An efficient strategy for reliability-based multidisciplinary design optimization of twin-web disk with non-probabilistic model

The twin-web disk holds big promise for increasing efficiency of the aircraft engine. Its reliability-based multidisciplinary design optimization involves several disciplines including fluid mechanics, heat transfer, structural strength, and vibration. The solution to this optimization problem requires three-loop calculations including loops for optimization, reliability, and interdisciplinary consistence often making its computational cost unacceptably high. The lack of sufficient amount of probabilistic data, especially for this brand-new turbine disk, makes matters worse. In this paper, the non-probabilistic uncertain variables are described by an evidence theory-based fuzzy set method, which we extend to general structure of uncertain data. We also propose two modifications of the active learning kriging model: one of them for the purpose of optimization with respect to the distance from the optimum point and another one for the purpose of assessing reliability by introducing the importance concept. Applications of these two modifications are demonstrated in this paper. Finally, a multi-adaptive learning kriging strategy for non-probabilistic reliability-based multidisciplinary design optimization of twin-web disk is proposed to improve its power efficiency and reliability in a computationally effective way.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.     

结构的失效可能度及模糊概率计算方法

依据模糊可能性理论,系统地建立含模糊变量时结构的可靠性计算模型。旨在解决模糊结构、模糊-随机结构和模糊状态假设下结构的可靠性计算问题。所建模型可给出模糊结构失效的可能度和模糊-随机结构失效概率的可能性分布。研究表明:对同时含模糊变量和随机变量的混合可靠性计算问题,把失效概率(或可靠度)作为模糊变量,能更客观地反映系统的安全状况。算例分析说明了文中方法的合理性和有效性。     

A new hybrid uncertainty optimization method for structures using orthogonal series expansion

This paper proposes a new hybrid uncertain design optimization method for structures which contain both random and interval variables simultaneously. The optimization model is formulated with the feasible robustness and the reliability of the worst scenario. The hybrid uncertainty is quantified by using the orthogonal series expansion method that integrates the Polynomial Chaos (PC) expansion method and the Chebyshev interval method within a uniform framework. The design sensitivity of objective and constraints will be developed to greatly facilitate the use of gradient-based optimization algorithms. The numerical results show that this method will be more possible to seek the feasible solution.     

A new nonlinear interval programming method for uncertain problems with dependent interval variables

This paper proposes a new nonlinear interval programming method that can be used to handle uncertain optimization problems when there are dependencies among the interval variables. The uncertain domain is modeled using a multidimensional parallelepiped interval model. The model depicts single-variable uncertainty using a marginal interval and depicts the degree of dependencies among the interval variables using correlation angles and correlation coefficients. Based on the order relation of interval and the possibility degree of interval, the uncertain optimization problem is converted to a deterministic two-layer nesting optimization problem. The affine coordinate is then introduced to convert the uncertain domain of a multidimensional parallelepiped interval model to a standard interval uncertain domain. A highly efficient iterative algorithm is formulated to generate an efficient solution for the multi-layer nesting optimization problem after the conversion. Three computational examples are given to verify the effectiveness of the proposed method.     

结构模糊可靠性分析方法的泛灰求解

针对现有的基于区间求解结构模糊可靠度方法的缺陷,提出了一种新的求解结构模糊可靠度方法.该方法利用泛灰数描述与结构基本变量概率分布相关的不确定参数,并将这些泛灰数引入到结构模糊可靠度计算中,得出了较为精确的结构可靠度计算结果.数值算例表明,该方法得到的结构可靠度区间更窄,实现了利用较少的信息量得到较精确的可靠度计算结果,相比传统的结构模糊可靠度计算方法能提供更多、更精确的关于结构安全程度的有用信息.     

