# Design optimization of a small-scale hydropower harvesting device

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## Abstract

This paper presents a semi-analytical model that facilitates the optimum design of small-scale hydropower systems, so that maximum possible energy can be harvested under low head and streamflow conditions in run-of-the-stream settings. The hydropower harvesting model developed and tested at the University of Connecticut (Malla et al., Renewable Energy 36(5):1568–1577, 2011) comprises a rotating cylinder attached to a piston producing reciprocating motion when placed in moving water. Taking this model as the 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 the stroke of the piston. The semi-analytical model is tested in steps: first with single-objective optimization for the time period and the stroke of the piston separately and then using multi-objective optimization to produce a set of Pareto optimal solutions. 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. Compared to the hydropower harvesting model tested before (Malla et al., Renewable Energy 36(5):1568–1577, 2011), the semi-analytical model is able to reduce the arms’ dimensions while obtaining a much higher stroke of piston for much lower time period. The model has some limitations but is able to produce optimization results comparable to laboratory experimental data and applicable to actual flow data from Shetucket River in Willimantic, CT, USA.

## Keywords

Hydropower Reciprocating cylinder system Power harvesting Optimization Genetic algorithm Robins–Magnus effect## Notes

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

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