scholarly journals Reliability-Based Multidisciplinary Design Optimization under Correlated Uncertainties

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Huanwei Xu ◽  
Xin Wang ◽  
Wei Li ◽  
Mufeng Li ◽  
Suichuan Zhang ◽  
...  
Keyword(s):  
Design Optimization ◽  
Set Theory ◽  
Multidisciplinary Design Optimization ◽  
Reliability Assessment ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
Engineering System ◽  
First Order ◽  
Reliability Method

Complex mechanical system is usually composed of several subsystems, which are often coupled with each other. Reliability-based multidisciplinary design optimization (RBMDO) is an efficient method to design such complex system under uncertainties. However, the present RBMDO methods ignored the correlations between uncertainties. In this paper, through combining the ellipsoidal set theory and first-order reliability method (FORM) for multidisciplinary design optimization (MDO), characteristics of correlated uncertainties are investigated. Furthermore, to improve computational efficiency, the sequential optimization and reliability assessment (SORA) strategy is utilized to obtain the optimization result. Both a mathematical example and a case study of an engineering system are provided to illustrate the feasibility and validity of the proposed method.

Possibility-Based Multidisciplinary Design Optimization in the Framework of Sequential Optimization and Reliability Assessment

2009 ◽  
Author(s):  
Xudong Zhang ◽  
Hong-Zhong Huang ◽  
Shengkui Zeng ◽  
Zhili Wang
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Multidisciplinary Design Optimization ◽  
High Reliability ◽  
Reliability Assessment ◽  
Coupled Systems ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
Sufficient Information ◽  
Early Design

Reliability Based Multidisciplinary Design Optimization (RBMDO) has received increasing attention to reach high reliability and safety in complex and coupled systems. In early design of such systems, however, information is often not sufficient to construct the precise probabilistic distributions required by the RBMDO and consequently RBMDO can not be carried out effectively. The present work proposes a method of Possibility Based Multidisciplinary Design Optimization (PBMDO) within the framework of the Sequential Optimization and Reliability Assessment (PBMDO-SORA). The proposed method enables designers to solve MDO problems without sufficient information on the uncertainties associated with variables, and also to efficiently decrease the computational demand. The efficiency of the proposed method is illustrated with an engineering design.

Reliability-Based Multidisciplinary Design Optimization Using Subset Simulation Analysis and Its Application in the Hydraulic Transmission Mechanism Design

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Debiao Meng ◽  
Yan-Feng Li ◽  
Hong-Zhong Huang ◽  
Zhonglai Wang ◽  
Yu Liu
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Simulation Analysis ◽  
High Reliability ◽  
Reliability Evaluation ◽  
Transmission Mechanism ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
Subset Simulation ◽  
Small Failure Probability

The Monte Carlo simulation (MCS) can provide high reliability evaluation accuracy. However, the efficiency of the crude MCS is quite low, in large part because it is computationally expensive to evaluate a very small failure probability. In this paper, a subset simulation-based reliability analysis (SSRA) approach is combined with multidisciplinary design optimization (MDO) to improve the computational efficiency in reliability-based MDO (RBMDO) problems. Furthermore, the sequential optimization and reliability assessment (SORA) approach is utilized to decouple an RBMDO problem into a sequential of deterministic MDO and reliability evaluation problems. The formula of MDO with SSRA within the framework of SORA is proposed to solve a design optimization problem of a hydraulic transmission mechanism.

Reliability-Based Multidisciplinary Design Optimization Using Subset Simulation Analysis and its Application in the Hydraulic Transmission Mechanism Design

2014 ◽  
Author(s):  
Debiao Meng ◽  
Hong-Zhong Huang ◽  
Zhonglai Wang ◽  
Xiaoling Zhang ◽  
Yu Liu
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Computational Efficiency ◽  
Multidisciplinary Design Optimization ◽  
Simulation Analysis ◽  
High Reliability ◽  
Transmission Mechanism ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
Subset Simulation

The traditional Monte Carlo Simulation (MCS) approach can provide high reliability analysis accuracy, however, with low computational efficiency. Especially, it is computationally expensive to evaluate a very small failure probability. In this paper, a Subset Simulation-based Reliability Analysis (SSRA) approach is combined with the Multidisciplinary Design Optimization (MDO) to improve the computational efficiency in the Reliability based Multidisciplinary Design Optimization (RBMDO) problems. Furthermore, the Sequential Optimization and Reliability Assessment (SORA) approach is utilized to decouple the RBMDO into MDO and reliability analysis. The formula of MDO with SSRA within the framework of SORA (MDO-SSRA-SORA) is proposed to solve the design optimization problem of hydraulic transmission mechanism.

Uncertainty-Based Multidisciplinary Design Optimization for Feedback-Coupled Systems Under Both Parametric and Metamodeling Uncertainties

2020 ◽  
Author(s):  
Zhao Liu ◽  
Zhouzhou Song ◽  
Ping Zhu ◽  
Can Xu
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Simulation Models ◽  
Coupled Systems ◽  
Multidisciplinary Design ◽  
Engineering System ◽  
Optimization Approach ◽  
Satellite Design ◽  
Consistency Constraints

Abstract Uncertainty-based multidisciplinary design optimization (UMDO) is an effective methodology to deal with uncertainties in the engineering system design. In order to shorten the design cycle and improve the design efficiency, the time-consuming computer simulation models are often replaced by metamodels, which consequently introduces metamodeling uncertainty into the UMDO procedure. The optimal solutions may deviate from the true results or even become infeasible if the metamodeling uncertainty is neglected. However, it is difficult to quantify and propagate the metamodeling uncertainty, especially in the UMDO process with feedback-coupled systems since the interdisciplinary consistency needs to be satisfied. In this paper, a new approach is proposed to solve the UMDO problem for the feedback-coupled systems under both parametric and metamodeling uncertainties. This approach adopts the decoupled formulation and it applies the Kriging technique to quantify the metamodeling uncertainty. The polynomial chaos expansion (PCE) technique is applied to propagate the two types of uncertainties and represent the interdisciplinary consistency constraints. In the optimization approach, the proposed method uses the iterative construction of PCE models for response means and variances to satisfy the multidisciplinary consistency at the optimal solution. The proposed approach is verified by a mathematical example and applied to the fire satellite design. The results demonstrate the proposed approach can solve the UMDO problem for coupled systems accurately and efficiently.

Multidisciplinary design optimization of a gear train transmission

2019 ◽  
Vol 27 (3) ◽  
pp. 268-281 ◽  
Author(s):  
Brahim Mahiddini ◽  
Taha Chettibi ◽  
Khaled Benfriha ◽  
Amézian Aoussat
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Integrated Design ◽  
Population Based ◽  
Gear Train ◽  
Multidisciplinary Design ◽  
Customer Preference ◽  
Integrated Design Process ◽  
Pareto Optimal Fronts

This article presents a method for multidisciplinary design optimization of a one-stage gear train transmission for an industrial application. The formulation and implementation that enable the integrated design of the gearbox elements (gears, shafts, and bearings) are detailed. The analytical formulation problem is based on four disciplines: product reliability, customer preference, product cost, and structure. The proposed integrated design process takes into account constraints imposed by quality standards. The optimization of the gear train transmission is performed according to a multidisciplinary feasible architecture and uses a population-based evolutionary algorithm (non-dominated sorting genetic algorithm II) to generate Pareto-optimal fronts. Finally, a detailed case study is presented to illustrate the effectiveness of the proposed approach.

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