• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1429-1433
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• 2004

본 연구에서는 쇄파대에서 정현파의 쇄파에 대해 수리모형실험과 수치모형실험을 수행하였다. 수치해석 모형에서는 Reynolds 방정식을 지배방정식으로 사용하고 난류해석을 위해 $k-\varepsilon$모델을 적용하였으며, 자유수면변위를 추적하기 위해 VOF기법을 사용하져다. 사면 및 평탄지형상에서 발생하는 쇄파양상을 서로 다르게 설정하기 위해 수심과 입사파의 주기와 파고를 변화시킨다. 발생된 정현파의 파형은 해석해와 잘 일치하였으며, 입사파와 파고계가 설치된 위치에서 측정된 파고비 $H/H_0$는 관측값과 비교해 본 길과 놀은 정확도를 나타내었다.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1991.07a
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• pp.89-92
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• 1991

선형파 이론 자체로서는 쇄파대내에서의 파고를 직접 계산할 수 없으므로 쇄파가 발생하기 전까지만 계산할 수 있다. 그리고 쇄파대내에서의 파고는 수심에 대하여 일정한 비율로 감소시켜 해석해 왔다. 그러나 쇄파가 발생한 후의 파고는 선형적으로 감소하지 않고 해저경사에 따라서 그 감소경향이 다르게 나타남을 여러문헌에서 알 수 있다.(중략)

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2000.09a
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• pp.166-170
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• 2000

연안역에서의 파랑운동 모형개발에 있어 봉착하는 가장 중요한 문제는 쇄파대에서의 파고변형의 예측이라 할 수 있다. 쇄파대에서의 파고변형 예측 모형의 재발은 파동에너지 손실의 평가를 이용한 Le Mehaute(1962)의 해석적 방법 이후로 예측모델을 개발하기 위한 많은 연구가 수행되어져 왔다. (중략)

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.165-172
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• 2021

Breaking wave is one of the important design factors in the design of coastal and port structures as they are directly related to various physical phenomena occurring on the coast, such as onshore currents, sediment transport, shock wave pressure, and energy dissipation. Due to the inherent complexity of the breaking wave, many empirical formulas have been proposed to predict breaker indices such as wave breaking height and breaking depth using hydraulic models. However, the existing empirical equations for breaker indices mainly were proposed via statistical analysis of experimental data under the assumption of a specific equation. In this study, a new Munk-type empirical equation was proposed to predict the height of breaking waves based on a representative linear supervised machine learning technique with high predictive performance in various research fields related to regression or classification challenges. Although the newly proposed breaker height formula was a simple polynomial equation, its predictive performance was comparable to that of the currently available empirical formula.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.4
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• pp.262-272
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• 2020

A large number of studies on wave breaking have been carried out, and many experimental data have been documented. Moreover, on the basis of various experimental data set, many empirical or semi-empirical formulas based primarily on regression analysis have been proposed to quantitatively estimate wave breaking for engineering applications. However, wave breaking has an inherent variability, which imply that a linear statistical approach such as linear regression analysis might be inadequate. This study presents an alternative nonlinear method using an neural network, one of the machine learning methods, to estimate breaking wave height and breaking depth. The neural network is modeled using Tensorflow, a machine learning open source platform distributed by Google. The neural network is trained by randomly selecting the collected experimental data, and the trained neural network is evaluated using data not used for learning process. The results for wave breaking height and depth predicted by fully trained neural network are more accurate than those obtained by existing empirical formulas. These results show that neural network is an useful tool for the prediction of wave breaking.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.2
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• pp.136-145
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• 2007

When waves propagating over an underwater shoal break at the top of the shoal, wave heights are drastically decreased in the downstream breaking zone, but a secondary current shooting downstream with strong velocity can be induced by the breaking waves themselves. In the case that an offshore structure is placed in the breaking zone, the estimation of wave farce purely based on the visible wave height may cause an under-design of the structure. Thus, for the safe design of the structure, the breaking wave induced current should be necessarily considered in the comprehensive estimation of design load. In the present study, Boussinesq equation model to calculate the wave height distribution and breaking wave induced current was set up and applied to the scheme of a hydraulic model test previously undertaken. Based on the results of the Boussinesq model, fluid forces acting on the model structure were calculated and compared with the experimental results. The importance of the breaking wave induced current was quantitatively assessed by comparing fluid forces with or without current.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.11 no.3
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• pp.149-155
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• 1999

A treatment of wave breaking is included in the extended Boussinesq equations of Nwogu. A spatially distributed source function and sponge layers are used to reduce the reflected waves in the computa¬tional domain. The model uses fourth-order Adams predictor-corrector method to advance in time, and discretizes first-order spatial derivatives to fourth-order accuracy, and thus reducing all truncation errors to a level smaller than the dispersive terms. The generated wave fields are found to be good and the corresponding wave heights are very close to their target values. For the tests of wave breaking, although agreement is considered to be reasonable, wave heights in the inner surf zone are over-predicted. This indicates the breaking parameters in the model should be adjusted.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.3 no.2
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• pp.65-71
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• 1991

Many investigations of wave deformation without currents have been carried out experimentally and theoretically but, studies treating the effect of longshore current on the wave deformation are few. It is thus necessary to evaluate the effect of longshore current on the wave deformation after breaking. In the paper the wave height attenuation. the wave direction and the variation of mean water level are calculated in which effects of longshore current are involved. To assess the effect of longshore current on the wave deformation, factors above with longshore current are compared with them without longshore current by using calculated results.

• 한국해양학회지
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• v.23 no.2
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• pp.53-61
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• 1988

A capacitance wavestaff system was devised, which is suitable for the wave measurement near to surf zone. Laboratory tests show that the performance of the wavestaff is not significantly affected by the sea water characteristics such as temperature and salinity, and output signals of the wavestaff are linearly proportional to sea surface elevations. The major error source of the wavestaff system is the improper setting of the wavestaff in field experiment.