Novel reliability-based optimization method for thermal structure with hybrid random,interval and fuzzy parameters

In this paper, novel reliability-based optimization model and method are proposed for thermal structure design with random, interval and fuzzy uncertainties in material properties, external loads and boundary conditions. Random variables are used to quantify the probabilistic uncertainty with sufficient sample data; whereas, interval variables and fuzzy variables are adopted to model the non-probabilistic uncertainty associated with objective limited information and subjective expert opinions, respectively. Using the interval ranking strategy, the level-cut limit state function is precisely quantified to represent the safety state. The eventual safety possibility is derived based on multiple integral, where the cut levels of different fuzzy variables are considered to be independent. Then a hybrid reliability-based optimization model is established with considerable computational cost caused by three-layer nested loop. To improve the computational efficiency, a subinterval vertex method is presented to replace the inner-loop and middle-loop. Comparing numerical results with traditional reliability model, a mono-objective example and a multi-objective example are provided to demonstrate the feasibility of proposed method for hybrid reliability analysis and optimization in practical engineering.     

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.     

Una formulación de mínimo peso con restricciones en tensión en optimización topológica de estructuras

Optimum design of structures has been traditionally focused on the analysis of shape and dimensions optimization problems. However, more recently a new discipline has emerged: the topology optimization of the structures. This discipline states innovative models that allow to obtain optimal solutions without a previous definition of the type of structure being considered. These formulations obtain the optimal topology and the optimal shape and size of the resulting elements. The most usual formulations of the topology optimization problem try to obtain the structure of maximum stiffness. These approaches maximize the stiffness for a given amount of material to be used. These formulations have been widely analyzed and applied in engineering but they present considerable drawbacks from a numerical and from a practical point of view. In this paper the author propose a different formulation, as an alternative to maximum stiffness approaches, that minimizes the weight and includes stress constraints. The advantages of this kind of formulations are crucial since the cost of the structure is minimized, which is the most frequent objective in engineering, and they guarantee the structural feasibility since stresses are constrained. In addition, this approach allows to avoid some of the drawbacks and numerical instabilities related to maximum stiffness approaches. Finally, some practical examples have been solved in order to verify the validity of the results obtained and the advantages of the proposed formulation.     

Super parametric convex model and its application for non-probabilistic reliability-based design optimization

In this study, we attempt to propose a new super parametric convex model by giving the mathematical definition, in which an effective minimum volume method is constructed to give a reasonable enveloping of limited experimental samples by selecting a proper super parameter. Two novel reliability calculation algorithms, including nominal value method and advanced nominal value method, are proposed to evaluate the non-probabilistic reliability index. To investigate the influence of non-probabilistic convex model type on non-probabilistic reliability-based design optimization, an effective approach based on advanced nominal value method is further developed. Four examples, including two numerical examples and two engineering applications, are tested to demonstrate the superiority of the proposed non-probabilistic reliability analysis and optimization technique.     

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.     

Determination of critical chain project buffer based on information flow interactions

It has been well accepted in the literature that co-dependency between project activity durations is caused by resource tightness and network complexity. However, we show that information flow interaction between activities is the key factor for it. Based on whether there exist spliced relationships between activities, we introduce the concept of rework safety time. We propose a method to compute the rework safety time using the information output and input time factors, rework probability matrix, and rework impact matrix. We achieve the optimization of the critical chain sequencing via the design structure matrix so that the dependency between activities is reduced. The project buffer is then determined by the tail concentration method based on the optimized chain. The empirical results show that, as opposed to the traditional RSEM method, our approach improves the project buffer consumption rate, shortens project duration, reduces project cost, and increases project on-time completion rate.     

具有强非线性状态方程的机械零部件可靠性灵敏度分析方法

基于可靠性灵敏度设计的随机摄动技术,结合可靠性分析的矩方法、矩阵微分理论和Kronecker代数的相关理论,讨论了实际中存在着高度非线性极限状态方程结构的可靠性灵敏度问题．在已知随机变量前4阶矩的前提下,对基于摄动法的可靠性灵敏度计算方法进行了修正,提出了具有高度非线性结构的可靠性灵敏度计算方法．并结合实例证明了采用此方法大大提高了可靠性灵敏度的计算精度,并为工程实际提供了更加可信的理论依据．     

Better Optimization of Nonlinear Uncertain Systems (BONUS) for Vehicle Structural Design

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Nonlinear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties.     

Partitioned Random Search for Global Optimization with Sampling Cost and Discounting Factor

The method of partitioned random search has been proposed in recent years to obtain an as good as possible solution for the global optimization problem (1). A practical algorithm has been developed and applied to real-life problems. However, the design of this algorithm was based mainly on intuition. The theoretical foundation of the method is an important issue in the development of efficient algorithms for such problems. In this paper, we generalize previous theoretical results and propose a sequential sampling policy for the partitioned random search for global optimization with sampling cost and discounting factor. A proof of the optimality of the proposed sequential sampling policy is given by using the theory of optimal stopping.     

