Passive Drag Reduction Optimization for Complex Commercial Vehicle Models

Liao, G.; Xue, M.; Yue, L.; Yang, H.; Guo, P.; Hu, X.; Wang, Z.; Ma, T.

doi:10.47176/jafm.18.6.2983

Passive Drag Reduction Optimization for Complex Commercial Vehicle Models

Document Type : Regular Article

Authors

¹ FAW Liberation Co., Ltd. Commercial Vehicle Development Institute, Changchun 130000, China

² State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China

10.47176/jafm.18.6.2983

Abstract

The long-distance transportation of heavy commercial vehicles is facing increasing pressure. Low wind resistance and low fuel consumption will become the objective requirements and development trend of heavy commercial vehicles. In this paper, the 1:1 complex model of commercial vehicle is taken as the research object to study the passive drag reduction of the commercial vehicle. First, simulation analysis is conducted, and then the wind tunnel test of the 1:2.5 complex model is performed to verify the accuracy of the simulation scheme and results. Then, the geometric shape of the cab is parameterized and controlled by 13 parameters. After determining the range of parameter changes, Latin hypercube sampling is selected, and large eddy simulation is used for numerical simulation to construct the sample space. Taking the shape parameter as the input factor and the coefficient of drag C_D as the target response, the initial surrogate model is constructed, and the sample points are supplemented by the combination of global and local point addition strategies to improve the accuracy of the surrogate model. Finally, R²=0.812. The local details of the optimization results are optimized, and the low-wind-resistance shapes of the cabs of the three styling styles are obtained. Among them, the bullet model has the lowest C_D. Compared with the basic model, the drag reduction rate is 28%, and the coefficient of drag is simulated. The error between the value and the test value is within 1%.

Keywords

Main Subjects

Computational Fluid Dynamics

References

Aleksandra, A. R., & Alvin, G. (2022). On the drag reduction of road vehicles with trailing edge-integrated lobed mixers. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 236(7). 1515-1545. https://doi.org/10.1177/09544070211039697

Arabacı, S. K., & Pakdemirli, M. (2023). Aerodynamic improvements of buses inspired by beluga whales. Journal of Applied Fluid Mechanics, 16(12), 2569-2580. https://doi.org/10.47176/jafm.16.12.1694

Aultman, M., Wang, Z., & Duan, L. (2021) Effect of time-step size on flow around generic car models. Journal of Wind Engineering & Industrial Aerodynamics, 219: 104764 https://doi.org/10.1016/j.jweia.2021.104764

Ballabio, D., & Vasighi, M. (2012). A MATLAB toolbox for self organizing maps and supervised neural network learning strategies. Chemometrics and Intelligent Laboratory Systems, 118, 24–32. https://doi.org/10.1016/j.chemolab.2012.07.005

Bates, S., Hastie, T., & Tibshirani, R. (2023). Cross-validation: what does it estimate and how well does it do it?. Journal of the American Statistical Association, 119(546), 1434-1445. https://doi.org/10.1080/01621459.2023.2197686

Bayindirli, C. (2021). Reducing of pressure based drag force of a bus model by flow control rod in wind tunnel. International Journal of Automotive Science and Technology, 5(4), 412-418. https://doi.org/10.30939/ijastech..994351

Davis, S. C., & Boundy, R. G. (2022). Transportation energy data book. Edition 40. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). https://doi.org/10.2172/1767864

Farghaly, M. B., Sarhan, H. H., & Abdelghany, E. S. (2023). Aerodynamic performance enhancement of a heavy trucks using experimental and computational investigation. CFD Letters, 15(3), http://doi.org/10.37934/cfdl.15.8.7394

Garcia-Ribeiro, D., Bravo-Mosquera, P. D., Ayala-Zuluaga, J. A., Martinez-Castañeda, D. F., Valbuena-Aguilera, J. S., Cerón-Muñoz, H. D., & Vaca-Rios, J. J. (2023). Drag reduction of a commercial bus with add-on aerodynamic devices. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 237(7), 1623-1636. https://doi.org/10.1177/09544070221098209

Kim, S. H., & Kim, J. J. (2023). Study on drag reduction of commercial vehicle using flow control device. Journal of the Korean Society of Visualization, 21(2), 8–13. https://doi.org/10.5407/JKSV.2023.21.2.008

Levin, J., & Chen, S. H. (2023). Aerodynamic of a refrigerated truck and improvement to reduce its aerodynamic drag. Proceedings of the Institution of Mechanical Engineers, Part D. https://doi.org/10.1177/09544070221113128

Lv, L. Y., Lu, Y. J., Wang, S., et al. (2019). Agent model technology and its application: current situation and prospect. Chinese Journal of Mechanical Engineering, 60(3), 254-281. http://dx.doi.org/10.3901/JME.2024.03.254.

