A Sequential Robust Optimization Approach for Multidisciplinary Design Optimization With Uncertainty

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Tingting Xia ◽  
Mian Li ◽  
Jianhua Zhou
Keyword(s):  
Robust Optimization ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Procedure ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
Optimization Approach ◽  
Time Saving ◽  
Robust Solution ◽  
Optimization Stage

One real challenge for multidisciplinary design optimization (MDO) problems to gain a robust solution is the propagation of uncertainty from one discipline to another. Most existing methods only consider an MDO problem in the deterministic manner or find a solution which is robust for a single-disciplinary optimization problem. These rare methods for solving MDO problems under uncertainty are usually computationally expensive. This paper proposes a robust sequential MDO (RS-MDO) approach based on a sequential MDO (S-MDO) framework. First, a robust solution is obtained by giving each discipline full autonomy to perform optimization without considering other disciplines. A tolerance range is specified for each coupling variable to take care of uncertainty propagation in the coupled system. Then the obtained robust extreme points of global variables and coupling variables are dispatched into subsystems to perform robust optimization (RO) sequentially. Additional constraints are added in each subsystem to keep the consistency and to guarantee a robust solution. To find a solution with such strict constraints, genetic algorithm (GA) is used as a solver in each optimization stage. The proposed RS-MDO can save significant amount of computational efforts by using the sequential optimization procedure. Since all iterations in the sequential optimization stage can be processed in parallel, this robust MDO approach can be more time-saving. Numerical and engineering examples are provided to demonstrate the availability and effectiveness of the proposed approach.

A Sequential Robust Approach for Multi-Disciplinary Design Optimization With Uncertainty

2016 ◽  
Author(s):  
Tingting Xia ◽  
Mian Li ◽  
Jianhua Zhou
Keyword(s):  
Design Optimization ◽  
Uncertainty Propagation ◽  
Extreme Points ◽  
Coupled System ◽  
Sequential Optimization ◽  
Time Saving ◽  
Robust Solution ◽  
Propagation Of Uncertainty ◽  
Optimization Stage ◽  
Global Variable

The real challenge for multi-disciplinary design optimization (MDO) problems to gain a robust solution is the propagation of uncertainty from one discipline to another. Most existing methods only consider a MDO problem in deterministic manner or find a solution which is robust for a single-disciplinary optimization problem. These rare methods for solving MDO problems under uncertainty are usually computational expensive. This research proposes a robust sequential MDO (RS-MDO) approach based on a sequential MDO (S-MDO) framework. Firstly, a robust solution is obtained by giving each discipline full autonomy to perform optimization. Tolerance range is specified for the coupling variable to model uncertainty propagation in the original coupled system. Then the obtained robust extreme points of global variable and coupling variable are dispatched into subsystems to perform optimization sequentially. Additional constraints are added to keep consistency and guarantee a robust solution. To find a solution with such strict constraints, genetic algorithm (GA) is used as a solver in each optimization stage. Since all iterations in the sequential optimization stage can be processed in parallel, this robust MDO approach can be more time-saving. Numerical examples are provided to demonstrate the availability and effectiveness of proposed approach.

A Collaborative Approach for Multidisciplinary Systems Reliability Design and Optimization

2013 ◽  
Vol 694-697 ◽  
pp. 911-914 ◽  
Author(s):  
Jun Zhang ◽  
Bing Zhang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Procedure ◽  
Performance Measure ◽  
Multidisciplinary Design ◽  
Sequential Optimization ◽  
Optimization Strategy ◽  
Reliability Design ◽  
Design And Optimization ◽  
Subspace Optimization

In order to improve the efficiency and robustness of reliability-based multidisciplinary design optimization (RBMDO), a new collaborative strategy (named C-RBMDO) which integrates performance measure approach (PMA) and concurrent subspace optimization strategy (CSSO) is proposed. Both the mathematical model and optimization procedure are put forward. The traditional triple-level nested flowchart of RBMDO is decoupled with the sequential optimization and reliability assessment (SORA). The deterministic multidisciplinary design optimization and the multidisciplinary reliability analysis are executed by CSSO and PMA respectively. Finally, the proposed method is verified through the design example of gear transmission.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Multidisciplinary Design Optimization of a Lunar Lander’s Soft-Landing Gear

2010 ◽  
Vol 42 ◽  
pp. 118-121
Author(s):  
Yun Tong Lu ◽  
Chun Jie Wang ◽  
Ang Li ◽  
Han Wang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Rapid Development ◽  
Fem Analysis ◽  
Landing Gear ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Soft Landing ◽  
Multi Body

The rapid development of Multidisciplinary Design Optimization (MDO) approach can simultaneously guarantee the cut of cost on design and optimal performance of spacecraft. Based on the theory of Collaborative Optimization approach (CO) of MDO, present paper proposes the method of CO by integrating Pro/E(3D modeling), Patran/Nastran(FEM analysis) and ADAMS(multi-body dynamic analysis) with the Isight software. In the analysis of the soft-landing gear of Lunar Lander, this method can optimize the mass of the landing gear and meanwhile ensures the reliability of structure statics, structure dynamics and multi-body dynamics. Thus the feasibility, applied value and guideline significance of this method in spacecraft structural design are proven.

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.

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