Date of Completion

5-12-2019

Embargo Period

10-21-2019

Advisors

Amvrossios C. Bagtzoglou, Zoi Dokou, Ramesh B. Malla

Field of Study

Environmental Engineering

Degree

Master of Science

Open Access

Open Access

Abstract

This study presents a semi-analytical model that facilitates the optimum design of small-scale hydropower systems, so that maximum possible energy can be harvested under such settings. The model comprises a rotating cylinder attached to a piston producing reciprocating motion when placed in moving water. Taking this model as base, the semi-analytical model employs Genetic Algorithm based optimization and develops optimal dimensions for the system, with the objective to minimize the time period while at the same time maximizing stroke of the piston. The model is tested first with single-parameter optimization and then with multi-objective optimization. As many energy harvesting approaches are based on the reciprocating motion of the mechanical/structural system, which is greatly affected by the geometric dimensions of the system, optimization of the system geometry becomes crucial for energy harvesting. The semi-analytical model is able to reduce the arms dimension while obtaining a higher stroke of piston for lower time period. The model has limitations but is able to produce optimization results comparable to laboratory data and applicable to flow data from Shetucket River in Willimantic, Connecticut, USA.

As energy is needed to rotate the cylinder, we propose to make it self-sustainable by attaching fins to the cylinder and use the river flow to rotate the cylinder. A fluid-structure interaction type CFD model is developed to study the rotation of the cylinder and the flow measurements are used as input velocities for simulations. Two different types of self-sustaining models are studied, namely Crank-slider model and Quick-return model.

Major Advisor

Amvrossios C. Bagtzoglou

Available for download on Monday, October 21, 2019

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