Transient gap resonance between two closely-spaced boxes triggered by nonlinear focused wave groups

Gao, Junliang; Mi, Chenglong; Song, Zhiwei; Liu, Yingyi

doi:10.1016/j.oceaneng.2024.117938

Cited by 44 publications

(1 citation statement)

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“…The first one, [ 24 ], analyzes the fluid dynamics of a ducted Savonius wind turbine for OWC Wave Energy Converters. Using a 2D Computational Fluid Dynamics (CFD) [ 25 , 26 , 27 ] model validated against experimental data, the research demonstrates the turbine’s potential in OWC systems. Results indicate that the ducted Savonius rotor, with blockage effects considered, achieves a significantly higher power coefficient than bare Savonius turbines.…”

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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