Analysis of cavitation for the optimized design of hydrokinetic turbines using BEM

Silva, Paulo Augusto Strobel Freitas; Shinomiya, Léo Daiki; Oliveira, Taygoara Felamingo de; Vaz, Jerson Rogério Pinheiro; Mesquita, André Luiz Amarante; Brasil, Antônio

doi:10.1016/j.apenergy.2016.02.098

Cited by 43 publications

(40 citation statements)

References 26 publications

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“…which is used widely for the determination of cavitation onset, e.g., [16]. It is noted from Equation (3) that further reduction of σ from σ i increases cavitation.…”

Section: Cavitationmentioning

confidence: 99%

“…The fluids are not mixed on a microscopic scale, rather they are mixed on a macroscopic scale with a discernible interface between them. With this model, the imposed kinematic and dynamic conditions are that water and air share the same pressure and velocity fields as well as the turbulence fields [2,16]. The free-surface model attempts to resolve the interface between the fluids by imposing these conditions [2,16].…”

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

“…With this model, the imposed kinematic and dynamic conditions are that water and air share the same pressure and velocity fields as well as the turbulence fields [2,16]. The free-surface model attempts to resolve the interface between the fluids by imposing these conditions [2,16]. For brevity, the continuity and momentum equations of the multiphase flow are omitted here; a more detailed discussions on multiphase model can be found in [14,16].…”

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

“…The free-surface model attempts to resolve the interface between the fluids by imposing these conditions [2,16]. For brevity, the continuity and momentum equations of the multiphase flow are omitted here; a more detailed discussions on multiphase model can be found in [14,16].…”

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

“…For brevity, the transport equations for the SST k-ω turbulence model have been omitted and can be found in [17,20]. For solving the RANS equations, high-resolution upwind scheme with double precision was used in order to ensure convergence, accuracy and consistency in the computations [16].…”

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

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Cavitation Inception in Crossflow Hydro Turbines

2016

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Cavitation is a common flow phenomena in most hydraulic turbines and has the potential to cause vibration, blade surface damage and performance loss. Despite the fact that crossflow turbines have been used in small-scale hydropower systems for a long time, cavitation has not been studied in these turbines. In this paper, we present the findings of a computational study on cavitation inception in crossflow turbines. Cavitation inception was assessed using three-dimensional (3D) Reynolds-Averaged Navier-Stokes (RANS) computations. A homogeneous, free-surface two-phase flow model was used. Pressure distributions on the blades were examined for different flow rates, heads and impeller speeds to assess cavitation inception. The results showed that cavitation occurs in the second stage of the turbine and was observed on the suction side near the inner edge of the blades. For the particular turbine studied, cavitation always occurred at shaft speeds greater than that, giving the maximum efficiency for each combination of flow rate and head. The implication is that the useful operating range of crossflow turbines is up to and including the maximum efficiency point.

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“…which is used widely for the determination of cavitation onset, e.g., [16]. It is noted from Equation (3) that further reduction of σ from σ i increases cavitation.…”

Section: Cavitationmentioning

confidence: 99%

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

Section: Multiphase and Turbulence Modellingmentioning

confidence: 99%

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Cavitation Inception in Crossflow Hydro Turbines

2016

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Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

Sun

Fan

et al. 2019

Energy Science & Engineering

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As a new type of energy, tidal current energy is increasingly gaining attention worldwide. However, the tidal current energy development field faces a key technical problem of how to improve the generation efficiency and service life of tidal current generators. In this study, an innovative optimized design method of the horizontal axis tidal turbine was applied to the rotor blade design. To be specific, blade cavitation was considered as a factor for optimization and then was improved using a modern optimization method that combined artificial neural networks and genetic algorithms, solving for a numerical solution. In the optimization program, the annual energy output and the superficial area of the rotor blade served as the optimization goals, and the chord length and the twist angle served as the optimization variables. The cavitation constraint was different from the traditional method in that the judgment criterion of occurrence of cavitation was no longer the minimum pressure coefficient but the maximum stress coefficient. The combination of the artificial neural network and the genetic algorithm not only realized high optimization accuracy but also greatly saved computation time. To verify the validity of the optimization method, a 1 MW turbine was designed, and the optimal solution was chosen from the formed Pareto optimal solution set for modeling, and the new model was compared with the model quoted in the reference. The simulation result shows that the turbine designed using the optimization method not only reduces the surface area of the rotor blade and improves the annual energy output, but also remarkably improves the blades' cavitation resistance. The optimization analysis and comparison using different cavitation judgment criteria shows that the cavitation resistance of blades obtained using the maximum stress coefficient as the judgment criterion is more advantageous.

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