周期性功能梯度结构稳态热传导拓扑优化设计

提出了一种考虑周期性约束的功能梯度结构稳态热传导拓扑优化设计方法。建立了基于变密度理论的固体各向同性微结构惩罚（SIMP）模型的周期性功能梯度拓扑优化模型。以整体结构散热弱度最小化为目标函数、体积分数为约束条件进行宏观拓扑优化,提取了最优构型中各预设梯度层的体积分数;通过重新分配单元散热弱度,实现了梯度层周期性约束设置。借助基于偏微分方程的灵敏度过滤方法消除数值不稳定问题,并采用移动渐近线法对设计变量进行了迭代更新。通过2D和3D数值算例分析了全局周期以及周期性分层梯度设置下,不同离散单元和子区域个数对宏观结构和微观构型的影响规律。研究结果表明：所提方法能够实现周期性约束下功能梯度结构的拓扑优化设计,不同子区域个数条件下均能获得清晰的周期性功能梯度结构且所获得的结构具有良好的散热性能。     

基于Epsilon算法加速的导重法拓扑优化求解研究

针对拓扑优化模型求解过程中需要多次迭代才能得到满足一定精度要求的收敛结果的问题,提出了一种基于向量Epsilon算法加速迭代序列收敛的方法。在求解大型连续体结构拓扑优化过程中,依据导重法迭代格式首先迭代了k次,然后对所得到的迭代序列的后m项作Epsilon算法运算,将所得到新向量作为下次导重法迭代的初始值,以此类推直到满足收敛条件。通过两个算例验证了所提出方法的有效性。计算及研究结果表明,用Epsilon算法加速后的迭代格式求解拓扑优化问题能够减少迭代次数,具有更高求解效率。     

基于最大应力约束的柔顺机构拓扑优化设计

采用拓扑优化方法获得柔顺机构构型容易出现类铰链结构,导致应力集中、疲劳可靠性差。为了抑制类铰链结构,提出了一种基于最大应力约束的柔顺机构拓扑优化设计方法。采用改进的固体各向同性材料惩罚模型（Solid isotropic material with penalization,SIMP）,以柔顺机构的互应变能最大化作为目标函数,采用P范数方法对所有单元的局部应力凝聚化成一个全局化应力约束,利用自适应约束缩放法使得P范数应力更加接近最大应力,以机构的最大应力和体积作为约束,建立柔顺机构最大应力约束拓扑优化模型,采用全局收敛移动渐近线算法求解柔顺机构最大应力约束拓扑优化问题。结果表明,采用P范数方法进行柔顺机构最大应力约束拓扑优化设计,能够有效抑制类铰链结构。随着应力约束极限值减少,获得机构构型由集中式柔顺机构逐渐转变为分布式柔顺机构,应力分布更加均匀,但机构的互应变能逐渐减小。     

基于并行策略的多材料柔顺机构多目标拓扑优化*

多材料柔顺机构能够让设计者充分利用各种材料的优良属性,在力,位移,以及能量转移等方面获得更大的设计自由度,因而受到重视。针对受到广泛研究的柔顺机构,结合多目标拓扑优化的方法,提出相应的基于并行策略的求解模型。该方法的核心是将一个复杂的多材料多目标问题离散成为单材料子问题,然后并行求解,再根据整体目标的需要,对所有子问题的解进行调整以得到原始问题的解。针对多目标情形,提出新的材料与输出目标关系,从而在将多材料问题离散成单材料子问题的同时,也将多目标问题离散成单目标子问题。对所有的单材料单目标子问题采用各向同性材料的刚度插值-惩罚法并行独立求解。以上方法有其独特优势：在理论和实践上都比较简单,可以处理任意多种材料,可以避免零碎的拓扑结构因而有利于制造。通过算例说明了此方法的有效性。研究结果表明,该方法在某些输出需要特定材料的设计场合更具优势。     

