A Modified Bi-Directional Evolutionary Structural Optimization Procedure with Variable Evolutionary Volume Ratio Applied to Multi-Objective Topology Optimization Problem

Natural frequency and dynamic stiffness under transient loading are two key performances for structural design related to automotive,aviation and construction industries.This article aims to tackle the multi-objective topological optimization problem considering dynamic stiffness and natural frequency using modified version of bi-directional evolutionary structural optimization(BESO).The conventional BESO is provided with constant evolutionary volume ratio(EVR),whereas low EVR greatly retards the optimization process and high EVR improperly removes the efficient elements.To address the issue,the modified BESO with variable EVR is introduced.To compromise the natural frequency and the dynamic stiffness,a weighting scheme of sensitivity numbers is employed to form the Pareto solution space.Several numerical examples demonstrate that the optimal solutions obtained from the modified BESO method have good agreement with those from the classic BESO method.Most importantly,the dynamic removal strategy with the variable EVR sharply springs up the optimization process.Therefore,it is concluded that the modified BESO method with variable EVR can solve structural design problems using multi-objective optimization.

A Smooth Bidirectional Evolutionary Structural Optimization of Vibrational Structures for Natural Frequency and Dynamic Compliance

A smooth bidirectional evolutionary structural optimization(SBESO),as a bidirectional version of SESO is proposed to solve the topological optimization of vibrating continuum structures for natural frequencies and dynamic compliance under the transient load.A weighted function is introduced to regulate the mass and stiffness matrix of an element,which has the inefficient element gradually removed from the design domain as if it were undergoing damage.Aiming at maximizing the natural frequency of a structure,the frequency optimization formulation is proposed using the SBESO technique.The effects of various weight functions including constant,linear and sine functions on structural optimization are compared.With the equivalent static load(ESL)method,the dynamic stiffness optimization of a structure is formulated by the SBESO technique.Numerical examples show that compared with the classic BESO method,the SBESO method can efficiently suppress the excessive element deletion by adjusting the element deletion rate and weight function.It is also found that the proposed SBESO technique can obtain an efficient configuration and smooth boundary and demonstrate the advantages over the classic BESO technique.

Stress Relaxation and Sensitivity Weight for Bi-Directional Evolutionary Structural Optimization to Improve the Computational Efficiency and Stabilization on Stress-Based Topology Optimization

Stress-based topology optimization is one of the most concerns of structural optimization and receives much attention in a wide range of engineering designs.To solve the inherent issues of stress-based topology optimization,many schemes are added to the conventional bi-directional evolutionary structural optimization(BESO)method in the previous studies.However,these schemes degrade the generality of BESO and increase the computational cost.This study proposes an improved topology optimization method for the continuum structures considering stress minimization in the framework of the conventional BESO method.A global stress measure constructed by p-norm function is treated as the objective function.To stabilize the optimization process,both qp-relaxation and sensitivity weight scheme are introduced.Design variables are updated by the conventional BESO method.Several 2D and 3D examples are used to demonstrate the validity of the proposed method.The results show that the optimization process can be stabilized by qp-relaxation.The value of q and p are crucial to reasonable solutions.The proposed sensitivity weight scheme further stabilizes the optimization process and evenly distributes the stress field.The computational efficiency of the proposed method is higher than the previous methods because it keeps the generality of BESO and does not need additional schemes.

Topology Optimization with Aperiodic Load Fatigue Constraints Based on Bidirectional Evolutionary Structural Optimization

Because of descriptive nonlinearity and computational inefficiency,topology optimization with fatigue life under aperiodic loads has developed slowly.A fatigue constraint topology optimization method based on bidirectional evolutionary structural optimization(BESO)under an aperiodic load is proposed in this paper.In viewof the severe nonlinearity of fatigue damagewith respect to design variables,effective stress cycles are extracted through transient dynamic analysis.Based on the Miner cumulative damage theory and life requirements,a fatigue constraint is first quantified and then transformed into a stress problem.Then,a normalized termination criterion is proposed by approximatemaximum stress measured by global stress using a P-normaggregation function.Finally,optimization examples show that the proposed algorithm can not only meet the requirements of fatigue life but also obtain a reasonable configuration.

