Abstract
This study contrasts Reynolds-Averaged Navier-Stokes (RANS) and (Numerical) Large-Eddy Simulation (NLES) for their use in predicting conjugate heat transfer for a low Reynolds number flow over a surface roughness element. The (N)LES predictions are in good agreement with experimental data, the heat transfer estimate is within 7.7% error. The linear RANS model shows larger errors up to 40%, especially in modelling the turbulent stresses. Using a one-equation LES turbulence model slightly increases the heat transfer prediction compared to a numerical LES. This indicates that more advanced turbulence models might not give more accurate heat transfer predictions.Additionally, this study investigates the time dependant development of the flow and temperature fields and how long data needs to be collected for statistically stationary results. The flow needs to develop for approximately 1800 through-flow times, TL, and 150 TL is required for collection of statistics. A significant range in turbulence length scale prediction between (N)LES and RANS was found. (N)LES scales appeared physically reasonable and should inform mesh resolution in future studies. Two additional cases were run with different cube heights showing changes in the time dependant development of the temperature field and greater sensitivity to turbulence modelling.
Highlights
The use of electronic systems is a cornerstone of modern life and electronic systems become more complex every year, with more components and advanced manufacturing methods
Since this flow can be classified as a top-down flow, where the large turbulent structures are more important to resolve for the heat transfer, it is surprising that the Reynolds Averaged Navier–Stokes (RANS) model was able to accurately predict the surface temperatures without resolving the complex time-dependent features
In the cases discussed in this project, the RANS heat transfer predictions vary from average errors of 9.1% to 5.7%
Summary
The use of electronic systems is a cornerstone of modern life and electronic systems become more complex every year, with more components and advanced manufacturing methods. A surface roughness elements has the shape of electronic components, such as voltage regulators, memory chips, capacitors, and heat sinks. The power density in electric cars continues to increase and the fast charging of more than 100 kW leads to an increase in rejected heat that needs to be dissipated [6]. Car batteries and electronic components need to be able to withstand continuous increases in power density and heat dissipation. Aeroengine turbine blade cooling contains bluff body features of varying aspect which experience similar life degradation as electronic devices. The power density of the electrical components needs to be quadrupled [9] to 10–15 kW/kg by 2030 [10] which will lead to significantly more excess heat that needs to be dissipated via the electronics packaging. Computational fluid dynamics (CFD) is an important part of the design process because it provides deeper insight into the underlying flow features and heat transfer of roughness elements
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