Deterministic Sea Wave Prediction Research Articles

The Propagation Velocity and Influences of Environmental Factors of Deterministic Sea Wave Prediction in the Long Crest Wave

Ocean waves are one of the leading environmental factors that cause motion of the ocean’s structure. Wave prediction is of great significance for the safety of marine structures. The deterministic sea wave prediction (DSWP) has been focused on because it provided an accurate temporal wave surface. The propagation velocity of wave components is one of the critical problems in DSWP. In this paper, the research of propagation velocity is focused on. The Taylor expansion to wave number is used to prove that the group velocity is the propagation velocity of wave components. The simulated irregular long crest wave data is generated. Utilizing the simulated data, the calculated wave surfaces based on group velocity are consistent with the simulated results. Meanwhile, the comparisons of calculated results based on the group velocity and phase velocity are given. Then, a tank experiment is set to verify the prediction results. To further investigate the prediction performance under different conditions, the influences of environmental factors, including the wind speed, water depth and sea state are analyzed in this paper.

A real-time wave prediction in directional wave fields: Strategies for accurate continuous prediction in time

Previous research on real-time deterministic sea wave prediction has generally focused on evaluating the accuracy and efficiency of short-term wave fields within a specific prediction zone. However, for a real-time wave prediction system, it is necessary to provide a continuous description of the ocean wave surface based on short-term prediction segments. In this regard, we have developed algorithms for continuous wave prediction in directional wave fields based on the “practical” prediction zone. The practical prediction zone refers to the time interval available for generating a continuous wave forecast by excluding the reconstructed waves from the prediction zone proposed by Kim et al. (2023). We also introduce and discuss several important time factors, such as the update interval of the spatio-temporal wave dataset, the total computation time, and the length of the practical prediction zone. By gaining a deeper understanding of numerical modeling setups, we have established strategies to reduce computational costs, which are directly related to the accuracy of continuous wave prediction. In particular, the development of these strategies suggests guidance in specifying direction and frequency bandwidths for continuous wave prediction.

Modelling and Control Tank Testing Validation for Attenuator Type Wave Energy Converter - Part II: Linear Noncausal Optimal Control and Deterministic Sea Wave Prediction Tank Testing

Tank testing results from applying Linear non-causal optimal control (LNOC) to the Mocean M100 wave energy converter (WEC) are presented in this paper. Based on the two state-space models derived from Part I, two LNOC algorithms designed to increase power capture of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/20$</tex-math></inline-formula> scaled M100 WEC prototype are tested separately in real-time under various irregular wave conditions. The deterministic sea wave prediction (DSWP) algorithm is employed to provide the required wave elevation/excitation force predictions for the LNOC using real-time wave measurements. Experimental data shows that the DSWP can predict the incoming waves with sufficiently high accuracy in various sea states. Compared with a well-tuned passive damper, LNOC based on a hydrodynamic model increases the power capture by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$128\%$</tex-math></inline-formula> while LNOC based on a system identification (SysID) model increases the power capture by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$323\%$</tex-math></inline-formula> . The technologies are transferrable. The testing procedure and the results also provide informative guidance for the tank testing of other advanced optimal controllers and their implementations on other types of WECs.

Energy capture optimization of heave oscillating buoy wave energy converter based on model predictive control

The optimal constant damping control based on the peak frequency of irregular wave is commonly adopted to improve the wave energy capture efficiency for the oscillating buoy wave energy converter (WEC) while the influence of the wave frequency change cannot be considered. In this paper, a real-time PTO force control optimization method is proposed based on model predictive control (MPC). Firstly, the dynamic model of the buoy is built based on the Cummins equation. Secondly, the additional penalty terms which may make the optimization problem convex are introduced into the original maximized energy objective, and their effects on the efficiency are investigated. Then, the deterministic sea wave prediction method is used to predict the future wave force aiming to make up the shortage that the classical MPC cannot compensate the external disturbance. Finally, the energy capture performance of MPC for the oscillating buoy WEC was compared with the resistive control. The results show that the MPC method can improve the energy capture by about 40%–60% under the irregular wave conditions.

Deterministic Sea Wave Prediction Based on Least Squares With Regularization Algorithm Using Coherent Microwave Radar

Deterministic Sea Wave Prediction Based on Least Squares With Regularization Algorithm Using Coherent Microwave Radar

In-Ocean Validation of a Deterministic Sea Wave Prediction (DSWP) System leveraging X-Band Radar to Enable Optimal Control in Wave Energy Conversion Systems

Advanced controls optimization offers a promising pathway to improve performance, reduce structural loads and thereby reduce the Levelized Cost of Electricity (LCOE) for wave energy conversion (WEC) systems. Model predictive control (MPC), which remains the preferred algorithm framework for WEC devices requires a wave excitation force forecast on the order of 30 seconds into the future to be effective. To establish the feasibility of forecasting the wave resource within this time-horizon, we benchmarked a wave prediction system that leverages an X-band ship navigation radar using a set of field campaigns carried out offshore Santa Cruz in California. For quantitative assessment of the prediction accuracy, we evaluated error metrics based on correlation and normalized mean absolute error between measurement and prediction. The relationship between prediction accuracy and key environmental factors prevalent during data collection (wave height and wind-speed) was analyzed through a set of sensitivity studies. Overall, the results show that wave prediction accuracy is good enough to enable MPC based control, which establishes a credible technology pathway to utilize these control techniques on commercial devices at sea.

