Development of an Intelligent Passive Device Generator for Road Vehicle Applications

Document Type : Regular Article


Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, Ontario, L1G0C5, Canada



Flow control has a tremendous technological and economic impact, such as aerodynamic drag reduction on road vehicles which translates directly into fuel savings, with a consequent reduction in greenhouse gas emissions and operating costs. In recent years, machine learning has also been used to develop new approaches to flow control in place of more laborious methods, such as parametric studies, to find optimal parameters with few exceptions. This paper proposes an intelligent passive device generator (IPDG) that combines computational fluid dynamics (CFD) and genetic algorithm, more specifically, the Non-dominated Sorting Genetic Algorithm II (NSGA II). The IPDG is not application specific and can be applied to generate various devices in the given design space. In particular, it creates three-dimensional passive flow control devices with unique shapes that are aerodynamically efficient in terms of the cost function (i.e., aerodynamic drag and lift). In this paper, the IPDG is demonstrated using a rear flap and an underbody diffuser as passive devices. The three-dimensional Reynolds-averaged Navier-stokes (RANS) equations were used to solve the problem. Relative to the baseline, the IPDG generated flap-only, and diffuser-only provide drag reductions of 6.3% and 5.4%, respectively, whereas the flap-diffuser combination provides a drag reduction of 7.4%. Furthermore, the increase in the downforce is significant from 624.4% in flap-only to 4930% and 4595% in the diffuser and flap-diffuser combination. The proposed method has the potential to evolve into a universal passive device generator with the integration of machine learning.


