Identifying the influence of number of blades and distance between blades on tesla pump characteristics

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.268975

Keywords:

tesla turbine, open-flow, blades number, blades distance, wall shear

Abstract

The main principle of the Tesla pump is to increase the shear stresses as a result of the rotation of the pump blades, and thus increase the kinetic energy of the fluid to form a mass flow. The types of mechanical pumps are many and the ways of their use are wide. Over the years, scientists have contributed to developing types of pumps to get the best pump efficiency. The rotational energy can be converted into a mass flow of the fluid that can be pumped. As the Tesla pump is one of the types that gives a wide impression of fluid mechanics, where the viscosity and shear stress of the fluid will be in the movement of the fluid particles and the formation of a centrifugal force that gives an active flow of the fluid. Tesla pump is one of the primitive pumps that can be modified to study this research paper and know the number of fins used and the optimal distance between them to obtain the best mechanical efficiency of the pump. Where the Tesla pump was designed with variable fins, 3, 6 and 11 fins were taken to compare them, and the distance between the fins was reduced from 10 mm to 5 mm with a change of 2.5 mm, where the changes that occur on the pump can be observed. Where the results proved that the value of the fins increases the flow velocity of the fluid, as the best case was at the fins number 11, where the flow velocity reached 13 m/s. As for the change of distance, it is an inverse relationship as the small distance between the fins impedes the movement of the fluid flow and thus reduces the value of the flow. In the case where the number of turbine blades is 11, shear stresses reached 401 Pa. Which is the best case compared to the rest of the cases. The mechanical movement of the water was significantly increased

Author Biographies

Mohammed Wahhab Aljibory, University of Kerbala

Assistant Professor Doctor, Higher Studies Coordinator

Department of Mechanical Engineering

Mokdad Hayawi Rahman, Al-Farahidi University

Aeronautical Engineering Department Chief

Department of Aeronautical Engineering

References

  1. Niknam, P. H., Talluri, L., Ciappi, L., Fiaschi, D. (2021). Numerical assessment of a two-phase Tesla turbine: Parametric analysis. Applied Thermal Engineering, 197, 117364. doi: https://doi.org/10.1016/j.applthermaleng.2021.117364
  2. Rusin, K., Wróblewski, W., Rulik, S. (2021). Efficiency based optimization of a Tesla turbine. Energy, 236, 121448. doi: https://doi.org/10.1016/j.energy.2021.121448
  3. Galindo, Y., Reyes-Nava, J. A., Hernández, Y., Ibáñez, G., Moreira-Acosta, J., Beltrán, A. (2021). Effect of disc spacing and pressure flow on a modifiable Tesla turbine: Experimental and numerical analysis. Applied Thermal Engineering, 192, 116792. doi: https://doi.org/10.1016/j.applthermaleng.2021.116792
  4. Aghagoli, A., Sorin, M. (2020). CFD modelling and exergy analysis of a heat pump cycle with Tesla turbine using CO2 as a working fluid. Applied Thermal Engineering, 178, 115587. doi: https://doi.org/10.1016/j.applthermaleng.2020.115587
  5. Talluri, L., Dumont, O., Manfrida, G., Lemort, V., Fiaschi, D. (2020). Experimental investigation of an Organic Rankine Cycle Tesla turbine working with R1233zd(E). Applied Thermal Engineering, 174, 115293. doi: https://doi.org/10.1016/j.applthermaleng.2020.115293
  6. Pacini, L., Ciappi, L., Talluri, L., Fiaschi, D., Manfrida, G., Smolka, J. (2020). Computational investigation of partial admission effects on the flow field of a tesla turbine for ORC applications. Energy, 212, 118687. doi: https://doi.org/10.1016/j.energy.2020.118687
  7. Talluri, L., Dumont, O., Manfrida, G., Lemort, V., Fiaschi, D. (2020). Geometry definition and performance assessment of Tesla turbines for ORC. Energy, 211, 118570. doi: https://doi.org/10.1016/j.energy.2020.118570
  8. Sheikhnejad, Y., Simões, J., Martins, N. (2020). Introducing Tesla turbine to enhance energy efficiency of refrigeration cycle. Energy Reports, 6, 358–363. doi: https://doi.org/10.1016/j.egyr.2019.08.073
  9. Andres, J. F., Loretero, M. E. (2019). Performance of tesla turbine using open flow water source. International Journal of Engineering Research and Technology, 12 (12), 2191–2199. Available at: http://www.irphouse.com/ijert19/ijertv12n12_16.pdf
  10. Ntatsis, C. K., Chatziangelidou, N. A., Efstathiadis, G. T., Gkoutzamanis, G. V., Silvestri, P., Kalfas, I. A. (2019). CFD analysis of a tesla turboexpander using single phase steam. Proceedings of Global Power and Propulsion Society Technical Conference 2019. Available at: https://gpps.global/wp-content/uploads/2021/02/GPPS-TC-2019_paper_89.pdf
  11. Rustamov, N., Meirbekova, O., Kibishov, А., Babakhan, S., Berguzinov, А. (2022). Creation of a hybrid power plant operating on the basis of a gas turbine engine. Eastern-European Journal of Enterprise Technologies, 2 (8 (116)), 29–37. doi: https://doi.org/10.15587/1729-4061.2022.255451
  12. Ciappi, L., Fiaschi, D., Niknam, P. H., Talluri, L. (2019). Computational investigation of the flow inside a Tesla turbine rotor. Energy, 173, 207–217. doi: https://doi.org/10.1016/j.energy.2019.01.158
  13. Borisenko, V., Ustenko, S., Ustenko, I. (2022). Devising an approach to the geometric modeling of railroad tracks along curvilinear sections. Eastern-European Journal of Enterprise Technologies, 1 (1 (115)), 29–35. doi: https://doi.org/10.15587/1729-4061.2022.251983
  14. Hrudkina, N., Aliieva, L., Markov, O., Marchenko, I., Shapoval, A., Abhari, P., Kordenko, M. (2020). Predicting the shape formation of hollow parts with a flange in the process of combined radial-reverse extrusion. Eastern-European Journal of Enterprise Technologies, 4 (1 (106)), 55–62. doi: https://doi.org/10.15587/1729-4061.2020.203988
  15. Quang, N. H., Linh, N. H., Huy, T. Q., Lam, P. D., Tuan, N. A., Ngoc, N. D. et al. (2022). Optimizing the partial gear ratios of the two-stage worm gearbox for minimizing total gearbox cost. Eastern-European Journal of Enterprise Technologies, 1 (1 (115)), 6–15. doi: https://doi.org/10.15587/1729-4061.2022.252301
Identifying the influence of number of blades and distance between blades on tesla pump characteristics

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Published

2022-12-30

How to Cite

Wahhab Aljibory, M., & Hayawi Rahman, M. (2022). Identifying the influence of number of blades and distance between blades on tesla pump characteristics . Eastern-European Journal of Enterprise Technologies, 6(8 (120), 48–54. https://doi.org/10.15587/1729-4061.2022.268975

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Section

Energy-saving technologies and equipment