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CFD-enabled sustainable design and manufacturing of cooling fan for unmanned helicopter
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  • Published: 17 January 2026

CFD-enabled sustainable design and manufacturing of cooling fan for unmanned helicopter

  • Liang Si1,2,
  • Zhixin Liu1,
  • Nannan Xiao1,
  • Yuwen Zhang1,
  • Yebao Liu1,
  • Shuai Deng1,
  • Yuchuan Li1,
  • Haisheng Yang1,
  • Xiongjian Zhang1,
  • Guoqiang Fu1 &
  • …
  • Joon Phil Choi3,4 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Engineering

Abstract

Due to the demand of unmanned helicopters for drag reduction and rain-proof, the helicopter nacelle must be sealed. It will lead to a decrease in the heat transfer efficiency of the radiator, and the output power of the engine will drop dramatically. The helicopter’s flight safety will be seriously jeopardized, especially when the helicopter is hovering (maximum engine power is needed). Currently, active cooling by equipping the radiator with a fan is the only solution, and the heat transfer efficiency of the radiator could be controlled by fan. Therefore, the performance of the fan directly affects the flight safety of the whole system. In this study, the Airfoils 30, suitable for low Reynolds number flows, is adopted to design the fan blade considering the characteristics of helicopter heat sink miniaturization and high integration. Then, a three-dimensional CFD (computational fluid dynamics) k-omega SST model is developed to investigate the effects of fan blade torsion angle, chord length, mounting angle and the number of blades on the performance of the fan. Furthermore, the constraints of radiator dimension, air flow resistance on the performance of the fan are considered comprehensively to finalize the new fan configuration. The optimised parameters of fan suitable for high flow rate (above 1.17 kg/s) are chord length is 55 mm, torsion angle is 26°, mounting angle is 39° and the blade number is 7. The fan efficiency increases about 13.6%. The power consumption decreases about 9.5% (about 73 W). The fan rotational speed decreases 10.5%. The improvement of fan efficiency is a key measure for energy conservation and carbon reduction in unmanned helicopter systems. The 73 W power consumption of the fan decrease indicates that 1.2 kg green-house gas emission reduces per day. The lower power consumption will result in a 0.14% cruising endurance increase. The fan is then manufactured by additive manufacturing based on CFD optimization results. This deep integration between CFD and additive manufacturing reduces trial and error costs and energy consumption. It also shows the promising future of UAV components autonomous manufacturing. Finally, the experiment is conducted in lab under 40℃. The experimental results indicate that the maximum output power of the engine is over than 90 kW. Based on the helicopter main rotor performance curve, the helicopter could hover indefinitely with 500 kg loading under 40 °C. It is a criterion to identify the designing success.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Shuhong, L. I. U., Yulin, W. U. & Zhigang, Z. U. O. Applied Fluid Mechanics (Tsinghua University Press, 2012) ((In Chinese)).

    Google Scholar 

  2. Moreau, S. Improvement of fan design using CFD. SAE Technical Paper 970934, (2002).

  3. Henner M. Kessaci S. Moreau S. Latest improvements of CFD Models of engine cooling axial fan systems. SAE Technical Paper Series.

  4. Yi-You, W., Min, W. U., Dun-Lu. L. Noise Calculation of engine cooling fan based on CFD. Mechan. Eng. (2009).

  5. Wang, A., Hui, X. Z. & Ghazialam, H. Evaluation of the Multiple Reference Frame (MRF) Model in a Truck Fan Simulation (SAE Paper, 2005).

    Google Scholar 

  6. Ota, H. et al. Development of High Efficient Radiator Cooling Fan for Automotive Application (SAE Technical Paper, 2013).

    Google Scholar 

  7. Kobayashi, Y., Kohri, I. & Matsushima, Y. Study of Influence of MRF Method on the Prediction of the Engine Cooling Fan Performance (SAE Technical Paper, 2011).

