Building a model of the flow in a nozzle-flapper valve of the HP-3 control pump to improve the stability of characteristics

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.329024

Keywords:

nozzle-flapper, numerical modeling, stagnant zone, vortex flow

Abstract

The object of this study is the flow of a viscous incompressible fluid in a nozzle-flapper valve used as part of the free turbine speed controller in the HP-3 pump-regulator of the TV3-117 turboprop helicopter engine. The task addressed relates to a need for detailed calculations of the fluid flow because of unsatisfactory operation of the valve under actual operating conditions. An additional difficulty was the contradictory data on the characteristics of such valves in the literature, which made it impossible to determine the flow characteristics and directions for improving the design.

This paper reports the results of numerical calculations of the flow in the valve performed in the SolidWorks Flow Simulation environment. A mathematical model is proposed that takes into account the influence of the design mesh on the accuracy and computational time volume, as well as ways to improve accuracy without a significant increase in resources. The model was verified by comparing it with the manufacturer’s experimental data. The results have made it possible to solve the problem through the detailed construction of the model taking into account the valve geometry and optimization of the computational mesh, which ensured a balance between accuracy and computational speed.

The results are attributed to the application of state-of-the-art hydrodynamic calculation software, precise mesh tuning, as well as proper validation of the model to reflect the actual physical processes in the valve. The model built makes it possible to study the flow in the valve and could be used to analyze the impact of manufacturing defects. The model is suitable for parametric studies and modification of valves in helicopter engines of the TV3-117 type or similar systems. The model could also be adapted for other systems requiring flow analysis in similar valves

