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Jongmin Lim Byungchai Lee Ikjin Lee 《International journal for numerical methods in engineering》2014,100(10):773-792
First‐order reliability method (FORM) has been mostly utilized for solving reliability‐based design optimization (RBDO) problems efficiently. However, second‐order reliability method (SORM) is required in order to estimate a probability of failure accurately in highly nonlinear performance functions. Despite accuracy of SORM, its application to RBDO is quite challenging due to unaffordable numerical burden incurred by a Hessian calculation. For reducing the numerical efforts, a quasi‐Newton approach to approximate the Hessian is introduced in this study instead of calculating the true Hessian. The proposed SORM with the approximated Hessian requires computations only used in FORM, leading to very efficient and accurate reliability analysis. The proposed SORM also utilizes a generalized chi‐squared distribution in order to achieve better accuracy. Furthermore, SORM‐based inverse reliability method is proposed in this study. An accurate reliability index corresponding to a target probability of failure is updated using the proposed SORM. Two approaches in terms of finding an accurate most probable point using the updated reliability index are proposed. The proposed SORM‐based inverse analysis is then extended to RBDO in order to obtain a reliability‐based optimum design satisfying probabilistic constraints more accurately even for a highly nonlinear system. The numerical study results show that the proposed reliability analysis and RBDO achieve efficiency of FORM and accuracy of SORM at the same time. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Reliability analysis may involve random variables and interval variables. In addition, some of the random variables may have interval distribution parameters owing to limited information. This kind of uncertainty is called second order uncertainty. This article develops an efficient reliability method for problems involving the three aforementioned types of uncertain input variables. The analysis produces the maximum and minimum reliability and is computationally demanding because two loops are needed: a reliability analysis loop with respect to random variables and an interval analysis loop for extreme responses with respect to interval variables. The first order reliability method and nonlinear optimization are used for the two loops, respectively. For computational efficiency, the two loops are combined into a single loop by treating the Karush–Kuhn–Tucker (KKT) optimal conditions of the interval analysis as constraints. Three examples are presented to demonstrate the proposed method. 相似文献
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提出一种有效的求解结构最小质量设计,同时满足动位移和动应力约束的二阶优化设计方法。在有限元法和纽马克法基础上导出一种高效的动应力、动位移对设计变量一阶导数和二阶导数的算法。建立含时间参数,以结构质量最小化为目标,同时满足动位移、动应力和设计变量约束的优化数学模型,通过积分型内点罚函数将含时间参数的不等式约束优化问题转变为一系列不含时间参数的无约束优化问题。利用动位移、动应力对设计变量一阶导数和二阶导数的信息计算内点罚函数的梯度和海森矩阵,利用梯度和海森矩阵构造求解优化设计问题高效有效的二阶优化算法。算例结果表明该文的优化设计方法能获得刚架结构的局部最优设计,优化的效率高于增广拉格朗日乘子法。 相似文献
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该文提出一种求解不确定性结构模态的二阶区间优化算法,首先应用拉格朗日乘子法将带有约束条件的模态优化问题转化为非约束优化,再用区间扩展的二阶泰勒展开式近似表述不确定性结构的模态区间函数。由于其二阶常数项(海森矩阵)的计算十分繁琐,这里采用DFP方法(Davidon and Fletcher-Powell method)近似迭代计算该常数项,同时计算满足约束条件和优化目标的结构参数和参数不确定性区间。在结构重分析中采用Epsilon算法,从而在保证计算精度的同时节省了计算时间。通过算例计算进一步证明该方法对于板壳加筋不确定结构的优化是有效的。 相似文献
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It is nowadays widely acknowledged that optimal structural design should be robust with respect to the uncertainties in loads and material parameters. However, there are several alternatives to consider such uncertainties in structural optimization problems. This paper presents a comprehensive comparison between the results of three different approaches to topology optimization under uncertain loading, considering stress constraints: (1) the robust formulation, which requires only the mean and standard deviation of stresses at each element; (2) the reliability-based formulation, which imposes a reliability constraint on computed stresses; (3) the non-probabilistic formulation, which considers a worst-case scenario for the stresses caused by uncertain loads. The information required by each method, regarding the uncertain loads, and the uncertainty propagation approach used in each case is quite