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.     

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.     

A hybrid self-adjusted mean value method for reliability-based design optimization using sufficient descent condition

Due to the efficiency and simplicity, advanced mean value (AMV) method is widely used to evaluate the probabilistic constraints in reliability-based design optimization (RBDO) problems. However, it may produce unstable results as periodic and chaos solutions for highly nonlinear performance functions. In this paper, the AMV is modified based on a self-adaptive step size, named as the self-adjusted mean value (SMV) method, where the step size for reliability analysis is adjusted based on a power function dynamically. Then, a hybrid self-adjusted mean value (HSMV) method is developed to enhance the robustness and efficiency of iterative scheme in the reliability loop, where the AMV is combined with the SMV on the basis of sufficient descent condition. Finally, the proposed methods (i.e. SMV and HSMV) are compared with other existing performance measure approaches through several nonlinear mathematical/structural examples. Results show that the SMV and HSMV are more efficient with enhanced robustness for both convex and concave performance functions.     

Coupled Aerostructural Design Optimization Using the Kriging Model and Integrated Multiobjective Optimization Algorithm

The paper develops and implements a highly applicable framework for the computation of coupled aerostructural design optimization. The multidisciplinary aerostructural design optimization is carried out and validated for a tested wing and can be easily extended to complex and practical design problems. To make the framework practical, the study utilizes a high-fidelity fluid/structure interface and robust optimization algorithms for an accurate determination of the design with the best performance. The aerodynamic and structural performance measures, including the lift coefficient, the drag coefficient, Von-Mises stress and the weight of wing, are precisely computed through the static aeroelastic analyses of various candidate wings. Based on these calculated performance, the design system can be approximated by using a Kriging interpolative model. To improve the design evenly for aerodynamic and structure performance, an automatic design method that determines appropriate weighting factors is developed. Multidisciplinary aerostructural design is, therefore, desirable and practical. The authors acknowledge the support of a Korea Research Foundation Grant funded by the Korean Government and the second stage of Brain Korea 21st project.     

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.     

Branch and bound algorithm for multidimensional scaling with city-block metric

A two level global optimization algorithm for multidimensional scaling (MDS) with city-block metric is proposed. The piecewise quadratic structure of the objective function is employed. At the upper level a combinatorial global optimization problem is solved by means of branch and bound method, where an objective function is defined as the minimum of a quadratic programming problem. The later is solved at the lower level by a standard quadratic programming algorithm. The proposed algorithm has been applied for auxiliary and practical problems whose global optimization counterpart was of dimensionality up to 24.     

Implementable Algorithm for Stochastic Optimization Using Sample Average Approximations

We develop an implementable algorithm for stochastic optimization problems involving probability functions. Such problems arise in the design of structural and mechanical systems. The algorithm consists of a nonlinear optimization algorithm applied to sample average approximations and a precision-adjustment rule. The sample average approximations are constructed using Monte Carlo simulations or importance sampling techniques. We prove that the algorithm converges to a solution with probability one and illustrate its use by an example involving a reliability-based optimal design.     

A new multi-objective discrete robust optimization algorithm for engineering design

This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality.     

Globally solving a nonlinear UAV task assignment problem by stochastic and deterministic optimization approaches

In this paper, we consider a task allocation model that consists of assigning a set of m unmanned aerial vehicles (UAVs) to a set of n tasks in an optimal way. The optimality is quantified by target scores. The mission is to maximize the target score while satisfying capacity constraints of both the UAVs and the tasks. This problem is known to be NP-hard. Existing algorithms are not suitable for the large scale setting. Scalability and robustness are recognized as two main issues. We deal with these issues by two optimization approaches. The first approach is the Cross-Entropy (CE) method, a generic and practical tool of stochastic optimization for solving NP-hard problem. The second one is Branch and Bound algorithm, an efficient classical tool of global deterministic optimization. The numerical results show the efficiency of our approaches, in particular the CE method for very large scale setting.     

A time-space Kriging-based sequential metamodeling approach for multi-objective crashworthiness optimization

A time-space Kriging-based sequential metamodeling approach is proposed for multi-objective crashworthiness optimization (MOCO) in this paper. By defining the novel time-space design criteria, the constructed metamodels for the optimization objectives include the characteristic mechanical responses with respect to both the structural space domain and crash time domain, compared to standard metrics with the extremum of the time history of the entire structure. The adaptive addition of new samples is performed to gradually improve the approximation accuracy during the optimization with the guidance of an adaptive weighted sum method. The effectiveness of the proposed method is demonstrated by investigating a multi-cell thin-walled crashworthiness design problem. Finally, its effectiveness in practical engineering is validated by the crashworthiness design for a vehicle under full-overlap frontal crash loadcase.