A review on comparative study of Savonius wind turbine rotor performance parameters

Shende, Vikas; Patidar, Harsh; Baredar, Prashant; Agrawal, Meena

doi:10.1007/s11356-022-22399-w

Cited by 7 publications

(1 citation statement)

References 118 publications

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“…Moreover, the Savonius turbine’s ability to handle turbulent and irregular flows, which are common in wave environments, further enhances its suitability for OWC systems. Its vertical-axis orientation means it does not need to be reoriented to face the direction of the flow, unlike horizontal-axis turbines, thus continuously capturing energy regardless of changes in wave direction [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device

Sfravara,

Barberi,

Bongiovanni

et al. 2024

Sensors

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Oscillating Water Column (OWC) systems harness wave energy using a partially submerged chamber with an underwater opening. The Savonius turbine, a vertical-axis wind turbine, is well-suited for this purpose due to its efficiency at low speeds and self-starting capability, making it an ideal power take-off (PTO) mechanism in OWC systems. This study tested an OWC device with a Savonius turbine in an air duct to evaluate its performance under varying flow directions and loads. An innovative aspect was assessing the influence of power augmenters (PAs) positioned upstream and downstream of the turbine. The experimental setup included load cells, Pitot tubes, differential pressure sensors and rotational speed sensors. Data obtained were used to calculate pressure differentials across the turbine and torque. The primary goal of using PA is to increase the CP–λ curve area without modifying the turbine geometry, potentially enabling interventions on existing turbines without rotor dismantling. Additionally, another novelty is the implementation of a regression Machine-Learning algorithm based on decision trees to analyze the influence of various features on predicting pressure differences, thereby broadening the scope for further testing beyond physical experimentation.

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Section: Introductionmentioning

confidence: 99%