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A numerical study on the performance of a superhydrophobic coated very low head (VLH) axial hydraulic turbine using entropy generation method

Author

Listed:
  • Sotoude Haghighi, Mohammad Hadi
  • Mirghavami, Seyed Mohammad
  • Ghorani, Mohammad Mahdi
  • Riasi, Alireza
  • Chini, Seyed Farshid

Abstract

Fluids slip on superhydrophobic surfaces. The slip velocity is modeled by Navier's slip-length. A user defined function (UDF) in ANSYS Fluent 15.0 was developed to implement the slip boundary condition. The UDF was validated for different values of slip length by two benchmark solutions in laminar and turbulent flows. We utilized a periodic approach to model the VLH turbine in steady-state condition. For modeling, single-phase is assumed and the shear stress transport model was used for turbulence modeling. The results of employing partial slip at different positions of the runner blade and other turbine components with a constant slip-length of 50 μm showed an approximately 4% improvement in the turbine hydraulic efficiency, at the design point and when only the runner blade is superhydrophobic. In addition to the design point analysis, the turbine efficiency values were studied at part and high load ranges. The entropy generation method was applied to the output results of the simulated cases to determine the energy dissipating zones and to detect the effect of superhydrophobic walls on the dissipation mechanisms. Since this work is the first numerical evaluation of superhydrophobic surfaces for VLH, the conclusions can be meaningful for future numerical and experimental studies.

Suggested Citation

  • Sotoude Haghighi, Mohammad Hadi & Mirghavami, Seyed Mohammad & Ghorani, Mohammad Mahdi & Riasi, Alireza & Chini, Seyed Farshid, 2020. "A numerical study on the performance of a superhydrophobic coated very low head (VLH) axial hydraulic turbine using entropy generation method," Renewable Energy, Elsevier, vol. 147(P1), pages 409-422.
  • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:409-422
    DOI: 10.1016/j.renene.2019.09.003
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    References listed on IDEAS

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