Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

Abstract

to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure while only incurring a reasonable computational expense. Both of these approaches have different strengths and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-ofconcept uncertainty quantification analyses of a simple component under prototypical conditions. The builtin stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

Grizzly Development for Light Water Reactor and Advanced Reactor Applications in Fiscal Year 2021

“…In this section we present a parameter sensitivity analysis of the LAROMANCE and NEML Grade 91 material models for the high temperature creep response of the two engineering components shown in Figure 11 . This study is a continuation of the parameter sensitivity study carried out in our previous report [1 ] for 2D geometries of a pipe and nozzle component.…”

“…The effects of these improvements are demonstrated here on several engineering-scale components in twoand three-dimensions subjected to high temperature environments expected for advanced nuclear reactors. This work is a continuation of a previous report [1 ] summarizing the joint effort between Argonne…”

“…In the previous report [1 ], a parameter study was performed on a two-dimensional axisymmetric pressurized nozzle geometry containing 3,300 Quad8 elements. This model contains enough degrees of freedom that performance and timing data of various C++ routines can be profiled.…”

“…Default parameter values were used for the LAROMANCE Grade 91 model. The parameters for the NEML model were chosen as the mean values for the normal distributions given in our previous report for T=550 • C [1 ]. The NEML model gives a nearly flat maximum principal strain response as a function of time, showing very little creep and a mostly elastic linear response.…”

“…These are referred to as surrogate models because they rely on data from these simulations rather than directly running the underlying microstructure-sensitive model. This is an extremely powerful approach, and the previous work that the current effort builds on [1 ] successfully applied this model to engineering-scale analyses of Grade 91 components.…”