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    Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables

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    Type
    Article
    Authors
    Pourhabib, Arash
    Huang, Jianhua Z.
    Wang, Kan
    Zhang, Chuck
    Wang, Ben
    Ding, Yu
    KAUST Grant Number
    KUSCI-016-04
    Date
    2015
    Permanent link to this record
    http://hdl.handle.net/10754/672263
    
    Metadata
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    Abstract
    Buckypapers are thin sheets produced from Carbon NanoTubes (CNTs) that effectively transfer the exceptional mechanical properties of CNTs to bulk materials. To accomplish a sensible tradeoff between effectiveness and efficiency in predicting the mechanical properties of CNT buckypapers, a multi-fidelity analysis appears necessary, combining costly but high-fidelity physical experiment outputs with affordable but low-fidelity Finite Element Analysis (FEA)-based simulation responses. Unlike the existing multi-fidelity analysis reported in the literature, not all of the input variables in the FEA simulation code are observable in the physical experiments; the unobservable ones are the latent variables in our multi-fidelity analysis. This article presents a formulation for multi-fidelity analysis problems involving latent variables and further develops a solution procedure based on nonlinear optimization. In a broad sense, this latent variable-involved multi-fidelity analysis falls under the category of non-isometric matching problems. The performance of the proposed method is compared with both a single-fidelity analysis and the existing multi-fidelity analysis without considering latent variables, and the superiority of the new method is demonstrated, especially when we perform extrapolation.
    Citation
    Pourhabib, A., Huang, J. Z., Wang, K., Zhang, C., Wang, B., & Ding, Y. (2014). Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables. IIE Transactions, 47(2), 141–152. doi:10.1080/0740817x.2014.917777
    Sponsors
    The authors would like to acknowledge the generous support from their sponsors. Ding and Pourhabib are partially supported by NSF under grant no. CMMI-1000088; Ding and Huang are partially supported by AFOSR DDDAS program under grant no. FA9550-13-1-0075 and King Abdullah University of Science and Technology award KUSCI-016-04; Huang is partially supported by NSF under grant no. DMS-1208952.
    Publisher
    Informa UK Limited
    Journal
    IIE TRANSACTIONS
    DOI
    10.1080/0740817X.2014.917777
    Additional Links
    http://www.tandfonline.com/doi/abs/10.1080/0740817X.2014.917777
    ae974a485f413a2113503eed53cd6c53
    10.1080/0740817X.2014.917777
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