Selection of turbulence models via multiscaling analysis of an axisymmetric pipe flow and heat transfer

TL;DR

This study assesses the effectiveness of RANS turbulence models in predicting axisymmetric pipe flow and heat transfer, using multiscaling analysis to compare with DNS data and evaluate model accuracy.

Contribution

It introduces a multiscaling analysis approach to select suitable RANS turbulence models for pipe flow and heat transfer, demonstrating their reasonable accuracy compared to DNS.

Findings

RANS models match DNS data within acceptable error margins.

Multiscaling analysis effectively evaluates turbulence model performance.

RANS models are viable for industrial applications involving pipe flow and heat transfer.

Abstract

To fully evaluate a turbulent flow, Direct Numerical Simulation (DNS) is the most accurate method by far and requires considerable computational power and time; not optimum for industry standards. Developing an alternative model, providing results with reasonable accuracy would resolve this issue. Reynolds Averaged Navier Stokes (RANS) modeling has proven its worth in addressing this phenomenon. In this study, we investigated the RANS turbulence models from COMSOL for fully developed single-phase flow in a two-dimensional axisymmetric pipe domain with constant heating at the wall and periodic boundary conditions at the inlet and outlet. Heat transfer in the fluid module has been added to address the heat transfer phenomenon. We evaluated the computed results with existing DNS data to match the accuracy of the RANS models. RANS simulations are conducted for friction Reynolds number, i.e., Re_{\tau}= 180, 314, and 395 with varying Prandtl numbers, i.e., Pr = 0.71, 2, 5, and 7. Multiscaling analyses in the flow's inner, outer, and meso scaling regions are performed for fluid and heat transfer profiles, i.e., mean streamwise velocity, Reynolds shear stress, mean streamwise temperature, and turbulent heat flux, to compare with the DNS data. The investigation reports the scaling analysis's effectiveness and shows that RANS turbulence models can be used to describe such flow with reasonable accuracy.