Reliability of Engineering Methods in Heavy-Vehicle Aerodynamics

2017-01-7001

08/25/2017

Authors Abstract
Content
The improved performance of heavy-duty vehicles as transport carriers is essential for economic reasons and to fulfil new emission standards in Europe. A key parameter is the aerodynamic vehicle drag. An enormous potential still exists for fuel saving and reducing exhaust emission by aerodynamic optimisation. Engineering methods are required for developments in vehicle aerodynamics. To assess the reliability of the most common experimental testing and numerical simulation methods in the industrial design process is the objective of this article.
Road tests have been performed to provide realistic results, which are compared to the results obtained by scale-model wind tunnel experiments and time-averaged computational fluid dynamics (CFD). These engineering methods are evaluated regarding their deployment in the industrial development process. The investigations focus on the separated flow region behind the vehicle rear end. For this, the velocity field behind a full-scale tractor-trailer configuration was measured under realistic conditions using the laser-based optical measurement method particle image velocimetry (PIV). The flow field measurements were complemented by measurements of the induced pressure field on the base.
The comparison revealed that CFD predictions agree well with measurements if the mean flow structure in the wake is considered. However, the pressure distribution on the base is underestimated, which also reduces the accuracy of the predicted aerodynamic drag. Geometrical simplifications on the wind tunnel model made a comparison to the full-scale data difficult. Nevertheless, scale-model experiments are still an effective method in heavy-vehicle aerodynamics.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-7001
Pages
14
Citation
Haff, J., Jönsson, M., Loose, S., and Wagner, C., "Reliability of Engineering Methods in Heavy-Vehicle Aerodynamics," SAE Technical Paper 2017-01-7001, 2017, https://doi.org/10.4271/2017-01-7001.
Additional Details
Publisher
Published
Aug 25, 2017
Product Code
2017-01-7001
Content Type
Technical Paper
Language
English