Structural Topology Optimization by Combining BESO with Reinforcement Learning

In this paper,a new algorithm combining the features of bi-direction evolutionary structural optimization(BESO)and reinforcement learning(RL)is proposed for continuum structural topology optimization(STO).In contrast to conventional approaches which only generate a certain quasi-optimal solution,the goal of the combined method is to provide more quasi-optimal solutions for designers such as the idea of generative design.Two key components were adopted.First,besides sensitivity,value function updated by Monte-Carlo reinforcement learning was utilized to measure the importance of each element,which made the solving process convergent and closer to the optimum.Second,ε-greedy policy added a random perturbation to the main search direction so as to extend the search ability.Finally,the quality and diversity of solutions could be guaranteed by controlling the value of compliance as well as Intersection-over-Union(IoU).Results of several 2D and 3D compliance minimization problems,including a geometrically nonlinear case,show that the combined method is capable of generating a group of good and different solutions that satisfy various possible requirements in engineering design within acceptable computation cost.

Computational Simulations of Bone Remodeling under Natural Mechanical Loading or Muscle Malfunction Using Evolutionary Structural Optimization Method

Live bone inherently responds to applied mechanical stimulus by altering its internal tissue composition and ultimately biomechanical properties, structure and function. The final formation may structurally appear inferior by design but complete by function. To understand the loading response, this paper numerically investigated structural remodeling of mature sheep femur using evolutionary structural optimization method (ESO). Femur images from Computed Tomography scanner were used to determine the elastic modulus variation and subsequently construct finite element model of the femur with stiffest elasticity measured. Major muscle forces on dominant phases of healthy sheep gait were imposed on the femur under static mode. ESO was applied to progressively alter the remodeling of numerically simulated femur from its initial to final design by iteratively removing elements with low strain energy density (SED). The computations were repeated with two different mesh sizes to test the convergence. The elements within the medullary canal had low SEDs and therefore were removed during the optimization. The SEDs in the remaining elements varied with angle around the circumference of the shaft. Those elements with low SED were inefficient in supporting the load and thus fundamentally explained how bone remodels itself with less stiff inferior tissue to meet load demand. This was in line with the Wolff’s law of transformation of bone. Tissue growth and remodeling process was found to shape the sheep femur to a mechanically optimized structure and this was initiated by SED in macro-scale according to traditional principle of Wolff’s law.

Topology Optimization in Damping Structure Based on ESO

The damping material optimal placement for the structure with damping layer is studied based on evolutionary structural optimization (ESO) to maximize modal loss factors. A mathematical model is constructed with the objective function defined as the maximum of modal loss factors of the structure and design constraints function defined as volume fraction of damping material. The optimal placement is found. Several examples are presented for verification. The results demonstrate that the method based on ESO is effective in solving the topology optimization of the structure with unconstrained damping layer and constrained damping layer. This optimization method suits for free and constrained damping structures.

基于应变能与频率灵敏度的静动力双目标ESO

当前在运用渐进结构优化(ESO)时,大多仅设定了单一的静力或动力目标,难以满足工程结构设计的需求。为此,将单目标优化常用的应变能灵敏度和频率灵敏度进行无量纲处理,再与多目标优化理论结合,开发出静动力双目标ESO。通过多个不同边界条件的深受弯构件数值算例,证实了新方法的运行稳定性和普遍适用性,同时还得到了静力优化与动力优化间的权重系数比取值建议。有限元对比分析结果表明,该新方法相较于传统的单目标优化,能够兼顾结构的静动力性能,使结构耗材减少但静力刚度基本维持,同时材料利用率和一阶固有频率还能不断提升。

A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time.

Effectiveness Assessment of the Search-Based Statistical Structural Testing

Search-based statistical structural testing(SBSST)is a promising technique that uses automated search to construct input distributions for statistical structural testing.It has been proved that a simple search algorithm,for example,the hill-climber is able to optimize an input distribution.However,due to the noisy fitness estimation of the minimum triggering probability among all cover elements(Tri-Low-Bound),the existing approach does not show a satisfactory efficiency.Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time.Tri-Low-Bound is considered a strong criterion,and it is demonstrated to sustain a high fault-detecting ability.This article tries to answer the following question:if we use a relaxed constraint that significantly reduces the time consumption on search,can the optimized input distribution still be effective in faultdetecting ability?In this article,we propose a type of criterion called fairnessenhanced-sum-of-triggering-probability(p-L1-Max).The criterion utilizes the sum of triggering probabilities as the fitness value and leverages a parameter p to adjust the uniformness of test data generation.We conducted extensive experiments to compare the computation time and the fault-detecting ability between the two criteria.The result shows that the 1.0-L1-Max criterion has the highest efficiency,and it is more practical to use than the Tri-Low-Bound criterion.To measure a criterion’s fault-detecting ability,we introduce a definition of expected faults found in the effective test set size region.To measure the effective test set size region,we present a theoretical analysis of the expected faults found with respect to various test set sizes and use the uniform distribution as a baseline to derive the effective test set size region’s definition.