New simulation-based frameworks for multi-objective reliability-based design optimization of structures

In the present study, two new simulation-based frameworks are proposed for multi-objective reliability-based design optimization (MORBDO). The first is based on hybrid non-dominated sorting weighted simulation method (NSWSM) in conjunction with iterative local searches that is efficient for continuous MORBDO problems. According to NSWSM, uniform samples are generated within the design space and, then, the set of feasible samples are separated. Thereafter, the non-dominated sorting operator is employed to extract the approximated Pareto front. The iterative local sample generation is then performed in order to enhance the accuracy, diversity, and increase the extent of non-dominated solutions. In the second framework, a pseudo-double loop algorithm is presented based on hybrid weighted simulation method (WSM) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that is efficient for problems including both discrete and continuous variables. According to hybrid WSM-NSGA-II, proper non-dominated solutions are produced in each generation of NSGA-II and, subsequently, WSM evaluates the reliability level of each candidate solution until the algorithm converges to the true Pareto solutions. The valuable characteristic of presented approaches is that only one simulation run is required for WSM during entire optimization process, even if solutions for different levels of reliability be desired. Illustrative examples indicate that NSWSM with the proposed local search strategy is more efficient for small dimension continuous problems. However, WSM-NSGA-II outperforms NSWSM in terms of solutions quality and computational efficiency, specifically for discrete MORBDOs. Employing global optimizer in WSM-NSGA-II provided more accurate results with lower samples than NSWSM.     

不确定环境下服务资源配置优化

在服务资源配置过程中,按维修状态将服务对象分成三类:完全维修、部分维修及最小维修.由于维修服务的特性,服务成本与服务时间均是一个模糊数,并且维修对象在服务时所处的维修状态也具有一定的不确定性.针对这类情况下的服务资源配置问题,提出了同时考虑模糊服务成本和模糊服务时间及不确定维修状态的最小化服务成本为优化指标的服务资源配置模型.在模型的求解过程中采用多粒子群算法,通过仿真计算表明了该方法的可行性和有效性.     

Time-dependent reliability-based design optimization with probabilistic and interval uncertainties

Time-dependent reliability-based design optimization with both probabilistic and interval uncertainties is a cost-consuming problem in engineering practice which generally needs huge computational burden. In order to deal with this issue, a sequential single-loop optimization strategy is established in this work. The established sequential single-loop optimization strategy converts the original triple-loop optimization into a sequence of deterministic optimization, the estimations of time instant and interval value that corresponding to the worst case scenario, and the minimum performance target point searching. Two key points in the sequential single-loop optimization strategy guarantee the high efficiency of the proposed strategy. One is that no iterative searching step is needed to find the minimum performance target point at each iteration in the proposed sequential single-loop optimization strategy. The other is that only the correction step needs the reliability analysis to correct the design parameter solutions. In the example section, four minimum performance target point searching techniques are combined with the sequential single-loop optimization strategy to solve the corresponding optimization problems so to illustrate the effectiveness of the established strategy.     

模糊运算和模糊有限元静力控制方程的求解

根据模糊数的区间形式表达和区间运算的性质,给出了模糊数和模糊变量的运算规则.据此并依据区间有限元理论,提出了结构模糊有限元静力控制方程的几种求解方法.方法可根据输入模糊数的隶属函数,给出结构响应量的可能性分布.且计算量小,易于实施.算例分析说明了方法是实用和可行的.     

Structural reliability analysis with imprecise random and interval fields

This paper investigates the issue of reliability assessment for engineering structures involving mixture of stochastic and non-stochastic uncertain parameters through the Finite Element Method (FEM). Non-deterministic system inputs modelled by both imprecise random and interval fields have been incorporated, so the applicability of the structural reliability analysis scheme can be further promoted to satisfy the intricate demand of modern engineering application. The concept of robust structural reliability profile for systems involving hybrid uncertainties is discussed, and then a new computational scheme, namely the unified interval stochastic reliability sampling (UISRS) approach, is proposed for assessing the safety of engineering structures. The proposed method provides a robust semi-sampling scheme for assessing the safety of engineering structures involving multiple imprecise random fields with various distribution types and interval fields simultaneously. Various aspects of structural reliability analysis with multiple imprecise random and interval fields are explored, and some theoretically instructive remarks are also reported herein.     

