基于OpenFOAM的槽道内液态金属湍流换热直接数值模拟
Direct Numerical Simulation on Heat Transfer of Liquid Metal in Channels Based on OpenFOAM
DOI: 10.12677/NST.2021.94022, PDF,    国家自然科学基金支持
作者: 童彦钧, 吴应杰, 赵后剑*, 牛风雷：华北电力大学，非能动核能安全技术北京重点实验室，北京
关键词: 直接数值模拟；液态金属；湍流换热；Direct Numerical Simulation (DNS)； Liquid Metal； Turbulent Heat Transfer
摘要: 液态金属被广泛用作先进核能系统的一回路冷却剂。液态金属的普朗特数较低，其换热特征与常规流体区别较大。本文采用OpenFOAM对槽道内液态金属湍流换热过程进行了直接数值模拟。模拟采用的摩擦雷诺数为180，分子普朗特数分别为1，0.71，0.25，0.125，0.05，0.025，0.005，0.001。通过与前人研究的数值结果进行对比，验证了本文模型的准确性。分析了普朗特数对温度分布的影响，随着普朗特数的降低，常见的温度分布对数律区在截面内的占比明显减小。采用指数函数对截面内的无量纲温度分布进行拟合，得到了普朗特数在0.001到1范围内的温度分布计算式。结合速度幂律，在温度分布公式的基础上推导出了努赛尔数关系式。
Abstract: The liquid metal is widely used as the primary coolant in many advanced nuclear systems. Due to the low Prandtl number, the turbulent heat transfer characteristics of liquid metal are different from the conventional fluids. Using the method of Direct Numerical Simulation (DNS), turbulent convection of liquid metal is simulated with OpenFOAM. The friction Reynolds number is 180. The molecular Prandtl numbers (Pr) are 1, 0.71, 0.25, 0.125, 0.05, 0.025, 0.005 and 0.001. The accuracy of present numerical model is validated by comparing with the numerical results of previous stud-ies. Prandtl number effects on temperature distribution are analyzed. The region of logarithmic law for temperature in the cross section is decreased with the decreasing of Prandtl number. Exponent functions for dimensionless temperature distribution in the cross section are obtained by regres-sion analysis of numerical results with 0.001 ≤ Pr ≤ 1. Combined with the power law for dimension-less velocity distribution, the Nusselt number correlation for turbulent convection of liquid metal is derived.
文章引用：童彦钧, 吴应杰, 赵后剑, 牛风雷. 基于OpenFOAM的槽道内液态金属湍流换热直接数值模拟[J]. 核科学与技术, 2021, 9(4): 187-196. https://doi.org/10.12677/NST.2021.94022

参考文献

[1] Komen, E., Shams, A., Camilo, L., et al. (2014) Quasi-DNS Capabilities of Open FOAM for Different Mesh Types. Computers & Fluids, 96, 87-104. [Google Scholar] [CrossRef] 
[2] Kim, J. and Moin, P. (1990) Transport of Passive Scalars in a Turbulent Channel Flow. Springer, Heidelberg. [Google Scholar] [CrossRef] 
[3] Kawamura, H., et al. (1999) DNS of Turbulent Heat Transfer in Channel Flow with Respect to Reynolds and Prandtl Number Effects. International Journal of Heat and Fluid Flow, 20, 196-207. [Google Scholar] [CrossRef] 
[4] Kawamura, H., et al. (1998) DNS of Turbulent Heat Trans-fer in Channel Flow with Low to Medium-High Prandtl Number Fluid. International Journal of Heat and Fluid Flow, 19, 482-491. [Google Scholar] [CrossRef] 
[5] Kawamura, H., Abe, H., Shingai, K. (2000) DNS of Turbu-lence and Heat Transport in a Channel Flow with Different Reynolds and Prandtl Numbers and Boundary Conditions. Proceedings of the 3rd International Symposium on Turbulence Heat & Mass Transfer, 3, 15-32.
[6] Tiselj, I. and Cizelj, L. (2012) DNS of Turbulent Channel Flow with Conjugate Heat Transfer at Prandtl Number 0.01. Nuclear Engi-neering and Design, 253, 153-160. [Google Scholar] [CrossRef] 
[7] Abe, H., Kawamura, H. and Matsuo, Y. (2004) Surface Heat-Flux Fluctuations in a Turbulent Channel Flow up to Reτ = 1020 with Pr = 0.025 and 0.71. International Journal of Heat and Fluid Flow, 25, 404-419. [Google Scholar] [CrossRef] 
[8] Kozuka, M., Seki, Y. and Kawamura, H. (2009) DNS of Turbulent Heat Transfer in a Channel Flow with a High Spatial Resolution. International Journal of Heat and Fluid Flow, 30, 514-524. [Google Scholar] [CrossRef] 
[9] Villiers, E.D. (2006) The Potential of Large Eddy Simu-lation for the Modeling of Wall Bounded Flows. Ph.D. Thesis, Imperial College of Science, Technology and Medicine, London.
[10] Patankar, S.V., Liu, C.H. and Sparrow, E.M. (1977) Fully Developed Flow and Heat Transfer in Ducts Having Streamwise-Periodic Variations of Cross-Sectional Area. Journal of Heat Transfer, 99, 180-186. [Google Scholar] [CrossRef] 
[11] Komen, E.M.J., Camilo, L.H., Shams, A., et al. (2017) A Quantification Method for Numerical Dissipation in Quasi-DNS and Under-Resolved DNS, and Effects of Numerical Dissipation in Quasi-DNS and Under-Resolved DNS of Turbulent Channel Flows. Journal of Computational Physics, 345, 565-595. [Google Scholar] [CrossRef] 
[12] Vreman, A.W. and Kuerten, J.G.M. (2014) Comparison of Direct Numerical Simulation Databases of Turbulent Channel flow at Reτ = 180. Physics of Fluids, 26, 133-166. [Google Scholar] [CrossRef] 
[13] Kays, W.M., Crawford, M.E. and Weigand, B. (2004) Convective Heat and Mass Transfer. McGraw-Hill, New York.