scholarly journals Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network

2022 ◽  
Vol 10 (1) ◽  
pp. 50
Author(s):  
Miyoung Yun ◽  
Jinah Kim ◽  
Kideok Do
Keyword(s):  
Neural Network ◽  
Wave Height ◽  
Water Depth ◽  
Wave Breaking ◽  
Water Wave ◽  
Breaking Wave ◽  
Empirical Equations ◽  
Bottom Slope ◽  
Proposed Model ◽  
Deep Water Wave

Estimating wave-breaking indexes such as wave height and water depth is essential to understanding the location and scale of the breaking wave. Therefore, numerous wave-flume laboratory experiments have been conducted to develop empirical wave-breaking formulas. However, the nonlinearity between the parameters has not been fully incorporated into the empirical equations. Thus, this study proposes a multilayer neural network utilizing the nonlinear activation function and backpropagation to extract nonlinear relationships. Existing laboratory experiment data for the monochromatic regular wave are used to train the proposed network. Specifically, the bottom slope, deep-water wave height and wave period are plugged in as the input values that simultaneously estimate the breaking-wave height and wave-breaking location. Typical empirical equations employ deep-water wave height and length as input variables to predict the breaking-wave height and water depth. A newly proposed model directly utilizes breaking-wave height and water depth without nondimensionalization. Thus, the applicability can be significantly improved. The estimated wave-breaking index is statistically verified using the bias, root-mean-square errors, and Pearson correlation coefficient. The performance of the proposed model is better than existing breaking-wave-index formulas as well as having robust applicability to laboratory experiment conditions, such as wave condition, bottom slope, and experimental scale.

Prediction of Water Wave Breaking Height and Depth Using ANFIS

2011 ◽  
Author(s):  
Ehsan Delavari ◽  
Ahmad Reza Mostafa Gharabaghi ◽  
Mohammad Reza Chenaghlou
Keyword(s):  
Wave Height ◽  
Water Depth ◽  
Fuzzy Inference System ◽  
Wave Breaking ◽  
Fuzzy Inference ◽  
Breaking Point ◽  
Empirical Equations ◽  
Anfis Model ◽  
Inference System ◽  
Semi Empirical

Wave height as well as water depth at the breaking point are two basic parameters which are necessary for studying coastal processes. In this paper, the application of Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) and semi-empirical models are investigated. The data sets used in this study are published laboratory data obtained from regular wave breaking on plane, impermeable slopes collected from 22 sources. Results indicate that the developed ANFIS model provides more accurate and reliable estimation of breaking wave height, compared to semi-empirical equations. However, some of semi-empirical equations provide better predictions of water depth at the breaking point compared to the ANFIS model.

scholarly journals Cubipod® Armor Design in Depth-Limited Regular Wave-Breaking Conditions

2018 ◽  
Vol 6 (4) ◽  
pp. 150 ◽  
Author(s):  
M. Gómez-Martín ◽  
María Herrera ◽  
Jose Gonzalez-Escriva ◽  
Josep Medina
Keyword(s):  
Water Depth ◽  
Wave Breaking ◽  
Experimental Methodology ◽  
Small Scale ◽  
Breaking Wave ◽  
Regular Wave ◽  
Empirical Formulas ◽  
Physical Tests ◽  
Wave Conditions ◽  
Deep Water Wave

Armor stability formulas for mound breakwaters are commonly based on 2D small-scale physical tests conducted in non-overtopping and non-breaking conditions. However, most of the breakwaters built around the world are located in breaking or partially-breaking wave conditions, where they must withstand design storms having some percentage of large waves breaking before they reach the structure. In these cases, the design formulas for non-breaking wave conditions are not fully valid. This paper describes the specific 2D physical model tests carried out to analyze the trunk hydraulic stability of single- and double-layer Cubipod® armors in depth-limited regular wave breaking and non-overtopping conditions with horizontal foreshore (m = 0) and armor slope (α) with cotα = 1.5. An experimental methodology was established to ensure that 100 waves attacked the armor layer with the most damaging combination of wave height (H) and wave period (T) for the given water depth (hs). Finally, for a given water depth, empirical formulas were obtained to estimate the Cubipod® size which made the armor stable regardless of the deep-water wave storm.

