面向液态铅铋数值传热的湍流热通量模型构建综述

Review of Turbulent Heat Flux Model Construction for Numerical Heat Transfer in Liquid Lead-bismuth

液态铅铋(LBE)数值传热受制于极低普朗特数特性,传统雷诺比拟方法不能准确封闭和描述平均能量方程中的湍流热通量(THF)及温度输运过程,因此需构建针对低普朗特数流体THF的封闭模型。模型分为4类:湍流普朗特数模型、代数热通量模型、二阶矩微分热通量模型及利用机器学习的数据驱动模型。本文通过梳理其研究进展、建模思想,对模型所展现的复杂程度、在工程应用中的适用性等特点展开评述,为后续THF封闭建模研究、优化及创新提供见解。在燃料棒束间LBE的强制对流换热模拟中,显式代数热通量模型展现出较高的应用前景,机器学习则为THF封闭提供了全新的视角。

Numerical heat transfer of liquid lead-bismuth eutectic(LBE)is limited by the extremely low-Prandtl-number characteristic,conventional Reynolds analogy method fails to accurately close and describe the turbulent heat flux(THF)in the averaged energy equation and the temperature transport process.Therefore,it is necessary to construct models specifically for the closure of THF in low-Prandtl-number fluids.The models are divided into four categories:turbulent-Prandtl-number models,algebraic heat flux models,second-moment differential heat flux models,and data-driven models using machine learning.This study provides insights into subsequent research,optimization,and innovation in THF closure modeling by reviewing their research progress,modeling concepts,the complexity demonstrated by the models,and their applicability in engineering applications.In the simulation of forced convection heat transfer between fuel rod bundles in LBE,explicit algebraic heat flux models show promising application prospects,while machine learning provides a fresh perspective for THF closure.