Main Subjects

Ahmed, S. R., Ramm, G., & Faltin, G. (1984). Some salient features of the time-averaged ground vehicle wake. SAE Transactions, 93(2), 473–503.
Aider, J. L., Beaudoin, J. F., & Wesfreid, J. E. (2010). Drag and lift reduction of a 3D bluff-body using active vortex generators. Experiments in Fluids, 48(5), 771–789.
ANSYS, I. (2018). ANSYS User’s Guide.##
Banzhaf, W., Nordin, P., Keller, R. E., & Francone, F. D. (1998). Genetic programming. An introduction on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc.##
Beaudoin, J. F., & Aider, J. L. (2008). Drag and lift reduction of a 3D bluff body using flaps. Experiments in Fluids, 44(4), 491–501.
Cornejo MacEda, G. Y., Li, Y., Lusseyran, F., Morzyński, M., & Noack, B. R. (2021). Stabilization of the fluidic pinball with gradient-enriched machine learning control. Journal of Fluid Mechanics, 917, A42.
Cornejo Maceda, G. Y., Noack, B. R., Lusseyran, F., Deng, N., Pastur, L., & Morzynski, M. (2019). Artificial intelligence control applied to drag reduction of the fluidic pinball. PAMM, 19(1), e201900268.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Delassaux, F., Mortazavi, I., Itam, E., Herbert, V., & Ribes, C. (2021). Sensitivity analysis of hybrid methods for the flow around the Ahmed body with application to passive control with rounded edges. Computers and Fluids, 214, 104757.
Doyle, J. B., Hartfield, R. J., & Roy, C. (2008). Aerodynamic optimization for freight trucks using a genetic algorithm and CFD. 46th AIAA Aerospace Sciences Meeting and Exhibit (p.323).
Duriez, T., Brunton, S. L., & Noack, B. R. (2013). Machine learning dynamics and taming nonlinear control – turbulence. Springer.
Fan, D., Zhang, B., Zhou, Y., & Noack, B. R. (2020). Optimization and sensitivity analysis of active drag reduction of a square-back Ahmed body using machine learning control. Physics of Fluids, 32(12), 125117.
Fourrié, G., Keirsbulck, L., Labraga, L., & Gilliéron, P. (2011). Bluff-body drag reduction using a deflector. Experiments in Fluids, 50(2), 385–395.
Gautier, N., Aider, J. L., Duriez, T., Noack, B. R., Segond, M., & Abel, M. (2015). Closed-loop separation control using machine learning. Journal of Fluid Mechanics, 770, 442-457.
George, A. R., & Donis, J. E. (1983, November 13-18). Flow patterns, pressures, and forces on the underside of idealized ground effect vehicles. Aerodynamics of Transportation-ii, ASME Winter Annual Meeting, Boston, USA.##
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, Springer.
Guilmineau, E. (2008). Computational study of flow around a simplified car body. Journal of Wind Engineering and Industrial Aerodynamics, 96(6–7), 1207–1217.
Hanfeng, W., Yu, Z., Chao, Z., & Xuhui, H. (2016). Aerodynamic drag reduction of an Ahmed body based on deflectors. Journal of Wind Engineering and Industrial Aerodynamics, 148, 34–44.
Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., & Prasath, V. B. S. (2019). Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information (Switzerland), 10(12).
Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4(2), 87-112.
Lee, C., Kim, J., Babcock, D., & Goodman, R. (1997). Application of neural networks to turbulence control for drag reduction. Physics of Fluids, 9(6), 1740-1747.
Li, R., Noack, B. R., Cordier, L., Borée, J., & Harambat, F. (2017). Drag reduction of a car model by linear genetic programming control. Experiments in Fluids, 58(8), 1–20.
Li, Y., Cui, W., Jia, Q., Li, Q., Yang, Z., Morzyński, M., & Noack, B. R. (2022). Explorative gradient method for active drag reduction of the fluidic pinball and slanted Ahmed body. Journal of Fluid Mechanics, 932, A7.
Lienhart, H., Stoots, C., & Becker, S. (2002). Flow and turbulence structures in the wake of a simplified car model (Ahmed Modell). New Results in Numerical and Experimental Fluid Mechanics III, Springer Berlin Heidelberg.
Liu, C., Bu, W., & Xu, D. (2017). Multi-objective shape optimization of a plate-fin heat exchanger using CFD and multi-objective genetic algorithm. International Journal of Heat and Mass Transfer, 111, 65-22.
Meile, W., Brenn, G., Reppenhagen, A., Lechner, B., & Fuchs, A. (2012). Experiments and numerical simulations on the aerodynamics of the Ahmed body.  CFD letters, 3(1), 32-39.##
Moghimi, P., & Rafee, R. (2018). Numerical and experimental investigations on aerodynamic behavior of the Ahmed body model with different diffuser angles. Journal of Applied Fluid Mechanics, 11(4),1101-1113.
Muñoz-Paniagua, J., & García, J. (2020). Aerodynamic drag optimization of a high-speed train. Journal of Wind Engineering and Industrial Aerodynamics, 204, 104215.
Muyl, F., Dumas, L., & Herbert, V. (2004). Hybrid method for aerodynamic shape optimization in automotive industry. Computers and Fluids, 33(5–6).
Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7(4).
Noack, B. R. (2019). Closed-loop turbulence control-from human to machine learning (And retour). Lecture Notes in Mechanical Engineering, 23–32.
Patrikalakis, N. M., & Maekawa, T. (2010). Shape interrogation for computer aided design and manufacturing. Shape Interrogation for Computer Aided Design and Manufacturing Heidelberg, Springer.
Raibaudo, C., Zhong, P., Noack, B. R., & Martinuzzi, R. J. (2020). Machine learning strategies applied to the control of a fluidic pinball. Physics of Fluids, 32(1), 015108.
Ren, F., Wang, C., & Tang, H. (2019). Active control of vortex-induced vibration of a circular cylinder using machine learning. Physics of Fluids, 31(9), 093601.
Ren, K., Chen, Y., Gao, C., & Zhang, W. (2020). Adaptive control of transonic buffet flows over an airfoil. Physics of Fluids, 32(9),096106.
Siddiqui, N. A., & Agelin-Chaab, M. (2021). A simple passive device for the drag reduction of an Ahmed body. Journal of Applied Fluid Mechanics, 14(1), 147–164.
Tian, J., Zhang, Y., Zhu, H., & Xiao, H. (2017). Aerodynamic drag reduction and flow control of Ahmed body with flaps. Advances in Mechanical Engineering, 9(7), 1–17.
Wu, Z., Fan, D., Zhou, Y., Li, R., & Noack, B. R. (2018). Jet mixing optimization using machine learning control. Experiments in Fluids, 59(8), 1-17.
Yang, X. S. (2013). Optimization and metaheuristic algorithms in engineering. Metaheuristics in Water, Geotechnical and Transport Engineering,1, 23.
Yu, M. G., Zhang, J. Y., & Zhang, W. H. (2013). Multi-objective optimization design method of the high-speed train head. Journal of Zhejiang University: Science A, 14(9).
Yu, Z., & Bingfu, Z. (2021). Recent advances in wake dynamics and active drag reduction of simple automotive bodies. Applied Mechanics Reviews, 73(6), 060801.
Yuan, C. Y., & Li, M. Q. (2017). Multi-objective optimization for the aerodynamic noise of the high-speed train in the near and far field based on the improved NSGA-II algorithm. Journal of Vibroengineering, 19(6), 4759–4782.
Zdravkovich, M. M. (1981). Review and classification of various aerodynamic and hydrodynamic means for suppressing vortex shedding. Journal of Wind Engineering and Industrial Aerodynamics,
7(2), 145–189.
Zhang, X., Senior, A., & Ruhrmann, A. (2004). Vortices behind a bluff body with an upswept aft section
in ground effect. International Journal
of Heat and Fluid Flow
, 25(1),1-9.
Zhou, Y., Zhou, Y., Fan, D., Zhang, B., Li, R., Li, R., Noack, B. R., Noack, B. R., & Noack, B. R. (2020). Artificial intelligence control of a turbulent jet.
Journal of Fluid Mechanics, 897, A27.