    Google Scholar 

  8. Kohri, I., Kobayashi, Y. & Matsushima, Y. Prediction of the performance of the engine cooling fan with CFD simulation. SAE Int. J. Passenger Cars-Mechan. Syst. 3, 508–522 (2010).

    Google Scholar 

  9. Yuntao, C. CFD Analysis and Low Noise Optimization Design of Engine Cooling Fan for Sedan (Jilin University, 2007) ((In Chinese)).

    Google Scholar 

  10. Xingrong, W. CFD Analysis and Simulation Process Optimization of Engine Cooling Fan Aerodynamic Performance (South China University of Technology, 2013) ((In Chinese)).

    Google Scholar 

  11. Shengfu, Li. & Xinxin, W. Optimization design analysis of automotive cooling fan blade parameters. Mechan. Design Manuf. 07, 48–52 (2019) ((In Chinese)).

    Google Scholar 

  12. Yaozhen, Z. CFD Analysis and Low Noise Optimization Design of Car Engine Cooling Fan (Jilin University, 2007) ((In Chinese)).

    Google Scholar 

  13. Wang, Z,N., Wang, H. Design of automobile cooling fan based on computational fluid dynamics. Optimized design of automobile cooling fan based on computational fluid dynamics. Mechanical Design and Manufacturing, 2016 (10): 182–186

  14. Wang, Y., Lu J., Jiang, B., et al. Study on the effect of blade inclination on the performance of cross-flow wind turbine using CFD technology. Journal of Hefei University of Technology: Natural Science Edition, (2012). (In Chinese)

  15. Li, Y. E., Feng, X. U., Yan, S. H. I., Haibo, L. I. N. & Chao, S. U. N. Research and optimization of aerodynamic performance of automobile engine cooling fan. Pract. Energy Saving Technol. 5, 76–80 (2018) ((In Chinese)).

    Google Scholar 

  16. Tang Zhao. Research and optimization of engine cooling fan blade parameters. Guangzhou: South China University of Technology, (2012). (In Chinese)

  17. Jung, Y. S., Lee, B. & Baeder, J. Prediction of Coaxial rotor hub flow using mercury framework. J. Am. Helicopter Soc. 69(2), 1–4 (2024).

    Google Scholar 

  18. Renaud, T., Pape, A. L. & Péron, S. Numerical analysis of hub and fuselage drag breakdown of a helicopter configuration. CEAS Aeronaut. J. 4(4), 409–419 (2013).

    Google Scholar 

  19. Zaharia, S. M. et al. Material extrusion additive manufacturing of the composite UAV Used for search-and-rescue missions. Drones 7, 602 (2023).

    Google Scholar 

  20. Helge, K. et al. Additive manufacturing of porous structures for unmanned aerial vehicles applications. Adv. Eng. Mater. 20, 1800290 (2018).

    Google Scholar 

  21. Bozkurttas, M. et al. CFD-Based Analysis and Design of Biomimetic Flexible Propulsor for Autonomous Underwater Vehicles (AIAA, 2007).

    Google Scholar 

  22. Zhao, J. et al. CFD analysis of Ducted-Fan UAV Based on Magnus Effect (IEEE, 2012).

    Google Scholar 

  23. Yin, S. et al. Electric Scissors for precise generation of organic droplets in microfluidics: a universal approach that goes beyond surface wettability. J. Phys. Chem. C 123, 25643 (2019).

    Google Scholar 

  24. Yin, S. et al. Triple-layered encapsulation through direct droplet impact. J. Colloid Interface Sci. 615, 887 (2023).

    Google Scholar 

  25. VA113-BBL504P fan performance experiment report.

  26. Subramanya, R. Modelling and Simulation of Fan Performance Using CFD Group (Linkopings University, 2020).

    Google Scholar 

  27. Zhang, S., et al. Intelligent simulation and optimization of axial flow fan based on CFD. (2023).

  28. Low Reynolds Number, Low drag, high lift airfoil. Michael S. Selig, William Holmes, Frank Stauder. Patent.

  29. Ding, D., Ng, B. F., Liu, H., Lu, X. & Wan, M. P. The characteristics and formation mechanism of emissions from thermal decomposition of 3D printer polymer filaments. Sci. Total Environ. 20, 984–994 (2019).

