Numerical simulation of a full spine of Edinburgh Duck modules in uni- and multi-directional irregular wave climates, with a view to design optimisation

Cotten, Alfred; Forehand, David

doi:10.1016/j.oceaneng.2023.115214

Cited by 3 publications

(1 citation statement)

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“…This distinctive characteristic enhances overall performance, allowing for the successful conversion of both kinetic and potential energies generated by the waves into rotational mechanical energy. The evolutionary trajectory of the asymmetric WEC has witnessed the integration of experimental approaches [19][20][21] and numerical methods [22][23][24] to enhance its performance across a spectrum of sea states. Notably, the efficiency of this technology experiences a substantial decline when confronted with the intricate variations in incoming waves, encompassing factors such as high steep waves, 3D nonlinear effects, and the influence of waves breaking in the proximity of the WEC.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models

Poguluri,

Bae

2024

JMSE

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The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter’s duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location.

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