随机参数平面连续体结构的频率拓扑优化设计

摘 要 研究几何和物理参数均为随机变量的平面连续体结构在结构基频约束下的拓扑优化设计问题。以结构总质量均值极小化为目标函数,以结构的形状拓扑信息为设计变量,以结构基频概率可靠性指标为约束条件,构建了随机结构拓扑优化设计数学模型。利用代数综合法,导出了随机参数结构动力响应的均值和均方差的计算表达式。采用渐进结构优化的求解策略与方法,通过两个算例验证了文中模型及求解方法的合理性和可行性。     

稳态热传导结构非概率可靠性拓扑优化设计

研究具有区间参数的稳态热传导结构在散热弱度非概率可靠性约束下的拓扑优化设计问题。建立了以单元相对导热系数为设计变量,导热材料体积极小化为目标函数,满足散热弱度非概率可靠性为约束条件的稳态热传导结构的拓扑优化设计数学模型。基于区间因子法,推导出散热弱度的均值及离差的计算表达式。采用渐进结构优化法的求解策略与方法,并利用过滤技术消除优化过程中的数值不稳定性现象。通过算例验证文中模型及求解策略、方法的合理性和有效性。     

多工况下杆系结构的概率优化设计

研究了基于概率的杆系结构多工况优化设计问题。建立了以杆系截面积为设计变量,结构位移、单元应力可靠度和尺寸限为约束条件,使结构重量极小化设计的数学模型。通过将概率约束等价化处理使之转变为常规约束形式,以紧约束处理策略确定有效约束。最后利用齿行法求解。算例表明文中的模型和方法是合理的和有效的。     

基于6σ的车身噪声传递函数稳健优化设计

为提高设计稳健性,将6σ稳健优化设计引入车身噪声传递函数优化过程。将6σ质量管理、可靠性设计稳健设计相结合,考虑设计变量、约束条件目标函数在内所有不确定性信息,不仅满足优化目标函数、提高系统可靠性要求,使系统响应均方差最小化,即提高稳健性。以某型汽车为例,在汽车声固耦合有限元模型基础上采用基于试验设计的二阶多项式响应面模型,以车身总质量一阶模态频率为约束条件驾驶员耳旁声压级响应均方根值为目标函数,在基本随机变量概率特性已知情况下对车身噪声传递函数进行6σ稳健优化设计,与传统确定性优化设计相比表明该方法的有效性。     

非概率不确定性及其对船舶坐墩配墩优化的影响

讨论了船舶坐墩配墩设计中存在的非概率不确定性及其描述。提出了一个非概率不确定性条件下船舶坐墩支墩配置优化设计的数学模型。着重分析了船体梁载荷和支墩组合刚度不确定性对设计结果的影响。计算结果表明,考虑参数不确定性后,配墩方案发生了变化,支墩结构重量也有所增加。增加的支墩材料是用于提高结构物抵抗不确定性参数波动变化的能力。相对于船体梁载荷不确定性,支墩组合刚度不确定性对设计结果的影响较大。     

基于概率-凸集混合模型的汽车正面碰撞结构可靠性优化设计

本文基于概率和凸集模型研究汽车正面碰撞可靠性优化设计问题。根据汽车吸能结构厚度、材料参数等不确定参数类型,分别采用概率和多椭球凸模型进行描述,以汽车正面碰撞安全性可靠性指标为约束,考虑汽车吸能结构质量为优化目标,建立了一种基于混合模型的可靠性优化设计模型。采用拉丁方试验设计构造了目标函数和约束函数的Kriging近似模型,利用功能度量法求解可靠度指标值,通过基于移动因子序列优化与可靠性评定将嵌套优化解耦为单层次优化。实际算例表明算法具有较高的计算效率及精度,对实际设计工作有一定参考价值。     

区间参数平面连续体结构频率非概率可靠性拓扑优化

研究了具有区间参数的平面连续体结构在固有频率非概率基频约束和频率禁区约束下的拓扑优化设计问题。考虑结构弹性模量、质量密度和频率约束限具有区间不确定性,根据SIMP材料插值方法和区间变量运算法则,构建了基于频率非概率可靠性约束的弯曲薄板和平面应力薄板结构的拓扑优化数学模型表达式,并给出了进化优化准则。算例及其结果表明文中模型和方法的有效性。     

鲁棒优化设计方法在结构动力学中的应用

在传统的静力学鲁棒优化设计基础上,考虑时间t参数,通过优化系统目标函数和约束条件的鲁棒性,将鲁棒优化设计方法运用在动力学问题中。通过一个主系统的质量和刚度均有微小波动的二自由度模型减振器设计算例,与传统的优化设计方法相比,显示了鲁棒优化设计的优越性,能使结构具有更稳定的性能。     

