一种薄壁吸能结构的设计优化

研究了一种薄壁结构在耐撞指标下的轴向冲击吸能特性。采用非线性有限元软件ABAQUS对结构的冲击过程进行仿真,并结合径向基函数法(RBF),根据耐撞性指标优化了这种吸能结构的截面,得到了较为理想的结构形式。数值结果表明采用该方法可以精确地确定优化参数,使结构比吸能(Es)得到明显提高。     

新颖圆形多胞复合填充结构的耐撞性

提出一种新颖的圆形多胞复合填充结构,该结构采用蜂窝和泡沫两类材料的交错复合填充。采用实验验证与数值研究相结合的方法,系统地研究了蜂窝和泡沫材料在全填充、部分填充及交互填充结构中的耐撞性。研究结果表明,针对单一材料填充的多胞圆管,部分填充结构比全填充结构具有更好的耐撞性能,其中,环形蜂窝填充结构（H40）和中心泡沫填充结构（F01）具有更优异的能量吸收特性。针对双材料复合填充的多胞圆管,则是中心泡沫填充与环形蜂窝填充的复合结构（F01H40）具有最佳的耐撞吸能性。最后,进一步结合Kriging近似技术与粒子群数值优化方法,对复合填充结构进行多目标优化设计,探索其最优耐撞性与最优参数匹配。结果表明,环形蜂窝部分填充结构（H40）、中心泡沫填充与环形蜂窝填充的复合全填充结构（F01H40）具有最优的耐撞性能。     

Application of manufacturing constraints to structural optimization of thin-walled structures

Topology optimization can be a very useful tool for creating conceptual designs for vehicles. Structures suggested by topology optimization often turn out to be difficult to implement in manufacturing processes. Presently, rail vehicle structures are made by welding sheet metal parts. This leads to many complications and increased weight of the vehicle. This article presents a new design concept for modern rail vehicle structures made of standardized, thin-walled, closed, steel profiles that fulfil the stress and manufacturing requirements. For this purpose, standard software for topology optimization was used with a new way of preprocessing the design space. The design methodology is illustrated by an example of the topology optimization of a freight railcar. It is shown that the methodology turns out to be a useful tool for obtaining optimal structure design that fulfils the assumed manufacturing constraints.     

Multi-objective optimization based on meta-modeling by using support vector regression

Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.     

Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm

This article presents a novel framework for the multi-objective optimization of offshore renewable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a floating offshore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the offshore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally efficient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of optimal solutions characterizing how design changes impact the trade-off between these two competing objectives.     

Topology and sizing optimization of discrete structures using a cooperative coevolutionary genetic algorithm with independent ground structures

This article proposes a method called the cooperative coevolutionary genetic algorithm with independent ground structures (CCGA-IGS) for the simultaneous topology and sizing optimization of discrete structures. An IGS strategy is proposed to enhance the flexibility of the optimization by offering two separate design spaces and to improve the efficiency of the algorithm by reducing the search space. The CCGA is introduced to divide a complex problem into two smaller subspaces: the topological and sizing variables are assigned into two subpopulations which evolve in isolation but collaborate in fitness evaluations. Five different methods were implemented on 2D and 3D numeric examples to test the performance of the algorithms. The results demonstrate that the performance of the algorithms is improved in terms of accuracy and convergence speed with the IGS strategy, and the CCGA converges faster than the traditional GA without loss of accuracy.     

Analysis,design and optimization of structures using force method and genetic algorithm

In the first part of this paper, the energy formulation of the force method is presented and analysis is performed using genetic algorithm. Two simple examples are provided to show the accuracy of the approach. In the second part, an efficient method is developed for designing structures with prescribed stress ratios for its members. The genetic algorithm performed very well and designs with specified stress ratios were achieved with a good convergence rate. A unit value of c_i for all the members of a structure corresponds to the well known fully stressed design. In the third part, minimum weight design is formulated by the additional conditions being imposed on the design process. Again, genetic algorithm showed to be a powerful means for optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

