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 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.

Reliability-Based MDSDO for Co-Design of Stochastic Dynamic Systems

2019 ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Design Optimization ◽  
System Design ◽  
Design Approach ◽  
Probabilistic Constraints ◽  
Sequential Optimization ◽  
Stochastic Dynamic ◽  
Embodiment Design ◽  
And Control ◽  
The Impact ◽  
Stochastic Dynamic Systems

Abstract Optimization of dynamic engineering systems requires an integrated approach that accounts for the coupling between embodiment design and control system design, simultaneously. Generally known as combined design and control (co-design) optimization, these methods offer superior system performance and reduced costs. Despite the widespread use of co-design approaches in the literature, not much work has been done to address the issue of uncertainty in co-design problem formulations. This is problematic as all engineering models contain some level of uncertainty that might negatively affect the systems performance, if overlooked. While in our previous study we developed a robust co-design approach, a more rigorous evaluation of probabilistic constraints is required to obtain the targeted reliability levels for probabilistic constraints. Therefore, we propose and implement a novel stochastic co-design approach based on the principles of reliability-based design optimization (RBDO). In particular, a reliability-based, multidisciplinary dynamic system design optimization (RB-MDSDO) formulation is developed using the sequential optimization and reliability assessment (SORA) algorithm, such that the dynamic equality constraints are satisfied at the mean values of random variables, as well as their most probable points (MPPs). The proposed approach is then implemented for two case studies to indicate the impact of including reliability measures in co-design formulations.

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.

Design Optimization of Hierarchically Decomposed Multilevel Systems Under Uncertainty

2005 ◽  
Vol 128 (2) ◽  
pp. 503-508 ◽  
Author(s):  
Michael Kokkolaras ◽  
Zissimos P. Mourelatos ◽  
Panos Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Uncertainty Propagation ◽  
Mean Value ◽  
Multilevel Systems ◽  
System A ◽  
Engine Design ◽  
Advanced Mean Value ◽  
Mean Value Method ◽  
Propagation Technique ◽  
Bottom To Top

This paper presents a methodology for design optimization of hierarchically decomposed systems under uncertainty. We propose an extended, probabilistic version of the deterministic analytical target cascading (ATC) formulation by treating uncertain quantities as random variables and posing probabilistic design constraints. A bottom-to-top coordination strategy is used for the ATC process. Given that first-order approximations may introduce unacceptably large errors, we use a technique based on the advanced mean value method to estimate uncertainty propagation through the multilevel hierarchy of elements that comprise the decomposed system. A simple yet illustrative hierarchical bilevel engine design problem is used to demonstrate the proposed methodology. The results confirm the applicability of the proposed probabilistic ATC formulation and the accuracy of the uncertainty propagation technique.

An Efficient Approach for Reliability-Based Design Optimization Combined Sequential Optimization with Approximate Models

2018 ◽  
Vol 15 (04) ◽  
pp. 1850018 ◽  
Author(s):  
Bao Quoc Doan ◽  
Guiping Liu ◽  
Can Xu ◽  
Minh Quang Chau
Keyword(s):  
Design Optimization ◽  
Reliability Assessment ◽  
Latin Hypercube Sampling ◽  
Assessment Method ◽  
Probabilistic Constraints ◽  
Sequential Optimization ◽  
Reliability Based Design Optimization ◽  
Efficient Approach ◽  
Reliability Based Design ◽  
Approximate Models

Reliability-based design optimization (RBDO) involves evaluation of probabilistic constraints which can be time-consuming in engineering structural design problems. In this paper, an efficient approach combined sequential optimization with approximate models is suggested for RBDO. The radial basis functions and Latin hypercube sampling are used to construct approximate models of the probabilistic constraints. Then, a sequential optimization with approximate models is carried out by the sequential optimization and reliability assessment method which includes a serial of cycles of deterministic optimization and reliability assessment. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

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.

Sign in / Sign up

Export Citation Format

Share Document