复合材料机翼鲁棒气动弹性优化设计

针对气动弹性结构, 利用遗传-敏度混合算法开展鲁棒优化设计。以大展弦比复合材料机翼的鲁棒气动弹性结构优化设计为例验证了鲁棒设计方法的适用性和有效性, 比较了鲁棒结构优化设计与传统优化设计的区别。研究结果表明: 在设计变量存在不确定性的情况下, 考虑鲁棒性约束优化得到的结构较传统优化结构具有更好的抗干扰性; 但鲁棒性的满足是以增加结构质量为代价的, 鲁棒性要求越高, 结构增重越明显。      

不确定性简谐激励下连续体结构可靠性拓扑优化

针对不确定性简谐激励下连续体结构设计问题,提出了一种有效的考虑载荷振幅和频率不确定性的谐响应可靠性拓扑优化方法。建立了概率可靠性约束下结构体积比最小化的可靠性设计优化模型,其中极限状态函数定义为所关注自由度振幅平方和。利用伴随变量法推导了极限状态函数关于设计变量和随机变量的解析灵敏度列式,采用功能度量法实现结构可靠性分析,并基于移动渐进线方法实现设计变量的更新。最后,通过3个数值算例及蒙特卡罗仿真,验证了所提方法对不确定性简谐激励下连续体结构设计问题的有效性和稳定性,并讨论了简谐激励的振幅大小和频率不确定性、可靠度指标及变异系数对优化结果的影响。     

Optimal design of composite shells based on minimum weight and maximum feasibility robustness

A robust design optimization (RDO) approach for minimum weight and safe shell composite structures with minimal variability into design constraints under uncertainties is proposed. A new concept of feasibility robustness associated to the variability of design constraints is considered. So, the feasibility robustness is defined through the determinant of variance–covariance matrix of constraint functions introducing in this way the joint effects of the uncertainty propagations on structural response. A new framework considering aleatory uncertainty into RDO of composite structures is proposed. So, three classes of variables and parameters are identified: deterministic design variables, random design variables and random parameters. The bi-objective optimization search is performed using on a new approach based on two levels of dominance denoted by Co-Dominance-based Genetic Algorithm (CoDGA). The use of evolutionary concepts together sensitivity analysis based on adjoint variable method is a new proposal. The examples with different sources of uncertainty show that the Pareto front definition depends on random design variables and/or random parameters considered in RDO. Furthermore, the importance to control the uncertainties on the feasibility of constraints is demonstrated. CoDGA approach is a powerfully tool to help designers to make decision establishing the priorities between performance and robustness.     

Reliability-based robust multi-objective optimization applied to engineering system design

ABSTRACT

Probabilistic and non-probabilistic methods have been proposed to deal with design problems under uncertainties. Reliability-based design and robust design are probabilistic strategies traditionally used for this purpose. In the present contribution, reliability-based robust design optimization (RBRDO) is formulated as a multi-objective problem considering the interaction of both approaches. The proposed methodology is based on the differential evolution algorithm associated with two strategies to deal with reliability and robustness, respectively, namely inverse reliability analysis and the effective mean concept. This multi-objective optimization problem considers the maximization of reliability and robustness coefficients as additional objective functions. The effectiveness of the methodology is illustrated by two classical test cases and a rotor-dynamics application. The results demonstrate that the proposed methodology is an alternative method to solve RBRDO problems.     

Development of a simple and efficient method for robust optimization

Robust optimization problems are newly formulated and an efficient computational scheme is proposed. Both design variables and design parameters are considered as random variables about their nominal values. To ensure the robustness of objective performance, we introduce a new performance index bounding the performance together with a constraint limiting the performance variation. The constraint variations are regulated by considering the probability of feasibility. Each probability constraint is transformed into a sub‐optimization problem by the advanced first‐order second moment (AFOSM) method for computational efficiency. The proposed robust optimization method has the advantages that the mean value and the variation of the performance function are controlled simultaneously and rationally and the second‐order sensitivity information is not required even in case of gradient‐based optimization process. The suggested method is examined by solving three examples and the results are compared with those for the deterministic case and those available in the literature. Copyright © 2002 John Wiley & Sons, Ltd.     

