Wind-turbine blades exposed to rain can be damaged by erosion if not protected. Although this damage does not typically influence the structural response of the blades, it could heavily degrade the aerodynamic performance, and therefore the power production. We present a method for computational analysis of rain erosion in wind-turbine blades. The method is based on a stabilized finite element fluid mechanics formulation and a finite element particle-cloud tracking method. Accurate representation of the flow would be essential in reliable computational turbomachinery analysis and design. The turbulent-flow nature of the problem is dealt with a RANS model and SUPG/PSPG stabilization, the particle-cloud trajectories are calculated based on the flow field and closure models for the turbulence-particle interaction, and one-way dependence is assumed between the flow field and particle dynamics. The erosion patterns are then computed based on the particle-cloud data.

SUPG/PSPG computational analysis of rain erosion in wind-turbine blades

CASTORRINI, Alessio;CORSINI, ALESSANDRO;
2016-01-01

Abstract

Wind-turbine blades exposed to rain can be damaged by erosion if not protected. Although this damage does not typically influence the structural response of the blades, it could heavily degrade the aerodynamic performance, and therefore the power production. We present a method for computational analysis of rain erosion in wind-turbine blades. The method is based on a stabilized finite element fluid mechanics formulation and a finite element particle-cloud tracking method. Accurate representation of the flow would be essential in reliable computational turbomachinery analysis and design. The turbulent-flow nature of the problem is dealt with a RANS model and SUPG/PSPG stabilization, the particle-cloud trajectories are calculated based on the flow field and closure models for the turbulence-particle interaction, and one-way dependence is assumed between the flow field and particle dynamics. The erosion patterns are then computed based on the particle-cloud data.
2016
978-3-319-40825-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/139507
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