3. 1 Model Validation
The results obtained from the CFD simulations for each case are firstly reported as concentration contours of the tracer gas on the rear surface of the bus in ppt.
Regarding the first validation stage, in the Fig. 4 the tracer gas concentration contours is plotted. A similar tendency in both contours, CFD and measured, is observed. This first approach of qualitative analysis is useful to observe that the model reproduces correctly the results from the previous study, however, minimal behavior differences are noted in the contours.
Fig. 4.
Rear surface tracer gas (SF6) concentration over the scholar bus: Previous study contours and CFD results. For the 5 cases evaluated.
The differences observed between the CFD results from this research and the previous studies can be related to geometry details built for the simulations, the mesh used and the set-up, as the previous study authors did not report the dimensions, the mesh type, elements and quality, nor the setup settings and models of the one they used. Considering this, the assumptions and settings from the setup used in this work CFD model are adequate to study the phenomena of interest.
In both contour sets exist a decreasing tendency in the tracer gas concentration as the velocity increases, this behavior could be explained by the streams and turbulence generated in the air by the transit of the bus, which helps in the dispersion. Dragging in the tracer gas due to the high speed of the air flow that carries along with the emission is evidenced. It is also notorious that there exists a relationship between the tailpipe position and the concentration of the tracer gas, in the positions 1 and 3 is where the highest concentration is detected, whereas for position 2 the proportion of the concentration is down to 10 times less than in the others.
Considering the similitude of the contours between the previous study and this work, a quantitative validation was performed. Graphically the scatter plots represent the accuracy of the CFD model according to the measured data. A linear correlation in these plots allows to determine the relationship between the two datasets; it is desirable that the Pearson coefficient and the R2 values are close to 1.0. As seen in the plots, for each case, R2 values where between 0.5 and 0.9 indicating an accurate performance of the CFD model implemented. However, it is important to consider that for one of the measured data there are several points of the CFD results points, this data pattern affects negatively the correlation in terms of Pearson Coefficient and R2. Nevertheless, it is important to mention that in the results from the previous study, not details such as the tailpipe or wheels were considered; this situation leads to a non-real reproduction of the tracer gas and pollution dispersion evaluated by the previous studies model. The source of error is in this case the previous work results which does not consider details improved in the current development.
The CFD model developed in this research improves the results obtained by previous studies, the accurate consideration of details such as considering the wheels and tailpipes geometry leads to a more realistic representation of the dispersion phenomenon. The avoidance of this details would not be conceptually correct this research aimed to consider as much real features as possible for the geometry involve in the CFD modeling.
The scatter plots from the Fig. 5 show that there exist some values in the X-axis that have multiple corresponding values in the Y-axis, as is shown in the case number 3, this results in a poor correlation (R2=0.5151), however, by reviewing the reference in detail, it is shown that the wheels and tailpipe were not considered in the model, this is a non-real situation due to the inexistence of the wheels in the bus geometry and this affects negatively the model results. In contrast, this study considers the wheels of the bus in the geometry, meshing and CFD simulations, resulting in a more accurate phenomena description and results. By considering the wheels in this study, the final correlation between both studies could achieve higher values.
Fig. 5.
Scatter plots of CFD surface tracer gas (SF6) concentration against reported data from previous study. For the 5 cases evaluated.
To assess the self-pollution inside the bus, comparison of the concentration results obtained from the CFD model with the data measured in the second previous study was the approach. The collected data from this study showed that there is experimentally a quantity of self-pollution inside the bus cabin, this phenomenon is also perceived by the CFD model developed during this research.
For the assessment of the CFD self-pollution accuracy inside the bus, the results from the CFD model where directly compared with the measurements of the second previous study. The results obtained from the simulations for this case showed a tendency to the infiltration of exhaust gases into the cabin of the bus, these results were obtained through a transient simulation and the contours of the evolution of the concentration of tracer gas (SF6), the contour maps of tracer gas are contained in the Fig. 6. The contours of concentration shown in the left column is the concentration of tracer gas inside the bus in ppt and the contours on the right column is the concentration of tracer gas in the surroundings of the bus in ppm. As the time step advances, according to the transient formulation used, the emission is dispersed behind the bus and this dispersion leads the gases from the tailpipe to the cabin of the bus.
Fig. 6.
CFD results: contours of tracer gas (SF6) inside the scholar bus and outside the scholar bus.
The numerical comparison with the previous study considered data measured inside the buses, three cases of the dataset reported in the study where used to validate in a second stage the results from the CFD model of this research. The concentrations measured inside and outside the bus where averaged and compared with the CFD results. Table 5 show the error between the measured data and the prediction of the CFD model.
Table 5.
CFD tracer gas (SF6) concentration results validation inside and outside the scholar bus.
Run number
(Table 4) |
Mean SF6 concentration
outside the bus (ppm) |
Mean SF6 concentration
inside the bus (ppt) |
30* |
4.61 |
848 |
33* |
2.15 |
519 |
36* |
3.19 |
807 |
Average |
3.32 |
725 |
CFD results |
3.7 |
845 |
Error |
12% |
17% |
The calculated error between CFD and measurement showed that a low level of error is presented by the prediction of the CFD, considering the assumptions made by the model it is an adequate approach to simulate the self-pollution phenomena.
The simulation results allow to determine that the most affected zone inside the bus due to self-pollution is the rear part of the cabin. In the center-rear part of the cabin an accumulation area was identified due to the air flow pattern inside the cabin. However, as the bus advances, the velocity and the exchange of air between the inside and outside of the vehicle, with the windows opened, trend to disperse the gas in the volume of the cabin, notwithstanding, the highest concentrations are in the rear part persists constantly due to the accumulation area identified.
This results leads to identified the rear art of the buses as the more exposed place for children inside scholar buses, and for users of transport systems based in this type of urban buses.
The plume of gases emitted from the tail pipe are dispersed back to the vehicle, and mainly to the left side of the flow domain (Fig. 6). This dispersion pattern evidences an important affectation to other vehicles, and mainly to motorcycles, making passengers of those traffic vehicles to be also exposed to the emission of this medium size buses.