High-precision shape approximation low-thrust trajectory optimization method satisfying bi-objective index

The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory; however, it still faces the challenges of large deviation from the optimal solution and inability to satisfy the specific flight time and fuel mass constraints. In this paper, a modified shape approximation low-thrust model is presented, and a novel constrained optimization algorithm is developed to solve this problem. The proposed method aims at settling the bi-objective optimization o...     

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.     

Topology optimization of compliant adaptive wing leading edge with composite materials

An approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber reinforced epoxy composite plates has been built based on the symmetric laminated plate theory. Then, an optimization objective function of compliant adaptive wing leading edge was used to minimize the least square error（LSE） between deformed curve and desired aerodynamics shape. After that, the topology structures of wing leading edge of different glass fiber ply-orientations were obtained by using the solid isotropic material with penalization（SIMP） model and sensitivity filtering technique. The desired aerodynamics shape of compliant adaptive wing leading edge was obtained based on the proposed approach. The topology structures of wing leading edge depend on the glass fiber ply-orientation. Finally, the corresponding morphing experiment of compliant wing leading edge with composite materials was implemented, which verified the morphing capability of topology structure and illustrated the feasibility for designing compliant wing leading edge. The present paper lays the basis of ply-orientation optimization for compliant adaptive wing leading edge in unmanned aerial vehicle（UAV） field.     

A hybrid original approach for prediction of the aerodynamic coe?cients of an ATR-42 scaled wing model

A new approach for the prediction of lift, drag, and moment coefficients is presented. This approach is based on the support vector machines (SVMs) methodology and an optimization meta-heuristic algorithm called extended great deluge (EGD). The novelty of this approach is the hybridization between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters. The training and validation of this new identification approach is realized using the aerodynamic coefficients of an ATR-42 wing model. The aerodynamic coefficients data are obtained with the XFoil software and experimental tests using the Price–Pa?doussis wind tunnel. The predicted results with our approach are compared with those from the XFoil software and experimental results for different flight cases of angles of attack and Mach numbers. The main pur-pose of this methodology is to rapidly predict aircraft aerodynamic coefficients.     

A hybrid original approach for prediction of the aerodynamic coefficients of an ATR-42 scaled wing model

A new approach for the prediction of lift, drag, and moment coefficients is presented.This approach is based on the support vector machines(SVMs) methodology and an optimization meta-heuristic algorithm called extended great deluge(EGD). The novelty of this approach is the hybridization between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters. The training and validation of this new identification approach is realized using the aerodynamic coefficients of an ATR-42 wing model. The aerodynamic coefficients data are obtained with the XFoil software and experimental tests using the Price–Pa?¨doussis wind tunnel.The predicted results with our approach are compared with those from the XFoil software and experimental results for different flight cases of angles of attack and Mach numbers. The main purpose of this methodology is to rapidly predict aircraft aerodynamic coefficients.     

Applications of numerical optimization techniques to design of axial compressor blades

This paper describes the shape optimization of NASA rotor 37 and rotor and stator blades in a single-stage transonic axial compressor.Shape optimization of the blades operating at the design flow condition has been performed using the response surface method and three-dimensional Navier-Stokes analysis.Thin-layer approximation is introduced to the Navier-Stokes equations,and an explicit Runge-Kutta scheme is used to solve the governing equations.The three design variables,blade sweep,lean and skew,are introduced to optimize the three-dimensional stacking line of the blades.The objective function of the shape optimization is an adiabatic efficiency.Throughout the optimization of rotor and stator blades, optimal blade shape can be obtained.It is noted the increase of adiabatic efficiency by optimization of the blade shape with the stacking line in the single-stage transonic axial compressor is more effective in a rotor blade rather than a stator blade because of the large deformation of blade shape in the stator blade.      

