brings the following advantages:(A) significantly reducing design variables;(B) easy to ensure the mechanical strength of rotating blades;(C) better physical understanding;(D) easy to achieve smooth surface. The Improved Artificial Bee Colony(IABC) algorithm, which significantly increases the convergence speed and global optimization ability, is adopted to find the optimal result. A new engineering optimization tool is constructed by combining the surface parametric control method, the IABC algorithm, with a verified Computational Fluid Dynamics(CFD) simulation method, and it has been successfully applied in the aerodynamic optimization for a single-row transonic rotor(Rotor 37) and a single-stage transonic axialflow compressor(Stage 35). With the constraint that the relative change in the flow rate is less than0.5% and the total pressure ratio does not decrease, within the acceptable time in engineering, the adiabatic efficiency of Rotor 37 at design point increases by 1.02%, while its surge margin 0.84%,and the adiabatic efficiency of Stage 35 0.54%, while its surge margin 1.11% after optimization, to verify the effectiveness and potential in engineering of this new tool for optimization of axial compressor blade.