迷你翼梢小翼增升减阻效应

选取某窄体客机的翼梢小翼为研究对象,采用Spalart Allmaras模型对无翼梢小翼、全尺寸翼梢小翼和迷你翼梢小翼3种机翼构型进行数值模拟,通过流场分析和速度分解等手段,研究翼梢小翼的增升减阻机理。结果表明：迷你翼梢小翼有恢复涡核流速、减弱涡流掺混程度和梳理翼梢气流的作用;增升减阻的关键在于迷你翼梢小翼对气流方向的修正;翼梢小翼的局部流动差异会对整体机翼近场造成影响。由于尺寸较小,迷你翼梢小翼能在较大攻角范围内改善传统翼梢小翼的性能,具有一定的实践意义。     

Aeroelastic topology optimization of membrane structures for micro air vehicles

This work considers the aeroelastic optimization of a membrane micro air vehicle wing through topology optimization. The low aspect ratio wing is discretized into panels: a two material formulation on the wetted surface is used, where each panel can be membrane (wing skin) or carbon fiber (laminate reinforcement). An analytical sensitivity analysis of the aeroelastic system is used for the gradient-based optimization of aerodynamic objective functions. An explicit penalty is added, as needed, to force the structure to a 0–1 distribution. The dependence of the solution upon initial design, angle of attack, mesh density, and objective function are presented. Deformation and pressure distributions along the wing are studied for various load-augmenting and load-alleviating designs (both baseline and optimized), in order to establish a link between stiffness distribution and aerodynamic performance of membrane micro air vehicle wings. The work concludes with an experimental validation of the superiority of select optimal designs.     

Vibration analysis of composite plate wing

Vibration characteristics, including natural frequencies and mode shapes for various shaped composite wings, are evaluated using finite elements based on the shear deformable theory. The present analysis gives the influence of the sweep angle, the fiber orientation, the aspect ratio, and the taper ratio of a composite wing on the vibration properties. Natural frequencies have been obtained for a composite wing which has symmetric stacking sequences of laminates. Frequency closeness phenomena of different modes can be observed for the negative fiber angles. The present analysis uses eight-node quadrilateral elements which provide very accurate results.     

A review of the current applications of genetic algorithms in assembly line balancing

Most of the problems involving the design and plan of manufacturing systems are combinatorial and NP-hard. A well-known manufacturing optimization problem is the assembly line balancing problem (ALBP). Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms. In this article, a survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms. In particular, we have summarized the main specifications of the problems studied, the genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms. Moreover, future research directions have been identified and are suggested.     

A Genetic Algorithm for Multiobjective Robust Design

The goal of robust design is to develop stable products that exhibit minimum sensitivity to uncontrollable variations. The main drawback of many quality engineering approaches, including Taguchi's ideology, is that they cannot efficiently handle presence of several often conflicting objectives and constraints that occur in various design environments.Classical vector optimization and multiobjective genetic algorithms offer numerous techniques for simultaneous optimization of multiple responses, but they have not addressed the central quality control activities of tolerance design and parameter optimization. Due to their ability to search populations of candidate designs in parallel without assumptions of continuity, unimodality or convexity of underlying objectives, genetic algorithms are an especially viable tool for off-line quality control.In this paper we introduce a new methodology which integrates key concepts from diverse fields of robust design, multiobjective optimization and genetic algorithms. The genetic algorithm developed in this work applies natural genetic operators of reproduction, crossover and mutation to evolve populations of hyper-rectangular design regions while simultaneously reducing the sensitivity of the generated designs to uncontrollable variations. The improvement in quality of successive generations of designs is achieved by conducting orthogonal array experiments as to increase the average signal-to-noise ratio of a pool of candidate designs from one generation to the next.     