基于模糊熵-熵权法的混合多属性决策方法

针对决策信息以区间数、直觉模糊数和语言变量给出的混合多属性决策问题,提出了基于模糊熵-熵权法的混合多属性决策方法。通过规范化的方法把区间数转化为直觉模糊数,建立了直觉模糊数与语言变量的对应关系,把混合多属性决策信息统一在同一决策框架下;然后利用熵权法确定属性的客观权重区间,通过求解属性信息模糊熵最小的线性规划模型得到属性客观权重;再与主观赋权方法相结合确定属性的组合权重;最后应用相对熵排序法得到方案的最终排序结果。算例分析表明方法的可行性和实用性。     

A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets

Soft set theory, originally proposed by Molodtsov, has become an effective mathematical tool to deal with uncertainty. A type-2 fuzzy set, which is characterized by a fuzzy membership function, can provide us with more degrees of freedom to represent the uncertainty and the vagueness of the real world. Interval type-2 fuzzy sets are the most widely used type-2 fuzzy sets. In this paper, we first introduce the concept of trapezoidal interval type-2 fuzzy numbers and present some arithmetic operations between them. As a special case of interval type-2 fuzzy sets, trapezoidal interval type-2 fuzzy numbers can express linguistic assessments by transforming them into numerical variables objectively. Then, by combining trapezoidal interval type-2 fuzzy sets with soft sets, we propose the notion of trapezoidal interval type-2 fuzzy soft sets. Furthermore, some operations on trapezoidal interval type-2 fuzzy soft sets are defined and their properties are investigated. Finally, by using trapezoidal interval type-2 fuzzy soft sets, we propose a novel approach to multi attribute group decision making under interval type-2 fuzzy environment. A numerical example is given to illustrate the feasibility and effectiveness of the proposed method.     

A novel reliability-based design method based on quantile-based first-order second-moment

Quantile-based first-order second-moment method is a novel reliability analysis method proposed by the authors that is with simplicity and efficiency close to the first-order second-moment method, yet with accuracy and robustness close to the first-order reliability method. However, the quantile-based first-order second-moment method is inefficient if directly applied to reliability-based design. The novelty of the current short communication is to re-formulate the quantile-based first-order second-moment method into inverse-reliability methods to enhance computational efficiency. Two inverse-reliability methods are proposed based on this re-formulation. One is more practical, whereas the other is often more efficient. The effectiveness of the proposed methods is illustrated by a friction pile design example and a spread footing design example for the National Geotechnical Experimentation Sites at Texas A&M University.     

Bivariate interval semi-infinite programming with an application to environmental decision-making analysis

This paper proposed a bivariate interval semi-infinite linear programming (BV-ISIP) method to address a type decision-making problem where various uncertainties exist in functional relations and parameter uncertainty. The performance of the method is also demonstrated via an illustrative example and an environmental decision-making problem. As BV-ISIP guarantees that each of the constraints is satisfied under all possible levels of independent variables, the system-failure risk can be reduced. The BV-ISIP solutions can be more robust to the variation of coefficients associated with independent variables than the ILP ones. Other features of BV-ISIP are as follows: (i) flexible decision-making schemes can be developed for decision makers in terms of the BV-ISIP solutions; (ii) BV-ISIP can conveniently be applied to many large-scale optimization problems as no significantly-increased computational costs are required; (iii) the method can easily be improved for addressing functional intervals associated with multiple independent variables.     

应急物资预置问题的可信性优化模型

基于可信性理论,研究了多受灾点、多出救点、多物资的应急设备选址和物资预置问题.考虑到运输费用、出救点的供应量、受灾点的需求量和道路容量的不确定性,用模糊变量来刻画,建立了模糊环境下应急物资预置的可信性优化模型以最小化期望总费用.当模型中的模糊变量相互独立且服从三角分布时,推导了总费用目标及服务质量和弧容量约束的解析表达式,从而将原模型转化为等价的确定模型.鉴于等价模型是一个混合整数规划,可采用Lingo软件编程求解.最后,数值算例演示所提建模思想.实验结果说明了所建模型的有效性.     