different. The robust formulation requires only mean and standard deviation of uncertain loads; stresses are computed via a first-order perturbation approach. The reliability-based formulation requires full probability distributions of random loads, reliability constraints are computed via a first-order performance measure approach. The non-probabilistic formulation is applicable for bounded uncertain loads; only lower and upper bounds are used, and worst-case stresses are computed via a nested optimization with anti-optimization. The three approaches are quite different in the handling of uncertainties; however, the basic topology optimization framework is the same: the traditional density approach is employed for material parameterization, while the augmented Lagrangian method is employed to solve the resulting problem, in order to handle the large number of stress constraints. Results are computed for two reference problems: similarities and differences between optimized topologies obtained with the three formulations are exploited and discussed. 相似文献
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A single-loop deterministic method (SLDM) has previously been proposed for solving reliability-based design optimization (RBDO) problems. In SLDM, probabilistic constraints are converted to approximate deterministic constraints. Consequently, RBDO problems can be transformed into approximate deterministic optimization problems, and hence the computational cost of solving such problems is reduced significantly. However, SLDM is limited to continuous design variables, and the obtained solutions are often trapped into local extrema. To overcome these two disadvantages, a global single-loop deterministic approach is developed in this article, and then it is applied to solve the RBDO problems of truss structures with both continuous and discrete design variables. The proposed approach is a combination of SLDM and improved differential evolution (IDE). The IDE algorithm is an improved version of the original differential evolution (DE) algorithm with two improvements: a roulette wheel selection with stochastic acceptance and an elitist selection technique. These improvements are applied to the mutation and selection phases of DE to enhance its convergence rate and accuracy. To demonstrate the reliability, efficiency and applicability of the proposed method, three numerical examples are executed, and the obtained results are compared with those available in the literature. 相似文献
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Ikjin Lee K. K. Choi David Gorsich 《International journal for numerical methods in engineering》2010,82(1):26-46
In gradient‐based design optimization, the sensitivities of the constraint with respect to the design variables are required. In reliability‐based design optimization (RBDO), the probabilistic constraint is evaluated at the most probable point (MPP), and thus the sensitivities of the probabilistic constraints at MPP are required. This paper presents the rigorous analytic derivation of the sensitivities of the probabilistic constraint at MPP for both first‐order reliability method (FORM)‐based performance measure approach (PMA) and dimension reduction method (DRM)‐based PMA. Numerical examples are used to demonstrate that the analytic sensitivities agree very well with the sensitivities obtained from the finite difference method (FDM). However, as the sensitivity calculation at the true DRM‐based MPP requires the second‐order derivatives and additional MPP search, the sensitivity derivation at the approximated DRM‐based MPP, which does not require the second‐order derivatives and additional MPP search to find the DRM‐based MPP, is proposed in this paper. A convergence study illustrates that the sensitivity at the approximated DRM‐based MPP converges to the sensitivity at the true DRM‐based MPP as the design approaches the optimum design. Hence, the sensitivity at the approximated DRM‐based MPP is proposed to be used for the DRM‐based RBDO to enhance the efficiency of the optimization. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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《工程优选》2012,44(1):1-21
ABSTRACTProbabilistic 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. 相似文献
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Jongmin Lim Byungchai Lee Ikjin Lee 《International journal for numerical methods in engineering》2016,107(2):93-108
