First Advisor

Raúl Bayoán Cal

Term of Graduation

Spring 2020

Date of Publication


Document Type


Degree Name

Master of Science (M.S.) in Mechanical Engineering


Mechanical and Materials Engineering




Wakes (Aerodynamics), Turbulence -- Mathematical models, Granular flow, Fluid mechanics



Physical Description

1 online resource (vii, 45 pages)


Impacting particles such as rain, dust, and other debris can have devastating structural effects on wind turbines, but little is known about the interaction of such debris within turbine wakes. This study aims to characterize behavior of inertial particles within the turbulent wake of a wind turbine and relative effects on wake recovery. Here a model wind turbine is subjected to varied two-phase inflow conditions, with wind as the carrier fluid (Reλ = 49 - 88) and polydisperse water droplets (26 to 45µm in diameter) at varied concentrations (Φv=0.24 x 10-5 - 1.3 x 10-5), comparing with sub-inertial particles that follow the flow streamlines. Phase doppler interferometry (PDI) and particle image velocimetry (PIV) were employed at multiple downstream locations, centered with respect to turbine hub height. Analysis considers momentum and particle size distribution within the wake focusing on turbulence statistics and preferential concentration. PDI data show droplet size varied with wake location and volume fraction, and the inflow velocity of Reλ = 66.58 demonstrated Φv-dependent increases in streamwise velocity deficits of 59.5% to 62.6% and 15.8% to 19.8% for near and far wake, respectively. PIV data indicated correlation of particle concentration to wake expansion and amplified downward trajectory over the entire interrogation field. Contributions to kinetic energy and momentum are diminished overall for inertial particle cases compared to single-phase, except turbulent momentum flux [stack u to the power of 1 v to the power of 1 with bar on top], where shearing effects are visible at the rotor top edge in near wake and concentrated magnitudes increase in far wake correlating with increased Φv. Application of Voronoi analysis identified clustering behavior in far wake and was validated as motivation for future studies.


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