Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables
KAUST Grant NumberKUSCI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/672263
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AbstractBuckypapers 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.
CitationPourhabib, 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
SponsorsThe 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.
PublisherInforma UK Limited