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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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 |
work_keys_str_mv |
AT elzbietambitnergregersen challengesindescriptionofnonlinearwavesduetosamplingvariability AT odingramstad challengesindescriptionofnonlinearwavesduetosamplingvariability AT annekarinmagnusson challengesindescriptionofnonlinearwavesduetosamplingvariability AT mikamalila challengesindescriptionofnonlinearwavesduetosamplingvariability |
_version_ |
1721563945767010304 |