Probabilistic modeling of ship powering performance using full-scale operational data |
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| Institution: | 1. National Engineering Laboratory for Pipeline Safety/ MOE Key Laboratory of Petroleum Engineering/ Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, China;2. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan;3. Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan;4. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan;5. Big Data Institute, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, New Territories, Hong Kong, China;1. Fluid Structure Interactions, Faculty of Engineering and Physical Sciences, Boldrewood Innovation Campus, University of Southampton, Southampton SO16 7QF, United Kingdom;2. Maritime Archaeology, Faculty of Arts and Humanities, University of Southampton, Avenue Campus, Highfield, Southampton, SO17 1BF, United Kingdom;3. Physical Geography, Faculty of Environmental and Life Sciences, Highfield Campus, University of Southampton, Southampton S017 1BJ, United Kingdom;1. Department of Maritime Transportation Management Engineering, Istanbul Technical University, Sahil Street, 34940 Tuzla, Istanbul, Turkey;2. Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, 100 Montrose Street, Glasgow G4 0LZ, United Kingdom;3. World Maritime University, PO Box 500, SE-20124 Malmö, Sweden;1. DAMEN Shipyard Singapore, R&D Department, 29 Tuas Crescent, 638720, Singapore;2. DIBRIS – University of Genova, Via Opera Pia 13, I-16145 Genova, Italy;3. Department of Shipping and Marine Technology, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden;4. DIBRIS – University of Genova, Via Opera Pia 13, I-16145 Genova, Italy;1. Dept. of Shipping and Marine Technology, Chalmers University of Technology, 41296 Gothenburg, Sweden;2. Dept. of Mathematical Sciences, Chalmers University of Technology, 41296 Gothenburg, Sweden;3. DNV-GL, No-1322, Hovik, Norway |
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| Abstract: | The energy efficiency of ocean-going vessels can be increased through various operational considerations, such as improved cargo arrangements and weather routing. The first step toward the goal of maximizing the energy efficiency is to analyze how the ship's powering performance changes under different operational settings and weather conditions. However, existing analytical models and empirical methods have limitations in reliably estimating the powering performance of full-scale ships in real operating conditions. In this study, machine learning techniques are employed to estimate the powering performance of a full-scale ship by constructing regression models using the ship's operational data. In order to minimize the risk of overfitting in the regression process, domain knowledge based on physical principles is combined into the regression models. Also, the uncertainty of the estimated performance is evaluated with consideration of the environmental uncertainties. The obtained regression models can be used to predict the ship speed and engine power under different operational settings and weather conditions. |
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| Keywords: | Ship powering performance Gaussian processes Domain knowledge Environmental uncertainty |
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