Experimental investigations on the predictable temporal-spatial zone for the deterministic sea wave prediction of long-crested waves

Experimental investigations on the predictable temporal-spatial zone for the deterministic sea wave prediction of long-crested waves

DETERMINISTIC SEA WAVES PREDICTION FROM X-BAND RADAR: NEW ALGORITHM AND SEA TRIAL

Many maritime operations can benefit from short-term deterministic sea wave prediction (DSWP). Conventional X-band radars constitute a convenient cheap source of measurements for DSWP. The radar backscatter measurements suffer from several imperfections such as the effect of larger waves shadowing smaller waves. In order to extract the wave profiles and build a reliable sea prediction model, multiple radar scans must be processed. In this paper, we present a new single-wavenumber least-squares spectral algorithm for wave prediction from radar backscatter. The proposed technique is evaluated using field data from a dedicated sea trial.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/g0ejshY_nhc

Nonlinear deterministic sea wave prediction using instantaneous velocity profiles

Optimizing the production of wave energy converters using Model Predictive Control (MPC) requires a real-time, deterministic prediction of the waves arriving at the device. This study presents a new method for deterministic sea wave prediction, using the horizontal velocity profile over the water column as a boundary condition for a dedicated nonlinear wave model. However, direct measurement of the horizontal velocity component over the whole vertical column is hardly achievable at sea. A method to reconstruct this profile from measurement devices currently at use, such as ADCPs, is thus presented and evaluated. The performance of the prediction method itself is then tested using synthetic numerical data. First, the reconstruction of the horizontal velocity profile as a boundary condition is evaluated. Then, the whole prediction procedure is assessed. In both these stages, the simulations are based on synthetic numerical data and the outcomes are compared with numerical reference solutions. The results show that the method is promising enough to justify further investigation through wave tank experiments.

Sea trial on deterministic sea waves prediction using wave-profiling radar

Short-term deterministic sea wave prediction (DSWP) can facilitate the safe implementation of a number of maritime operations. Wave profile radars, based on the conventional X-band radars, have recently been proven to provide a low-cost convenient source of two-dimensional wave measurement for DSWP. The Golden Arrow sea trial in the North Atlantic was dedicated to collecting wave and vessel motion data for DSWP purposes, particularly under the large sea conditions that are of practical interest in marine operations. The field data were collected using a wave profiling radar and other measurement technologies for validation. This paper reports on the successful application of a recently developed technique by Al-Ani et al. in DSWP to the dedicated sea trial.

Non-Causal Linear Optimal Control of Wave Energy Converters With Enhanced Robustness by Sliding Mode Control

Sea wave energy converter control is a non-causal optimal control problem, and the control performance relies on the accuracy of wave prediction information. However, the existing wave prediction methods, such as Auto-Regressive (AR) method, extended Kalman Filter (EKF), Artificial neural network and deterministic sea wave prediction (DSWP), inevitably introduce prediction errors. This paper presents a robust non-causal linear optimal control of wave energy converters to explicitly cope with the prediction error of sea wave prediction and simultaneously compensate the modelling uncertainty caused by wave force approximations. This is achieved by designing a non-causal linear optimal control (LOC) to maximize the energy output and a sliding mode control (SMC) to compensate unmodeled WEC dynamics and wave prediction error. The parameters of both SMC and non-causal LOC are calculated off-line, which significantly enhances the real-time implementation of the proposed controller with reasonably low computational load. Simulation results demonstrate the efficacy of the proposed control strategy.

Deterministic Sea Waves Prediction Using Mixed Space–Time Wave Radar Data

AbstractA number of maritime operations can benefit from a short-term deterministic sea wave prediction (DSWP). Conventional X-band radars have recently been shown to provide a low-cost convenient source of two-dimensional wave profile information for DSWP purposes. However, such rotating radars typically introduce temporal smearing into the data, which introduces errors when traditional Fourier transform–based wave prediction methods are used. The authors report on a new approach for DSWP that avoids such errors. Furthermore, it is not susceptible to the condition number problems that arise with any form of direct or indirect inversion-based approaches. Extensive numerical analyses are conducted to illustrate the effect of the mixed space–time nature of the data on DSWP and the efficiency of the proposed technique to handle it.