基于BESO算法的大型海洋垂直轴风力机支撑结构优化

大型海洋垂直轴风力机的研究对发展海洋风能具有重要意义,研究大型垂直轴风力机的合理支撑结构形式对风力发电结构安全至关重要.基于变删除率的双向渐进结构优化(BESO)算法,对大型海洋垂直轴风力机进行支撑结构优化,并通过风力机的动力响应特性分析,验证结构优化方法的可靠性.结果表明:反比例型变删除率的BESO算法能有效改善优化迭代速率,适用于垂直轴风力机的支撑结构优化设计;相比于初始结构,拓扑出的新结构模型在风荷载作用下的风致动力响应显著降低.研究成果可用于垂直轴风力机支撑结构设计优化.

基于BESO算法的涡轮盘拓扑优化

涡轮盘作为航空发动机的核心部件之一,其轻量化设计对航空发动机效率提升至关重要。基于双向渐进结构优化算法(bidirectional evolutionary structural optimization,BESO)拓扑优化模型,使用灵敏度过滤的方法抑制棋盘格现象,用ANSYS的参数化设计语言(ANSYS parametric design language,APDL)编写拓扑优化程序,对离心载荷作用下的涡轮盘进行结构优化,并通过施加叶片等效载荷的方式,考虑叶片对于优化结果的影响。结果表明,使用编写的算法进行拓扑优化迭代步数减少,显著提升优化效率,在减重26%的条件下,拓扑出的新的结构模型结构总应变能降低48%,结构刚度提升,最大等效应力降低25%且应变能和应力分布更均匀合理。

Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method

Weight reduction has attracted much attention among ship designers and ship owners.In the present work,based on an improved bi-directional evolutionary structural optimization(BESO) method and surrogate model method,we propose a hybrid optimization method for the structural design optimization of beam-plate structures,which covers three optimization levels:dimension optimization,topology optimization and section optimization.The objective of the proposed optimization method is to minimize the weight of design object under a group of constraints.The kernel optimization procedure(KOP) uses BESO to obtain the optimal topology from a ground structure.To deal with beam-plate structures,the traditional BESO method is improved by using cubic box as the unit cell instead of solid unit to construct periodic lattice structure.In the first optimization level,a series of ground structures are generated based on different dimensional parameter combinations,the KOP is performed to all the ground structures,the response surface model of optimal objective values and dimension parameters is created,and then the optimal dimension parameters can be obtained.In the second optimization level,the optimal topology is obtained by using the KOP according to the optimal dimension parameters.In the third optimization level,response surface method(RSM) is used to determine the section parameters.The proposed method is applied to a hatch cover structure design.The locations and shapes of all the structural members are determined from an oversized ground structure.The results show that the proposed method leads to a greater weight saving,compared with the original design and genetic algorithm(GA) based optimization results.

Structural-acoustic topology optimization analysis based on evolutionary structural optimization approach

The continuum structural-acoustic topology optimization with external loading is investigated herein. Finite element method （FEM） is used to obtain the structural frequency response and boundary element method （BEM） is adopted to perform exterior acoustic radiation analysis. The evolutionary structural optimization （ESO） is served as an optimization method in structural-acoustic radiation topology analysis. The acoustic radiation optimization of a plate under harmonic excitation is given for example. The numerical results show that using ESO solution to analyze structural-acoustic topology optimization is feasible and effective.