    Google Scholar 

  30. Zhang, H. & Moon, S. K. Hybrid machine learning method to determine the optimal operating process window in aerosol jet 3D printing. ACS Appl. Mater. Interface 11, 17994–18003 (2019).

    Google Scholar 

  31. Zhang, H., Moon, S. K. & Ngo, T. H. 3D printed electronics of non-contact ink writing techniques: status and promise. Int. J. Precis. Eng. Manuf. Green Technol. 7, 511–524 (2020).

    Google Scholar 

  32. Yang, X. et al. Active fabrics with controllable stiffness for robotic assistive interface. Adv. Mater. 36, 2404502 (2024).

    Google Scholar 

  33. Zhang H, Moon SK, Ngo TH, Tou J, Mohamed AB. A hybrid machine learning approach for the quality optimization of a 3D printed sensor. In 2018 International Conference on Intelligent Rail Transportation (ICIRT). IEEE.

  34. Li, M., Yin, S., Liu, Z. & Zhang, H. Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing. Sci. Rep. 14, 14614 (2024).

    Google Scholar 

  35. Wang, B. et al. A post-treatment method to enhance the property of aerosol jet printed electric circuit on 3D printed substrate. Materials 13, 5602 (2020).

    Google Scholar 

  36. Zhang, H., Huang, J., Zhang, X. & Wong, C. N. Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach. Additive Manuf 86, 104208 (2024).

    Google Scholar 

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Acknowledgements

This research was supported by the Anhui HAERY Aviation Power Co., Ltd. This work was also supported by the Technology development Program (No.RS-2025-25443487) funded by the Ministry of SMEs and Startups (MSS, Korea).

Funding

This research was supported by the Anhui province fundamental research funding (No. 2024AH051814). Valuable support from Anhui HAERY Aviation Power Co., Ltd. This work was also supported by the Technology development Program (No.RS-2025-25443487) funded by the Ministry of SMEs and Startups (MSS, Korea).

Author information

Authors and Affiliations

  1. Aerospace Times Feihong Technology Company Limited, Beijing, China

    Liang Si, Zhixin Liu, Nannan Xiao, Yuwen Zhang, Yebao Liu, Shuai Deng, Yuchuan Li, Haisheng Yang, Xiongjian Zhang & Guoqiang Fu

  2. National Elite Institute of Engineering, Northwestern Polytechnical University, Xi’an, China

    Liang Si

  3. Department of 3D Printing, Korea Institute of Machinery & Materials, Daejeon, Republic of Korea

    Joon Phil Choi

  4. School of Mechanical Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea

    Joon Phil Choi

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Contributions

Liang Si: Conceptualization, Methodology, Investigation, Writing original draft. Zhixin Liu: Conceptualization, Methodology, Investigation, Writing original draft. Nannan Xiao: Methodology, Investigation, Writing original draft. Yuwen Zhang: Methodology, Investigation, Writing original draft. Yebao Liu: Methodology, Writing original draft, Writing. Shuai Deng: Methodology, Writing original draft. Yuchuan Li: Methodology, Writing original draft. Haisheng Yang: Validation, Writing original draft. Xiongjian Zhang: Validation, Writing original draft. Guoqiang Fu; - review & Editing draft. Joon Phil Choi: Methodology, Writing—review & editing.

Corresponding authors

Correspondence to Zhixin Liu or Joon Phil Choi.

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Cite this article

Si, L., Liu, Z., Xiao, N. et al. CFD-enabled sustainable design and manufacturing of cooling fan for unmanned helicopter. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35901-y

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  • Received: 02 November 2025

  • Accepted: 08 January 2026

  • Published: 17 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-35901-y

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Keywords

  • Unmanned helicopter
  • CFD simulation
  • Fan design
  • Additive manufacturing
  • Performance curve
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