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Research Papers

Dynamics Model Validation Using Time-Domain Metrics

[+] Author and Article Information
Dan Ao

Department of Civil and Environmental
Engineering,
Vanderbilt University,
279 Jacobs Hall,
VU Mailbox: PMB 351831,
Nashville, TN 37235
e-mail: dan.ao@vanderbilt.edu

Zhen Hu

Department of Civil and Environmental
Engineering,
Vanderbilt University,
279 Jacobs Hall,
VU Mailbox: PMB 351831,
Nashville, TN 37235
e-mail: zhen.hu@vanderbilt.edu

Sankaran Mahadevan

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

1Corresponding author.

Manuscript received November 30, 2016; final manuscript received February 26, 2017; published online March 24, 2017. Assoc. Editor: David Moorcroft.

J. Verif. Valid. Uncert 2(1), 011004 (Mar 24, 2017) (15 pages) Paper No: VVUQ-16-1032; doi: 10.1115/1.4036182 History: Received November 30, 2016; Revised February 26, 2017

Validation of dynamics model prediction is challenging due to the involvement of various sources of uncertainty and variations among validation experiments and over time. This paper investigates quantitative approaches for the validation of dynamics models using fully characterized experiments, in which both inputs and outputs of the models and experiments are measured and reported. Existing validation methods for dynamics models use feature-based metrics to give an overall measure of agreement over the entire time history, but do not capture the model's performance at specific time instants or durations; this is important for systems that operate in different regimes in different stages of the time history. Therefore, three new validation metrics are proposed by extending the model reliability metric (a distance-based probabilistic metric) to dynamics problems. The proposed three time-domain model reliability metrics consider instantaneous reliability, first-passage reliability, and accumulated reliability. These three reliability metrics that perform time-domain comparison overcome the limitations of current feature-based validation metrics and provide quantitative assessment regarding the agreement between the simulation model and experiment over time from three different perspectives. The selection of validation metrics from a decision-making point of view is also discussed. Two engineering examples, including a simply supported beam under stochastic loading and the Sandia National Laboratories structural dynamics challenge problem, are used to illustrate the proposed time-domain validation metrics.

Copyright © 2017 by ASME
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References

Figures

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Fig. 1

Illustration of difference between experiment and model at one validation sites: (a) experimental observation and model prediction and (b) difference

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Fig. 2

Illustration of first-passage failure

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Fig. 3

Three steps of the first-passage reliability metric computation at one validation site

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Fig. 4

Simply supported beam under stochastic loads

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Fig. 5

Simulation and experimental output: (a) good model and (b) bad model

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Fig. 6

Validation results using time-instantaneous reliability metric for good model: (a) λ = 8% and (b) λ = 16%

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Fig. 7

Validation results using the first-passage reliability metric for good model: (a) λ = 8% and (b) λ = 16%

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Fig. 8

Validation results using the accumulated reliability metric for good model: (a) λ = 8% and (b) λ = 16%

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Fig. 10

Validation results using the first-passage reliability metric for bad model: (a) λ = 8% and (b) λ = 16%

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Fig. 11

Validation results using the accumulated reliability metric for bad model: (a) λ = 8% and (b) λ = 16%

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Fig. 9

Validation results using time-instantaneous reliability metric for bad model: (a) λ = 8% and (b) λ = 16%

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Fig. 12

Validation results using the first-passage reliability metric under different thresholds

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Fig. 13

Validation results using the accumulated reliability metric under different thresholds

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Fig. 14

Mass–spring–dampers on a beam

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Fig. 15

Simulation and experiment data of the good model and bad model: (a) good model and (b) bad model

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Fig. 16

Validation results using time-instantaneous reliability metric for good model: (a) λ = 8% and (b) λ = 16%

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Fig. 17

Validation results using the first-passage reliability metric for good model: (a) λ = 8% and (b) λ = 16%

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Fig. 18

Validation results using the accumulated reliability metric for good model: (a) λ = 8% and (b) λ = 16%

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Fig. 19

Validation results using time-instantaneous reliability metric for bad model: (a) λ = 8% and (b) λ = 16%

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Fig. 20

Validation results using the first-passage reliability metric for bad model: (a) λ = 8% and (b) λ = 16%

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Fig. 21

Validation results using the accumulated reliability metric for bad model: (a) λ = 8% and (b) λ = 16%

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