Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.
The phenomenon of spread of a (pathogenic) virus involves many physical variables, and is not amenable to satisfactory analysis via conventional methods. Dimensional Analysis (DA) is singled out as a simple and accessible way that can determine (at least qualitatively) how virus spread is related to seven physical quantities that are thought to influence it. However, classical DA deduces four dimensionless products only, none of which incorporates temperature and humidity, despite the obvious relevance of these two meteorological factors. This paper proposes an alternative version of dimensional analysis using a novel irredundant set of three fundamental quantities only. This new DA version produces five dimensionless products, four of which are essentially a replication of the old ones, while the fifth is a novel product that relates both humidity and temperature to other influencing factors. Our novel DA solution is a significant contribution, since it provides a more realistic model for virus spread rate, and it does not ignore any of the essential influencing factors. Such a model might lead to a better understanding of the determinants of spread for the novel coronavirus SARS-CoV-2 that causes the ongoing COVID-19 fatal pandemic.
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