Vehicle-induced Turbulence Research Articles

Enhanced modeling of vehicle-induced turbulence and pollutant dispersion in urban street canyon: Large-eddy simulation via dynamic overset mesh approach

This study presented a novel application of large-eddy simulation (LES) with a dynamic overset mesh approach to simulate vehicle-induced turbulence in a two-dimensional street canyon. The simulation incorporated moving vehicle entities to emulate two-way traffic, with each vehicle equipped with a pollutant source to simulate pollutant dispersion. Comprehensive long-term statistical analyses were conducted to compare the simulated turbulence with those produced by the conventional approach (where vehicle-induced momentum was not considered) and the quasi-steady method (where vehicle motion was simplified as momentum sources). The results revealed that the presence of moving vehicle entities significantly distorted the primary circulation within the canyon, altering the transport pathways of both lateral momentum and air pollutants. The motion of vehicle entities also induced a substantial amount of turbulence, resulting in different pollutant removal mechanisms at the top of the canyon. The ensemble-average analysis revealed a downwash followed by an upwash within a cycle of vehicle movement, which largely contributed to momentum and pollutant transport. These findings underscored the need for considering the moving entities in LES approaches to enhance vehicle-induced turbulence modeling. Other factors influencing the simulation were discussed, aiming to guide more accurate and reliable turbulence modeling in urban environments.

Estimating vehicular emission factors and vehicle-induced turbulence: Application of an air quality sensor array for continuous multipoint monitoring in a tunnel

In this study, air quality monitoring nodes (equipped with pollutants (NO, NO2, CO, and PM2.5), temperature, and humidity sensors) were deployed at seven locations inside a road tunnel for continuous multipoint monitoring of traffic emissions. At the same time, in-tunnel turbulence was measured with a 3D-sonic anemometer in the middle of the tunnel to estimate vehicle-induced turbulence (VIT). The emission factors (EFs) of all pollutants estimated from conventional two-point measurements varied significantly among arbitrarily chosen pairs of measurement locations (with a range of 53–193% compared to the mean value). These findings suggest the need for multipoint monitoring for more generalizable EF estimates. Our results indicate that installing a sensor array in a tunnel could enable long-term continuous multipoint monitoring of traffic-related pollutants with limited resources. The EF estimates obtained in this study were higher (∼7 times) than those calculated from an emission inventory based on actual traffic data, particularly for CO and wintertime NOX. One possible explanation for this inconsistency is an underestimation of high emitters in the emission inventory. We also estimated VIT as a function of the total traffic flow rate and the fraction of heavy-duty diesel vehicles in a fleet. We expect our results to be used as input data for local and regional air quality models, which can improve the model's performance. We also expect that long-term continuous monitoring of air pollutants in tunnels with multiple sensor nodes will aid evaluations of the effectiveness of air pollution reduction policies by tracking changes in traffic emissions.

Research on PM10 diffusion and distribution of moving vehicle in street canyon based on dynamic mesh

Particulate matter (PM) produced by moving vehicles causes the deterioration of urban air quality and seriously endangers the health of residents. By considering PM10 as discrete phase and adopting dynamic mesh updating technology, a mathematical model of PM10 dispersion in three-dimensional ideal street canyon is established to evaluate the contribution of vehicle-induced turbulence to the dispersion of PM10. The dynamic spatial distribution characteristics of PM10 are analyzed when the average vehicle speed is 60 km/h, 50 km/h and 40 km/h. PM10 diffuses outwards in the form of alternating turbulent vortices on both sides, and its diffusion law is mainly affected by vehicle-induced turbulence. After a quarter of total moving time, the peak concentration of PM10 on the horizontal monitoring line drops to 0.3%, 14.4% and 10.7%, respectively, and the peak concentration of PM10 on the vertical monitoring line drops to 0.03%, 9.6% and 4.3%, respectively. For car drivers, they need to maintain a following distance of more than 3.5 m. For roadside pedestrians, they only need to walk on the sidewalk as required. This paper studies the diffusion of PM10 under different moving conditions from microscopic perspective, and provides a theoretical basis for urban development of traffic emission control measures.