McAuliffe, B. R., Ghorbanishohrat, F., & Barber, H. (2022). Preliminary investigation towards next generation truck design for aerodynamic efficiency. Techni Cal Report, National Research Council Of Canada. Aerospace, Artwork Size. www. https://nrc -publications .canada .ca /eng /view /object /?id =610b10b1 -805a -4047 -a908 174b18a0ea07

McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometric, 42(1), 55-61. http://dx.doi.org/10.1080/00401706.2000.10485979

Mohamed-Kassim, Z., & Filippone, A. (2010). Fuel savings on a heavy vehicle via aerodynamic drag reduction. Transportation Research Part D： Transport and Environment, 15(5), 275-284. https://doi.org/10.1016/j.trd.2010.02.010

Nagelkerke, N. J. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691-692. https://doi.org/10.2307/2337038

Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3), 240-255. https://doi.org/10.1109/TEVC.2004.826071

Schiavo, D., Trevizan, L. C., Pereira-Filho, E. R., & Nóbrega, J. A. (2009). Evaluation of the use of multiple lines for determination of metals in water by inductively coupled plasma optical emission spectrometry with axial viewing. Spectrochimica Acta Part B: Atomic Spectroscopy, 64(6), 544-548. https://doi.org/10.1016/j.sab.2009.05.009

Wang, J. Y., Geng, Y. L., Hui, Z., Li, T. H., Liu, Z. C., Li, J. C., Hu, X. J. (2020). Research on drag reduction of square-backed vehicle model based on plasma flow control. Automotive Engineering, 42 (6), 753-758. https://doi.org/10.19562/j.chinasae.qcgc.2020.06.007

Wang, Y., Wu, C., Tan, G., & Deng, Y. (2017). Reduction in the aerodynamic drag around a generic vehicle by using a non-smooth surface. Proceedings of the Institution of Mechanical Engineers, Part D： Journal of Automobile Engineering, 231(1), 130-144. https://doi.org/10.1177/0954407016636970

Wiedemann, J. (1996). The influence of ground simulation and wheel rotation on aerodynamic drag optimization-potential for reducing fuel consumption. SAE Transactions, 810-819. https://doi.org/10.3354/cr030079

Willmott, C., & latsuura, K. (2005) Advantages of the mean absolute error (MAE) over the root mean square eror (RMSE) in assessing average model performance. Climate Research, 30(1)79-82. https://doi.org/10.3354/cr030079

Yadegari, M., & Bak Khoshnevis, A. (2021). Numerical and experimental study of characteristics of the wake produced behind an elliptic cylinder with trip wires. Iranian Journal of Science and Technology, Transactions of Mechanical Engineering, 45, 265-285. https://doi.org/10.1007/s40997-020-00373-6

Yadegari, M., Bak Khoshnevis, A., & Boloki, M. (2023). An experimental investigation of the effects of helical strakes on the characteristics of the wake around the circular cylinder. Iranian Journal of Science and Technology, Transactions of Mechanical Engineering, 47(1), 67-80. https://doi.org/10.1007/s40997-022-00494-0

Yang, W. Y., Cao, W., Kim, J., Park, K. W., Park, H. H., Joung, J., & Im, T. (2020). Applied numerical methods using MATLAB. John Wiley & Sons. https://doi.org/10.5860/choice.43-1615

You, D., & Moin, P. A. (2009). Dynamic globalcoefficient subgrid-scale model for large-eddy simulation of turbulent scalar transport in complexgeometries. Physics of Fluids, 21(4). https://doi.org/10.1063/1.3115068

Zhang, Y. C., Zheng, Z. Y., Wu, K. G., & Zhang, Z. (2020). Research on active drag reduction of MIRA fast back model. Automotive Engineering, 42 (5), 588-592. https://doi.org/10.19562/j.chinasae.qcgc.2020.05.004