A Hybrid Optimization Approach for SRM FINOCYL Grain Design

This article presents a method to design and optimize 3D FINOCYL grain （FCG） configuration for solid rocket motors （SRMs）. The design process of FCG configuration involves mathematical modeling of the geometry and parametric evaluation of various independent geometric variables that define the complex configuration. Virtually infinite combinations of these variables will satisfy the requirements of mass of propellant, thrust, and burning time in addition to satisfying basic needs for volumetric loading fraction and web fraction. In order to ensure the acquisition of the best possible design to be acquired, a sound approach of design and optimization is essentially demanded. To meet this need, a method is introduced to acquire the finest possible performance. A series of computations are carried out to formulate the grain geometry in terms of various combinations of key shapes inclusive of ellipsoid, cone, cylinder, sphere, torus, and inclined plane. A hybrid optimization （HO） technique is established by associating genetic algorithm （GA） for global solution convergence with sequential quadratic programming （SQP） for further local convergence of the solution, thus achieving the final optimal design. A comparison of the optimal design results derived from SQP, GA, and HO algorithms is presented. By using HO technique, the parameter of propellant mass is optimized to the minimum value with the required level of thrust staying within the constrained burning time, nozzle and propellant parameters, and a fixed length and outer diameter of grain.     

Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speed,Part Ⅰ–Aerodynamic optimization using genetic,bee colony and gradient descent algorithms

In this paper, an ‘in-house' genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm's performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods,namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated.     

Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics

This paper focuses on a method to solve structural optimization problems using particle swarm optimization (PSO), surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to find the global optimum. However, PSO needs many evaluations compared to gradient-based optimization. This means PSO increases the analysis costs of structural optimization. One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques. In this work, surrogate models are used, including the response surface method (RSM) and Kriging. When surrogate models are used, there are some errors between exact values and approximated values. These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models. In this paper, Bayesian statistics is used to obtain more reliable results. To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization, two numerical examples are optimized, and the optimization of a hub sleeve is demonstrated as a practical problem.     

Research of low boom and low drag supersonic aircraft design

Sonic boom reduction will be an issue of utmost importance in future supersonic transport, due to strong regulations on acoustic nuisance. The paper describes a new multi-objective optimization method for supersonic aircraft design. The method is developed by coupling Seebass–George–Darden(SGD) inverse design method and multi-objective genetic algorithm.Based on the method, different codes are developed. Using a computational architecture, a conceptual supersonic aircraft design environment(CSADE) is constructed. The architecture of CSADE includes inner optimization level and out optimization level. The low boom configuration is generated in inner optimization level by matching the target equivalent area distribution and actual equivalent area distribution. And low boom/low drag configuration is generated in outer optimization level by using NSGA-II multi-objective genetic algorithm to optimize the control parameters of SGD method and aircraft shape. Two objective functions, low sonic boom and low wave drag, are considered in CSADE. Physically reasonable Pareto solutions are obtained from the present optimization. Some supersonic aircraft configurations are selected from Pareto front and the optimization results indicate that the swept forward wing configuration has benefits in both sonic boom reduction and wave drag reduction. The results are validated by using computational fluid dynamics(CFD) analysis.     

基于遗传算法的柔性机构形状变化综合优化研究

 实现机翼在不同的飞行状态下的最优气动外形是变弯度自适应机翼的一项关键技术。针对传统铰链机构会使机翼表面产生不连续变化而导致气流提早分离的问题，从全柔性机构实现连续平滑的形状变化的技术出发，以目标形状与实际形状的边界曲线之差最小为优化目标，采用遗传算法(GA)对柔性机构的拓扑、尺寸、形状进行了综合优化。在优化方法上，以二进制编码技术和实数编码技术为基础建立初始离散柔性机构的混合变量遗传算法模型，将其映射为有限元模型并进行了结构分析。在优化过程中引入了渐进结构优化(ESO)算法的思想，消除GA优化过程中产生的自由单元，改善了优化效率和分析结果。结合机翼前缘形状变化实例，基于MATLAB进行优化设计，并用ANSYS10.0对优化结果进行了机构的仿真分析。分析结果表明，所提出的方法合理、有效。     

