Version 1
: Received: 10 May 2024 / Approved: 11 May 2024 / Online: 12 May 2024 (07:45:10 CEST)
How to cite:
Sfravara, F.; Barberi, E.; Bongiovanni, G.; Chillemi, M.; Brusca, S. Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device. Preprints2024, 2024050737. https://doi.org/10.20944/preprints202405.0737.v1
Sfravara, F.; Barberi, E.; Bongiovanni, G.; Chillemi, M.; Brusca, S. Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device. Preprints 2024, 2024050737. https://doi.org/10.20944/preprints202405.0737.v1
Sfravara, F.; Barberi, E.; Bongiovanni, G.; Chillemi, M.; Brusca, S. Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device. Preprints2024, 2024050737. https://doi.org/10.20944/preprints202405.0737.v1
APA Style
Sfravara, F., Barberi, E., Bongiovanni, G., Chillemi, M., & Brusca, S. (2024). Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device. Preprints. https://doi.org/10.20944/preprints202405.0737.v1
Chicago/Turabian Style
Sfravara, F., Massimiliano Chillemi and Sebastian Brusca. 2024 "Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device" Preprints. https://doi.org/10.20944/preprints202405.0737.v1
Abstract
An Oscillating Water Column device, specifically a Savonius turbine, underwent comprehensive testing within an air duct to evaluate its performance under varying rotational speeds, flow directions, and the presence of power augmenters positioned both in front of and behind the device. The experimental setup involved the utilization of load cells and pressure transducers, with their data utilized to calculate pressure differentials across the turbine and torque. Subsequently, a predictive model based on decision trees was developed using Machine Learning. This model was then used to analyze the influence of various features on predicting the pressure difference, considered as the output. The results of the validation (10-fold cross-validation) and test phases were thoroughly investigated. Moreover, obtaining a predictive model allows for the exploration of different scenarios without relying solely on physical experimentation, thereby broadening the scope for further testing.
Keywords
OWC; Machine Learning; Savonius; Predictive Model
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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