Uncertainty Quantification in Coupled Multiphysics Systems via Gaussian Process Surrogates: Application to Fuel Assembly Bow

TL;DR

This paper develops a Gaussian process surrogate framework for uncertainty quantification in coupled multiphysics systems, specifically applied to predicting fuel assembly bow in reactors, ensuring computational efficiency and stability.

Contribution

It introduces a mathematically rigorous coupling method for Gaussian process surrogates in multiphysics UQ, with theoretical stability analysis and practical application to reactor fuel assembly bow.

Findings

Stable uncertainty propagation demonstrated in fuel assembly bow simulations

Significant reduction in computational cost compared to direct simulations

Theoretical bounds ensure predictive variance remains controlled

Abstract

Predicting fuel assembly bow in pressurized water reactors requires solving tightly coupled fluid-structure interaction problems, whose direct simulations can be computationally prohibitive, making large-scale uncertainty quantification (UQ) very challenging. This work introduces a general mathematical framework for coupling Gaussian process (GP) surrogate models representing distinct physical solvers, aimed at enabling rigorous UQ in coupled multiphysics systems. A theoretical analysis establishes that the predictive variance of the coupled GP system remains bounded under mild regularity and stability assumptions, ensuring that uncertainty does not grow uncontrollably through the iterative coupling process. The methodology is then applied to the coupled hydraulic-structural simulation of fuel assembly bow, enabling global sensitivity analysis and full UQ at a fraction of the computational cost of direct code coupling. The results demonstrate accurate uncertainty propagation and stable predictions, establishing a solid mathematical basis for surrogate-based coupling in large-scale multiphysics simulations.