A practical extension of the recursive multi‐fidelity model for the emulation of hole closure experiments. (25th May 2021)

• Record Type: Journal Article
• Authors: Muyskens, Amanda; Schmidt, Kathleen; Nelms, Matthew; Barton, Nathan; Florando, Jeffrey; Kupresanin, Ana; Rivera, David
• Other Names: Morris Max D. guestEditor.
• Is Part Of: Statistical analysis and data mining. Volume 14:Number 6(2021)
• Page Start: 636
• Page End: 646
• Publication Date: 2021-05-25
• Subjects: computer experiments -- functional emulator -- Gaussian process -- material strength model -- multi‐fidelity emulator; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11513
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: In regimes of high strain rate, the strength of materials often cannot be measured directly in experiments. Instead, the strength is inferred based on an experimental observable, such as a change in shape, that is matched by simulations supported by a known strength model. In hole closure experiments, the rate and degree to which a central hole in a plate of material closes during a dynamic loading event are used to infer material strength parameters. Due to the complexity of the experiment, many computationally expensive, three‐dimensional simulations are necessary to train an emulator for calibration or other analyses. These simulations can be run at multiple grid resolutions, where dense grids are slower but more accurate. In an effort to reduce the computational cost, a combination of simulations with different resolutions can be combined to develop an accurate emulator within a limited training time. We explore the novel design and construction of an appropriate functional recursive multi‐fidelity emulator of a strength model for tantalum in hole closure experiments that can be applied to arbitrarily large training data. Hence, by formulating a multi‐fidelity model to employ low‐fidelity simulations, we were able to reduce the error of our emulator by approximately 81% with only an approximately 1.6% increase in computing resource utilization.