A Computationally Efficient Approach to Resolving Vehicle-Induced Turbulence for Near-Road Air Quality

Abstract Near-road air pollution is a worldwide public health concern, especially in urban areas. Vehicle-induced turbulence (VIT) has a major impact on the initial dispersion of traffic-related pollutants on the roadways, affecting their subsequent near-road impact. The current methods for high-fidelity VIT simulations using computational fluid dynamics (CFD) are often computationally expensive or prohibitive. Earlier studies adopted the turbulent kinetic energy (TKE) method, which models VIT as a fixed TKE volume source and produces turbulence uniformly in the computational traffic zones. This paper presents two novel methods, namely the force method and the moving force method, to generate VIT implicitly by injecting a force source into the computational domain instead of physical vehicles in the domain explicitly, thus greatly reducing the computational burden. The simulation results were evaluated against experimental data collected in a field study near a major highway in Las Vegas, NV, which included collocated measurements of traffic and wind speed. The TKE method systematically overestimated the turbulence produced on the highway by converting the drag force completely into turbulence. This indicates that the TKE method, currently being used to implicitly model VIT in CFD simulations, requires major improvements. In comparison, the proposed force and moving force methods performed favorably and were able to capture turbulence anisotropicity and fluid convection. The force method was shown to be a computationally efficient way to simulate VIT with adequate accuracy, while the moving force method has the potential to emulate vehicle motion and its impact on fluid flow.

Vehicle-induced turbulence and atmospheric pollution

Abstract. Theoretical models of the Earth's atmosphere adhere to an underlying concept of flow driven by radiative transfer and the nature of the surface over which the flow is taking place: heat from the sun and/or anthropogenic sources are the sole sources of energy driving atmospheric constituent transport. However, another source of energy is prevalent in the human environment at the very local scale – the transfer of kinetic energy from moving vehicles to the atmosphere. We show that this source of energy, due to being co-located with combustion emissions, can influence their vertical distribution to the extent of having a significant influence on lower-troposphere pollutant concentrations throughout North America. The effect of vehicle-induced turbulence on freshly emitted chemicals remains notable even when taking into account more complex urban radiative transfer-driven turbulence theories at high resolution. We have designed a parameterization to account for the at-source vertical transport of freshly emitted pollutants from mobile emissions resulting from vehicle-induced turbulence, in analogy to sub-grid-scale parameterizations for plume rise emissions from large stacks. This parameterization allows vehicle-induced turbulence to be represented at the scales inherent in 3D chemical transport models, allowing this process to be represented over larger regions than is currently feasible with large eddy simulation models. Including this sub-grid-scale parameterization for the vertical transport of emitted pollutants due to vehicle-induced turbulence in a 3D chemical transport model of the atmosphere reduces pre-existing North American nitrogen dioxide biases by a factor of 8 and improves most model performance scores for nitrogen dioxide, particulate matter, and ozone (for example, reductions in root mean square errors of 20 %, 9 %, and 0.5 %, respectively).

Numerical evaluation of turbulence induced by wind and traffic, and its impact on pollutant dispersion in street canyons

In cities, street canyons are common features that affect the exposure level and health of pedestrians, drivers, and passengers. The purpose of this study is to analyze the characteristics of velocity, turbulence, pollutant dispersion, and pedestrian exposure levels inside a nearly 400 m asymmetrical street canyon in Dalian, China. We present a realizable k–ε turbulence model, with additional vehicle-induced turbulence (VIT) source terms in the momentum equation, turbulent kinetic equation, and turbulence dissipation rate equation, to simulate velocity and pollution diffusion under the predominant wind directions, varying inlet wind flow conditions, and different vehicle speeds. The results showed that the VIT effect dominated the velocity and pollution distribution under continuous traffic flow conditions. A significant underestimation of up to 33% for velocity, and obvious overestimation of up to 78% for CO concentration, appeared if the effect of VIT was neglected. The results also indicated that more attention should be paid to pedestrian health, as extremely high exposure levels occurred during traffic rush hours due to high vehicle exhaust emissions.

