CFD Modeling and Validation of Tracer Gas Dispersion to Evaluate Self-pollution in School Buses

Abstract

The exhaust gases expelled from the tailpipe during the transit of the buses are composed by different pollutants, each of them dangerous to the human health in different ways. Self-pollution has been detected as a phenomenon related with the passengers exposition to pollutants inside transport buses during their travels. The emissions from the tailpipe make their way to the cabin of the bus, exposing the passengers to toxic gases and particulate matter during the whole time of the trip they are making. The quantitative and detailed modeling of the pollutants in-cabin is important due to the sensitive nature of the children population to respiratory disease related to air pollution. This study assesses self-pollution in school buses using CFD modeling, this is achieved by simulating the dispersion of a tracer gas Sulfur Hexa-Fluoride (SF6) inside and outside the bus. Two previous studies datasets were used to validate the CFD model performance, and accuracy in its capacity to quantitatively describe the phenomenon. The CFD model developed and validated considered the turbulence through the k-epsilon realizable model. The system was considered as a multicomponent single-eulerian-phase flow with the “species to transport” model. Transient formulation and energy were implemented. Mesh was optimized to polyhedral elements, reducing considerably the simulation time by 35%, and mesh size to 25%. The model was able to determine the behavior of the tailpipe emissions and the self-pollution phenomenon into the bus, showing that the highest concentrations (and therefore the maximum exposure) are located in the rear part of the bus. Validation of the CFD results with previous experimental measures and modeling results, reported in two previous studies, determined an error of 12% and 17% for the concentrations outside and inside the bus respectively, correlation coefficient (R2) values between 0.5 and 0.9 were obtained between CFD results for SF6 concentrations and validation dataset.

Keywords

• air quality modeling
• cfd
• self-pollution
• public transport
• tracer gas