Phase Resolving Analysis of Multidirectional Wave Trains

Janssen, T. T.; Dongeren, Ap van; Kuiper, C.

doi:10.1061/40604(273)39

Cited by 8 publications

(6 citation statements)

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“…In contrast to the mature discipline of the statistical prediction of waves (Pierson et al 1955;Kinsman 1984;Tucker and Pitt 2001), this is a relatively new area with a very modest-sized literature (Morris et al 1992(Morris et al , 1998Prislin et al 1997;Zhang et al 1999;Belmont et al 2003Belmont et al , 2006Wu et al 2000;Edgar et al 2000;Janssen et al 2002;Wu 2004;Blondel et al 2008;Naaijen and Huijsmams 2008;Naaijen et al 2009;Abusedra and Belmont 2011) that is only beginning to make the move from a research topic into practical applications.…”

Section: Introductionmentioning

confidence: 99%

An Examination of the Feasibility of Linear Deterministic Sea Wave Prediction in Multidirectional Seas Using Wave Profiling Radar: Theory, Simulation, and Sea Trials

Belmont

Christmas

Dannenberg

et al. 2014

Journal of Atmospheric and Oceanic Technology

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For a number of maritime tasks there is a short time period, typically only a few tens of seconds, where a critical event occurs that defines a limiting wave height for the whole operation. Examples are the recovery of fixed and rotary winged aircraft, cargo transfers, final pipe mating in fluid transfer operations, and launch/ recovery of small craft. The recovery of a 30-t rescue submersible onto a mother ship in the North Atlantic Treaty Organization (NATO) Submarine Rescue System is a prime example. In such applications short-term deterministic sea wave prediction (DSWP) can play a vital role in extending the sea states under which the system can be safely deployed. DSWP also has great potential in conducting experimental sea wave research at full scale. This report explores the feasibility of using data from an experimental wave profiling radar in achieving DSWP. The report includes theory, simulation, and field testing. Two forms of DSWP are employed: a fixed point system based upon a restricted set of wave directions from which some success is obtained and the other a fully two-dimensional technique that requires further development. The main finding is that using wave profiling radar for DSWP offers promise but requires improvements both to the spatial reliability and the resolution of the wave profiling radar and to the temporal resolution of its sweep before the technique can be considered to be viable as a usable tool.

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Section: Introductionmentioning

confidence: 99%

An Examination of the Feasibility of Linear Deterministic Sea Wave Prediction in Multidirectional Seas Using Wave Profiling Radar: Theory, Simulation, and Sea Trials

Belmont

Christmas

Dannenberg

et al. 2014

Journal of Atmospheric and Oceanic Technology

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“…The new post-processing method developed in this study can be viewed as a variation on the DPRA approach of Janssen et al (2001). Both DPRA and r-DPRA aim at analysing complex wave situations that include wave components from several different directions.…”

Section: Description Of the R-dpra Methodsmentioning

confidence: 99%

“…Besides the amplitude and direction of the different wave components, also the (relative) phases are required for such types of applications. Janssen et al (2001) developed a phase-resolving method for determining the 2D distribution of waves, called Directional Phase-Resolving Analysis (DPRA). Their method primarily aims at representing a directionally spread sea state using a small number (2-5) of discrete wave directions.…”

Section: Wave Analysis Toolsmentioning

confidence: 99%

A Practical Post-Processing Method to Obtain Wave Parameters from Phase-Resolving Wave Model Results

Jong

Borsboom

2012

The International Journal of Ocean and Climate Systems

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This paper describes a new method to obtain directional and amplitude information from the results of phase-resolving computational wave models. The output of this type of models is the wave field resulting from incoming and reflected wave components combined, whereas specific information on separate wave components (directions, heights, periods) is generally required for design purposes. The new post-processing method that we present to obtain this information has been validated using analytically prescribed wave signals as well as results from a mild-slope model, which is the phaseresolving wave model that has been considered so far. The versatility and usefulness of the method is illustrated in a number of application examples for ports. The results show that the new method is practical and produces reliable results.

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“…Similar to the scheme of Fisher et al [6], also validated using real ocean waves, the present algebraic model exploits all three degrees of freedom (DoF) translational displacements of each buoy: one vertically and two horizontally along the cardinal north and east. This significantly simplifies the deployment process that would be required with heave-only buoys or sensors as used in [5], [7]. Furthermore, the model employs a single frequencyindependent set of directions to represent wave propagation, following [7], though frequency-dependent spreading is allowed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of physics-based and machine learning methods for phase-resolved prediction of waves measured in the field

Chen,

Hlophe,

Zhao

et al. 2023

Proc. EWTEC

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Phase-resolved predictions of surface waves can be used to optimize a wide variety of marine applications. In this paper, we compare predictions obtained using two independent methods for field data, with horizons sufficient to control wave energy converters. The first method is physics-based prediction. In this method, a set of optimal representative angles, obtained using an optimization algorithm given time histories of a wave buoy motion in 3D, are used for forward propagation based on linear wave theory. The second method is a machine learning method using an Artificial Neural Network (ANN) which requires longer records for training. Field measurements were obtained from the Southern Ocean of Albany, WA. The field data were collected by an upwave ‘detection’ array of 3 Sofar Spotter wave buoys and a downwave ‘prediction’ point coincident with a Datawell Waverider-4. All buoys were soft-moored, and data were collected over 3 months in 2022. Selected intervals during this period are presented in the paper to compare and contrast the predictions made by the two different methods. We find that some wave fields can be predicted well over more than a period in advance, all that is required for active control of a wave power take-off in a renewable energy application. In contrast, highly spread sea states remain a challenge. The methods are also compared in terms of the complexity and time required for making predictions. Further discussions are made on the applicability of the results to other locations.

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Phase Resolving Analysis of Multidirectional Wave Trains

Cited by 8 publications

References 0 publications

An Examination of the Feasibility of Linear Deterministic Sea Wave Prediction in Multidirectional Seas Using Wave Profiling Radar: Theory, Simulation, and Sea Trials

An Examination of the Feasibility of Linear Deterministic Sea Wave Prediction in Multidirectional Seas Using Wave Profiling Radar: Theory, Simulation, and Sea Trials

A Practical Post-Processing Method to Obtain Wave Parameters from Phase-Resolving Wave Model Results

Comparison of physics-based and machine learning methods for phase-resolved prediction of waves measured in the field

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