Research Papers: Design Automation

Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

[+] Author and Article Information
Zhen Hu

Department of Industrial and Manufacturing
Systems Engineering,
University of Michigan-Dearborn,
2340 Heinz Prechter Engineering
Complex (HPEC),
Dearborn, MI 48128
e-mail: zhennhu@umich.edu

Sankaran Mahadevan

Department of Civil and Environmental
Vanderbilt University,
272 Jacobs Hall,
VU Mailbox: PMB 351831,
Nashville, TN 37235
e-mail: sankaran.mahadevan@vanderbilt.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 10, 2017; final manuscript received October 9, 2017; published online November 15, 2017. Assoc. Editor: Samy Missoum.

J. Mech. Des 140(2), 021401 (Nov 15, 2017) (13 pages) Paper No: MD-17-1331; doi: 10.1115/1.4038333 History: Received May 10, 2017; Revised October 09, 2017

Multidisciplinary systems with transient behavior under time-varying inputs and coupling variables pose significant computational challenges in reliability analysis. Surrogate models of individual disciplinary analyses could be used to mitigate the computational effort; however, the accuracy of the surrogate models is of concern, since the errors introduced by the surrogate models accumulate at each time-step of the simulation. This paper develops a framework for adaptive surrogate-based multidisciplinary analysis (MDA) of reliability over time (A-SMART). The proposed framework consists of three modules, namely, initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models from the reliability analysis perspective. Approaches are proposed to estimate the potential error of the failure probability estimate and to determine the locations of new training points. The three-level GSA method identifies the individual surrogate model for refinement. The combination of the three modules facilitates adaptive and efficient allocation of computational resources, and enables high accuracy in the reliability analysis result. The proposed framework is illustrated with two numerical examples.

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Fig. 5

Overview of the proposed A-SMART framework

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Fig. 4

Simulation of multidisciplinary system at a time instant

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Fig. 3

Illustration of multidisciplinary system with time-dependent uncertainty

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Fig. 2

Four-disciplinary analysis of a hypersonic vehicle panel

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Fig. 1

Two-disciplinary aero-elastic analysis

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Fig. 7

Uncertainty propagation in MDA over time

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Fig. 6

(a) Simulation of a multidisciplinary system with two disciplines at a time instant and (b) models of the kth discipline

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Fig. 9

A three-disciplinary system

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Fig. 8

Uncertainty propagation after introducing auxiliary variables

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Fig. 11

(a) A compound cylinder, (b) inner cylinder, and (c) outer cylinder

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Fig. 12

Multidisciplinary system of the compound cylinders

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Fig. 13

Comparison of convergence history

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Fig. 10

Comparison of the convergence history



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