In this study, a post optimization technique for a correction of inaccurate optimum obtained using first‐order reliability method (FORM) is proposed for accurate reliability‐based design optimization (RBDO). In the proposed method, RBDO using FORM is first performed, and then the proposed second‐order reliability method (SORM) is performed at the optimum obtained using FORM for more accurate reliability assessment and its sensitivity analysis. In the proposed SORM, the Hessian of a performance function is approximated by reusing derivatives information accumulated during previous RBDO iterations using FORM, indicating that additional functional evaluations are not required in the proposed SORM. The proposed SORM calculates a probability of failure and its first‐order and second‐order stochastic sensitivity by applying the importance sampling to a complete second‐order Taylor series of the performance function. The proposed post optimization constructs a second‐order Taylor expansion of the probability of failure using results of the proposed SORM. Because the constructed Taylor expansion is based on the reliability method more accurate than FORM, the corrected optimum using this Taylor expansion can satisfy the target reliability more accurately. In this way, the proposed method simultaneously achieves both efficiency of FORM and accuracy of SORM. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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很多实际问题都可归结为二阶周期性线性方程,这类方程的稳定性意味着所有解皆为周期解或拟周期解,于是初值如何都对应稳定的周期解或拟周期解。在应用上就说明该实际问题总有稳定运动状态。本文应用Hill方程的一个判别式给出了一个通过简单计算即可判定二阶方程稳定性的方法。数值例子说明该方法具有很好的实用性,同时有较高的精确度。 相似文献
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This article describes a second-order shape and cross-section optimization method of plane truss subjected to earthquake excitation. The method is based on gradient and Hessian matrix calculation. First, the first and second derivatives of dynamic response with respect to design variables are calculated based on the Newmark method. Second, the inequality time-dependent constraint problem is converted into a sequence of appropriately formed unconstrained problems using the integral interior penalty function method. Then, the gradient and Hessian matrix of the integral interior penalty function are computed. Third, Marquardt's method is employed to solve the unconstrained problems. Finally, the new approach is validated through several case studies. The results show that the new optimization method is an efficient and effective approach for minimum weight design of truss structures. 相似文献
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S. Sakata F. Ashida M. Zako 《International journal for numerical methods in engineering》2007,70(6):631-654
This paper describes a new hybrid algorithm that uses a Kriging and quadratic polynomial‐based approach for approximate optimization. The Kriging method is used for generating a global approximation model, and the polynomial‐based approximation method is used for generating a local approximation model. The Kriging system is only used to construct a polynomial‐based locally approximate model by estimating some function values and Hessian components of an estimated surface. The number of Kriging estimations can be reduced in comparison with direct Kriging‐based optimization, and a local optimum solution on an approximated surface can be clearly estimated without use of an optimization procedure based on a local appropriate quadratic polynomial model. Numerical examples of engineering optimization using the proposed method illustrate validity and effectiveness of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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信息融合多传感器可信度的确定方法及应用 总被引：6，自引：0，他引：6
借鉴层次分析法相对比较的思想,提出一种确定多传感器可信度的方法．该方法基于传感器提供的信息的相对可信度,对不同目标分别建立多传感器可信度判断矩阵,求得各传感器对该目标的可信度,进而求得各传感器的综合可信度．给出了一个数值算例．根据传感器的可信度,改变径向基函数神经网络的样本空间的大小,分析了训练时间和网络融合性能,验证了方法的有效性和实用性． 相似文献
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基于Kriging代理模型的改进EGO算法研究 总被引：1，自引：0，他引：1
代理模型是复杂工程优化设计问题的关键技术之一．基于Kriging代理模型的EGO算法作为一种贝叶斯全局优化算法引入了EI函数来确定校正点,保证了算法的全局收敛性．首先针对原始EGO算法的不足之处,提出改进EGO算法．然后采用改进EGO算法对4个经典函数和1个工程算例进行测试,最后从算法的收敛速度和精度两方面将不同的算法进行比较．结果表明改进后的EGO算法达到原始EGO算法精度时所需迭代步数更少,与基于响应面的优化算法相比在收敛速度和精度方面更具有优势．说明该方法适应性强,具有很高的工程实用价值． 相似文献
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