Publications

Abstract not provided.

Abstract not provided.

Steady-state Reynolds-averaged Navier-Stokes (RANS) simulations are presented for the three-dimensional flow over a simplified tractor/trailer geometry at zero degrees yaw angle. The simulations are conducted using a multi-block, structured computational fluid dynamics (CFD) code. The turbulence closure model employed is the two-equation Menter k-ω model. The discretization error is estimated by employing two grid levels: a fine mesh of 20 million cells and a coarser mesh of 2.5 million cells. Simulation results are compared to experimental data obtained at the NASA-Ames 7 × 10 ft wind tunnel. Quantities compared include vehicle drag, surface pressures, and time-averaged velocities in the trailer near wake. The results indicate that the RANS approach is able to accurately predict the surface pressure on the vehicle, with the exception of the base region. The pressure predictions in the base region are poor due to the inability of the RANS model to accurately capture the near-wake vortical structure. However, the gross pressure levels in the base region are in reasonable agreement with experiment, and thus the overall vehicle drag is well predicted. Copyright © 2006 by ASME.

For several years, Sandia National Laboratories and Lawrence Livermore National Laboratory have been part of a consortium funded by the Department of Energy to improve fuel efficiency of heavy vehicles such as Class 8 trucks through aerodynamic drag reduction. The objective of this work is to demonstrate the feasibility of using the steady Reynolds-Averaged Navier-Stokes (RANS) approach to predict the flow field around heavy vehicles, with special emphasis on the base region of the trailer, and to compute the aerodynamic forces. In particular, Sandia's computational fluid dynamics code, SACCARA, was used to simulate the flow on a simplified model of a tractor-trailer vehicle. The results are presented and compared with NASA Ames experimental data to assess the predictive capability of RANS to model the flow field and predict the aerodynamic forces.

Abstract not provided.