Author Biographies

Oleksandr Lytviak, National Aerospace University Kharkiv Aviation Institute

Doctor of Technical Sciences

Department of Mechatronics and Electrical Engineering

Roman Trishch, National Aerospace University Kharkiv Aviation Institute

Doctor of Technical Sciences

Department of Mechatronics and Electrical Engineering

Eduard Khomiak, National Aerospace University Kharkiv Aviation Institute

PhD

Department of Mechatronics and Electrical Engineering

Serhii Kochuk, National Aerospace University Kharkiv Aviation Institute

PhD

Department of Mechatronics and Electrical Engineering

Svitlana Khomenko, Berdyansk State Pedagogical University

PhD

Department of Vocational Education, Labor Training and Technologies

Ihor Tiupa, V. N. Karazin Kharkiv National University

PhD

Department of Аutomation, Metrology and Energy-Efficient Technologies

References

  1. SOLIDWORKS Installation and Administration. Available at: https://help.solidworks.com/2022/english/Installation/install_guide/c_install_admin_overview.htm
  2. Tabe Jamaat, A. G., Hattori, B. Y. (2022). Development of subgrid-scale model for LES of Burgers turbulence with large filter size. Physics of Fluids, 34 (4). https://doi.org/10.1063/5.0087761
  3. Lytviak, O., Komar, S., Derevyanko, O., Durieiev, V. (2021). Devising quality control criteria for manufacturing control valves of the type «nozzle-flap». Eastern-European Journal of Enterprise Technologies, 1 (1 (109)), 27–34. https://doi.org/10.15587/1729-4061.2021.224918
  4. Lytviak, O., Loginov, V., Komar, S., Martseniuk, Y. (2021). Self-Oscillations of The Free Turbine Speed in Testing Turboshaft Engine with Hydraulic Dynamometer. Aerospace, 8 (4), 114. https://doi.org/10.3390/aerospace8040114
  5. Liu, Y., Ren, Y., Zhang, M., Wei, K., Hao, L. (2022). Solenoid valves quality improvement based on Six Sigma management. International Journal of Lean Six Sigma, 14 (1), 72–93. https://doi.org/10.1108/ijlss-08-2021-0140
  6. Jafari, B., Mashadi, B. (2022). Valve control of a hydraulically interconnected suspension system to improve vehicle handling qualities. Vehicle System Dynamics, 61 (4), 1011–1027. https://doi.org/10.1080/00423114.2022.2056490
  7. Fedorovich, O., Lutai, L., Trishch, R., Zabolotnyi, О., Khomiak, E., Nikitin, A. (2024). Models for Reducing the Duration and Cost of the Aviation Equipment Diagnostics Process Using the Decomposition of the Component Architecture of a Complex Product. Information Technology for Education, Science, and Technics, 108–125. https://doi.org/10.1007/978-3-031-71801-4_9
  8. Cheng, Y., Tang, Y., Wu, J., Jin, H., Shen, L. (2024). Numerical Simulation Study on Hydraulic Characteristics and Wear of Eccentric Semi-Ball Valve under Sediment Laden Water Flow. Sustainability, 16 (17), 7266. https://doi.org/10.3390/su16177266
  9. Lopes, R., Eça, L., Vaz, G. (2020). On the Numerical Behavior of RANS-Based Transition Models. Journal of Fluids Engineering, 142 (5). https://doi.org/10.1115/1.4045576
  10. Laima, S., Zhou, X., Jin, X., Gao, D., Li, H. (2023). DeepTRNet: Time-resolved reconstruction of flow around a circular cylinder via spatiotemporal deep neural networks. Physics of Fluids, 35 (1). https://doi.org/10.1063/5.0129049
  11. Kim, M., Park, J., Choi, H. (2024). Large eddy simulation of flow over a circular cylinder with a neural-network-based subgrid-scale model. Journal of Fluid Mechanics, 984. https://doi.org/10.1017/jfm.2024.154
  12. SolidWorks Flow Simulation. Available at: https://www.goengineer.com/solidworks/simulation/solidworks-flow-simulation-cfd
  13. Popa, C. (2023). An aplication for the selection and sizing of control valve for control loop. Romanian Journal of Petroleum & Gas Technology, 4 (75) (2), 109–116. https://doi.org/10.51865/jpgt.2023.02.11
  14. Kim, J., Kim, H., Kim, J., Lee, C. (2022). Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence. Physics of Fluids, 34 (10). https://doi.org/10.1063/5.0106940
  15. Trishch, R., Cherniak, O., Zdenek, D., Petraskevicius, V. (2024). Assessment of the occupational health and safety management system by qualimetric methods. Engineering Management in Production and Services, 16 (2), 118–127. https://doi.org/10.2478/emj-2024-0017
  16. Cherniak, O., Trishch, R., Ginevičius, R., Nechuiviter, O., Burdeina, V. (2024). Methodology for Assessing the Processes of the Occupational Safety Management System Using Functional Dependencies. Integrated Computer Technologies in Mechanical Engineering - 2023, 3–13. https://doi.org/10.1007/978-3-031-60549-9_1
  17. Khomiak, E., Burdeina, V., Cherniak, O., Olesia, N., Bubela, T. (2024). Improving the Method of Quality Control of the Fuel Element Shell in Order to Improve the Safety of a Nuclear Reactor. Integrated Computer Technologies in Mechanical Engineering - 2023, 351–360. https://doi.org/10.1007/978-3-031-61415-6_30
  18. Khomiak, E., Trishch, R., Zabolotnyi, O., Cherniak, О., Lutai, L., Katrich, O. (2024). Automated Mode of Improvement of the Quality Control System for Nuclear Reactor Fuel Element Shell Tightness. Information Technology for Education, Science, and Technics, 79–91. https://doi.org/10.1007/978-3-031-71801-4_7
  19. Wu, Q., Chen, Z., Xu, H., Cai, Y. (2024). A novel wall model for large-eddy simulation of the flow around a circular cylinder. Physics of Fluids, 36 (6). https://doi.org/10.1063/5.0209462
  20. Nisters, C., Bauer, F., Brocker, M. (2020). Condition monitoring systems for hydraulic accumulators – improvements in efficiency, productivity and quality. Volume 2 - Conference, 195–203. https://doi.org/10.25368/2020.83
  21. Tong, Z., Xin, J., Song, J., Cao, X. E. (2023). A graphics-accelerated deep neural network approach for turbomachinery flows based on large eddy simulation. Physics of Fluids, 35 (9). https://doi.org/10.1063/5.0160968
  22. Abekawa, A., Minamoto, Y., Osawa, K., Shimamoto, H., Tanahashi, M. (2023). Exploration of robust machine learning strategy for subgrid scale stress modeling. Physics of Fluids, 35 (1). https://doi.org/10.1063/5.0134471
  23. Dong, X., Hong, H., Deng, X., Zhong, W., Hu, G. (2023). Surrogate model-based deep reinforcement learning for experimental study of active flow control of circular cylinder. Physics of Fluids, 35 (10). https://doi.org/10.1063/5.0170316
  24. Zhan, Q., Bai, C., Ge, Y., Sun, X. (2023). Flow time history representation and reconstruction based on machine learning. Physics of Fluids, 35 (8). https://doi.org/10.1063/5.0160296
  25. Wang, Y.-Z., Hua, Y., Aubry, N., Chen, Z.-H., Wu, W.-T., Cui, J. (2022). Accelerating and improving deep reinforcement learning-based active flow control: Transfer training of policy network. Physics of Fluids, 34 (7). https://doi.org/10.1063/5.0099699
Building a model of the flow in a nozzle-flapper valve of the HP-3 control pump to improve the stability of characteristics

Downloads

Published

2025-06-27

How to Cite

Lytviak, O., Trishch, R., Khomiak, E., Kochuk, S., Khomenko, S., & Tiupa, I. (2025). Building a model of the flow in a nozzle-flapper valve of the HP-3 control pump to improve the stability of characteristics. Eastern-European Journal of Enterprise Technologies, 3(1 (135), 51–57. https://doi.org/10.15587/1729-4061.2025.329024

Issue

Section

Engineering technological systems