scholarly journals Computational Fluid Dynamics Simulation of Deep-Water Wave Instabilities Involving Wave Breaking

2021 ◽  
Vol 144 (2) ◽  
Author(s):  
Yuzhu Li ◽  
David R. Fuhrman
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Water ◽  
Wave Breaking ◽  
Water Wave ◽  
Class Ii ◽  
Dynamics Simulation ◽  
Class I ◽  
Wave Instabilities ◽  
Deep Water Wave

Abstract Instabilities of deep-water wave trains subject to initially small perturbations (which then grow exponentially) can lead to extreme waves in offshore regions. The present study focuses on the two-dimensional Benjamin–Feir (or modulational) instability and the three-dimensional crescent (or horseshoe) waves, also known as Class I and Class II instabilities, respectively. Numerical studies on Class I and Class II wave instabilities to date have been mostly limited to models founded on potential flow theory; thus, they could only properly investigate the process from initial growth of the perturbations to the initial breaking point. The present study conducts numerical simulations to investigate the generation and development of wave instabilities involving the wave breaking process. A computational fluid dynamics (CFD) model solving Reynolds-averaged Navier–Stokes (RANS) equations coupled with a turbulence closure model in terms of the Reynolds stress model is applied. Wave form evolutions, Fourier amplitudes, and the turbulence beneath the broken waves are investigated.

scholarly journals A Modified k – ε Turbulence Model for a Wave Breaking Simulation

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2282
Author(s):  
Giovanni Cannata ◽  
Federica Palleschi ◽  
Benedetta Iele ◽  
Francesco Gallerano
Keyword(s):  
Energy Dissipation ◽  
Turbulence Model ◽  
Wave Breaking ◽  
A Priori ◽  
Time Dependent ◽  
Breaking Wave ◽  
Vertical Coordinate ◽  
Initial Wave ◽  
Proposed Model ◽  
Curvilinear Coordinate

We propose a two-equation turbulence model based on modification of the k − ε standard model, for simulation of a breaking wave. The proposed model is able to adequately simulate the energy dissipation due to the wave breaking and does not require any “a priori” criterion to locate the initial wave breaking point and the region in which the turbulence model has to be activated. In order to numerically simulate the wave propagation from deep water to the shoreline and the wave breaking, we use a model in which vector and tensor quantities are expressed in terms of Cartesian components, where only the vertical coordinate is expressed as a function of a time-dependent curvilinear coordinate that follows the free surface movements. A laboratory test is numerically reproduced with the aim of validating the turbulence modified k − ε model. The numerical results compared with the experimental measurements show that the proposed turbulence model is capable of correctly estimating the energy dissipation induced by the wave breaking, in order to avoid any underestimation of the wave height.

Laboratory Observations on the Evolution of Deep-Water Wave Packets

2011 ◽  
Author(s):  
Yuxiang Ma ◽  
Guohai Dong ◽  
Xiaozhou Ma
Keyword(s):  
Experimental Data ◽  
Deep Water ◽  
Wave Breaking ◽  
Water Wave ◽  
Wave Packets ◽  
Local Maximum ◽  
Peak Frequency ◽  
Higher Harmonics ◽  
Stokes Waves ◽  
Deep Water Wave

New experimental data for the evolution of deep-water wave packets has been presented. The present experimental data shows that the local maximum steepness for extreme waves is significantly above the criterion of the limiting Stokes waves. The wavelet spectra of the wave groups around the breaking locations indicate that the energy of higher harmonics can be generated quickly before wave breaking and mainly concentrate at the part of the wave fronts. After wave breaking, however, these higher harmonics energy is dissipated immediately. Furthermore, the variations of local peak frequency have also been examined. It is found that frequency downshift increases with the increase of initial steepness and wave packet size.

