The automotive industry is showing high demand for efficient design of cooling systems in electric vehicles. The development of complex aero-thermodynamic systems requires reliable, high-fidelity simulations of high Reynolds number flows. We implemented a numerical solver based on the double-distribution lattice-Boltzmann method (LBM). The main advantage of the LBM, compared to the Navier-Stokes-based solvers, is its computational efficiency and intrinsic parallelism, which allows for execution on massively parallel architectures (GPUs). We have validated our GPU implementation by simulating a natural convection flow and a heated cylinder in an enclosed cavity. Both cases show very good agreement with published literature. In the future, we aim to extend the usage of the LBM framework to industry-relevant cases like the simulation of various packaging concepts for electric vehicles.
«The automotive industry is showing high demand for efficient design of cooling systems in electric vehicles. The development of complex aero-thermodynamic systems requires reliable, high-fidelity simulations of high Reynolds number flows. We implemented a numerical solver based on the double-distribution lattice-Boltzmann method (LBM). The main advantage of the LBM, compared to the Navier-Stokes-based solvers, is its computational efficiency and intrinsic parallelism, which allows for execution...
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