Impact Factors on Airflow and Pollutant Dispersion in Urban Street Canyons and Comprehensive Simulations: a Review

The air quality management within urban street canyons can be improved by enhancing ventilation for dispersion of pollutants. The purpose of this review is to summarize effects of various impact factors on airflow and pollutant dispersion in urban street canyons. The relative intensity of different influence factors is reviewed, which should provide a useful comprehensive guide for modelling of these effects for urban developments. All reviewed numerical simulations, wind tunnel and outdoor scaled model experiments show that the various building heights and incoming airflow conditions could produce a clear influence on airflow and pollutant dispersion in urban street canyon. Outdoor scaled experiments have provided complex turbulent data and illustrated the complexity of airflow within urban street canyons, which would require comprehensive simulations to investigate the microclimate within these urban street canyons. Impacts of thermal and/or wall heating conditions have been fully studied, while the impact of inflow variation, building height difference, model scale and the coupling effect of different factors are current hot topics for research. Building height difference and time-varying inflow conditions are factors of most significant influence, while tree planting, vehicle-induced turbulence, thermal and/or wall heat conditions have a relatively weak influence.

Evaluation of the Relationship between Momentum Wakes behind Moving Vehicles and Dispersion of Vehicle Emissions Using Near-Roadway Measurements.

A parameterization of initial vertical dispersion coefficient (σz,init) was developed for incorporation into California line source dispersion model, version 4 (CALINE4) and AMS/EPA regulatory model (AERMOD) to better predict pollutant concentrations near roadways. The momentum wake theory of moving vehicles indicates that both vehicle-induced turbulence (VIT) and dispersion occur in the vehicle wake. Based on a literature review, it is postulated that σz,init near roadways can be estimated using a "wake area model" concept of effective wake area defined as the vehicle height times the wake length, vehicle density, and vehicle type. A total of 523 5-min near-roadway simultaneous measurements (2016-2018) of pollutant concentrations and meteorological and traffic information were used to evaluate the model. Two roadways with distinct fleet composition and simple road configurations were selected for monitoring. The near-roadway σz,init ranged from 1 to 4 m for light-duty vehicles (LDVs) and from 3 to 7 m for fleet-mix (LDVs and heavy-duty vehicles (HDVs)). The results demonstrate that the dispersion contribution from one HDV was 31 times larger than that from one LDV. Calculated pollutant dispersion using the wake area model compared favorably with measurements (R2 = 0.91, slope = 1.07). These results indicate that σz,init varies with vehicle density and HDVs. Pollutant dispersion related to the vehicle wakes can be used to correctly parameterize dispersion models and improve prediction of pollutant concentrations near roadways.

The effect of exhaust emissions from a group of moving vehicles on pollutant dispersion in the street canyons

Moving vehicles are the primary emission source of road pollution and the vehicle-induced turbulence (VIT) affecting the flow field in the street canyon. This paper used the dynamic mesh updating method to simulate the air pollution under the real situation of vehicle movement and exhaust emission in the street canyon. The dispersion characteristics of vehicular exhaust under various vehicle arrangements were analyzed. The personal intake fraction (P_IF) was introduced as an index to analyze the impact of factors such as vehicle speed and ambient wind velocity on the pollution exposure level of pedestrians. Based on the general assumptions in the study of street canyon pollution that vehicle-induced turbulence (VIT) is a continuous turbulence source and vehicle exhaust pollutants is a uniform pollution source, the conditions of the hypothesis are preliminarily explored. The research indicates that VIT can be regarded as a continuous turbulence source when vehicles maintain a “safe distance” on the road and form a stable traffic flow. Moreover, according to the range of dispersion of exhaust pollution, a “healthy distance” was proposed to guide driver to avoid direct impact of exhaust emitted from the vehicles in front.

Dispersion behaviors of exhaust gases and nanoparticle of a passenger vehicle under simulated traffic light driving pattern

