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

by   Kazuma Kobayashi, et al.

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.


page 1

page 2

page 3

page 4


Transfer learning based multi-fidelity physics informed deep neural network

For many systems in science and engineering, the governing differential ...

Enhanced multi-fidelity modelling for digital twin and uncertainty quantification

The increasing significance of digital twin technology across engineerin...

Physics-informed Gaussian Process for Online Optimization of Particle Accelerators

High-dimensional optimization is a critical challenge for operating larg...

The role of surrogate models in the development of digital twins of dynamic systems

Digital twin technology has significant promise, relevance and potential...

A physics-informed variational DeepONet for predicting the crack path in brittle materials

Failure trajectories, identifying the probable failure zones, and damage...

Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)

Multifidelity simulation methodologies are often used in an attempt to j...

Physics-informed CoKriging model of a redox flow battery

Redox flow batteries (RFBs) offer the capability to store large amounts ...

Please sign up or login with your details

Forgot password? Click here to reset