An efficient approximation-based robust design optimization framework for large-scale structural systems

2020 ◽  
pp. 179-201
Author(s):  
Tanmoy Chatterjee ◽  
Rajib Chowdhury
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Robust Design Optimization ◽  
Structural Systems ◽  
Optimization Framework ◽  
Efficient Approximation

scholarly journals Structural optimization considering the probabilistic system response

2004 ◽  
Vol 31 (3-4) ◽  
pp. 361-394 ◽  
Author(s):  
M. Papadrakakis ◽  
N.D. Lagaros ◽  
V. Plevris
Keyword(s):  
Structural Optimization ◽  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Optimization Problem ◽  
Structural Parameters ◽  
Dominant Role ◽  
Robust Design Optimization ◽  
Design Parameters ◽  
System Response

In engineering problems, the randomness and uncertainties are inherent and the scatter of structural parameters from their nominal ideal values is unavoidable. In Reliability Based Design Optimization (RBDO) and Robust Design Optimization (RDO) the uncertainties play a dominant role in the formulation of the structural optimization problem. In an RBDO problem additional non deterministic constraint functions are considered while an RDO formulation leads to designs with a state of robustness, so that their performance is the least sensitive to the variability of the uncertain variables. In the first part of this study a metamodel assisted RBDO methodology is examined for large scale structural systems. In the second part an RDO structural problem is considered. The task of robust design optimization of structures is formulated as a multi-criteria optimization problem, in which the design variables of the optimization problem, together with other design parameters such as the modulus of elasticity and the yield stress are considered as random variables with a mean value equal to their nominal value. .

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2003 ◽  
Vol 125 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition

2016 ◽  
Author(s):  
Xuchun Ren ◽  
Sharif Rahman
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Design Sensitivity ◽  
Robust Design Optimization ◽  
Score Functions ◽  
Design Sensitivities ◽  
Sparse Polynomial ◽  
Dimensional Decomposition ◽  
Polynomial Dimensional Decomposition

This work proposes a new methodology for robust design optimization (RDO) of complex engineering systems. The method, capable of solving large-scale RDO problems, involves (1) an adaptive-sparse polynomial dimensional decomposition (AS-PDD) for stochastic moment analysis of a high-dimensional stochastic response, (2) a novel integration of score functions and AS-PDD for design sensitivity analysis, and (3) a multi-point design process, facilitating standard gradient-based optimization algorithms. Closed-form formulae are developed for first two moments and their design sensitivities. The method allow that both the stochastic moments and their design sensitivities can be concurrently determined from a single stochastic simulation or analysis. Precisely for this reason, the multi-point framework of the proposed method affords the ability of solving industrial-scale problems with large design spaces. The robust shape optimization of a three-hole bracket was accomplished, demonstrating the efficiency of the new method to solve industry-scale RDO problems.

Robust Design Optimization of Structural Systems Under Evolutionary Stochastic Seismic Excitation

2014 ◽  
Author(s):  
Ioannis P. Mitseas ◽  
Ioannis A. Kougioumtzoglou ◽  
Michael Beer ◽  
Edoardo Patelli ◽  
John E. Mottershead
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Seismic Excitation ◽  
Robust Design Optimization ◽  
Structural Systems

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2001 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

Abstract In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed approach, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Adaptive Refined-Model-Based Approach for Robust Design Optimization

2018 ◽  
pp. 19-43
Author(s):  
Tanmoy Chatterjee ◽  
Rajib Chowdhury
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Model Building ◽  
Kriging Model ◽  
Robust Design Optimization ◽  
Engineering Systems ◽  
Selection Scheme ◽  
Meta Model ◽  
Systems Implementation

Robust design optimization (RDO) has been noteworthy in realizing optimal design of engineering systems in presence of uncertainties. However, computations involved in RDO prove to be intensive for real-time applications. For addressing such issues, a meta-model-assisted RDO framework has been proposed. It has been further observed in such approximation-based RDO frameworks that accuracy of the meta-model is an important factor and even slight deviation in intermediate iterations may eventually lead to false optima. Therefore, two-tier improvement has been incorporated within existing Kriging model so as to ensure accurate approximation of response quantities. Firstly, the trend portion has been refined so that the model is capable of approximating higher order non-linearity. Secondly, a sequential basis selection scheme has been merged during model building, which reduces computational complexity significantly in case of large-scale systems. Implementation of the proposed approach in a few examples clearly illustrates its potential for further complex problems.

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