基于参数化分析的柔性后缘优化设计

介绍了一种自适应柔性机翼后缘变形控制优化设计方法,该方法采用了由单个驱动器驱动的单块式分布柔性结构。该方法的关键在于设计一种合适的柔性后缘结构拓扑形式,使该自适应柔性机翼后缘能够达到精确的变形。阐明了柔性结构拓扑优化设计的数学模型,针对自适应柔性机翼后缘概念设计提出了基于参数化分析的拓扑优化设计方案,并进行了工程化圆整和尺寸优化。最终,通过非线性有限元分析和功能测试,验证了本文提出的柔性后缘优化设计方法的合理性。     

基于精英保留遗传算法的连续结构多约束拓扑优化

采用遗传算法对应力、位移及结构约束下的连续体结构进行拓扑优化。通过在遗传算法中引入精英保留的进化策略,保证算法的收敛性,改善优化结果。优化过程中,利用精英保留遗传算法进行结构优化,通过基于ANSYS软件的有限元技术进行结构的建模和分析。算例分析表明所提出的方法是合理、有效的。该方法可用于复杂工程结构的初始设计,并能够提供多种初始设计方案。      

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。     

基于超椭圆方程和序列响应面法的回转壳开孔形状优化

含有多种功能开孔的轻质回转壳结构是航空航天结构系统内的常用支撑构件,其开孔形状直接影响结构的静动态性能。以回转壳结构为对象,基于超椭圆方程和坐标映射变换推导了回转壳开孔边界的参数化表达,开展了开孔形状动力学优化研究。为提高结构优化计算的精度、效率和收敛性,提出了准等弧长方法和基于均匀设计的序列响应面近似建模方法(SRSM),以分别实现空间超椭圆曲线的精确逼近、减少结构有限元分析成本和加快迭代收敛。以非支配排序遗传算法Ⅱ(NSGA-Ⅱ)作为响应面模型求解算法,结合有限元分析构建了回转壳开孔形状优化设计流程,开展了最大化结构一二阶频率带隙的典型回转壳结构开孔形状优化设计。结果表明,基于超椭圆方程和序列响应面法的开孔优化方法获得了有效改进结构动态特性的回转壳开孔形状,对开展计算耗时工程结构形状优化设计具有一定应用价值。     

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。     

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm: Part I,building blocks detection and optimization in runway

This paper proposes a Linkage Learning Genetic Algorithm (LLGA) based on the messy Genetic Algorithm (mGA) to optimize the Min-Max fuel controller performance in Gas Turbine Engine (GTE). For this purpose, a GTE fuel controller Simulink model based on the Min-Max selection strategy is firstly built. Then, the objective function that considers both performance indices (response time and fuel consumption) and penalty items (fluctuation, tracking error, overspeed and acceleration/deceleration) is established to quantify the controller performance. Next, the task to optimize the fuel controller is converted to find the optimization gains combination that could minimize the objective function while satisfying constraints and limitations. In order to reduce the optimization time and to avoid trapping in the local optimums, two kinds of building block detection methods including lower fitness value method and bigger fitness value change method are proposed to determine the most important bits which have more contribution on fitness value of the chromosomes. Then the procedures to apply LLGA in controller gains tuning are specified stepwise and the optimization results in runway condition are depicted subsequently. Finally, the comparison is made between the LLGA and the simple GA in GTE controller optimization to confirm the effectiveness of the proposed approach. The results show that the LLGA method can get better solution than simple GA within the same iterations or optimization time. The extension applications of the LLGA method in other flight conditions and the complete flight mission simulation will be carried out in part II.     

基于遗传算法的复杂多峰函数优化

从传统的搜索、优化方法入手，分析了遗传算法对复杂、多峰函数优化的有效性；简单介绍了遗传算法；并归纳出从优化问题转化到用遗传算法求解的转化思路；最后，通过对一个多峰函数优化的遗传算法求解，验证了此方法在全局优化、鲁棒性等方面的更有效性。