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

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## Abstract

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## 1. Introduction

^{−1}m

^{−2}for all roads in OpenFOAM software, finding that trees can reduce the concentration of vehicle pollutant by 7%. Sun and Zhang [14] analyzed and discussed the effectiveness of avenue trees in dispersing vehicle pollutant in asymmetric street canyons, with a velocity inlet in ANSYS Fluent as a line source model, indicating that wind direction and canyon geometry play important roles in traffic exhaust diffusion. Buccolieri et al. [15] found that the ratio of street width to building height (W/H) is a crucial variable affecting pedestrian height level traffic originated pollutant concentration, with four tracer gas emitting line sources simulating on-road vehicle emissions. However, few studies have discussed the applicability and effectiveness of line source design in simulating vehicle pollutant sources. Meroney et al. [16] found that closely spaced point sources rather than uniformly continuous line sources can better approximate the traffic exhaust source. Within the city scale, the interaction between the urban traffic and atmosphere movement tends to induce complicated flow patterns. This fact produces a heterogeneous distribution of on-road emissions and the strong emission concentration gradient [17] in urban roads, which is difficult to simulate by using the extremely simplified line source model. The studies of automotive aerodynamics illustrate that the flow over a vehicle contains extremely complex mechanical turbulence [18]. Rao et al. [19] found that noticeable augmentation of turbulent kinetic energy appeared in heavy traffic conditions. Affected by the airflow blocking effect in car-following situations, vehicle induced turbulence (VIT) significantly influences exhaust dispersion in urban areas, especially in street canyons [20]. Due to the oversimplification of line source models, the impact of vehicle induced turbulence on traffic exhaust flow pattern and instantaneous exposure concentration of pedestrians has been ignored, resulting in deviation from reality. According to Deng and Guan [21], the simulated concentration error of the infinite line source emission model is at a 10

^{−1}order of magnitude, introducing considerable errors due to neglecting the influence of vehicle induced turbulence. Few studies have investigated the effect of vehicle induced turbulence and applicability of line source models under heavy traffic conditions in the urban environment.

## 2. Methodology and Modeling

#### 2.1. Field Measurement

#### 2.1.1. CO Concentration

#### 2.1.2. Traffic Flow

#### 2.2. Numerical Approach

#### 2.2.1. CFD Model Built-Up

#### 2.2.2. Mathematical Model

#### 2.2.3. CFD Parametrization

- Inflow boundary: Velocity-Inlet, Temperature = 300 K, CO Mass Fraction = 0.001.
- Outflow boundary: Pressure-Outlet, Static Pressure = 0.
- Ground and vehicle body surface: Stationary Wall, Roughness Height (m) = 0, Roughness Constant = 0.5.
- Top and side surfaces: SYMMETRY.
- Exhaust pipe: Velocity-Inlet (V
_{CO}= 4.8 m/s), CO Mass Fraction = 0.1. - Flow (air): Pressure = 1.01325 × 105 Pa, Temperature = 288 K, Density = 1.225 kg/m
^{3}, Dynamic Coefficient of Viscosity $\mu =1.7894\times {10}^{-5}\mathrm{Pa}\cdot \mathrm{s}$, Kinematic Coefficient of Viscosity $\upsilon =\mu /\rho =1.461\times {10}^{-5}{\mathrm{m}}^{2}/\mathrm{s}$.

#### 2.2.4. CFD Model Assumptions

**Assumption**

**1.**

**Assumption**

**2.**

**Assumption**

**3.**

**Assumption**

**4.**

**Assumption**

**5.**

**Assumption**

**6.**

#### 2.3. Grid Sensitivity Analysis

^{−5}was used for four cases. The grid quantity and ${C}_{d}$ results are summarized in Table 1.

#### 2.4. Model Calibration

- The monitoring duration was at the evening peak, with large motor vehicle flow and high CO emission intensity;
- There are tall buildings on both sides of the monitoring locations, and CO was easy to accumulate there;
- On the day of experiment, the wind speed was small, which makes it difficult for CO to enter the surrounding area.

## 3. Results and Discussion

#### 3.1. Steady-State Simulation Results

#### 3.1.1. Verification of Existence of VIT Influence

#### 3.1.2. Influence of VIT in Direction along the Road

#### 3.1.3. Influence of VIT in Direction Perpendicular to the Road

^{−3}, so there was a large concentration gradient area accounting for 99.85% of the total concentration gradient only within 1 m on both sides of the vehicle’s body.

^{−6}, and concentration at vehicle side 1 m was very close to the background concentration. Namely, the effect of vehicle induced turbulence caused by front- and rear-vehicles on the traffic exhaust flow pattern was limited. More importantly, within a range of 1 m on both sides of the vehicle body there was a rather large concentration gradient area, containing sharp mechanical turbulence and a complex on-road emission flow mechanism. In this region, VIT should be considered carefully as it could have a dominant influence on the emissions concentration.

