CFD Simulation Study on Airflow Dynamics Around a Cricket Ball: Effects of Velocity and Surface Modifications on Aerodynamic Performance

Catur Harsito, Enock Michael Kandimba, Raihan Danu Ramanda, Putra Adil Wicaksana, Yuki Trisnoaji, Singgih Dwi Prasetyo

Abstract

This study investigates the aerodynamic behavior of a cricket ball at various velocities using Computational Fluid Dynamics (CFD) in ANSYS Fluent, focusing on the effects of speed and surface modifications on aerodynamic forces and pressure distribution. The cricket ball geometry was simplified by replacing the seam with a protruding flat surface. Simulations were performed at airflow velocities of 20, 30, and 40 m/s using the realizable k-ε turbulence model, with air properties set to a density of 1.225 kg/m³ and dynamic viscosity of 1.81×10-⁵ Pa-s. At 20 m/s, the inlet and outlet mass flow rates were 50.306891 kg/s and -50.306901 kg/s, with a net imbalance of        -9.3×10-⁶ kg/s, generating a drag force of 0.5 N, a lift force of 0.2 N, and a pressure difference of 50 Pa. At 30 m/s, the inlet and outlet rates were 75.460373 kg/s and -75.464958 kg/s, respectively, resulting in a net imbalance of -0.004585 kg/s. The flow was fully turbulent, producing a drag force of 3.5 N, a lift force of 1.5 N, and a pressure difference of 250 Pa. Increasing velocity boosts drag, lift, and pressure differences. At the same time, the flat surface enhances asymmetry, vortices, and swing at higher speeds.

