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Uncertainty aggregation and reduction in structure–material performance prediction

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Abstract

An uncertainty aggregation and reduction framework is presented for structure–material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure–material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

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Acknowledgements

This study was supported by funds from the National Science Foundation (Grant No. 1404823, CDSE Program) and the Air Force Office of Scientific Research (Grant No. FA9550-15-1-0018, Program Officer: Dr. David Stargel). The support is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Vanderbilt University, 272 Jacobs Hall, VU Mailbox: PMB 351831, Nashville, TN, 37235, USA

    Zhen Hu, Sankaran Mahadevan & Dan Ao

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  1. Zhen Hu
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  2. Sankaran Mahadevan
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  3. Dan Ao
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Correspondence to Sankaran Mahadevan.

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Hu, Z., Mahadevan, S. & Ao, D. Uncertainty aggregation and reduction in structure–material performance prediction. Comput Mech 61, 237–257 (2018). https://doi.org/10.1007/s00466-017-1448-6

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  • DOI: https://doi.org/10.1007/s00466-017-1448-6

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