Rapid Meshing for CFD Simulations of Vehicle Aerodynamics

Kanade, Keyur; Khondge, Ashok; Yavuz, İbrahim; Mandloi, Padmesh; Ghazialam, Hamid; Sovani, Sandeep; Foley, Laz; Lietz, Robert

doi:10.4271/2009-01-0335

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Cited by 4 publications

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“…At the numerical level, Ford Motor Company in 2009 [63] presented a rapid meshing for CFD simulations of vehicle aerodynamics. The new technique presented can reduce the engineering time required for the CAD-to-Mesh process to just a few hours.…”

Section: Air Inlets and Outletsmentioning

confidence: 99%

Review of underhood aerothermal management: Towards vehicle simplified models

Khaled

Ramadan

El-Hage

et al. 2014

Applied Thermal Engineering

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Section: Air Inlets and Outletsmentioning

confidence: 99%

Review of underhood aerothermal management: Towards vehicle simplified models

Khaled

Ramadan

El-Hage

et al. 2014

Applied Thermal Engineering

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Exposure risk to carbon monoxide concentrations inside a diesel-based bus rapid transport system: a CFD-Monte Carlo modeling approach

Guevara-Luna

Casallas

et al. 2023

Model. Earth Syst. Environ.

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Bus rapid transit (BRT) vehicles are common microenvironments in urban areas. In some cities, these BRT vehicles are diesel-powered, which makes them highly pollutant. Recent studies report high levels and exposure risk to particulate matter in BRT vehicles. Nevertheless, extensive research has yet to be published, including gaseous pollutants (e.g., CO). Nevertheless, extensive research including gaseous pollutants (e.g., CO) has not been published. This research aims to evaluate the self-pollution of BRT buses in terms of exhaust gasses. For this, measurements and computational fluid dynamics (CFD) were used. Results suggest that pollutant concentrations stay low during most of the trips. However, some areas of the buses have significant swings and peaks due to the transit cycle. Here, we used CFD modeling to evaluate the dispersion of the exhaust CO inside and outside the bus. CFD results show that the bus rear has the highest concentrations, with a mean self-pollution ratio of 12%. Additionally, we developed a method based on the source-receptor relationship to quantify the impact of exhaust emissions reduction on self-pollution, showing that the technological replacement of current diesel buses would reduce self-pollution and, therefore, passenger exposure. Finally, since modeling results may be inaccurate, an uncertainty analysis was developed using the Monte Carlo method to obtain a confidence interval of 90% for the variables linked to the self-pollution.

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