Computational investigation and design optimization of vortex generator for a sport utility vehicle using CFD

Ganesan, S.; Allaudeen, Aymen Sharook; Hemanandh, J.; Mani, Purushothaman; Gnanamani, Senthilkumar; Raja, K B; Raja, T.

doi:10.1063/5.0034023

Cited by 1 publication

(1 citation statement)

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“…CFD studies of VGs are currently focused on two main goals. The first goal is to optimize the position and distribution of VGs, see the studies of Subbiah et al [13] and Yu et al [14]. The second goal is to analyze the swirling vortexes generated on the wake behind the VG, see the studies of Carapau and Janela [15] and Sheng et al [16] about this phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Testing the Accuracy of the Cell-Set Model Applied on Vane-Type Sub-Boundary Layer Vortex Generators

et al. 2021

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Vortex Generators (VGs) are applied before the expected region of separation of the boundary layer in order to delay or remove the flow separation. Although their height is usually similar to that of the boundary layer, in some applications, lower VGs are used, Sub-Boundary Layer Vortex Generators (SBVGs), since this reduces the drag coefficient. Numerical simulations of sub-boundary layer vane-type vortex generators on a flat plate in a negligible pressure gradient flow were conducted using the fully resolved mesh model and the cell-set model, with the aim on assessing the accuracy of the cell-set model with Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) turbulence modelling techniques. The implementation of the cell-set model has supposed savings of the 40% in terms of computational time. The vortexes generated on the wake behind the VG; vortical structure of the primary vortex; and its path, size, strength, and produced wall shear stress have been studied. The results show good agreements between meshing models in the higher VGs, but slight discrepancies on the lower ones. These disparities are more pronounced with LES. Further study of the cell-set model is proposed, since its implementation entails great computational time and resources savings.

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Section: Introductionmentioning

confidence: 99%