Multiobjective evolutionary computation for supersonic wing-shapeoptimization

This paper discusses the design optimization of a wing for supersonic transport (SST) using a multiple-objective genetic algorithm (MOGA). Three objective functions are used to minimize the drag for supersonic cruise, the drag for transonic cruise, and the bending moment at the wing root for supersonic cruise. The wing shape is defined by 66 design variables. A Euler flow code is used to evaluate supersonic performance, and a potential flow code is used to evaluate transonic performance. To reduce the total computational time, flow calculations are parallelized on an NEC SX-4 computer using 32 processing elements. The detailed analysis of the resulting Pareto front suggests a renewed interest in the arrow wing planform for the supersonic wing     

Real-coded adaptive range genetic algorithm applied to transonic wing optimization

Real-coded adaptive range genetic algorithm (ARGA) has been applied to a practical three-dimensional shape optimization for aerodynamic design of an aircraft wing. The real-coded ARGA possesses both advantages of the binary-coded ARGA and the floating-point representation to overcome the problems of having a large search space that requires continuous sampling. The results confirm that the real-coded ARGA consistently finds better solutions than the conventional real-coded genetic algorithms do.     

Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

This research is based on a new hybrid approach, which deals with the improvement of shape optimization process. The objective is to contribute to the development of more efficient shape optimization approaches in an integrated optimal topology and shape optimization area with the help of genetic algorithms and robustness issues. An improved genetic algorithm is introduced to solve multi-objective shape design optimization problems. The specific issue of this research is to overcome the limitations caused by larger population of solutions in the pure multi-objective genetic algorithm. The combination of genetic algorithm with robust parameter design through a smaller population of individuals results in a solution that leads to better parameter values for design optimization problems. The effectiveness of the proposed hybrid approach is illustrated and evaluated with test problems taken from literature. It is also shown that the proposed approach can be used as first stage in other multi-objective genetic algorithms to enhance the performance of genetic algorithms. Finally, the shape optimization of a vehicle component is presented to illustrate how the present approach can be applied for solving multi-objective shape design optimization problems.     

三维地效翼展向效应数值模拟

为研究地效翼的展向效应,利用FLUENT软件求解定常不可压N-S方程和标准k-ε湍流模型,对在地面效应下三维地效翼的流场进行数值模拟.首先,对给定面积和离地高度下不同展弦比的地效翼进行数值模拟;然后,对给定弦长和相对飞行高度下不同展弦比和带端板的地效翼进行数值研究.计算结果给出不同展弦比和带端板地效翼的气动特性曲线,揭示展弦比和端板对翼尖涡涡核位置和下洗角的影响规律.在地面效应下,机翼的展向效应更为明显,端板将进一步提高机翼的空气动力性能;翼尖涡的位置受地面影响向外移动,下洗角相应减小.研究结果为地效飞行器的设计与优化提供理论依据.     

Genetic algorithms in computer aided design

Design is a complex engineering activity, in which computers are more and more involved. The design task can often be seen as an optimization problem in which the parameters or the structure describing the best quality design are sought.Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way.Genetic algorithms transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they find solution(s) to a problem by maintaining a population of possible solutions according to the ‘survival of the fittest’ principle. We present here the main features of genetic algorithms and several ways in which they can solve difficult design problems. We briefly introduce the basic notions of genetic algorithms, namely, representation, genetic operators, fitness evaluation, and selection. We discuss several advanced genetic algorithms that have proved to be efficient in solving difficult design problems. We then give an overview of applications of genetic algorithms to different domains of engineering design.     

Steady-state genetic algorithms for discrete optimization of trusses

This paper presents the applications of steady-state genetic algorithms to discrete optimization of trusses. It is mathematically formulated as a constrained nonlinear optimization problem with discrete design variables. Discrete design variables are treated by a two-stage mapping process which is constructed by the mapping relationships between unsigned decimal integers and discrete values. With small generation gap and careful modification, steady-state genetic algorithms can significantly reduce the computational effort and promote the computational efficiency. The effectiveness, robustness and fast convergence of steady-state genetic algorithms are demonstrated through several examples. The performance of four crossover operators is also compared.     

