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The wake flow of a heavy truck model is investigated at Re=8.5×104 using particle image velocimetry measurements combined with computational fluids dynamics-simulations. Experimental measurements are carried out on a 1:28-scale model, focusing exclusively on the central longitudinal plane, in the rear of the truck model. Numerical simulations are performed based on the URANS (unsteady Reynolds averaged Navier–Stokes) approach using two statistical turbulence models, i.e., the shear stress transport k–ω and the baseline Reynolds stress (BSL-RSM) models. A comparison between the numerical and experimental results of the mean velocity profiles in the wake of the heavy truck is found to be relatively consistent. The BSL-RSM model, however, gives a better prediction of experiments, with a deviation of 6% in the near wake, against 13% for the SST k–ω. Both URANS models undervalue the streamwise and spanwise turbulence intensity components with a deviation around 24%, compared with the experimental results. The characteristic feature of the wake flow topology is the formation of a recirculation bubble resulting from the shear layers separated from the truck surfaces. Different identification methods, including visualization of closed streamlines, vorticity magnitude, and the Q-invariant criterion, are considered and highlight the existence of two particular vortex regions in the mean flow: a vortex-shedding area in the upper recirculation region and a back-truck attached vortical structure. It is found that the Q criterion-based technique is a relevant indicator of the vortex cores regions.
The wake flow of a heavy truck model is investigated at Re=8.5×104 using particle image velocimetry measurements combined with computational fluids dynamics-simulations. Experimental measurements are carried out on a 1:28-scale model, focusing exclusively on the central longitudinal plane, in the rear of the truck model. Numerical simulations are performed based on the URANS (unsteady Reynolds averaged Navier–Stokes) approach using two statistical turbulence models, i.e., the shear stress transport k–ω and the baseline Reynolds stress (BSL-RSM) models. A comparison between the numerical and experimental results of the mean velocity profiles in the wake of the heavy truck is found to be relatively consistent. The BSL-RSM model, however, gives a better prediction of experiments, with a deviation of 6% in the near wake, against 13% for the SST k–ω. Both URANS models undervalue the streamwise and spanwise turbulence intensity components with a deviation around 24%, compared with the experimental results. The characteristic feature of the wake flow topology is the formation of a recirculation bubble resulting from the shear layers separated from the truck surfaces. Different identification methods, including visualization of closed streamlines, vorticity magnitude, and the Q-invariant criterion, are considered and highlight the existence of two particular vortex regions in the mean flow: a vortex-shedding area in the upper recirculation region and a back-truck attached vortical structure. It is found that the Q criterion-based technique is a relevant indicator of the vortex cores regions.
Traffic‐related air pollutants inside vehicle cabins are often extremely high compared to background pollution concentrations. The study of the determinants of these concentrations is particularly important for professional drivers and commuters who spend long periods in vehicles. This study is aimed at identifying and quantifying the effect of several exposure determinants on carbon monoxide (CO), equivalent black carbon (eBC), two particulate matter (PM) fractions (PM0.3–1 and PM1–2.5), and ultrafine particle (UFP) concentrations inside a passenger car cabin. The novelty of this work consists in examining the effects of the emissions of the first vehicle ahead (henceforth called “leading vehicle”) on pollutant concentrations inside the cabin of the following vehicle (i.e., the car that was equipped with the air monitoring devices), with particular emphasis on the role of the leading vehicle characteristics (e.g., emission reduction technologies). The real‐time instrumentation was placed inside the cabin of a petrol passenger car, which was driven by the same operator two times per day on the same route in real driving conditions. The in‐cabin ventilation settings were set as follows: windows closed, air conditioning and recirculation modes off, and the fanned ventilation system on. The measurements were conducted over a total of 10 weekdays during two different seasons (i.e., summer and autumn). A video camera fixed to the windscreen was used to retrieve information about traffic conditions and leading vehicle characteristics through careful video analysis. The associations among pollutant concentrations and their potential determinants were evaluated using generalized estimating equation univariate and multiple models. The results confirmed the significant impact of several well‐known determinants such as seasonality, microclimatic parameters, traffic jam situations, and route characteristics. Moreover, the outcomes shed light on the key role of leading vehicle emissions as determinant factors of the pollutant concentrations inside car cabins. Indeed, in the tested cabin ventilation conditions, it was demonstrated that in‐cabin pollutant concentrations were significantly higher with leading vehicles ahead (from +14.6% to +67.5%) compared to empty road conditions, even though the introduction of newer technologies with better emissions reduction helped mitigate their effect. Additionally, diesel‐fuelled leading vehicles compared to petrol‐fuelled leading vehicles were impactful on in‐cabin CO (−7.2%) and eBC (+45.3%) concentrations. An important effect (+30.4%) on in‐vehicle PM1–2.5 concentrations was found with heavy‐duty compared to light‐duty leading vehicles. Finally, this research pointed out that road‐scale factors are more important determinant factors of in‐cabin concentrations than local pollution and meteorological conditions.
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