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Recently, computational fluid dynamics (CFD) was applied to investigate blockage (or velocity) corrections using the nozzle method for a climatic wind tunnel (CWT) test environment (SAE 2003-01-0936). The study included two blockage corrections to the nozzle method reference velocity: vehicle frontal velocity and vehicle upper surface pressure trace. These methods resulted in well correlated predictions between the open road and CWT flow conditions. These CFD predicted blockage corrections are experimentally verified in a climatic wind tunnel in this study. A non-intrusive method applying particle image velocimetry is applied to acquire the velocity field in front of the test vehicle. The experimental data verifies the blockage correction predictions derived from the previous CFD work.

Computational Fluid Dynamics (CFD) was applied to investigate blockage effects (or velocity correction) in a climatic wind tunnel (CWT) test environment. Different blockage effects in the CWT were modeled using four simplified vehicles that approximated a sedan, an SUV, a pickup truck, and a minivan. Blockage dependence on nozzle size and spacing between the nozzle exit plane (NEP) and the vehicle were also investigated. The study quantified the blockage effect using different correction methods based on vehicle frontal velocity profiles and upper surface pressure traces. The blockage-free solution was also simulated for each vehicle in an ‘open road’ or free air condition. The CFD study revealed that all the test cases resulted in blockage correction factors, defined by Vactual/Vsimulated greater than 1.0. This is a condition in which the uncorrected wind tunnel velocity was higher than the ‘open road’ condition.

This paper presents an uncertainty analysis of aerodynamic force and moment coefficients for production vehicles in an automotive wind tunnel. The analysis uses a Monte Carlo numerical simulation technique. Emphasis is placed on defining the elemental random and systematic uncertainties from the tunnel’s instrumentation, understanding how they propagate through the data reduction equations and under what conditions specific elemental error sources are or are not important, and how the approach to data reduction influences the overall uncertainties in the coefficients. The results of the analysis are used to address the issue of averaging time in the context of maintaining a maximum allowable uncertainty level. Also, a maximum error requirement in the vehicle’s installation is suggested to allow the use of rapid but approximate vehicle alignment methods without incurring errors that exceed the data uncertainty. Observed reproducibility results are presented spanning a 16 month period.