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

Marco Rosati,Thomas Kelly,John V Ringwood

doi:10.1109/access.2021.3125600

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.

Highlights

T HE global wave energy potential has been estimated by different authors [1]–[3], who report around 16000 − 18500 TWh/year, and a slow variation rate, of around 500 TWh/decade on average [3]
Since the validation normalized root mean squared error (NRMSE) has a minimum for nd = -4, the value of the input delay is set to -4, noting that a negative value of nd implies that the autoregressive with exogenous input (ARX) model is noncausal, meaning that both past and future values of the input are utilized in order to predict the output value, y(k)
Despite the fact that the multiple linear ARX models perform slightly better than the Kolmogorov-Gabor polynomial (KGP) models during the validation on their specific data sets, the procedure to implement in real-time a multi-linear ARX model, to cover the full range of operation, is found to be quite complex and time-consuming

Summary

INTRODUCTION

T HE global wave energy potential has been estimated by different authors [1]–[3], who report around 16000 − 18500 TWh/year, and a slow variation rate, of around 500 TWh/decade on average [3]. Rosati et al.: Nonlinear data-based hydrodynamic modelling of an oscillating water column wave energy device. System identification is employed to derive linear and nonlinear black-box hydrodynamic models of a scaled fixed OWC device. In order to develop the SI models, the time traces of the free surface elevation, η(t), and the water column displacement, y(t), are recorded and sampled during RWT experiments, where a scaled fixed OWC model is tested in irregular waves (IWs). Before using the data for OWC modelling, the time traces of the input need to be temporally aligned with those of the output To this end, the FSE measurements gathered from the up-wave probe (Fig. 4) during the two experiments are cross-correlated in order to estimate the time delay. The purpose of hydrodynamic control is to extend the bandwidth of the system effectively increasing the range of frequencies at which the device can resonate [30]

METHODOLOGY

MODEL VALIDATION PERFORMANCE

CONCLUSION