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JOM

, Volume 71, Issue 8, pp 2646–2656 | Cite as

Gaussian-Process-Driven Adaptive Sampling for Reduced-Order Modeling of Texture Effects in Polycrystalline Alpha-Ti

  • Aaron E. TallmanEmail author
  • Krzysztof S. Stopka
  • Laura P. Swiler
  • Yan Wang
  • Surya R. Kalidindi
  • David L. McDowell
Multiscale Computational Strategies for Heterogeneous Materials with Defects: Coupling Modeling with Experiments and Uncertainty Quantification
  • 285 Downloads

Abstract

Data-driven tools for finding structure–property (S–P) relations, such as the Materials Knowledge System (MKS) framework, can accelerate materials design, once the costly and technical calibration process has been completed. A three-model method is proposed to reduce the expense of S–P relation model calibration: (1) direct simulations are performed as per (2) a Gaussian process-based data collection model, to calibrate (3) an MKS homogenization model in an application to α-Ti. The new methods are compared favorably with expert texture selection on the performance of the so-calibrated MKS models. Benefits for the development of new and improved materials are discussed.

Notes

Acknowledgements

This work was sponsored by the Office of Naval Research (ONR), under Grant No. N00014-17-1-2036. The views and conclusions contained herein are those of the authors only and should not be interpreted as representing those of the ONR, the U.S. Navy, or the U.S. Government. Simulations were performed on Georgia Institute of Technology’s Partnership for an Advanced Computing Environment (PACE) supercomputing cluster. We also acknowledge the support of Sandia’s Laboratory Directed Research and Development Academic Alliance program. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.

Supplementary material

11837_2019_3553_MOESM1_ESM.pdf (103 kb)
Supplementary material 1 (PDF 102 kb)

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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Optimization and Uncertainty Quantification DepartmentSandia National LaboratoriesAlbuquerqueUSA
  4. 4.School of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaUSA

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