Structural design optimization based on hybrid time-variant reliability measure under non-probabilistic convex uncertainties

Structural safety assessment issue, considering the influence of uncertain factors, is widely concerned currently. However, uncertain parameters present time-variant characteristics during the entire structural design procedure. Considering materials aging, loads varying and damage accumulation, the current reliability-based design optimization (RBDO) strategy that combines the static/time-invariant assumption with the random theory will be inapplicable when tackling with the optimal design issues for lifecycle mechanical problems. In light of this, a new study on non-probabilistic time-dependent reliability assessment and design under time-variant and time-invariant convex mixed variables is investigated in this paper. The hybrid reliability measure is first given by the first-passage methodology, and the solution aspects should depend on the regulation treatment and the convex theorem. To guarantee the rationality and efficiency of the optimization task, the improved GA algorithm is involved. Two numerical examples are discussed to demonstrate the validity and usage of the presented methodology.     

Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation

The paper is concerned with a hybrid optimization of fuzzy inference systems based on hierarchical fair competition-based parallel genetic algorithms (HFCGA) and information granulation. The process of information granulation is realized with the aid of the C-Means clustering. HFCGA being a multi-population based parallel genetic algorithms (PGA) is exploited here to realize structure optimization and carry out parameter estimation of the fuzzy models. The HFCGA becomes helpful in the context of fuzzy models as it restricts a premature convergence encountered quite often in optimization problems. It concerns a set of parameters of the model including among others the number of input variables to be used, a specific subset of input variables, and the number of membership functions. In the hybrid optimization process, two general optimization mechanisms are explored. The structural development of the fuzzy model is realized via the HFCGA optimization and C-Means, whereas to deal with the parametric optimization we proceed with a standard least square method and the use of the HFCGA technique. A suite of comparative studies demonstrates that the proposed algorithm leads to the models whose performance is superior in comparison with some other constructs commonly used in fuzzy modeling.     

Decoupled reliability-based geotechnical design of deep excavations of soil with spatial variability

This paper presents a general decoupled method for reliability-based geotechnical design that takes into account the spatial variability of soil properties. In this method, reliability analyses that require a lot of computational resources are decoupled from the optimization procedure by approximating the failure probability function globally. Failure samples are iteratively generated over the entire design space so that their global distribution information can be extracted to construct the failure probability function. The method is computationally efficient, is flexible to implement, and is well suited for geotechnical problems that may involve sophisticated models. A design example of two-dimensional deep excavation against basal heave is discussed for Singapore marine clay where the density and normalized undrained shear strength of soil mass are modeled as random fields. Results demonstrate that the proposed method works well in practice and is advantageous over the coupled or locally decoupled reliability-based geotechnical design methods.     

Efficient strategy for reliability-based optimization design of multidisciplinary coupled system with interval parameters

Non-probabilistic reliability based multidisciplinary design optimization has been widely acknowledged as an advanced methodology for complex system design when the data is insufficient. In this work, the uncertainty propagation analysis method in multidisciplinary system based on subinterval theory is firstly studied to obtain the uncertain responses. Then, based on the non-probabilistic set theory, the interval reliability based multidisciplinary design optimization model is established. Considering that the gradient information of interval reliability cannot be acquired in the whole design domain, which causes convergence difficulties and prohibitive computation, an interval reliability displacement based multidisciplinary design optimization method is proposed to address the issue. In the proposed method, the interval reliability displacement is introduced to measure the degree of interval reliability. By doing so, not only the connotation of the interval reliability is guaranteed, but more importantly, the partial gradient region for interval reliability is equivalently converted into full gradient region for reliability displacement. Consequently, the gradient information can be acquired under any circumstances and thus the convergence process is highly accelerated by utilizing the gradient optimization algorithms.