基于RAMP插值模型结合导重法求解拓扑优化问题

在连续体拓扑优化领域中,寻求更好的建模方法和更快的求解算法一直是研究人员的研究重点。为此,针对拓扑优化设计方法中的变密度法进行深入分析。研究和比较各向同性惩罚微结构法(Solid isotropic microstructure with penalization,SIMP)和材料属性有理近似模型(Rational approximation of material properties,RAMP)的优缺点后,建立基于RAMP法的优化模型,并结合导重法求解算法,用于结构拓扑优化领域。详细推导单、多工况的最小柔度拓扑优化的迭代公式,给出导重法各变量的物理定义,并分别对单工况和多工况两个典型算例进行拓扑优化计算。算例结果令人满意,同时表明RAMP插值模型结合导重法求解结构拓扑优化问题具有设计变量少、迭代次数少、收敛速度快、优化效率高的特点,验证了其可行性和高效性。     

考虑拓扑相关热载荷的散热结构多相材料拓扑优化设计

针对热传导拓扑优化设计过程中拓扑相关热载荷问题,以结构散热弱度为目标函数,体积分数为约束条件,构建了多相材料散热结构拓扑优化数学模型。采用一种基于变密度理论的有序固体各向同性微结构材料惩罚模型法构建多相材料插值模型,分别进行拓扑相关热载荷与拓扑独立热载荷作用下的灵敏度分析,借助优化准则法推导设计变量的迭代格式,引入偏微分方程过滤方法抑制优化过程中出现的数值不稳定现象。通过2D与3D数值模型计算,获得考虑拓扑相关热载荷、拓扑独立热载荷以及耦合拓扑独立/相关热载荷在不同边界条件下对拓扑优化结果的影响规律。结果证明了所提方法在解决拓扑相关热载荷作用下散热结构多相材料拓扑优化设计问题方面的有效性与可行性。     

柔顺机构几何非线性拓扑优化设计

对柔顺机构几何非线性拓扑优化设计理论进行了深入研究。首先,建立增量形式平衡方程,采用Total-Lagrange描述方法和Newton-Raphson载荷增量求解技术获得几何非线性的结构响应。其次,基于固体各向同性材料插值方法,建立体积约束下,输出位移最大为目标函数的柔顺机构几何非线性拓扑优化数学模型,目标函数敏度分析采用伴随求解技术,并用移动近似算法进行迭代求解。最后,通过算例说明以上方法的正确性和有效性。研究结果表明,应用上述方法对柔顺机构进行几何非线性拓扑优化设计能够得到合理拓扑图,并比线性分析所得机构的稳定性更高,同时也说明了对柔顺机构进行几何非线性拓扑优化的必要性。     

Topology optimization based on the harmony search method

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclusions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

     

桥式起重机箱型主梁周期性拓扑优化设计

桥式起重机主梁长度方向尺寸远大于高度和宽度方向,常规的拓扑优化方法无法获得清晰的、周期性的拓扑形式或求解困难。为了实现桥式起重机主梁的拓扑优化,得到具有周期性的、易于加工的结构拓扑形式。把主梁优化域划分成若干个子域,构建子域与优化域之间的关系。建立以优化域内单元相对密度为设计变量、以体积约束下最小柔度为目标函数的主梁周期性拓扑优化数学模型。在主梁强度和静态刚度准则下开展主梁周期性拓扑优化应用研究。结果表明,在优化过程中,各子域内同时出现孔洞,且具有周期性。直到周期性拓扑优化结束,获得具有类似“桁架式”结构的拓扑形式。子域数目取值不同时,均可获得周期性的拓扑形式,且具有良好的一致性和工艺性。为桥式起重机结构轻量化研究提出一种可行的方法。     

汽车车架结构多目标拓扑优化方法研究

为了研究以静态多工况下刚度和动态振动频率为目标函数的车架拓扑结构,提出了一种结构的多目标拓扑优化研究方法。基于实体各向同性材料惩罚函数的拓扑优化方法,采用折衷规划法定义多目标拓扑优化和多刚度拓扑优化的目标函数,而振动固有频率拓扑优化的目标函数则采用平均频率法定义。通过优化得到了同时满足静态刚度和振动低阶频率要求的汽车车架结构拓扑。该方法避免了单目标拓扑优化无法考虑其他因素的缺点,适合连续体结构的多目标拓扑优化。     

Study of key algorithms in topology optimization

The theory of topology optimization based on the solid isotropic material with penalization model (SIMP) method is thoroughly analyzed in this paper. In order to solve complicated topology optimization problems, a hybrid solution algorithm based on the method of moving asymptotes (MMA) approach and the globally convergent version of the method of moving asymptotes (GCMMA) approach is proposed. The numerical instability, which always leads to a non-manufacturing result in topology optimization, is analyzed, along with current methods to control it. To eliminate the numerical instability of topology results, a convolution integral factor method is introduced. Meanwhile, an iteration procedure based on the hybrid solution algorithm and a method to eliminate numerical instability are developed. The proposed algorithms are verified with illustrative examples. The effect and function of the hybrid solution algorithm and the convolution radius in optimization are also discussed.     

