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.File | Dimensione | Formato | |
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