Estimating the space-time characteristics of a sea state is of crucial importance to a number of engineering applications, such as the ones involving three-dimensional waves interacting with marine structures. In this context, developing a technique that allows extrapolating information about the wave field utilizing only a relatively small number of records is highly impactful, as it allows minimizing the use of expensive and sophisticated measurement techniques. In this paper, a Compressive Sampling (CS) based technique is developed for extrapolating free surface displacement data. The technique relies on a directional spectrum compatible sparse representation in conjunction with formulating and solving an L⁠1-norm optimization problem. Further, the accuracy of the developed technique is significantly enhanced via the use of an adaptive basis re-weighting procedure. Pertinent numerical examples demonstrate that the technique is capable of reconstructing the time history of a free surface displacement record successfully, while capturing the main features of the target frequency spectrum and of the cross-correlation function satisfactorily.

Extrapolation of random wave field data via compressive sampling / Malara, G.; Kougioumtzoglou, I. A.; Arena, Felice. - In: OCEAN ENGINEERING. - ISSN 0029-8018. - 157:(2018), pp. 87-95. [10.1016/j.oceaneng.2018.03.044]

Extrapolation of random wave field data via compressive sampling

Malara G.
;
Arena Felice
2018-01-01

Abstract

Estimating the space-time characteristics of a sea state is of crucial importance to a number of engineering applications, such as the ones involving three-dimensional waves interacting with marine structures. In this context, developing a technique that allows extrapolating information about the wave field utilizing only a relatively small number of records is highly impactful, as it allows minimizing the use of expensive and sophisticated measurement techniques. In this paper, a Compressive Sampling (CS) based technique is developed for extrapolating free surface displacement data. The technique relies on a directional spectrum compatible sparse representation in conjunction with formulating and solving an L⁠1-norm optimization problem. Further, the accuracy of the developed technique is significantly enhanced via the use of an adaptive basis re-weighting procedure. Pertinent numerical examples demonstrate that the technique is capable of reconstructing the time history of a free surface displacement record successfully, while capturing the main features of the target frequency spectrum and of the cross-correlation function satisfactorily.
2018
Compressive sampling
Sparse representation
Adaptive basis
Random wave field
Directional spectrum
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/3228
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