The role of S-Shape mapping functions in the SIMP approach for topology optimization

The SIMP (solid isotropic material with penalization) approach is perhaps the most popular density variable relaxation method in topology optimization. This method has been very successful in many applications, but the optimization solution convergence can be improved when new variables, not the direct density variables, are used as the design variables. In this work, we newly propose S-shape functions mapping the original density variables nonlinearly to new design variables. The main role of S-shape function is to push intermediate densities to either lower or upper bounds. In particular, this method works well with nonlinear mathematical programming methods. A method of feasible directions is chosen as a nonlinear mathematical programming method in order to show the effects of the S-shape scaling function on the solution convergence.     

Solving topology optimization problems by the Guide-Weight method

Finding a good solution method for topology optimization problems is always paid attention to by the research field because they are subject to the large number of the design variables and to the complexity that occurs because the objective and constraint functions are usually implicit with respect to design variables. Guide-Weight method, proposed first by Chen in 1980s, was effectively and successfully used in antenna structures’ optimization. This paper makes some improvement to it so that it possesses the characteristics of both the optimality criteria methods and the mathematical programming methods. When the Guide-Weight method is applied into topology optimization, it works very well with unified and simple form, wide availability and fast convergence. The algorithm of the Guide-Weight method and the improvement on it are described; two formulations of topology optimization solved by the Guide-Weight method combining with SIMP method are presented; subsequently, three numerical examples are provided, and comparison of the Guide-Weight method with other methods is made.     

Bi-directional evolutionary 3D topology optimization with a deep neural network

The FEM-based topology optimization repeats usually finite element analyses many times to converge to the stopping criteria. If the near-optimal topology data are available in advance at the beginning of an optimization process, the iterative computation could be greatly reduced. In an effort to obtain swiftly optimum topology solutions, the deep learning and neural networks with a special segmentation scheme of digital images are combined with the BESO (bi-directional evolutionary structural optimization) topology method in this study. The pre-trained digital images of 3200 optimum topologies construct the design domain for the main topology optimization. Additionally, a new post-processor is developed in order to reconstruct the relative locations among finite elements in the raw outputs generated by the neural network. The proposed method has been demonstrated to be efficient in lowering the iterations with several 2D and 3D optimization examples. The iteration counts can be reduced 63% for a 2D example and by 72.5% for a 3D one, compared to BESO results alone.

     

含自重载荷的功能梯度材料结构时域动力学拓扑优化设计

提出了一种含自重载荷的功能梯度材料(FGM)结构时域动力学拓扑优化设计方法。在固体各向同性材料惩罚(SIMP)框架下,提出了一种针对FGM-SIMP的结构自重载荷分布策略。以FGM结构动柔度最小为优化目标、以结构体积为约束,建立了动力学结构优化列式。基于伴随法,在时域内进行了灵敏度推导,并用移动渐近线方法进行求解。通过二维和三维典型数值算例系统研究了含自重载荷下FGM结构的拓扑优化设计问题,并深入探讨了自重载荷和材料梯度分布方向对结构优化结果的影响,发现自重载荷和材料梯度分布方向对FGM结构的优化构型和动刚度具有很大影响。最后,以均一材料(FGM的特例)为例,通过数值仿真和实验测试方法验证了所提方法的有效性,并证实了所提方法可有效提高结构的固有频率和结构动刚度。     

Topology optimization for lightweight cellular material and structure simultaneously by combining SIMP with BESO

This paper presents a hybrid algorithm for topology optimization of lightweight cellular materials and structures simultaneously by combining solid isotropic material with penalization (SIMP) and bi-directional evolutionary structural optimization (BESO). Microstructure of the lightweight cellular material is assumed unique in the structure to make the proposed method feasible. A new sensitivity analysis formula with respect to the discrete variable is derived by a principal submatrix stiffness matrix, by which the material can be effectively removed from or added to cellular. Moreover, the validity of the proposed method is then demonstrated through two numerical examples (a simple supported beam and a cantilever beam), which can be easily applied in a variety of practical situations.