In the present study, the flow structure and pollutants dispersions were investigated by experiment and simulation on a typical passenger vehicle under simulated traffic light driving pattern. Some important findings were achieved: 1) gaseous pollutants diffuse drastically during first 0.3–0.6 m distance depending on wind velocity, at 1.25 m/s wind speed which is the similar level of exhaust gas, the pollutant concentration rises suddenly at ~0.6 m because exhaust plume is twisted by bottom gas flow, and a low velocity zone is produced; 2) as wind speed increases, the vehicle-induced turbulence is more and more important on pollutant dispersion pattern than exhaust plume dynamics. For instance, at 1.25 m/s and 4.17 m/s wind speeds, pollutants decrease to zero at ~1.6 m behind tail pipe, but at 0 m/s condition, pollutant relative fraction is still at ~0.12 level even at very long distance; 3) solid particle has larger attenuation rate than gaseous pollutants, only after ~0.6 m the particle number (PN) and diameter are very close to background values. Solid particle can diffuse to farther distance in vehicle transverse direction, when a car passes through the pedestrians with a 3 m distance, pedestrians expose to 2.6–3 times higher PN relative to atmosphere with diameters of 28–33 nm, this is very hazardous for human health; 4) exhaust pollutants disperse difficultly when followed by a car with a commonly waiting distance. At free dispersion scenario only behind ~0.6 m, PN decreases to 5800 #/cm3 (background value), but in-cabin PN of the following car (behind 0.8 m) rises to 3.5 × 104 #/cm3 (even after 2–3 times decay through ventilation system). This study provides implications for future studies on transport planning.

Assessing On-Road Emission Flow Pattern under Car-Following Induced Turbulence Using Computational Fluid Dynamics (CFD) Numerical Simulation

Research assessing on-road emission flow patterns from motor vehicles is essential in monitoring urban air quality, since it helps to mitigate atmospheric pollution levels. To reveal the influence of vehicle induced turbulence (VIT) caused by both front- and rear-vehicles on traffic exhaust and verify the applicability of the simplified line source emission model, a Computational Fluid Dynamics (CFD) numerical simulation was used to investigate the micro-scale vehicle pollutant flow patterns. The simulation results were examined through sensitivity analysis and compared with the field measured carbon monoxide (CO) concentration. Conclusions indicate that the vehicle induced turbulence caused by the airflow blocking effect of both front- and rear-vehicles impedes the diffusion of front-vehicle traffic exhaust, compared with that of the rear vehicle. The front-vehicle isosurface with the CO mass fraction of 0.0012 extended to 6.0 m behind the vehicle, while that of the rear-vehicle extends as far as 12.7 m. But for the entire motorcade, VIT is beneficial to the diffusion of pollutants in car-following situations. Meanwhile, within the range of 9 m behind the rear of the lagging vehicle lies a vehicle induced turbulence zone. Furthermore, the influence of vehicle induced turbulence on traffic exhaust flow pattern is obvious within a range of 1 m on both sides of the vehicle body, where the concentration gradient of on-road emission is larger and contains severe mechanical turbulence. As a result, in the large concentration gradient area of the pollutant flow field, which accounts for 99.85% of the total concentration gradient, using the line source models to represent the on-road emission might introduce considerable errors due to neglecting the influence of vehicle induced turbulence. Findings of this study may shed lights on predicting emission concentrations in multiple locations by selecting appropriate on-road emission source models.

Effect of moving vehicles on pollutant dispersion in street canyon by using dynamic mesh updating method

Moving vehicles exert a considerable influence on air flow and turbulence within urban street canyons, which is an important consideration for the pollutant dispersion and thus, local air quality management. In order to evaluate vehicle-induced turbulence and its contribution to pollutant dispersion, this study proposes a computational fluid dynamics based simulation methodology that uses a dynamic mesh updating method. The unsteady RNG k-ε turbulence model was employed to investigate the turbulent kinetic energy (TKE) induced by vehicle movement and pollutant profile in the target street canyon. The simulation accuracy is successfully validated against a published wind tunnel experiment. The results demonstrate that: (1) The TKE is mainly located behind the vehicle, and the magnitude of the TKE around the moving vehicle body far exceeds that of stationary vehicles; (2) Travelling speed has a significant influence on the TKE. When the moving speed is 15 m/s, its TKE near the tail is about 1.8 times and 6.3 times than that at 10 m/s and at 5 m/s, respectively; (3) Vehicle movement greatly contributes to pollutant dispersion. Compared to no vehicle movement (V = 0 m/s), vehicle-induced turbulence with V above 5 m/s can reduce the carbon monoxide concentration by about 4% at breathing height.

