Parametric POD-Galerkin Model Order Reduction for Unsteady-State Heat Transfer Problems

TL;DR

This paper introduces a parametric reduced order model using POD-Galerkin methods to efficiently simulate unsteady heat transfer in T-junction pipes, addressing thermal fatigue risks in nuclear plant cooling systems.

Contribution

It develops a novel parametric ROM considering 3D Navier-Stokes and heat equations, allowing efficient modeling with different inlet and viscosity parameters.

Findings

The ROM accurately predicts temperature fluctuations.

Parametric variations significantly affect thermal behavior.

The reduced model reduces computational cost compared to full simulations.

Abstract

A parametric reduced order model based on proper orthogonal decomposition with Galerkin projection has been developed and applied for the modeling of heat transport in T-junction pipes which are widely found in nuclear power plants. Thermal mixing of different temperature coolants in T-junction pipes leads to temperature fluctuations and this could potentially cause thermal fatigue in the pipe walls. The novelty of this paper is the development of a parametric ROM considering the three dimensional, incompressible, unsteady Navier-Stokes equations coupled with the heat transport equation in a finite volume approximation. Two different parametric cases are presented in this paper: parametrization of the inlet temperatures and parametrization of the kinematic viscosity. Different training spaces are considered and the results are compared against the full order model.