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C. Lindsay, “Beyond the seam : unveiling the secrets of swing bowling in elite Australian cricket,” no. June, 2024. 2. M. Aoyagi, M. Oshima, M. Oishi, S. Kita, K. Fujita, H. Imai, S. Oishi, H. Ohmori, and T. Ono, “Computational fluid dynamic analysis of the nasal respiratory function before and after postero-superior repositioning of the maxilla,” PLoS One, vol. 17, no. 4 April, pp. 1–20, 2022. 3. G. Pant, “Effect of Sports Drinks on Cardiovascular Endurance of Football Effect of Sports Drinks on Cardiovascular Endurance of,” no. June, 2015. 4. L. P. Parker, A. Svensson Marcial, T. B. Brismar, L. M. Broman, and L. Prahl Wittberg, “Computational Fluid Dynamics of the Right Atrium: A Comparison of Modeling Approaches in a Range of Flow Conditions,” J. Eng. Sci. Med. Diagnostics Ther., vol. 5, no. 3, pp. 1–11, 2022. 5. V. Resseguier, L. Li, G. Jouan, P. Dérian, E. Mémin, and B. Chapron, “New Trends in Ensemble Forecast Strategy: Uncertainty Quantification for Coarse-Grid Computational Fluid Dynamics,” Arch. Comput. Methods Eng., vol. 28, no. 1, pp. 215–261, 2021. 6. J. R. Anderson, “Jock ’ s Walkabout Story,” no. August 2022, 2024. 7. G. X. de Oliveira, S. Kuhn, H. G. Riella, C. Soares, and N. Padoin, “Combining computational fluid dynamics, photon fate simulation and machine learning to optimize continuous-flow photocatalytic systems,” React. Chem. Eng., vol. 8, no. 9, pp. 2119–2133, 2023. 8. S. V. Ponnaluri, P. Hariharan, L. H. Herbertson, K. B. Manning, R. A. Malinauskas, and B. A. Craven, “Results of the Interlaboratory Computational Fluid Dynamics Study of the FDA Benchmark Blood Pump,” Ann. Biomed. Eng., vol. 51, no. 1, pp. 253–269, 2023. 9. S. K. S, P. HV, and C. Nandini, “Data Science Approach to predict the winning Fantasy Cricket Team Dream 11 Fantasy Sports,” 2022. 10. E. Santolini, M. Bovo, A. Barbaresi, D. Torreggiani, and P. Tassinari, “Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses,” J. Agric. Eng., vol. LV, 2024. 11. Z. Wang, E. R. Galea, A. Grandison, J. Ewer, and F. Jia, “Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information ,” no. January, 2020. 12. C. Lindsay, R. Crowther, B. Clark, K. Middleton, R. Keegan, and W. Spratford, “Bowler and coach experiential knowledge of new ball swing bowling in elite cricket,” J. Sports Sci., vol. 42, no. 2, pp. 146–159, 2024. 13. H. Jordaan, P. Stephan Heyns, and S. Hoseinzadeh, “Numerical Development of a Coupled One-Dimensional/Three-Dimensional Computational Fluid Dynamics Method for Thermal Analysis with Flow Maldistribution,” J. Therm. Sci. Eng. Appl., vol. 13, no. 4, 2021. 14. R. Crowther, W. Spratford, K. Middleton, and J. Warmenhoven, “A Comparison of Inswing and Outswing Bowling Arm,” pp. 1–10, 2025. 15. T. Wilberforce, O. Ijaodola, O. Emmanuel, J. Thompson, A. G. Olabi, M. A. Abdelkareem, E. T. Sayed, K. Elsaid, and H. M. Maghrabie, “Optimization of fuel cell performance using computational fluid dynamics,” Membranes (Basel)., vol. 11, no. 2, pp. 1–21, 2021. 16. S. DC, “Artificial Intelligence in Sport: An Ethical Issue,” Unity J., vol. 3, no. 01, pp. 27–39, 2022. 17. C. Lindsay, R. Crowther, B. Clark, K. Middleton, R. Keegan, and W. Spratford, “Bowler and coach experiential knowledge of new ball swing bowling in elite cricket,” J. Sports Sci., vol. 42, no. 2, pp. 146–159, 2024. 18. Y. Morita, S. Rezaeiravesh, N. Tabatabaei, R. Vinuesa, K. Fukagata, and P. Schlatter, “Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems,” J. Comput. Phys., vol. 449, p. 110788, 2022. 19. J. A. Badra, F. Khaled, M. Tang, Y. Pei, J. Kodavasal, P. Pal, O. Owoyele, C. Fuetterer, B. Mattia, and F. Aamir, “Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach,” J. Energy Resour. Technol. Trans. ASME, vol. 143, no. 2, pp. 1–11, 2021. 20. S. D. Grimshaw, A. Briggs, and N. R. Atkins, “A review and reassessment of the aerodynamics of cricket ball swing,” Flow, vol. 4, 2024. 21. F. Plua, V. Hidalgo, P. A. López-Jiménez, and M. Pérez-Sánchez, “Analysis of applicability of cfd numerical studies applied to problem when pump working as turbine,” Water, vol. 13, no. 15, p. 2134, 2021. 22. R. Vinuesa and S. L. Brunton, “Emerging Trends in Machine Learning for Computational Fluid Dynamics,” Comput. Sci. Eng., vol. 24, no. October, pp. 33–41, 2022. 23. Y. Liu, E. M. Ozbayoglu, E. R. Upchurch, and S. Baldino, “Computational Fluid Dynamics Simulations of Taylor bubbles Rising in Pr ep rin t n ot pe er r Pr ep rin t n ot pe er ed.” 24. M. Elrefaie, F. Morar, A. Dai, and F. Ahmed, “DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks,” no. NeurIPS, 2024. 25. J. Otoko, “International Journal of Engineering Technology Research & Management MULTI OBJECTIVE OPTIMIZATION OF COST , CONTAMINATION CONTROL , AND SUSTAINABILITY IN CLEANROOM CONSTRU .... MULTI OBJECTIVE OPTIMIZATION OF COST , CONTAMINATION CONTROL , SUPPORT MODEL INTEGRATING LEAN SIX SIGMA , MONTE CARLO SIMULATION , AND COMPUTATIONAL FLUID DYNAMICS ( CFD ),” no. April, 2025. 26. R. Vinuesa and S. L. Brunton, “The Potential of Machine Learning to Enhance Computational Fluid Dynamics,” no. October, 2021. 27. M. Mani and A. J. Dorgan, “A Perspective on the State of Aerospace Computational Fluid Dynamics Technology,” Annu. Rev. Fluid Mech., vol. 55, pp. 431–457, 2023. 28. F. Pichi, F. Ballarin, G. Rozza, and J. A. N. S. Hesthaven, “AN ARTIFICIAL NEURAL NETWORK APPROACH TO BIFURCATING,” pp. 1–28. 29. X. Li and S. C. P. Cheung, “A learning-centred computational fluid dynamics course for undergraduate engineering students,” Int. J. Mech. Eng. Educ., 2024. 30. Q. Caz, P. Pepiot, and E. Riber, “A fully automatic procedure for the analytical reduction of chemical kinetics mechanisms for Computational Fluid Dynamics applications,” vol. 303, no. December 2020, 2021.

DOI: https://doi.org/10.20961/mekanika.v25i1.104182

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