The Review of Studies on Formulas for Calculating Wave Breaking Height

2013 ◽  
Vol 392 ◽  
pp. 958-961
Author(s):  
Jia Xuan Yang ◽  
Shou Xian Zhu ◽  
Xun Qiang Li ◽  
Wen Jing Zhang ◽  
Lei Wang
Keyword(s):  
Wave Height ◽  
Wave Breaking ◽  
Calculation Model ◽  
The Other ◽  
Breaking Wave ◽  
Present Calculation ◽  
Calculation Methods ◽  
Ocean Engineering ◽  
Wave Calculation ◽  
Computing Models

Wave breaking is the most complex and intensified physical process in coastal zone. And as the maximum in this area, the breaking wave height has a major impact on ocean engineering and ship sailing. In this paper, the present calculation methods for breaking height are concluded and divided into two categories: one is directly computing models using deep wave elements; the other is indirectly calculation models based on the surf wave calculation model and the criterion of breaking.

Predicting deep water wave breaking with a non-hydrostatic shock-capturing model

2020 ◽  
Vol 216 ◽  
pp. 108041
Author(s):  
Dongbin He ◽  
Yuxiang Ma ◽  
Guohai Dong ◽  
Marc Perlin
Keyword(s):  
Deep Water ◽  
Wave Breaking ◽  
Water Wave ◽  
Shock Capturing ◽  
Deep Water Wave

Prediction of Characteristics of Wave Breaking in Shallow Water Using Neural Network Techniques

2017 ◽  
Author(s):  
Nicholas Kouvaras ◽  
Manhar R. Dhanak
Keyword(s):  
Neural Network ◽  
Wave Height ◽  
Predictive Models ◽  
Wave Breaking ◽  
Laboratory Experiments ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Wave Setup ◽  
Fringing Reef

The characteristics of wave breaking over a fringing reef are considered using a set of laboratory experiments and the results are used to develop associated predictive models. Various methods are typically used to estimate the characteristics of nearshore wave breaking, mostly based on empirical, analytical and numerical techniques. Deo et al. (2003) used an artificial neural network approach to predict the breaking wave height and breaking depth for waves transforming over a range of simply sloped bottoms. The approach is based on using available representative data to train appropriate neural network models. The Deo et al. (2003) approach is extended here to predict other characteristics of wave breaking, including the type of wave breaking, and the position of breaking over a fringing reef, as well as the associated wave setup, and the rate of dissipation of wave energy, based on observations from a series of laboratory experiments involving monochromatic waves impacting on an idealized reef. Yao et al. (2013) showed that for such geometry, the critical parameter is the ratio of deep-water wave height to the depth of the shallow reef flat downstream of the position of wave breaking, H1/hs, rather than the slope of the reef. H1/hs, and the wave frequency parameter, fH1/g, are provided as inputs to the neural network models of the feed-forward type that are developed to predict the above characteristics of wave breaking. The models are trained using the experimental data. The breaker type classification model has a success rate of over 95%, implying that the neural networks method outperforms previously used criteria for classifying breaker types. The numeric prediction model for the dimensionless position of wave breaking also performs well, with a high degree of correlation between the predicted and actual positions of wave breaking. The performance is higher when only the plunging breaker instances are considered, but lower when only the spilling breaker instances are considered. The corresponding neural network models for wave setup within the surf zone and the difference in energy flux between the incident and broken wave have success rates of approximately 89% and 94% respectively. The method may be extended to provide predictive models for consideration of a range of natural coastal conditions, random waves, and various bottom profiles and complex geometry, based on training and testing of the models using representative field and laboratory observational data, in support of accurate prediction of near-shore wave phenomena.

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