Josh Davidson

Identification of Wave Energy Device Models From Numerical Wave Tank Data—Part 2: Data-Based Model Determination

In this paper and its companion, the identification of mathematical models describing the behaviour of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank experiments. When the wave amplitude and the WEC displacement are not negligible with respect to the WEC dimensions, nonlinear hydrodynamic effects may appear, and the accuracy of linear hydrodynamic models is reduced, leading to the necessity of introducing some nonlinearities in the model structure. This paper proposes, for WEC modelling, the use of discrete-time nonlinear autoregressive with exogenous input (NARX) models, as an alternative to continuous-time models. Techniques of model identification are also explained and applied to a case study.

Optimising Numerical Wave Tank Tests for the Parametric Identification of Wave Energy Device Models

While linear and nonlinear system identification is a well established field in the control system sciences, it is rarely used in wave energy applications. System identification allows the dynamics of the system to be quantified from measurements of the system inputs and outputs, without significant recourse to first principles modelling. One significant obstacle in using system identification for wave energy devices is the difficulty in accurately quantifying the exact incident wave excitation, in both open ocean and wave tank scenarios. However, the use of numerical wave tanks (NWTs) allow all system variables to be accurately quantified and present some novel system tests not normally available for experimental devices. Considered from a system identification perspective, this paper examines the range of tests available in a NWT from which linear and nonlinear dynamic models can be derived. Recommendations are given as to the optimal configuration of such system identification tests.Copyright

Identification of Wave Energy Device Models From Numerical Wave Tank Data—Part 1: Numerical Wave Tank Identification Tests

In this paper and its companion, the identification of mathematical models describing the behaviour of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank (NWT) experiments. This paper deals with the identification tests used to produce the data for the model identification. NWTs, implemented using computational fluid dynamics (CFD), are shown as an effective platform to perform the identification tests. The design of the NWT experiments, to ensure the production of information-rich data for the model identification, is discussed. A case study is presented to illustrate the design and implementation of NWT experiments for the identification of WEC models.

COMPARISON OF NUMERICAL SIMULATIONS WITH EXPERIMENTAL MEASUREMENTS FOR THE RESPONSE OF A MODIFIED SUBMERGED HORIZONTAL CYLINDER MOORED IN WAVES

To facilitate commercially relevant numerical design optimization in wave energy conversion accurate and validated simulations of wave body interactions are necessary. Wave energy, more so than almost any other industry, can benefit from such numerical optimization because of the high cost and long period of design iteration in experimental and field testing. For the foreseeable future wave energy device design and optimization will continue to rely heavily on potential flow solvers. Two important prerequisites to successfully using simulations based on these codes are firstly a need to validate the simulation implementation by comparison with experiment and secondly a need to supplement the potential flow solution with experimentally (or CFD) derived coefficients for the forces that are neglected by the potential flow solver. This paper attempts to address both of these prerequisites. A comparison of numerical simulations and physical wave tank experiments on a submerged horizontal cylinder moored in waves is presented. Good agreement between numerical model and experiment is achieved. At operating points where the body response is linear a numerical model based purely on potential flow and linear mooring stiffness achieves excellent results and at operating points where the body response is non-linear a time domain model with frequency independent quadratic damping is shown to give good agreement for a wide range of wave periods and amplitudes.

Identifying Models Using Recorded Data

The modelling approach presented in this chapter is that of system identification, where models are determined from input/output data measured from the system under study. Models identified from recorded wave energy converter (WEC) data can accurately describe WEC behaviour, provided the data is of a sufficiently high quality. The chapter details generating data for the system identification process and outlines the requirements of the data to ensure representative models are obtained. Different parametric model structures are presented, along with identification algorithms to determine the optimum parameter values for the models. A number of case studies illustrating the use of system identification to obtain numerical models of WECs are given.