A Study of the Spatial Variation of Vehicle-Induced Turbulence on Highways Using Measurements from a Mobile Platform

During July 2016, an on-road study was conducted in and around the Toronto, Canada region to investigate the spatial variation of vehicle-induced turbulence on highways. The power spectral density of turbulent kinetic energy (TKE) while following on-road vehicles is significantly enhanced for frequencies greater than 0.5 Hz. This increase is not present while driving isolated from traffic, demonstrating that TKE is enhanced considerably on highways in the presence of vehicles. The magnitude of normalized TKE is found to decay following a power-law relationship with increasing normalized distance behind on-road vehicles, which is most pronounced behind heavy-duty trucks. The results suggest that the TKE in the vehicle wake is maximized in the upper shear layer near the vehicle top. An extended parametrization is outlined that describes the total on-road TKE enhancement due to a composition of vehicles, which includes a vertical dependence on the magnitude of TKE.

The effects of roadside vegetation characteristics on local, near-road air quality

Roadside vegetation has been shown to impact downwind, near-road air quality, with some studies identifying reductions in air pollution concentrations and others indicating increases in pollutant levels when vegetation is present. These widely contradictory results have resulted in confusion regarding the capability of vegetative barriers to mitigate near-road air pollution, which numerous studies have associated with significant adverse human health effects. Roadside vegetation studies have investigated the impact of many different types and conditions of vegetation barriers and urban forests, including preserved, existing vegetation stands usually consisting of mixtures of trees and shrubs or plantings of individual trees. A study was conducted along a highway with differing vegetation characteristics to identify if and how the changing characteristics affected downwind air quality. The results indicated that roadside vegetation needed to be of sufficient height, thickness, and coverage to achieve downwind air pollutant reductions. A vegetation stand which was highly porous and contained large gaps within the stand structure had increased downwind pollutant concentrations. These field study results were consistent with other studies that the roadside vegetation could lead to reductions in average, downwind pollutant concentrations by as much as 50% when this vegetation was thick with no gaps or openings. However, the presence of highly porous vegetation with gaps resulted in similar or sometimes higher concentrations than measured in a clearing with no vegetation. The combination of air quality and meteorological measurements indicated that the vegetation affects downwind pollutant concentrations through attenuation of meteorological and vehicle-induced turbulence as air passes through the vegetation, enhanced mixing as portions of the traffic pollution plume are blocked and forced over the vegetation, and through particulate deposition onto leaf and branch surfaces. Computational fluid dynamic modeling highlighted that density of the vegetation barrier affects pollutant levels, with a leaf area density of 3.0 m2 m-3 or higher needed to ensure downwind pollutant reductions for airborne particulate matter. These results show that roadside bushes and trees can be preserved or planted along highways and other localized pollution sources to mitigate air quality and human health impacts near the source if the planting adheres to important characteristics of height, thickness, and density with full coverage from the ground to the top of the canopy. The results also highlight the importance of planting denser vegetation and maintaining the integrity and structure of these vegetation barriers to achieve pollution reductions and not contribute to unintended increases in downwind air pollutant concentrations.

Large Eddy Simulation of Vehicle Induced Turbulence in an Urban Street Canyon with a New Dynamically Vehicle-Tracking Scheme

ABSTRACTMoving vehicles could have a considerable influence on air flow and turbulence within urban street canyons, especially the vehicle induced turbulence (VIT), which is an important consideration for pollutants’ dispersion and local air quality management. To simulate air flow in urban street canyons with moving vehicles, an Euler-Lagrangian method was further developed by this study. The method involved dynamically tracking each moving vehicle and determining the vehicle induced drag force on surrounding air. Simulations under different background wind velocities and vehicle speeds show that, moving vehicles could pose a strong effect on wind turbulence, but have a lesser effect on average wind field within a street canyon. The presence of VIT was mainly in the lower areas of a street canyon. The average standard deviation of velocity (σw0) in the lower areas is about 0.5 m s–1. In comparison with theoretical and experimental VIT models, VIT values produced by our simulations were higher than those obtained by experimental model, and were slightly lower than those produced by theoretical model. Instantaneous air flow fields within a street canyon were also investigated. Our results have indicated that the existence of local eddies could be a contribution factor to VIT.

