Performance optimization of a very low head (VLH) axial turbine using surrogate model

Mohsen Balavand, Amir Bahreini and Alireza Riasi

Renewable Energy, 2024, vol. 229, issue C

Abstract: The use of very low head (VLH) axial turbines in rivers and canals is one of the new methods of utilizing hydropower on a small scale. These types of turbines are environmentally friendly and, due to the lack of need for a dam, penstock, spiral casing, and draft tube, have low construction costs. Higher efficiency and improved turbine performance also lead to lower overall costs. In this study, the primary objective is to pinpoint the key factors that affect the VLH turbine performance and optimize it accordingly. The design variables encompass various elements, including leading and trailing edge angles, maximum thickness across three blade sections, tip clearance, outlet angles of guide vanes, and the count of guide vanes and runner blades. In this regard, during the optimization phase, only the most influential factors as ascertained through sensitivity analysis are considered. Subsequently, employing surrogate modeling, an evolutionary optimization algorithm is applied to identify the optimal configuration of design variables. Critical design variables were identified, including the blade angles in the middle section, the trailing edge angles, the outlet angle of the guide vanes, and the number of runner blades. Following the optimization process, the optimal turbine exhibited notable improvements, with a 2.26 % increase in efficiency and a remarkable 10.4 % boost in power output compared to the original configuration. Moreover, the optimized turbine showcased improved performance beyond the design point, with approximately a 2 % efficiency increase across all discharge rates.

Keywords: VLH turbine; CFD; Surrogate modeling; Optimization; Evolutionary algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:229:y:2024:i:c:s0960148124007821

DOI: 10.1016/j.renene.2024.120714

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