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

2026-01-26

AI Summary

This study addresses the challenge of predicting bending deformation in pressurized water reactor fuel assemblies, a problem governed by strongly coupled fluid–structure interactions that render full-order multiphysics simulations computationally prohibitive for large-scale uncertainty quantification. To overcome this, the authors propose a Gaussian process surrogate modeling framework that replaces expensive high-fidelity simulations, enabling efficient and rigorous uncertainty propagation and global sensitivity analysis. The key innovation lies in the development of the first theoretical framework guaranteeing bounded predictive variance for coupled Gaussian processes, thereby preventing uncertainty divergence during iterative refinement and establishing a rigorous mathematical foundation for reliable surrogate use in multiphysics systems. Numerical experiments demonstrate that the method achieves high prediction accuracy and stability while substantially reducing computational cost, confirming its effectiveness and scalability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• uncertainty quantification
• multiphysics systems
• fuel assembly bow
• fluid-structure interaction
• computational cost

Innovation

• Gaussian Process Surrogates
• Uncertainty Quantification
• Multiphysics Coupling
• Fuel Assembly Bow
• Predictive Variance Boundedness