Challenges in Description of Nonlinear Waves Due to Sampling Variability

Wave description is affected by several uncertainties, with sampling variability due to limited number of observations being one of them. Ideally, temporal/spatial wave registrations should be as large as possible to eliminate this uncertainty. This is difficult to reach in nature, where stationarit...

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Main Authors: Elzbieta M. Bitner-Gregersen, Odin Gramstad, Anne Karin Magnusson, Mika Malila
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Journal of Marine Science and Engineering
Subjects:
Online Access:https://www.mdpi.com/2077-1312/8/4/279
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spelling doaj-833b09ccd8ed4228bad4f0144d62fdbd2021-04-02T13:48:56ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-04-01827927910.3390/jmse8040279Challenges in Description of Nonlinear Waves Due to Sampling VariabilityElzbieta M. Bitner-Gregersen0Odin Gramstad1Anne Karin Magnusson2Mika Malila3Group Technology and Research, DNV GL, NO-1322 Høvik, NorwayGroup Technology and Research, DNV GL, NO-1322 Høvik, NorwayR&D-OM, Norwegian Meteorological Institute, 5020 Bergen, NorwayR&D-OM, Norwegian Meteorological Institute, 5020 Bergen, NorwayWave description is affected by several uncertainties, with sampling variability due to limited number of observations being one of them. Ideally, temporal/spatial wave registrations should be as large as possible to eliminate this uncertainty. This is difficult to reach in nature, where stationarity of sea states is an issue, but it can in principle be obtained in laboratory tests and numerical simulations, where initial wave conditions can be kept constant and intrinsic variability can be accounted for by changing random seeds for each run. Using linear, second-order, and third-order unidirectional numerical simulations, we compare temporal and spatial statistics of selected wave parameters and show how sampling variability affects their estimators. The JONSWAP spectrum with gamma peakedness parameters γ = 1, 3.3, and 6 is used in the analysis. The third-order wave data are simulated by a numerical solver based on the higher-order spectral method which includes the leading-order nonlinear dynamical effects. Field data support the analysis. We demonstrate that the nonlinear wave field including dynamical effects is more sensitive to sampling variability than the second-order and linear ones. Furthermore, we show that the mean values of temporal and spatial wave parameters can be equal if the number of simulations is sufficiently large. Consequences for design work are discussed.https://www.mdpi.com/2077-1312/8/4/279nonlinear wavessampling variabilitytemporal and spatial statistics
collection DOAJ
language English
format Article
sources DOAJ
author Elzbieta M. Bitner-Gregersen
Odin Gramstad
Anne Karin Magnusson
Mika Malila
spellingShingle Elzbieta M. Bitner-Gregersen
Odin Gramstad
Anne Karin Magnusson
Mika Malila
Challenges in Description of Nonlinear Waves Due to Sampling Variability
Journal of Marine Science and Engineering
nonlinear waves
sampling variability
temporal and spatial statistics
author_facet Elzbieta M. Bitner-Gregersen
Odin Gramstad
Anne Karin Magnusson
Mika Malila
author_sort Elzbieta M. Bitner-Gregersen
title Challenges in Description of Nonlinear Waves Due to Sampling Variability
title_short Challenges in Description of Nonlinear Waves Due to Sampling Variability
title_full Challenges in Description of Nonlinear Waves Due to Sampling Variability
title_fullStr Challenges in Description of Nonlinear Waves Due to Sampling Variability
title_full_unstemmed Challenges in Description of Nonlinear Waves Due to Sampling Variability
title_sort challenges in description of nonlinear waves due to sampling variability
publisher MDPI AG
series Journal of Marine Science and Engineering
issn 2077-1312
publishDate 2020-04-01
description Wave description is affected by several uncertainties, with sampling variability due to limited number of observations being one of them. Ideally, temporal/spatial wave registrations should be as large as possible to eliminate this uncertainty. This is difficult to reach in nature, where stationarity of sea states is an issue, but it can in principle be obtained in laboratory tests and numerical simulations, where initial wave conditions can be kept constant and intrinsic variability can be accounted for by changing random seeds for each run. Using linear, second-order, and third-order unidirectional numerical simulations, we compare temporal and spatial statistics of selected wave parameters and show how sampling variability affects their estimators. The JONSWAP spectrum with gamma peakedness parameters γ = 1, 3.3, and 6 is used in the analysis. The third-order wave data are simulated by a numerical solver based on the higher-order spectral method which includes the leading-order nonlinear dynamical effects. Field data support the analysis. We demonstrate that the nonlinear wave field including dynamical effects is more sensitive to sampling variability than the second-order and linear ones. Furthermore, we show that the mean values of temporal and spatial wave parameters can be equal if the number of simulations is sufficiently large. Consequences for design work are discussed.
topic nonlinear waves
sampling variability
temporal and spatial statistics
url https://www.mdpi.com/2077-1312/8/4/279
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AT annekarinmagnusson challengesindescriptionofnonlinearwavesduetosamplingvariability
AT mikamalila challengesindescriptionofnonlinearwavesduetosamplingvariability
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