Numerical Simulation on the Effect of Vehicle Movement on Pollutant Dispersion in Urban Street

Combined processes of ambient crosswind and vehicle-induced turbulence have a very significant effect on the distribution of pollutants emitted by vehicles in street canyon. So the purpose of this study is to analyze the process of pollutants dispersion when moving vehicles pass the urban streets based on the basic theory of hydromechanics. The air flow and pollutants dispersion in a street canyon in the presence of a moving vehicle by a dynamic mesh approach using computational fluid dynamics (CFD) is investigated. The standard k-epsilon model and enhanced wall functions are employed to establish airflow field, and species transport model and the tracer gas technique are combined to simulate pollutants dispersion. The simulation results are compared with the published experimental data of mean velocity and turbulence for the validation. Then on the base of this validation, we further explore the effect of vehicle speed and the vehicle driving lanes. This study is a good understanding of the mechanism of vehicle-induced pollutants dispersion within the urban street canyon. Thanks to the different piston effect formed by moving vehicle in different lanes, the concentration on the leeward side in the descending order are L1, No vehicle, L4, L3, L2. Meanwhile, the concentration of pollutant behind the vehicle on the leeward side decreases with increasing vehicle moving speed. Higher moving speed could lead to a reduction of concentration on the windward side.

Influence of vehicle-induced turbulence on pollutant dispersion in street canyon and adjacent urban area

This paper presents an assessment of an influence of turbulence intensity in the close vicinity of a linear source of pollution on the consequent process of air pollutant dispersion. Turbulent flow structure in the vicinity of vehicles in motion significantly influences the dispersion of air pollutants generated by road traffic into the environment of a traffic path. The detailed investigation of a cross-section vortex in the street canyon is done by the computational parametrical study. Vehicles' motion along the traffic path has been considered as an input parameter for the consequent quantification of the kinetic energy of turbulence generation above the road. The influence of generated kinetic energy of turbulence by a linear road on the concentration of PM10 at receptor points located in the studied area is obtained by using the mathematical modelling method CFD.

Influence of vehicle-induced turbulence on pollutant dispersion in street canyon and adjacent urban area

Influence of vehicle-induced turbulence on pollutant dispersion in street canyon and adjacent urban area

A new approach to quantifying vehicle induced turbulence for complex traffic scenarios

Traffic-related pollutants adversely affect air quality, especially in regions near major roadways. The vehicle-induced turbulence (VIT) is a significant factor that controls the initial dilution, dispersion, and ultimately the chemical and physical fate of pollutants by altering the conditions in the microenvironment. This study used a computational fluid dynamics (CFD) software FLUENT to model the vehicle-induced turbulence (VIT) generated on roadways, with a focus on impact of vehicle-vehicle interactions, traffic density and vehicle composition on turbulent kinetic energy (TKE). We show, for the first time, that the overall TKE from multiple vehicles traveling in series can be estimated by superimposing the TKE of each vehicle, without considering the distance between them while the distance is greater than one vehicle length. This finding is particularly significant since it enables a new approach to VIT simulations where the overall TKE is calculated as a function of number of vehicles. We found that the interactions between vehicles traveling next to each other in adjacent lanes are insignificant, regardless the directions of the traffic flow. Consequently, simulations of different traffic scenarios can be substantially simplified by treating two-way traffic as one-way traffic, with less than 5% difference in the overall volume-averaged TKE. We also developed equations that allow the estimation of the overall volume-averaged TKE as a function of the number and the type of vehicles.

The impact of traffic-flow patterns on air quality in urban street canyons

We investigated the effect of different urban traffic-flow patterns on pollutant dispersion in different winds in a real asymmetric street canyon. Free-flow traffic causes more turbulence in the canyon facilitating more dispersion and a reduction in pedestrian level concentration. The comparison of with and without a vehicle-induced-turbulence revealed that when winds were perpendicular, the free-flow traffic reduced the concentration by 73% on the windward side with a minor increase of 17% on the leeward side, whereas for parallel winds, it reduced the concentration by 51% and 29%. The congested-flow traffic increased the concentrations on the leeward side by 47% when winds were perpendicular posing a higher risk to health, whereas reduced it by 17–42% for parallel winds. The urban air quality and public health can, therefore, be improved by improving the traffic-flow patterns in street canyons as vehicle-induced turbulence has been shown to contribute significantly to dispersion.