Analysis and Optimization of Shrouded Horizontal Axis Wind Turbines

Date of Award

2018

Degree Name

M.S. in Aerospace Engineering

Department

Department of Mechanical and Aerospace Engineering and Renewable and Clean Energy

Advisor/Chair

Advisor: Markus Rumpfkeil

Abstract

So-called wind-lens turbines offer the potential for improved energy efficiency and better suitability for urban and suburban environments compared to unshrouded or bare wind turbines. Wind-lenses, which are typically comprised of a diffuser shroud equipped with a flange, can enhance the wind velocity at the rotor plane due to the generation of a lower back pressure. This work comprises of two main studies which aim to develop fast and accurate simulation tools for the performance prediction and design of shrouded horizontal axis wind turbines. In the first study, a low-order theoretical model of ducted turbines is developed to establish a better understanding of the basic aerodynamics of shrouded wind turbines. Then a cost-effective CFD tool coupled with a multi-objective genetic algorithm is developed and employed to improve the performance of shrouded wind turbines.A low-order semi empirical model, which offers performance prediction for the power and thrust coefficients, is developed and applied to shrouded turbines. This 1D model is based on assumptions and approximations to calculate optimal power coefficients and power extraction, as well as augmentation ratios. It is revealed that the power enhancement is proportional to the mass stream rise produced by the nozzle diffuser-augmented wind turbine (NDAWT). Such mass flow rise can only be accomplished through two essential principles: an increase in the area ratios and/or by reducing the negative back pressure at the exit. The thrust coefficient for optimal power production of a conventional bare wind turbine is known to be 8/9, whereas the theoretical analysis of the NDAWT predicts an ideal thrust coefficient either lower or higher than 8/9 depending on the back-pressure coefficient at which the shrouded turbine operates. Computed performance expectations demonstrate a good agreement with numerical and experimental results, and it is demonstrated that much larger power coefficients than for traditional wind turbines are achievable. Lastly, the developed model is found to be very well suited for the preliminary design of shrouded wind turbines where typically many trade-off studies need to be conducted inexpensively.Then a higher fidelity model is developed and implemented to calculate the power, thrust, and drag coefficients by solving the Reynolds-averaged Navier-Stokes (RANS) equations with the k-epsilon turbulence model for the flow within and around diffuser augmented wind turbines using the open source software OpenFOAM. To reduce the computational cost, the turbine rotor itself is modeled by incorporating blade element momentum body forces into the RANS equations. Realistic rotor data for the sectional lift and drag coefficients for all angles of attacks are utilized via look-up tables. Grid convergence studies for verification and comparisons with experiments for validation are carried out to demonstrate that the adopted methodology is able to accurately predict the performance of a wind-lens prior to performing shape optimizations.Finally, the wind-lens performance is increased by designing the shroud and wind turbine shapes as well as flange height through an optimization process that seeks to maximize the power while minimizing drag and thrust forces. The employed optimizer is a multi-objective genetic algorithm (MOGA). Bezier curves are used to define the chord and twist distribution of the turbine blades and a piece-wise quadratic polynomial is utilized to define the shroud shape. It is demonstrated that the resulting optimal designs yield significant improvements in the output power.

Keywords

Aerospace Engineering, Mechanical Engineering, Shrouded Wind Turbine, Ducted wind turbines, Flanged diffuser, Wind Lens, DAWT, NDAWT, multi-objective genetic algorithm, 1D model of wind lens turbines, Bezier curves, chord and twist distributions

Rights Statement

Copyright © 2018, author

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