Convergence Error Quantification for Road Vehicle Aerodynamics with Wind Tunnel Modeling Effects
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Date
2023-05
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The Ohio State University
Abstract
The goal of this work was to assess the statistical convergence error in computational fluid dynamics for simulating air flow around a benchmark passenger sedan vehicle within a digital wind tunnel using different mesh densities. The study analyzed the effects of averaging window size (window size for statistical averaging) and stabilization time (window size for washing out the initial condition), each with fine and coarse resolution levels to assess mesh resolution effects. Simulations were performed for three applications: an empty wind tunnel, DrivAer vehicle in open road, and DrivAer vehicle in a wind tunnel. The flow conditions and geometric dimensions for the empty tunnel and vehicle-in-tunnel applications were representative of the recently commissioned full-scale automotive aeroacoustic wind tunnel at Honda Automotive Laboratories of Ohio (HALO). Simulations were computed using a steady Reynolds-Averaged Navier-Stokes (RANS) model in STAR-CCM+, and important flow parameters such as lift, drag, and velocity profiles were extracted. The uncertainties of those parameters associated with the averaging window and stabilization time were quantified using T-test, F-test, standard error, and a convergence parameter for oscillating amplitudes (CPA). The uncertainties from each uncertainty-quantification method were also compared at different mesh densities to understand the robustness of the method.
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Keywords
Convergence, Computational, Wind Tunnel