An Examination of the Feasibility of Linear Deterministic Sea Wave Prediction in Multidirectional Seas Using Wave Profiling Radar: Theory, Simulation, and Sea Trials

Abstract For a number of maritime tasks there is a short time period, typically only a few tens of seconds, where a critical event occurs that defines a limiting wave height for the whole operation. Examples are the recovery of fixed and rotary winged aircraft, cargo transfers, final pipe mating in fluid transfer operations, and launch/recovery of small craft. The recovery of a 30-t rescue submersible onto a mother ship in the North Atlantic Treaty Organization (NATO) Submarine Rescue System is a prime example. In such applications short-term deterministic sea wave prediction (DSWP) can play a vital role in extending the sea states under which the system can be safely deployed. DSWP also has great potential in conducting experimental sea wave research at full scale. This report explores the feasibility of using data from an experimental wave profiling radar in achieving DSWP. The report includes theory, simulation, and field testing. Two forms of DSWP are employed: a fixed point system based upon a restricted set of wave directions from which some success is obtained and the other a fully two-dimensional technique that requires further development. The main finding is that using wave profiling radar for DSWP offers promise but requires improvements both to the spatial reliability and the resolution of the wave profiling radar and to the temporal resolution of its sweep before the technique can be considered to be viable as a usable tool.

Wave energy converter control by wave prediction and dynamic programming

We demonstrate that deterministic sea wave prediction (DSWP) combined with constrained optimal control can dramatically improve the efficiency of sea wave energy converters (WECs), while maintaining their safe operation. We focus on a point absorber WEC employing a hydraulic/electric power take-off system. Maximizing energy take-off while minimizing the risk of damage is formulated as an optimal control problem with a disturbance input (the sea elevation) and with both state and input constraints. This optimal control problem is non-convex, which prevents us from using quadratic programming algorithms for the optimal solution. We demonstrate that the optimum can be achieved by bang–bang control. This paves the way to adopt a dynamic programming (DP) algorithm to resolve the on-line optimization problem efficiently. Simulation results show that this approach is very effective, yielding at least a two-fold increase in energy output as compared with control schemes which do not exploit DSWP. This level of improvement is possible even using relatively low precision DSWP over short time horizons. A key finding is that only about 1 second of prediction horizon is required, however, the technical difficulties involved in obtaining good estimates necessitate a DSWP system capable of prediction over tens of seconds.

Prediction diagrams for deterministic sea wave prediction and the introduction of the data extension prediction method

Prediction diagrams for deterministic sea wave prediction and the introduction of the data extension prediction method

Filters for linear sea-wave prediction

Deterministic sea-wave prediction (DSWP) models are appearing in the literature designed for quiescent interval prediction in marine applications dominated by large swell seas. The approach has focused upon spectral methods which are straightforward and intuitively attractive. However, such methods have the disadvantage that while the sea is aperiodic in nature, the standard discrete spectral processing techniques force an absolutely periodic structure onto the resulting sea surface prediction models. As it is the shape of the sea surface that is important in such applications, particularly near the end of the domain which is important, the standard windowing techniques used in signal processing work to reduce leakage artifacts cannot be employed. This has necessitated the use of end matching methods that can be both inconvenient and may reduce the fraction of the time for which legitimate predictions are available. As a result, an investigation has been undertaken of the use of finite impulse response prediction filters to provide the necessary dispersive phase shifting required in DSWP systems. The present work examines the theoretical basis for such filters and explores their properties together with their application to both long and short crested swell seas. It is shown that wide band forms of such filters are only convergent in the sense of distributions having both infinite duration impulse responses and asymptotically divergent first derivatives. However, appropriate band limitation can produce useful finite impulse responses allowing implementation via standard discrete convolution methods. It is demonstrated that despite the prediction filters having a non-causal impulse response such filters can be used in practice due to a combination of the asymmetric nature of the impulse response and the fundamental nature of the prediction process. The findings are confirmed against actual sea-wave data.

Avoidance of phase shift errors in short term deterministic sea wave prediction

Short term deterministic sea wave prediction (DSWP) offers the promise of considerably extending the operational envelope of many marine activities such as aircraft launch and recovery onto vessels, cargo transfers, station-keeping roles and shuttle tanker activities. A short term prediction model first builds a description of the local sea surface profile then appropriately shifts this in time and space to the prediction site. It is shown that using the discrete Fourier series description conventionally proposed for DSWP implies assumptions about spectral resolution which can give rise to large errors in the shifting process. By reformulating the traditional linearised gravity wave equations it is possible to remove the need for discrete approximations to the shifting process and thus entirely avoid the consequent errors. The resulting shifting algorithms are shown to involve little increase in computational overhead compared to the fully discretised DSWP methods, and thus do not significantly detract from the available prediction times.

Adaptive phase measurement system for surface profile measurement

A hybrid phase measuring system is described which is an adaptive development of traditional phase locked loops. It is based around a digital delay line. The target application is a slope-measuring continuous wave lidar designed to determine surface profiles at shallow angles of incidence. The lidar unit, of which the phase shifter is a key element, is a pioneering system designed to obtain a vector field representation of propagating deep water ocean gravity waves. The application of the system is in the new discipline of deterministic sea wave prediction (DWSP) and is intended to operate in fixed time mode. In the normal tracking mode, the phase difference to be estimated is much less than /spl pi//2, but however, due to the nature of the physical situation, sudden large jumps of several times /spl pi/ are possible. The system time constants adapt to such jumps, producing an evolving compromise between phase error and the time taken to recover the tracking mode.