Characterizing random wave surface elevation data |
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| Institution: | 1. Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA;1. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA;2. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA;3. Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing, 100124, China;4. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA;5. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, 100872, China;6. Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, 100872, China;7. School of Engineering Science, University of Chinese Academy of Science, Beijing, 101408, China;1. School of Environment, Harbin Institute of Technology, Harbin 150090, China;2. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;3. School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia;4. Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA;5. Department of Civil Engineering, Indian Institute of Technology Bombay, Powai 400076, India;6. State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China;1. The Centre for Hydrogeology & Geothermics (CHYN), University of Neuchâtel, Rue Emile Argand 11, CH-2000 Neuchâtel, Switzerland;2. ETH Zürich, Institute of Structural Engineering, Chair of Risk, Safety & Uncertainty Quantification, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland;3. Andra, 1-7 rue Jean Monnet, 92298 Châtenay-Malabry Cedex, France;1. University of Genoa, DISTAV, Corso Europa, 26, 16132 Genova, Italy;2. SEAMap srl, Environmental Consulting, Via Ponti 11, 17052 Borghetto S.S., Italy;3. CEREGE, CNRS UMR, 7330 Aix-en-Provence, France;4. Lamont Doherty Earth Observatory, Columbia University, 61 Route 9w, Palisades 10964, USA;5. MARUM, University of Bremen & Leibniz Center for Tropical Marine Ecology (ZMT), Leobener Str., 28359 Bremen, Germany;6. Freelancer and Professional UAV Pilot, Italy;7. DHI Italia, Via degli Operai, 40, 16149 Genova, Italy;1. DiSTAV, Department of Earth, Environmental and Life Sciences, University of Genoa, Corso Europa 26, Genoa, Italy;2. College of Life and Environmental Sciences, University of Exeter, UK;3. DICAM, Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Viale Risorgimento 2, Bologna, Italy;4. Department of Environmental Biology, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, Rome, Italy;5. GIS Posidonie, Aix-Marseille University, Pythéas Institute, Campus of Luminy, Marseille, France;6. DiSTeM, Department of Earth and Marine Sciences, University of Palermo, Via Archirafi 22, Palermo, Italy;7. Departamento de Ciencias del Mar y Biología Aplicada, Unidad de Biología Marina, Universidad de Alicante, Apdo 99, Alicante, Spain;8. DSV, Department of Life Science, University of Trieste, Via Weiss 2, Trieste, Italy;9. DI4A, Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, Udine, Italy;10. FRES 3041, EqEL, Department of Ecology Biology, University of Corsica “Pascal Paoli”, Corte, France |
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| Abstract: | The definition and subsequent use of dimensional and dimensionless parameters to characterize various nonlinear aspects of ocean surface waves has again become a matter of great interest to the offshore community. The desire to ascertain whether laboratory simulations are adequately representing the surface waves found in the oceans and the concern over the mechanisms behind platform response phenomena, like ringing, has driven this resurgence of interest. This paper presents a depth independent characterization of single design waves, from which improved estimates of localized wave crest front and back slopes follow that are consistent with discrete time series analysis. Characterization of the nature of the entire wave data recorded requires a combination of spectral parameters and probabilistic models in addition to those used in the design wave characterization. A new expression for the direct evaluation of the kurtosis from knowledge of the spectral bandwidth, the relationship between some of the common spectral parameters, and some modified spectral parameters are presented and discussed. Three illustrative examples are presented. The first example provides a detailed examination of wave data measured from a series of random amplitude and random phase tests in a large model basin. The second presents estimates of the various parameters for the Pierson-Moskowitz and Wallops wave spectrum models. The third example investigates the use of the spectral peakedness ratio for comparing data with selected wave spectrum models. The examples illustrate how the formulae can provide a comprehensive local and global parametric characterization of surface wave elevation data. |
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