Numerical simulation of floating bodies in extreme free surface waves

In this paper, we use the in-house Computational Fluid Dynamics (CFD) flow code AMAZON-SC as a numerical wave tank (NWT) to study wave loading on a wave energy converter (WEC) device in heave motion. This is a surface-capturing method for two fluid flows that treats the free surface as contact surface in the density field that is captured automatically without special provision. A time-accurate artificial compressibility method and high resolution Godunov-type scheme are employed in both fluid regions (air/water). The Cartesian cut cell method can provide a boundary-fitted mesh for a complex geometry with no requirement to re-mesh globally or even locally for moving geometry, requiring only changes to cut cell data at the body contour. Extreme wave boundary conditions are prescribed in an empty NWT and compared with physical experiments prior to calculations of extreme waves acting on a floating Bobber-type device. The validation work also includes the wave force on a fixed cylinder compared with theoretical and experimental data under regular waves. Results include free surface elevations, vertical displacement of the float, induced vertical velocity and heave force for a typical Bobber geometry with a hemispherical base under extreme wave conditions

Numerical hydrodynamic modelling of a pitching wave energy converter

Two computational methodologies – computational fluid dynamics (CFD) and the numerical modelling using linear potential theory based boundary element method (BEM) are compared against experimental measurements of the motion response of a pitching wave energy converter. CFD is considered as relatively rigorous approach offering non-linear incorporation of viscous and vortex phenomenon and capturing of the flow turbulence to some extent, whereas numerical approach of the BEM relies upon the linear frequency domain hydrodynamic calculations that can be further used for the time-domain analysis offering robust preliminary design analysis. This paper reports results from both approaches and concludes upon the comparison of numerical and experimental findings

On global-local artificial neural networks for function approximation

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria

A Blind Comparative Study of Focused Wave Interactions with a Fixed FPSO-like Structure (CCP-WSI Blind Test Series 1)

Results from Blind Test Series 1, part of the Collaborative ComputationalProject in Wave Structure Interaction (CCP-WSI), are presented. Partici-pants, with a range of numerical methods, simulate blindly the interactionbetween a fixed structure and focused waves ranging in steepness and di-rection. Numerical results are compared against corresponding physicaldata. The predictive capability of each method is assessed based on pres-sure and run-up measurements. In general, all methods perform well inthe cases considered, however, there is notable variation in the results(even between similar methods). Recommendations are made for appro-priate considerations and analysis in future comparative studies

Appropriate model use for predicting elevations and inundation extent for extreme flood events

Flood risk assessment is generally studied using flood simulation models; however, flood risk managers often simplify the computational process; this is called a “simplification strategy”. This study investigates the appropriateness of the “simplification strategy” when used as a flood risk assessment tool for areas prone to flash flooding. The 2004 Boscastle, UK, flash flood was selected as a case study. Three different model structures were considered in this study, including: (1) a shock-capturing model, (2) a regular ADI-type flood model and (3) a diffusion wave model, i.e. a zero-inertia approach. The key findings from this paper strongly suggest that applying the “simplification strategy” is only appropriate for flood simulations with a mild slope and over relatively smooth terrains, whereas in areas susceptible to flash flooding (i.e. steep catchments), following this strategy can lead to significantly erroneous predictions of the main parameters—particularly the peak water levels and the inundation extent. For flood risk assessment of urban areas, where the emergence of flash flooding is possible, it is shown to be necessary to incorporate shock-capturing algorithms in the solution procedure, since these algorithms prevent the formation of spurious oscillations and provide a more realistic simulation of the flood levels

Numerical and experimental investigation of turbulent flow around a vertical circular cylinder

The present study is aimed at analyzing the flow around a vertical circular cylinder exposed to a steady current. A Detached Eddy Simulation (DES) turbulent model is used in the numerical simulations, while the physical experiments adopt Particle Image Velocimetry (PIV) and Acoustic Doppler Velocimetry (ADV) techniques to collect centreline flow velocities on a rigid smooth bed around a scaled monopile. Comparisons between the numerical and experimental results for the mean velocity and some turbulence characters distribution in the wake of a cylinder are compared

Neural network architectures and overtopping predictions

Overtopping of seawalls presents a considerable hazard to people and property near the coast and accurate predictions of overtopping volumes are essential in informing seawall construction. The methods most commonly used for the prediction of time-averaged overtopping volumes are parametric regression and numerical modelling. In this paper overtopping volumes are predicted using artificial neural networks. This approach is inherently non-parametric and accepts data from a variety of structural configurations and sea-states. Two different types of neural network are considered: multi-layer perceptron networks and radial basis function networks. It was found that the radial basis function networks considerably outperform both the multi-layer perceptron networks and the curve-fitting (parametric regression) regime, and approach bespoke numerical simulations in accuracy. Unlike numerical simulation, the neural network approach gives generic prediction across a range of structures and sea-states and therefore incurs considerably less computational cost

A global-local artificial neural network with application to wave overtopping prediction

We present a hybrid Radial Basis Function (RBF) - sigmoid neural network with a three-step training algorithm that utilises both global search and gradient descent training. We test the effectiveness of our method using four synthetic datasets and demonstrate its use in wave overtopping prediction. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower errors are often achievable using fewer hidden neurons and with less need for regularisation. Our Global-Local Artificial Neural Network (GL-ANN) is also seen to compare favourably with both Perceptron Radial Basis Net (PRBFN) and Regression Tree RBF