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Research Papers: Design Automation

Active Resource Allocation for Reliability Analysis With Model Bias Correction

[+] Author and Article Information
Mingyang Li

Department of Mechanical Engineering-
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: mli7@mtu.edu

Zequn Wang

Department of Mechanical Engineering-
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: zequnw@mtu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 13, 2018; final manuscript received December 16, 2018; published online January 11, 2019. Assoc. Editor: Gary Wang.

J. Mech. Des 141(5), 051403 (Jan 11, 2019) (13 pages) Paper No: MD-18-1565; doi: 10.1115/1.4042344 History: Received July 13, 2018; Revised December 16, 2018

To account for the model bias in reliability analysis, various methods have been developed to validate simulation models using precise experimental data. However, it still lacks a strategy to actively seek critical information from both sources for effective uncertainty reduction. This paper presents an active resource allocation approach (ARA) to improve the accuracy of reliability approximations while reducing the computational, and more importantly, experimental costs. In ARA, the Gaussian process (GP) modeling technique is employed to fuse both simulation and experimental data for capturing the model bias, and further predicting actual system responses. To manage the uncertainty due to the lack of data, a two-phase updating strategy is developed to improve the fidelity of GP models by actively collecting the most valuable simulation and experimental data. With the high-fidelity predictive models, sampling-based methods such as Monte Carlo simulation are used to calculate the reliability accurately while the overall costs of conducting simulations and experiments can be significantly reduced. The effectiveness of the proposed approach is demonstrated through four case studies.

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Figures

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Fig. 1

Illustration of the active resource allocation for accurate reliability analysis

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Fig. 2

Flowchart of the model bias correction using Gaussian process modeling

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Fig. 3

Flowchart of the proposed active resource allocation approach

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Fig. 4

Iterative updating process for simulations

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Fig. 5

Iterative updating process for experiments

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Fig. 6

Cumulative confidence level and estimated reliability history during ARA in (a) phase I and (b) phase II

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Fig. 7

Comparison between true experimental responses and ARA predictions at 50 validation points

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Fig. 8

Comparison of estimated Pf for 30 repetitive runs using different methods

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Fig. 9

Comparison between true bias and ARA predictions at 50 validation points

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Fig. 10

Comparison of estimated Pf for 30 repetitive runs using different methods

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Fig. 11

Geometry of the cantilever beam

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Fig. 12

Active resource allocation predicted responses versus true experimental responses for the validation points

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Fig. 13

ANSYS results of maximum deformation (unit: m)

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