Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel

TL;DR

This paper introduces a physics-informed multi-fidelity Kriging surrogate modeling approach to improve digital twin simulations for accident tolerant fuel, especially with limited and noisy data.

Contribution

It demonstrates the application of multi-fidelity Kriging in modeling thermal conductivity data for ATF, addressing data scarcity and noise challenges in digital twin frameworks.

Findings

MFK outperforms conventional Kriging with limited data

Effective in modeling noisy and outlier data

Facilitates integration into digital twin systems for ATF

Abstract

The Gaussian Process (GP)-based surrogate model has the inherent capability of capturing the anomaly arising from limited data, lack of data, missing data, and data inconsistencies (noisy/erroneous data) present in the modeling and simulation component of the digital twin framework, specifically for the accident tolerant fuel (ATF) concepts. However, GP will not be very accurate when we have limited high-fidelity (experimental) data. In addition, it is challenging to apply higher dimensional functions (>20-dimensional function) to approximate predictions with the GP. Furthermore, noisy data or data containing erroneous observations and outliers are major challenges for advanced ATF concepts. Also, the governing differential equation is empirical for longer-term ATF candidates, and data availability is an issue. Physics-informed multi-fidelity Kriging (MFK) can be useful for identifying and predicting the required material properties. MFK is particularly useful with low-fidelity physics (approximating physics) and limited high-fidelity data - which is the case for ATF candidates since there is limited data availability. This chapter explores the method and presents its application to experimental thermal conductivity measurement data for ATF. The MFK method showed its significance for a small number of data that could not be modeled by the conventional Kriging method. Mathematical models constructed with this method can be easily connected to later-stage analysis such as uncertainty quantification and sensitivity analysis and are expected to be applied to fundamental research and a wide range of product development fields. The overarching objective of this chapter is to show the capability of MFK surrogates that can be embedded in a digital twin system for ATF.