#### 3.2. Transient Simulation Results

#### 3.2.1. Judgment of Stable Driving State

#### 3.2.2. Influence of VIT over Time

## 4. Conclusions

- The vehicle induced turbulence caused by front- and rear-vehicles impedes the diffusion of traffic exhaust of the front car. Until the convergence timing occurred in the steady simulation, the front-vehicle isosurface with the CO mass fraction of 0.0012 extended to 6.0 m behind the vehicle, while rear-vehicle isosurface with the CO mass fraction of 0.0012 extended to 12.7 m behind the vehicle. Thus, in the direction along the road, the dispersion speed of the rear car pollutant was about twice that of the front one. According to Wang et al. [45], the CO concentration of a single car drops to a number near the background concentration within 4 m of the vehicle rear, which is lower than the results in this paper. By comparison, we can draw the conclusion that although the emissions of the front vehicle disperse slower than that of the rear vehicle, from an overall perspective, VIT is beneficial to the diffusion of pollutants of a motorcade.
- In the direction perpendicular to the road, the VIT influence area is generally concentrated within 1 m of the vehicle side. This result is consistent with the research of Wang et al. [45], which concluded that the concentration is relatively high within the radius of the exhaust pipe from 1 m to 1.5 m. That is, within a range of 1 m on both sides of vehicle there is a large concentration gradient area, which accounts for 99.85% of the total concentration gradient between the background environment and the exhaust pipe, which contains sharp mechanical turbulence and a complex traffic exhaust flow mechanism. In the large concentration gradient region, VIT should be considered carefully since it might affect the on-road emissions concentration.
- In this research, with the fleet average speed of 9.29 m/s and the average space headway of 24.15 m, the vehicle induced turbulence zone was approximated within a range of 9 m behind the rear car, afterwards the influence of VIT weakened.

^{−1}order of magnitude. Future research can focus on supplementing the vehicle model and the improvement of real traffic flow fluctuant imitation in simulations. Other vehicle models, such as trucks, or combinations of vehicle models can be used to explore the differences between VIT impacts on various motorcycle types. Moreover, a dynamic motorcade model which considers the distribution frequency of the vehicle speed may be established to improve simulation accuracy and better reflect actual vehicle movement. Further studies may be conducted to reveal the specific manifestation of the concentration differences between line source models and the actual situations by incorporating dense monitoring data, refined models, and even large amounts of big data [46].

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Obtained saturated headway from 600 effective headways measured, (

**a**) Counts–effective time headway scatter diagram, (

**b**) Probability distribution curve.

**Figure 3.**Computational domain diagram, (

**a**) Simulation model (unit: mm), (

**b**) Boundary conditions used for the simulation.

**Figure 4.**Road side carbon monoxide (CO) concentration curve, (

**a**) Averaged monitoring concentration curve of A, B, C, and D points, (

**b**) Comparison of CO concentration between simulation and field measurement.

**Figure 6.**Diffusion in the directions along the road and perpendicular to the road, (

**a**) Velocity vector contrastive diagram of the longitudinal section, (

**b**) CO concentration contrastive diagram of the longitudinal section, (

**c**) Concentration distribution map with a CO mass fraction of 0.0012, (

**d**) CO concentration contrastive diagram of 4 m behind the tail of the front car, 4 m behind the tail of the rear car, and 6 times the vehicle length behind the tail of the rear car, (

**e**) CO mass fraction at pedestrian breathing height (1.5 m) on both sides of the road.

Precision | Low | Medium-Low | Normal | High |
---|---|---|---|---|

Max grid size (m) | 0.40 | 0.35 | 0.30 | 0.25 |

Grid quantity | 300,000 | 450,000 | 650,000 | 1,150,000 |

C_{d} | 0.366 | 0.364 | 0.320 | 0.312 |

Error (%) | 19.80 | 19.20 | 4.70 | 2.10 |

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**MDPI and ACS Style**

Shi, X.; Sun, D.; Fu, S.; Zhao, Z.; Liu, J. Assessing On-Road Emission Flow Pattern under Car-Following Induced Turbulence Using Computational Fluid Dynamics (CFD) Numerical Simulation. *Sustainability* **2019**, *11*, 6705.
https://doi.org/10.3390/su11236705

**AMA Style**

Shi X, Sun D, Fu S, Zhao Z, Liu J. Assessing On-Road Emission Flow Pattern under Car-Following Induced Turbulence Using Computational Fluid Dynamics (CFD) Numerical Simulation. *Sustainability*. 2019; 11(23):6705.
https://doi.org/10.3390/su11236705

**Chicago/Turabian Style**

Shi, Xueqing, Daniel (Jian) Sun, Song Fu, Zhonghua Zhao, and Jinfang Liu. 2019. "Assessing On-Road Emission Flow Pattern under Car-Following Induced Turbulence Using Computational Fluid Dynamics (CFD) Numerical Simulation" *Sustainability* 11, no. 23: 6705.
https://doi.org/10.3390/su11236705