Doctoral Dissertations

Keywords and Phrases

Blade Element Momentum Theory; Computational Fluid Dynamics; Hydrokinetic Turbines; Multi-Turbine System; Neural Networks; Particle Image Velocimetry

Abstract

“Hydrokinetic energy conversion systems (HECSs) are emerging as viable solutions for harnessing the kinetic energy in river streams and tidal currents due to their low operating head and flexible mobility. This study is focused on the experimental and numerical aspects of developing an axial HECS for applications with low head ranges and limited operational space. In Part I, blade element momentum (BEM) and neural network (NN) models were developed and coupled to overcome the BEM’s inherent convergence issues which hinder the blade design process. The NNs were also used as a multivariate interpolation tool to estimate the blade hydrodynamic characteristics required by the BEM model. The BEM-NN model was able to operate without convergence problems and provide accurate results even at high tip speed ratios. In Part II, an experimental setup was developed and tested in a water tunnel. The effects of flow velocity, pitch angle, number of blades, number of rotors, and duct reducer were investigated. The performance was improved as rotors were added to the system. However, as rotors added, their contribution was less. Significant performance improvement was observed after incorporating a duct reducer. In Part III, a computational fluid dynamics (CFD) simulation was conducted to derive the optimum design criteria for the multi-turbine system. Solidity, blockage, and their interactive effects were studied. The system configuration was altered, then its performance and flow characteristics were investigated. The experimental setup was upgraded to allow for blockage correction. Particle image velocimetry was used to investigate the wake velocity profiles and validate the CFD model. The flow characteristics and their effects on the turbines performance were analyzed”--Abstract, page iv.

Advisor(s)

Chandrashekhara, K.

Committee Member(s)

Wang, Cheng
Homan, Kelly
Kimball, Jonathan W.
Samaranayake, V. A.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Mechanical Engineering

Comments

The authors would like to acknowledge the financial support received from the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology and the support of the Office of Naval Research (Grant # N000141010923). This work was supported by EPA grant # DW-14-95799601 and the USGS Toxic Substances Hydrology Program.

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2020

Journal article titles appearing in thesis/dissertation

  • A neural network approach to enhance blade element momentum theory performance for horizontal axis hydrokinetic turbine application
  • Experimental evaluation of coaxial horizontal axis hydrokinetic composite turbine system
  • Experimental evaluation of coaxial horizontal axis hydrokinetic composite turbine system

Pagination

xvi, 165 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2020 Abdulaziz Abutunis, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11865

Electronic OCLC #

1300808124

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