响应表面法在薄壁构件耐撞性优化设计中的应用研究

汽车结构的耐撞性及碰撞吸能优化是现代汽车工业重要的研究内容。耐撞性的优化涉及材料与结构的众多参数。传统的设计、碰撞仿真及试验往往只能在一定程度上改善结构的碰撞性能而无法达到限定条件下的最优状态。利用国际上近年来新发展起来的一种优化理论方法--响应表面法,结合传统的优化手段以及非线性有限元程序对薄壁构件的耐撞性问题进行了优化研究。耐撞性优化的结果表明,该方法具有较高的精确性和有效性。     

基于遗传算法的圆柱空腔吸声覆盖层参数优化研究

基于圆柱空腔吸声覆盖层二维理论推导吸声覆盖层单元-粘弹性圆柱管等效阻抗,结合传递矩阵法建立吸声覆盖层声学性能分析简化模型。采用遗传算法对圆柱-圆台空腔吸声覆盖层多个材料、结构参数进行综合优化研究。结果表明,较仅优化吸声覆盖层材料属性,对材料属性、结构多参数优化能获得更好宽频吸声性能。     

基于多目标遗传算法的实验目标车底盘结构优化

在实际道路上开展实验目标车碰撞试验是智能汽车开发过程中的重要测试手段之一.为保证智能汽车的安全性,实验目标车的底盘必须具有抵抗较大碾压冲击的能力.由于实验目标车底盘的碾压过程涉及接触非线性和几何非线性等问题,仅基于仿真分析对其进行结构设计和优化存在较大困难.为此,首先采用显式非线性有限元法对实验目标车底盘的抗碾压性能进...     

An algorithm for optimization of non-linear shell structures

An algorithm for optimal design of non-linear shell structures is presented. The algorithm uses numerical optimization techniques and nonlinear finite element analysis to find a minimum weight structure subject to equilibrium conditions, stability constraints and displacement constraints. A barrier transformation is used to treat an apparent non-smoothness arising from posing the stability constraints in terms of the eigenvalues of the Hessian of the potential energy of the structure. A sequential quadratic programming strategy is used to solve the resulting non-linear optimization problem. Matrix sparsity in the constraint Jacobian is exploited because of the large number of variables. The usefulness of the proposed algorithm is demonstrated by minimizing the weight of a number of stiffened thin shell structures.     

压电智能连续体结构/控制一体化广义拓扑优化

对于以往较少涉及到的同时考虑结构拓扑、作动器位置与数目和控制器参数等多种优化设计变量参与的压电智能板结构的一体化优化设计问题,研究了结构/控制一体化广义拓扑优化设计的方法。提出采用基于耦合模态空间的二次型最优控制系统设计与基于遗传算法和数学形态学处理的策略进行一体化拓扑优化设计实现。数值算例的结果表明,所提方法合理、有效,能够得到清晰的结构拓扑和良好的可控性。     

Topology optimization for crashworthiness of thin-walled structures under axial crash considering nonlinear plastic buckling and locations of plastic hinges

Although topology optimization is established for linear static problems, more effort is required for solving nonlinear plastic problems. A new topology optimization approach with equivalent static loads (ESLs) is suggested to find the optimum topologies and locations of plastic hinges of thin-walled crash boxes by considering crash-induced deformation, the main crash energy-absorbing mechanism. Together with finite element method crashworthiness analyses, considering all nonlinearities with rate-dependent plasticity, the method was developed using an appropriate time-incremental scheme of ESLs without removing any high values of loads. Analyses show that the crash boxes with optimum topologies have energy-absorbing capabilities equivalent to the original structure. The proposed method is evaluated for two crashes: a crash box at low speed and a double cell subjected to high-speed collision. The results indicate that this method captures nonlinear crushing behaviours and accurate locations of plastic hinges where, if proper reinforcements are made, energy absorption can be enhanced.     

Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position-resetting. The purpose of the algorithm is to optimize the design of truss structures. Inspired by a heuristic, the boundary-shifting approach forces particles to move to the boundary between feasible and infeasible regions in order to increase the convergence rate in searching. The purpose of the particle-position-resetting approach, motivated by mutation scheme in genetic algorithms (GAs), is to increase the diversity of particles and to prevent the solution of particles from falling into local minima. The performance of the AugPSO algorithm was tested on four benchmark truss design problems involving 10, 25, 72 and 120 bars. The convergence rates and final solutions achieved were compared among the simple PSO, the PSO with passive congregation (PSOPC) and the AugPSO algorithms. The numerical results indicate that the new AugPSO algorithm outperforms the simple PSO and PSOPC algorithms. The AugPSO achieved a new and superior optimal solution to the 120-bar truss design problem. Numerical analyses showed that the AugPSO algorithm is more robust than the PSO and PSOPC algorithms.     

