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Modeling Emission Flow Pattern of a Single Cruising Vehicle on Urban Streets with CFD Simulation and Wind Tunnel Validation

Author

Listed:
  • Xueqing Shi

    (State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Daniel (Jian) Sun

    (State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    China Institute of Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Ying Zhang

    (Shanghai Municipal Engineering Design Institute (Group) Co., Ltd., Shanghai 200092, China)

  • Jing Xiong

    (China Institute of Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Zhonghua Zhao

    (Shanghai Institute of Tourism, Shanghai Normal University, Shanghai 200234, China)

Abstract

Transportation has become one of the primary sources of urban atmospheric pollutants and it causes severe diseases among city residents. This study focuses on assessing the pollutant dispersion pattern using computational fluid dynamics (CFD) numerical simulation, with the effect and results validated by the results from wind tunnel experiments. First, the wind tunnel experiment was carefully designed to preliminarily assess the flow pattern of vehicle emissions. Next, the spatiotemporal distribution of pollutant concentrations around the motor vehicle was modeled using a CFD numerical simulation. The pollutant concentration contours indicated that the diffusion process of carbon monoxide mainly occurred in the range of 0−2 m above the ground. Meanwhile, to verify the correctness of the CFD simulation, pressure distributions of seven selected points that were perpendicular along the midline of the vehicle surface were obtained from both the wind tunnel experiment and the CFD numerical simulation. The Pearson correlation coefficient between the numerical simulation and the wind tunnel measurement was 0.98, indicating a strong positive correlation. Therefore, the distribution trend of all pressure coefficients in the numerical simulation was considered to be consistent with those from the measurements. The findings of this study could shed light on the concentration distribution of platoon-based vehicles and the future application of CFD simulations to estimate the concentration of pollutants along urban street canyons.

Suggested Citation

  • Xueqing Shi & Daniel (Jian) Sun & Ying Zhang & Jing Xiong & Zhonghua Zhao, 2020. "Modeling Emission Flow Pattern of a Single Cruising Vehicle on Urban Streets with CFD Simulation and Wind Tunnel Validation," IJERPH, MDPI, vol. 17(12), pages 1-17, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4557-:d:376006
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    References listed on IDEAS

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    1. Xueqing Shi & Daniel (Jian) Sun & Song Fu & Zhonghua Zhao & Jinfang Liu, 2019. "Assessing On-Road Emission Flow Pattern under Car-Following Induced Turbulence Using Computational Fluid Dynamics (CFD) Numerical Simulation," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    2. Ilie, Marcel, 2019. "Fluid-structure interaction in turbulent flows; a CFD based aeroelastic algorithm using LES," Applied Mathematics and Computation, Elsevier, vol. 342(C), pages 309-321.
    3. Jog, C.S. & Agrawal, Manish & Nandy, Arup, 2016. "The time finite element as a robust general scheme for solving nonlinear dynamic equations including chaotic systems," Applied Mathematics and Computation, Elsevier, vol. 279(C), pages 43-61.
    4. Ryi, Jaeha & Rhee, Wook & Chang Hwang, Ui & Choi, Jong-Soo, 2015. "Blockage effect correction for a scaled wind turbine rotor by using wind tunnel test data," Renewable Energy, Elsevier, vol. 79(C), pages 227-235.
    5. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
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