Bryson Robertson

Prediction of Incipient Breaking Wave-Heights Using Artificial Neural Networks and Empirical Relationships

The accurate prediction of shallow water breaking heights is paramount to better understanding complex nonlinear near shore coastal processes. Over the past 150 years, numerous empirical relationships have been proposed based on scaled laboratory datasets. This study utilizes a newly available field collected full-scale dataset of breaking wave conditions to investigate the accuracy of published empirical models and a novel artificial neural networks (ANN) model in predicting the final breaking wave-height for laboratory-scaled and full-scaled ocean waves. Performance is measured by comparison against both the field datasets and 465 separate datasets from 11 independent laboratory studies. The relationship of Rattanapitikon and Shibayama [2000 “Verification and modification of breaker height formulas,” Coastal Eng. J. 42 (4), 389–406.] outperformed all available empirical models when tested against only laboratory datasets, but was superseded by the relationship of Robertson et al. [2015 “Remote sensing of irregular breaking wave parameters in field conditions,” J. Coastal Res. 31 (2), 348–363.] when tested against only field datasets. However, this study noted that models developed based on scaled laboratory tests tend to underestimate the ocean full-scale breaking wave-heights. The training and testing of the ANN model were accomplished using 75% and 25% of the combined field and laborartory datasets. The ANN models consistently outperformed predictive accuracy of empirical models. Sensitivity analysis of the trained ANN models quantified the relative impact of individual wave parameters on the final breaking wave-height.

Remote Sensing of Irregular Breaking Wave Parameters in Field Conditions

ABSTRACT Robertson, B.; Hall, K.; Nistor, I.; Zytner, R., and Storlazzi, C., 2015. Remote sensing of irregular breaking wave parameters in field conditions. The analysis of wave breaking in shallow water has been ongoing for almost 150 years. Numerous research papers have been published that approximate both the local conditions and geometric characteristics of breaking waves. However, much of this knowledge is based on laboratory results or limited field investigations because traditional methods of extracting breaking wave measurements from the surfzone are expensive, dangerous, and feature low-resolution data. Unfortunately, laboratory studies are prone to scaling and friction effects that introduce unwanted variability in the data. This study presents a novel, safe, and low-cost method of extracting relevant breaking-wave properties from irregular waves in the surfzone, using optical and in situ measurement systems. Published, contradictory breaking-water depth definitions are compared, and the water depth at the wave-trough depth, corrected for optical offsets using a still-water correction of one-third of the wave height, is found to exhibit the least variability. A new, effective seafloor-slope definition, based on individual, breaking wavelength-to-depth ratios, was found to increase predictive ability over previously variable seafloor slope extraction methods. Collected field data are compared against established breaking-wave height formulas with the general exponential form consistently finding the best correlation. Finally, an optimized breaking-wave height-prediction method finds a root mean square relative error of just 1.672% within the ranges of the measured data set. Irregular waves investigated on an individual wave basis are shown to follow regular wave-breaker height and depth prediction methods.

Predicting Breaking Wave Conditions Using Gene Expression Programming

The forces and loading resulting from shallow water breaking waves are one of the most important drivers in coastal engineering design and morphological change. The importance of accurately and precisely predicting breaking wave conditions cannot be overstated. Using a novel dataset of laboratory and field scale breaking wave conditions, this study assesses the performance of widely applied empirical relationships for breaking waves and uses newly available artificial neural networks and gene expression programming (GEP) numerical methods to develop an accurate and easily applied predictor of breaking conditions for coastal engineers and planners. A novel GEP model is developed and shown to: provide excellent predictive ability at all scales, greatly improve prediction compared with previous works at laboratory scale, and clearly identify the relevant importance of seafloor slope and the water depth to wavelength ratio.

Development of Control Strategies for Interconnected Pneumatic Wave Energy Converters

In the field of wave energy converter control, high fidelity numerical models have become the predominant tool for the development of accurate and comprehensive control strategies. In this study, a numerical model of a novel wave energy converter, employing a pneumatic power take-off, is created to provide a low-cost method for the development of a power-maximizing control strategy. Device components and associated architectures are developed in the time domain solvers Proteus DS and MATLAB/Simulink. These two codes are dynamically coupled at run time to produce a complete six degree of freedom, time domain simulation of the converter. Utilizing this numerical framework, a genetic algorithm optimization procedure is implemented to optimally select eight independent parameters governing the PTO geometry. Optimality is measured in terms of estimated annual energy production at a specific deployment location off the West Coast of Canada. The optimization exercise is one layer of PTO force control — the parameters selected are seen to provide significant improvements in the annual power output, while also smoothing the WEC power output on both a sea-state by sea-state and wave-by-wave basis.Copyright