Sensitivity importance‐based robust optimization of structures with incomplete probabilistic information

Robust design optimization (RDO) is usually performed by minimizing the nominal value of a performance function and its dispersion considering equal importance to each individual gradient of the performance function. However, it is well known that all gradients are not equally important. An efficient sensitivity importance‐based RDO technique is proposed in the present study for optimum design of structures characterized by bounded uncertain input parameters. The basic idea of the proposed RDO formulation is to improve the robustness of a performance function by using a new gradient index that utilizes the importance factors proportional to the importance of the gradients of the performance function. The same concept is also extended to the constraints. To enhance the robustness of the constraints, the constraint functions are also modified by using the importance factor proportional to the importance of the associated gradient of the constraint. Because all the variables are not equally important to capture the presence of uncertainty, an improved robust solution is obtained by the proposed approach compared with the conventional RDO approach. The present formulation is illustrated with the help of three informative examples. The results are compared with the conventional RDO results to study the effectiveness of the proposed RDO approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Reliability analysis of structures based on a probability‐uncertainty hybrid model

The traditional reliability analysis method based on probabilistic method requires probability distributions of all the uncertain parameters. However, in practical applications, the distributions of some parameters may not be precisely known due to the lack of sufficient sample data. The probabilistic theory cannot directly measure the reliability of structures with epistemic uncertainty, ie, subjective randomness and fuzziness. Hence, a hybrid reliability analysis (HRA) problem will be caused when the aleatory and epistemic uncertainties coexist in a structure. In this paper, by combining the probability theory and the uncertainty theory into a chance theory, a probability‐uncertainty hybrid model is established, and a new quantification method based on the uncertain random variables for the structural reliability is presented in order to simultaneously satisfy the duality of random variables and the subadditivity of uncertain variables; then, a reliability index is explored based on the chance expected value and variance. Besides, the formulas of the chance theory‐based reliability and reliability index are derived to uniformly assess the reliability of structures under the hybrid aleatory and epistemic uncertainties. The numerical experiments illustrate the validity of the proposed method, and the results of the proposed method can provide a more accurate assessment of the structural system under the mixed uncertainties than the ones obtained separately from the probability theory and the uncertainty theory.     

COMPUTER AIDED OPTIMUM STRUCTURAL DESIGN UNDER UNCERTAINTY

This paper is concerned with the incorporation of different forms of imprecision and uncertainty into the formulation and solution of optimum structural design problems. The statistical uncertainties inherent in material properties and structural loadings are modelled by a first-order second-moment reliability approach. Resulting constraint boundaries are then treated as “soft” boundary regions in which the non-statistical uncertainties, and quality aspects of design and construction are handled by fuzzy set operations.     

Multidimensional parallelepiped model—a new type of non-probabilistic convex model for structural uncertainty analysis

Non-probabilistic convex models need to be provided only the changing boundary of parameters rather than their exact probability distributions; thus, such models can be applied to uncertainty analysis of complex structures when experimental information is lacking. The interval and the ellipsoidal models are the two most commonly used modeling methods in the field of non-probabilistic convex modeling. However, the former can only deal with independent variables, while the latter can only deal with dependent variables. This paper presents a more general non-probabilistic convex model, the multidimensional parallelepiped model. This model can include the independent and dependent uncertain variables in a unified framework and can effectively deal with complex ‘multi-source uncertainty’ problems in which dependent variables and independent variables coexist. For any two parameters, the concepts of the correlation angle and the correlation coefficient are defined. Through the marginal intervals of all the parameters and also their correlation coefficients, a multidimensional parallelepiped can easily be built as the uncertainty domain for parameters. Through the introduction of affine coordinates, the parallelepiped model in the original parameter space is converted to an interval model in the affine space, thus greatly facilitating subsequent structural uncertainty analysis. The parallelepiped model is applied to structural uncertainty propagation analysis, and the response interval of the structure is obtained in the case of uncertain initial parameters. Finally, the method described in this paper was applied to several numerical examples. Copyright © 2015 John Wiley & Sons, Ltd.     

The interval estimation of reliability for probabilistic and non-probabilistic hybrid structural system

The aim of this paper is to improve evaluation of the reliability of probabilistic and non-probabilistic hybrid structural system. Based on the probabilistic reliability model and interval arithmetic, a new model of interval estimation for reliability of the hybrid structural system was proposed. Adequately considering all uncertainties affecting the hybrid structural system, the lower and upper bounds of reliability for the hybrid structural system were obtained through the probabilistic and non-probabilistic analysis. In the process of non-probabilistic analysis, the interval truncation method was used. In addition, a recognition method of the main failure modes in the hybrid structural system was presented. A five-bar statically indeterminate truss structure and an intermediate complexity wing structure were used to demonstrate the new model is more suitable for analysis and design of these structural systems in comparison with the probabilistic model. The results also show that the method of recognition of main failure modes is effective. In addition, range obtained through interval estimation is shown to be more credible than certain results of other reliability models.