     

双材料结构非概率可靠性拓扑优化设计

利用凸模型描述和非概率可靠性的量化定义,研究存在材料属性、几何及荷载不确定性的双材料结构拓扑优化问题。基于扩展的相对密度惩罚方法,建立优化模型为给定材料体积约束下,同时满足可靠性要求的连续型极小极大优化问题,以寻找两种不同实心材料的最优联合材料分布布局。采用序列近似规划策略,结合不确定参数直接迭代公式和移动渐近线方法来求解该极小极大优化问题。该方法可把原问题转化为一系列近似的确定性优化问题,从而极大减少了计算量。数值研究表明,存在的不确定性可能对双材料结构的最优联合布局产生较大影响,优化模型和数值算法为双材料结构拓扑设计提供了一个有效途径。     

基于面光滑有限元的复杂三维结构拓扑优化

为了增强拓扑优化计算对任意复杂模型的适应性,改进基于线性四面体有限元的拓扑优化结果,引入了一种新型高精度的基于面光滑有限元模型(FS-FEM)来进行拓扑优化,通过每次迭代时提供很好的梯度解及位移解,从而达到改善拓扑优化结果的目的。在基于面光滑有限元模型的拓扑优化中,以柔度最小作为目标函数,建立了基于固体各向同性材料惩罚插值(SIMP)的拓扑优化数学模型,该数学模型通过最优准则进行求解。多个不同载荷的拓扑优化数值算例说明,采用基于面光滑有限元进行拓扑优化,结果都能够单调收敛,且采用该方法建立的拓扑优化模型能抑制棋盘格现象。与商业软件OptiStruct的计算比较表明,该方法相比有限元方法能得到更合理的拓扑结构。     

考虑载荷随机性的可行域调整的结构拓扑优化设计

考虑载荷大小和方向的不确定性,以结构柔顺度的期望和方差的加权和为目标函数,结构体积为约束函数,研究稳健结构拓扑优化方法。首先给出载荷大小和方向不确定情况下结构柔顺度期望和方差及其导数的显式近似式,而后改进可行域调整方案,提出具有收敛特征的稳健结构拓扑优化设计方法,探讨结构柔顺度期望和方差的权重因子以及随机量的方差对拓扑构型的影响。给出的算例表明,不论是确定载荷拓扑优化还是随机载荷拓扑优化,方法是可行和有效的,且可获得一系列清晰的拓扑构型和稳健的优化拓扑。     

Topology optimization of compressor bracket

Topology optimization is very useful engineering technique especially at the concept design stage. It is common habit to design depending on the designer’s experience at the early stage of product development. Structural analysis methodology of compressor bracket was verified on the static and dynamic loading condition with 2 bracket samples for the topology optimization base model. Topology optimization is able to produce reliable and satisfactory results with the verified structural model. Base bracket model for the topology optimization was modeled considering the interference with the adjacent vehicle parts. Objective function was to minimize combined compliance and the constraint was the first natural frequency over 250 Hz. Multiple load cases such as normal mode calculation and gravity load conditions with 3-axis direction were also applied for the optimization, expecting an even stress distribution and vibration durability performance. Commercial structural optimization code such as optistruct of Altair Engineering was used for the structural topology optimization. Optimization was converged after 14 iterations with the satisfaction of natural frequency constraint. New bracket shape was produced with the CATIA based on the topology optimization result. The new bracket from topology optimization result was compared with the traditional concept model and topology optimization base model under 4 load cases. 14 % 1’st natural frequency of new bracket with only 4 % mass increment increased compared to the concept model. 31 % mass decreased compared to the base model without the increment of stress under gravity load cases. It was analyzed thata new bracket would not fail during a vibration durability test, and these results were verified with a fabricated real sample under the durability condition.