Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

Huang, Chaofan; Aduloju, Sunday; Fritz, John; Joseph, V. Roshan; Messner, Mark; Truster, Timothy

doi:10.1115/1.4067584

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

Journal Article · Wed Jan 29 00:00:00 EST 2025 · Journal of Computing and Information Science in Engineering

DOI:https://doi.org/10.1115/1.4067584· OSTI ID:2574418

Huang, Chaofan ^[1]; Aduloju, Sunday ^[2]; Fritz, John ^[3]; Joseph, V. Roshan ^[1]; ^[4]; Truster, Timothy ^[3]

Georgia Institute of Technology, Atlanta, GA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357; AC05-00OR22725

OSTI ID:: 2574418

Journal Information:: Journal of Computing and Information Science in Engineering, Journal Name: Journal of Computing and Information Science in Engineering Journal Issue: 3 Vol. 25; ISSN 1530-9827; ISSN 1944-7078

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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