A Consistent Dual-Mesh Framework for Hybrid LES/RANS Simulations of Vehicle Exhaust Plumes: Implications for Remote Emission Sensing 2023-24-0105
Remote emission sensing (RES) is a non-intrusive measurement method based on absorption spectroscopy, which allows for the determination of pollutant concentrations in vehicle exhaust plumes. By measuring the absorption of the exhaust plume from the roadside using a light/laser barrier, concentration ratios of pollutants, such as nitrogen oxides to carbon dioxide, can be estimated. Computational fluid dynamics (CFD) has been employed to simulate vehicle exhaust plumes due to uncertainties in RES capabilities.
In a previous study, Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations were conducted to investigate the dispersion of vehicle exhaust plumes under various ambient/driving conditions and provide insights for RES applications. However, the accuracy of these simulations can be further improved. Therefore, this study focuses on enhancing the simulation accuracy by employing large eddy simulations (LES).
The computational cost of LES approximately scales with Re1.8 in wall-bounded flows, making it impractical for this application given that the computational mesh has to be substantially refined in the near-wall region. To overcome this challenge, a consistent hybrid LES/RANS dual-mesh approach is adopted. The hybrid turbulence modeling framework is extended to account for a species- and temperature-dependent (density-varying) flow to accurately simulate the vehicle exhaust plume.
To ensure consistency between the RANS and averaged LES solutions, additional drift terms are introduced in the corresponding equations. The results demonstrate that the hybrid LES/RANS solver leads to significantly improved consistency in comparison to the standalone RANS and LES results. This turbulence-modeling framework is not only very promising for industrial flow simulations but also provides valuable guidance for the future development of RES devices.
This work highlights the potential of hybrid LES/RANS simulations in enhancing the accuracy of vehicle exhaust plume dispersion predictions, which is crucial for optimizing RES measurements. The findings pave the way for further advancements in RES technology and contribute to the ongoing development of efficient and accurate emission monitoring systems.