An optimization technique using the characteristics of genetic algorithm

Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient‐based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.     

Elastoplastic analysis of compact and thin-walled structures using classical and refined beam finite element models

The paper presents results on the elastoplastic analysis of compact and thin-walled structures via refined beam models. The application of Carrera Unified Formulation (CUF) to perform elastoplastic analysis of isotropic beam structures is discussed. Particular attention is paid to the evaluation of local effects and cross-sectional distortions. CUF allows formulation of the kinematics of a one-dimensional (1D) structure by employing a generalized expansion of primary variables by arbitrary cross-section functions. Two types of cross-section expansion functions, TE (Taylor expansion) and LE (Lagrange expansion), are used to model the structure. The isotropically work-hardening von Mises constitutive model is incorporated to account for material nonlinearity. A Newton–Raphson iteration scheme is used to solve the system of nonlinear algebraic equations. Numerical results for compact and thin-walled beam members in plastic regime are presented with displacement profiles and beam deformed configurations along with stress contour plots. The results are compared against classical beam models such as Euler–Bernoulli beam theory and Timoshenko beam theory, reference solutions from literature, and three-dimensional (3D) solid finite element models. The results highlight: (1) the capability of the present refined beam models to describe the elastoplastic behavior of compact and thin-walled structures with 3D-like accuracy; (2) that local effects and severe cross-sectional distortions can be detected; (3) the computational cost of the present modeling approach is significantly lower than shell and solid model ones.     

New conceptual design of aeroelastic wing structures by multi-objective optimization

Internal structural layouts and component sizes of aircraft wing structures have a significant impact on aircraft performance such as aeroelastic characteristics and mass. This work presents an approach to achieve simultaneous partial topology and sizing optimization of a three-dimensional wing-box structure. A multi-objective optimization problem is assigned to optimize lift effectiveness, buckling factor and mass of a structure. Design constraints include divergence and flutter speeds, buckling factor and stresses. The topology and sizing design variables for wing internal components are based on a ground element approach. The design problem is solved by multi-objective population-based incremental learning (MOPBIL). The Pareto optimum results lead to unconventional wing structures that are superior to their conventional counterparts.     

工程项目管理多目标均衡优化研究综述

寻求工程项目各目标之间的均衡最优是工程项目管理的重要方面,近年来相关研究发展迅速,已取得了较为丰硕的研究成果。本文通过文献研究,对该领域的研究现状进行了综述。分别从确定条件下的工程项目多目标均衡优化模型和不确定条件下的工程项目多目标均衡优化模型两个方面,对该领域的研究现状进行了分析,并展望了该领域的研究趋势和发展方向。旨在总结当前工程项目多目标均衡优化领域中的研究成果并揭示当前的热点研究问题,为今后研究提供一定的参考和建议。     

混沌优化法在机械设计中的应用

针对一般的二次载波的混沌优化方法收敛慢的弱点,提出了一些改进的方法.主要是利用当前解的信息,自动改变最优点的搜索路径,能明显提高首次载波寻找最佳点大概位置的速度和效能;同时能更快更精确实现二次载波的精细搜索.将该算法用于一机械设计问题———箱形盖板优化设计计算之中,取得了优于常规方法———Powell法的结果,说明该方法在机械优化设计中具有较好的应用价值.     

Discrete material optimization of general composite shell structures

A novel method for doing material optimization of general composite laminate shell structures is presented and its capabilities are illustrated with three examples. The method is labelled Discrete Material Optimization (DMO) but uses gradient information combined with mathematical programming to solve a discrete optimization problem. The method can be used to solve the orientation problem of orthotropic materials and the material selection problem as well as problems involving both. The method relies on ideas from multiphase topology optimization to achieve a parametrization which is very general and reduces the risk of obtaining a local optimum solution for the tested configurations. The applicability of the DMO method is demonstrated for fibre angle optimization of a cantilever beam and combined fibre angle and material selection optimization of a four‐point beam bending problem and a doubly curved laminated shell. Copyright © 2005 John Wiley & Sons, Ltd.