A Data-driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation

Abstract

Over the past decades, several computer codes were developed for simulation and analysis of thermal-hydraulics of system behaviors in nuclear reactors under operating, abnormal transient and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-driven framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of coarse mesh size and models for low-fidelity system-level thermal-hydraulic simulation, such as coarse-mesh Computational Fluid Dynamics-like (CFD-like) codes, to achieve accuracy comparable to that of high-fidelity simulation, such as high-resolution CFD. Based on high-fidelity data and massive fast-running low-fidelity simulations, error database is built and used to train a machine learning model and find the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS framework is designed as a modularized six-step procedure and accomplished with methods and algorithms in the state of the art. A mixed convection case study was performed to illustrate the entire framework.

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• Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part I
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• Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data
• Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments
• Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning