Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel

Tallman, Aaron Ellis; Kumar, Mariyappan Arul; Castillo, John Andrew Redwing; Wen, Wei; Capolungo, Laurent; Tome, Carlos N.

doi:10.1007/s40192-020-00181-5

Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel

Journal Article · Fri Oct 02 00:00:00 EDT 2020 · Integrating Materials and Manufacturing Innovation

DOI:https://doi.org/10.1007/s40192-020-00181-5· OSTI ID:1876786

^[1]; ^[1]; Castillo, John Andrew Redwing ^[2]; Wen, Wei ^[1]; ^[1]; ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Georgia Institute of Technology, Atlanta, GA (United States)

Here, predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally onerous. To address this limitation, a new approach is proposed whereby a surrogate constitutive model (SM) of the inelastic response of 316H steel is derived from a mechanistic crystal plasticity-based polycrystal model tracking the evolution of dislocation densities on all slip systems. The latter is used to generate a database of the expected plastic response and dislocation content evolution associated with several instances of creep loading. From the database, a SM is developed. It relies on the use of orthogonal polynomial regression to describe the evolution of the dislocation content. The SM is then validated against predictions of the dead load creep response given by the polycrystal model across a range of temperatures and stresses. When the SM is used to predict the response of 316H during complex non monotonic loading, extrapolating to new loading conditions, it is found that predictions compare particularly well against those from the physics-based polycrystal model.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE), Nuclear Energy Advanced Modeling and Simulation (NEAMS); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1876786

Report Number(s):: LA-UR-20-21212

Journal Information:: Integrating Materials and Manufacturing Innovation, Journal Name: Integrating Materials and Manufacturing Innovation Journal Issue: 4 Vol. 9; ISSN 2193-9764

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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