Abstract
In many real world applications the physical knowledge of a phenomenon and data science can be combined together in order to get mutual benefits. As a result, it is possible to formulate a so-called hybrid model from the combination of the two approaches. In this work, we propose an hybrid approach for the prediction of the ship propeller cavitating vortex noise, adopting real data collected during extensive model scale tests in a cavitation tunnel. Results will show the effectiveness of the proposal.
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Cipollini, F., Miglianti, F., Oneto, L., Tani, G., Viviani, M., Anguita, D. (2020). Cavitation Noise Spectra Prediction with Hybrid Models. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-16841-4_16
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DOI: https://doi.org/10.1007/978-3-030-16841-4_16
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