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    Nonlinear Data-Based Hydrodynamic Modeling of a Fixed Oscillating Water Column Wave Energy Device


    Rosati, Marco and Kelly, Thomas and Ringwood, John (2021) Nonlinear Data-Based Hydrodynamic Modeling of a Fixed Oscillating Water Column Wave Energy Device. IEEE Access, 9. pp. 149756-149765. ISSN 2169-3536

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    Abstract

    System identification (SI) techniques represent an alternative strategy to provide the hydrodynamic model of oscillating water column (OWC) devices, compared to more traditional physics-based methods, such as linear potential theory (LPT) and computational fluid dynamics (CFD). With SI, the parameters of the model are obtained, by minimizing a model-related cost function, from input-output data. The main advantage of SI is its simplicity, as well as its potential validity range, where the dynamic model is valid over the full range for which the identification data was recorded. The paper clearly shows the value of a global nonlinear model, both in terms of accuracy and computational simplicity, over an equivalent multi-linear modelling solution. To this end, the validation performance of the nonlinear model is compared to the results provided by a range of linear models. Furthermore, in order to provide a more comprehensive comparative analysis, some practical aspects related to real-time implementation of multi-linear and nonlinear SI models are discussed. For the experimental campaign, real wave tank (RWT) data of a scaled OWC model are gathered from the narrow tank experimental facility at Dundalk Institute of Technology (DkIT). Particular attention is paid to the selection of suitable input signals for the experimental campaign, in order to ensure that the model is subjected to the entire range of equivalent frequencies, and amplitudes, over which model validity is required.

    Item Type: Article
    Keywords: Data-based hydrodynamic modelling, linear ARX model, nonlinear KGP model, oscillating water column; real wave tank; system identification; wave energy;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
    Item ID: 16112
    Identification Number: https://doi.org/10.1109/ACCESS.2021.3125600
    Depositing User: Professor John Ringwood
    Date Deposited: 16 Jun 2022 12:06
    Journal or Publication Title: IEEE Access
    Refereed: Yes
    URI:

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