Space-filling single square and square fractal grids induced turbulence: Reynolds stress model parameters-optimization

• Published in: Results in engineering Vol. 17; p. 100806
• Main Authors: Mok, Michael Chee Hoe, Yeoh, Chin Vern, Tan, Ming Kwang, Foo, Ji Jinn
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2023; Elsevier
• Subjects: Computational Fluid Dynamics; Fractal geometry; Reynolds Stress Model; Simplex optimization; Turbulence; Turbulence; Simplex optimization; Fractal geometry; Computational Fluid Dynamics; SFG; SSG; RSM; Reynolds Stress Model
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2022.100806

The employment of grid-induced turbulent flow structures as a passive means of augmenting solid-fluid heat transfer is receiving considerable attention, especially in HVAC applications. However, there presently exists a gap in simulation approaches, where industry-accessible CFD packages are unable to accurately express crucial second-order turbulent flow statistics generated by space-filling single square grids (SSGs) and square fractal grids (SFGs). The present study reports a successful application of a revised Reynolds Stress Model (RSM), which accurately replicates the streamwise distributions of centerline mean flow velocity and turbulence intensity in the lee of one SSG (operating under three different flow Reynolds number ReDh) and five geometrically different SFG test cases after undergoing Nelder-Mead downhill simplex optimization of key RSM kernel parameters. The optimized RSM presents a disagreement of, at worst, 4.30% and 9.98%, respectively against experimental hot-wire anemometry measurements of first-order and second-order statistics, and is the first known instance of the RSM being validated for turbulence intensity predictions of SSG- and SFG-induced turbulence. Examination of RSM parameters reveals that the pre-factors for the rates of turbulence dissipation production and destruction (C1,ε and C2,ε) hold greatest effect on simulation accuracy, with additional optimization of the turbulent viscosity pre-factor (Cμ) required for SFG cases. This is attributed to the effect of enhanced turbulent transport due to the cascading and multiscale nature of SFG turbulence, which insofar could not be replicated by Reynolds-Averaged Navier-Stokes (RANS) models. The values of optimized C1,ε, C2,ε, and Cμ range between 1.057 to 1.697, 2.226 to 2.556, and 0.17 to 2.27, respectively. This leads to the largest deviation of −26.6%, 33.1%, and 200% for C1,ε, C2,ε, and Cμ, respectively, when compared to their corresponding default values. With regards to the sensitivity of this parameter set on grid design, it is shown that the grid's fractal iteration number N and thickness ratio tr have greatest influence on the variation of C1,ε, C2,ε, and Cμ, while the effect of ReDh is insignificant. Overall, this study presents an alternative approach to capture the anisotropic and inhomogeneous nature of SSG- and SFG-induced turbulence for industrial heat-transfer applications via an accessible RANS package, which was previously constrained to expensive DNS and LES studies. •RSM parameters simplex optimization to predict multilength-scale grid turbulence.•RSM(C1,ε, C2,ε) optimization ensue a 9.70% deviation in SSG-induced turbulence.•RSM(C1,ε,C2,ε,Cμ) optimization gives 9.98% deviation on SFG-induced turbulence.•SSG-promoted turbulent statistics are independent of flow Reynolds number ReDh.•SFG-induced turbulence intensity is sensitive towards grid geometric properties.