Validation of Wind Noise for Class-8 Truck Using Lattice Boltzmann Method

Aug 29, 2025·
Nicolas Fougere
Nicolas Fougere
,
Guleria et al
· 0 min read
Image credit: Guleria et al. 2025
Abstract
The transportation and mobility industry trend toward electrification is rapidly evolving and in this specific scenario, wind noise aeroacoustics becomes one of the major concerns for OEMs, as new propulsion systems are notably quieter than traditional ones. There is, however, very limited references available in the literature regarding validation of computational fluid dynamics (CFD) simulations applied to the prediction of aeroacoustics contribution to the noise generated by large commercial trucks. Thus, in this work, high-fidelity CFD simulations are performed using lattice Boltzmann method (LBM), which uses very large eddy simulation (VLES) turbulence model and compared to on-road physical tests of a heavy-duty truck to validate the approach. Furthermore, the effect of realistic wind conditions is also analyzed. Two different truck configurations are considered one with side mirror (Case A) and the other without (Case B) side mirrors. The main focus othis work is to assess the accuracy of the commercial CFD software PowerFLOW to predict greenhouse wind noise analysis for heavy vehicles as a tool to complement or replace physical testing during the vehicle design process. From this study, we found that external microphone measurements at the passenger-side glass demonstrate strong correlation with simulation results, highlighting the importance of including a typical level of on-road free-stream turbulence to achieve accurate sound pressure level (SPL) correlations for the full frequency range available from the physical test (i.e., 100 Hz to 2000 Hz) for both configurations.
Type
Publication
SAE International Journal of Commercial Vehicle
Status
Peer-reviewed Open access
publications
Nicolas Fougere
Authors
Senior Portfolio Manager

Nicolas Fougere is a technology leader with experience helping global industrial organizations accelerate innovation through digital engineering and transformation. As a Senior Portfolio Manager at Dassault Systèmes, he works with manufacturers and technology leaders to develop strategies around virtual twins, simulation, systems engineering, and AI.

Before joining the private sector, Nicolas contributed to scientific research supporting NASA and the European Space Agency’s Rosetta mission and authored numerous peer-reviewed publications. He holds advanced engineering and scientific degrees from the University of Michigan.

Nicolas is passionate about connecting technology, business strategy, and customer success to help organizations solve complex challenges, build high-performing teams, and create lasting business value.