航空结构大规模并行分析与优化应用

针对飞机设计精细化数值分析模型自由度已经达到亿级,对高性能计算的要求也越来越高的问题,围绕大规模并行计算环境下结构分析和优化的若干关键问题,研究满足高性能计算体系特点的区域分解并行算法、超大规模结构变量敏度高效求解和结构非线性振动特性求解等关键技术.对国产CAE软件HAJIF进行并行化改造,初步实现基于最大航程的气动结构综合优化设计和基于精细化模型的复合材料机翼综合优化设计.HAJIF的计算效率和精度得到明显提高.     

飞机多目标优化设计网格的研究与应用

针对飞机多目标拓扑优化提出一种通用的遗传算法计算模型,在此模型基础上,基于对等计算(P2P)技术将分布的计算资源整合为高性能计算环境,以网格服务方式提供统一的资源服务和可视化的用户使用环境,实现多目标优化设计网格,解决飞机设计中遇到的复合材料多目标拓扑优化问题.首先对系统体系结构以及多目标遗传算法做出较详细的描述,然后以优化某型大展弦比机翼为例,给出一组实验数据.结果证明,该系统大大缩短了计算时间,具有良好的并行加速效果.     

Parallelized structural topology optimization and CFD coupling for design of aircraft wing structures

A set of structural optimization tools are presented for topology optimization of aircraft wing structures coupled with Computational Fluid Dynamics (CFD) analyses. The topology optimization tool used for design is the material distribution technique. Because reducing the weight requires numerous calculations, the CFD and structural optimization codes are parallelized and coupled via a code/mesh coupling scheme. In this study, the algorithms used and the results obtained are presented for topology design of a wing cross-section under a given critical aerodynamic loading and two different spar positions to determine the optimum rib topology.     

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.     

Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.     

An optimization study on load alleviation techniques in gliders using morphing camber

This study explores wing morphing for load alleviation as a means to reduce the required wing structural weight without compromising aircraft performance. A comparative study between the lift-to-drag ratio (L/D) performance of a fixed wing glider (FWG) and a cambered morphing wing glider (CMWG) is presented. Both aircraft are aero-structurally optimized for the best L/D for a given speed and payload mass. A combination of lifting-line theory and 2D viscous calculations is used for the aerodynamics and an equivalent beam model is employed for the structural analysis. Pull-up and -down maneuvers at 25 m/s and near stall angle of attack are assumed as critical load cases. Results of the FWG optimization are shown for several trimmed flight conditions with varying mass and velocity. Results are compared to the ones from the CMWG optimization and conclusions are drawn on the improvement in the L/D ratio throughout the flight envelope and on potential reductions in the wing structural mass due to the load alleviation strategy. The wing camber adaptation provides significant performance gains in a large range of flight speeds with negligible penalties in the low speeds range. However, maneuverability is penalized.     

基于递阶遗传算法的模糊控制器的规则生成和参数整定

提出了一种基于递阶遗传算法的模糊控制器的优化设计方法．采用具有层次结构染色体编码方式的遗传算法来设计模糊控制器，实现了语言控制规则的自动生成和隶属函数参数的自动整定．设计过程无需系统的先验知识和训练数据，具有自组织、自学习的特点．仿真结果表明，该方法优化得到的模糊控制器结构简单、性能优良．     

Topologies and performance of intelligent algorithms: a comprehensive review

Recently, optimization makes an important role in our day-to-day life. Evolutionary and population-based optimization algorithms are widely employed in several of engineering areas. The design of an optimization algorithm is a challenging endeavor caused of physical phenomena in order to obtain appropriate local and global search operators. Generally, local operators are fast. In contrast, global operators are used to find best solution in the search space; therefore they are slower compare to the local ones. The best review-knowledge of papers show that there are many optimization algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and etc in the engineering as a powerful tools. However, there is not a comprehensive review for theirs topologies and performance; therefore, the main goal of this paper is filling of this scientific gap. Moreover, several aspects of optimization heuristic designs and analysis are discussed in this paper. As a result, detailed explanation, comparison, and discussion on AI are achieved. Furthermore, some future research fields on AI are well summarized.     

Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACT

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization algorithms which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and particle swarm optimization algorithms for the given test scenarios in terms of yearly power output and efficiency.