渐进密度AESO方法及其在热传导结构拓扑优化中的应用

研究用于结构拓扑优化的基于材料添加策略的进化算法(AESO方法)。基于进化算法的思想,利用单元材料相对密度的变化描述材料的添加(从0到1)或删除(从1到0)。当某些单元满足进化准则时,单元的相对密度进行0-1变化。研究发现,基于一步变化策略的AESO方法往往不能获得正确的拓扑形式,其原因可能是,进化后的响应量是基于密度为0或很小时的敏度经线性近似获得的,与实际相差很大。这种敏度的计算误差问题在ESO、BESO等硬杀算法中都存在。本文提出将进化过程分成多步,以软杀的思想进行硬杀优化,即使材料密度逐渐由0变化到1,实现材料的逐步添加。基于该策略,提出了渐进密度AESO方法,并比较分析了这种逐步添加的做法对结果的影响。算例验证了该方法的正确性和有效性。渐进密度AESO方法为双向进化算法(BESO)提供了有效的进化(材料添加)策略。

ESO在2-D结构模型优化中的改进及应用

针对ESO在2-D结构模型优化中出现的畸变单元以及优化结果可能落入局部最优问题,提出2种算法。其一,基于畸变单元特征的筛选和删除算法。畸变单元的修正只需通过一个参数控制,该参数由其周围单元的联接个数决定;其二,通过区间逼近算法确定满足单元删除判定条件所对应的最小初始删除率R_(0min),以该删除率作为起始值,通过设定一定大小的增量确定多个初始删除率R_0。将以上算法植入到原有ESO优化步骤中,分别以得到的不同初始删除率开始优化,根据性能参数从优化得到的多个拓扑结构中筛选出可用结构。对多个2-D结构拓扑优化表明,改进的ESO方法在工程适应性和稳定性上优于现有ESO方法,且避免了单一结果落入局部最优解的不足。

简单立方点阵结构静态平压性能分析

为了提高简单立方(SC)点阵结构的平压力学性能,在ABAQUS中对SC单胞建立了周期边界约束方程,并通过ESO算法对周期边界条件下的SC单胞进行了拓扑优化设计。随后对优化SC单胞的等效弹性模量进行了求解,发现优化SC单胞的等效弹性模量明显优于传统SC单胞,从外部去除单胞材料可使优化单胞等效压缩模量提高27.14%,从内部去除单胞材料可使单胞等效剪切模量提高46.18%。最后将优化SC单胞从单胞层面扩展到宏观结构中,探究了三类SC点阵结构的静态平压性能。研究表明,周期边界条件与ESO相结合的拓扑优化方法,可使SC结构静态平压时的抵抗力得到明显提升。相比传统SC点阵结构,优化后的SC点阵结构抵抗力提高了20%以上。

基于ESO的起重机刚架结构离散拓扑优化研究

基于离散渐进结构拓扑优化方法,优化了岸桥门框支撑斜杆布局。对于研究的岸桥刚架结构,给定一个初始基结构,该基结构由若干杆件及节点组成,每个节点由若干杆件交汇而成。根据渐进结构优化法,研究给定设计域离散变量(杆件)单向删减操作,形成具有较低应变能的拓扑结构算法。应用离散杆系单元单向删减策略,在4种典型工况下,对岸桥门框基结构进行拓扑优化,给出斜撑的合理布局。

声子晶体能带结构仿真的CS-FEM方法及其在拓扑优化设计中的应用

针对声子晶体拓扑优化设计时能带结构和灵敏度计算效率低的缺陷,构造了一种Cell-based光滑有限元法模型(CS-FEM),并结合双向渐进结构优化法(BESO)实现了声子晶体的带隙最大化设计。基于四边形单元构造光滑子域,将梯度光滑技术与Bloch定理结合,构建了声子晶体能带结构计算的CS-FEM数值模型,并将其用于正问题的仿真模拟。在渐进优化准则下,通过BESO算法完成了声子晶体的优化设计。数值算例表明:CS-FEM能够适当软化离散系统的刚度,提供更加准确、高效的能带结构仿真结果;基于CS-FEM进行正问题的计算,在优化设计中得到了最优的拓扑构型,并有效地提高了优化效率,对于实现声子晶体的高效设计具有重要的参考价值。

基于ESO的反射镜拓扑优化设计

针对目前反射镜拓扑优化存在的局限性和渐进结构优化的优点,提出了用渐进结构优化的方法对镜体进行拓扑寻优。分析了渐进结构优化的原理并进行了改进,同时对比了不同模型和删除参数的影响,对镜体在竖直和水平两种工况下分别进行分析,得到了在复合工况下材料的最佳分布(载荷传递路径),分析了这个结果与现有镜体结构形式的异同,给出了改进建议,根据上述最佳材料分布加以整理,提出了一种多点支撑下的镜体结构形式,对以后相关设计具有一定的参考价值。