On the effectiveness of Reynolds-averaged and subgrid scale models in predicting flows inside car cabins

Giorgio Grossi*, Fausto Arpino, Michele Bertone, Gino Cortellessa, Andrea Sciacchitano

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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The aim of the present study is to analyze the performances of unsteady Reynolds-averaged Navier-Stokes (URANS) and large eddy simulation (LES) approaches in predicting the airflow patterns inside car cabins and to give insight in the design of computational fluid dynamics simulations of a real car cabin. For this purpose, one eddy viscosity-based turbulence model (shear stress transport k-ω) and two subgrid scale models (wall-adapting local eddy-viscosity and dynamic kinetic energy) were tested, and numerical results were compared with particle image velocimetry measurements carried out on a commercial car. The URANS model exhibited great accuracy in predicting the mean flow behavior and was appreciably outperformed by the LES models only far from the inlet sections. For this reason, it was deemed suitable for conducting further analyses, aimed at characterizing the airflow patterns in winter and summer conditions and performing a thermal comfort analysis. The thermal regime was found to have a very little effect on the air flow patterns, once the quasi-steady state regime is achieved; in fact, both in winter and in summer, the temperature field is fairly uniform within the car cabin, making the contribution of buoyancy negligible and velocity fields to be very similar in the two seasons. Findings also reveal that thermal comfort sensation can be different for passengers sharing the same car but sitting on different seats; this aspect should be considered when designing and operating the ventilation system, since the minimum comfort requirements should be met for all the occupants.

Original languageEnglish
Article number015137
Number of pages21
JournalPhysics of Fluids
Issue number1
Publication statusPublished - 2024


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