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Wake of a catamaran navigating in restricted waters
Published in C. Guedes Soares, T.A. Santos, Progress in Maritime Technology and Engineering, 2018
G.T.P. McSullea, J.M. Rodrigues, C. Guedes Soares
The results taken from these measurements are shown in Table 7. The wave heights are measured in meters and are measured from the height of the wave peak to the bottom of the next trough, except for at the bow where the height is just the surface elevation from the undisturbed free surface. The different combinations of hull characteristics have a significant impact on the wave heights in the near-field region and at the bow, however these effects become less apparent at the 45 m lateral line. This is due to wave height attenuation and at a distance far from the ship (in deep water) it would not matter which hull was chosen, as the waves heights would all be extremely similar. However, in congested waterways or where the bathymetry is varied, this type of data is very important for understanding what effects each characteristic has on the wave heights and wave energy.
Ocean Environment/Sea States
Published in Sukumar Laik, Offshore Petroleum Drilling and Production, 2018
Four factors – wind speed, duration, fetch and water depth – determine wave height. Wind speed is the most important. The faster or stronger the wind blows, the higher the waves will be although wind speed affects wave height only up to a certain extent. Duration of wind speed means that the longer the strong wind blows, the higher the waves will be. For example, if a 30-knot wind blew over an area of unlimited fetch for 5 hours, 7½ ft waves would develop. If that same 30-knot wind blew over the same area of water for 30 hours, the waves would reach 16 ft. Similarly, the longer the fetch, the higher are the waves. For example, if a 35-knot wind was blowing across a 1-mile area (short fetch), waves might reach only 2 ft in height. If that same 35-knot wind blew across an area of unlimited fetch, waves of at least 30 ft in height would eventually develop. Water depth is significant only in shallow seas and bays, where the shallowness causes waves to be relatively small. In an open ocean, water depth is sufficient for maximum wave development.
Hydrographic characteristics
Published in E. B. Welch, J. M. Jacoby, T. Lindell, Pollutant Effects in Freshwater, 2004
E. B. Welch, J. M. Jacoby, T. Lindell
Surface waves are ecologically interesting primarily in near-shore areas. In the deep parts of lakes the waves are non-transporting and in general each water particle describes an orbital movement, the circle (wave amplitude) diameter of which is halved for each change in depth of λ/g, where λ= wavelength. The wave height is usually about 1/20 of the wavelength. The wave height is a function of the distance along the water surface in the direction of the wind (=fetch) and a function of wind speed and duration. In the nearshore zone where the wave breaks, the orbital movement is transferred to a horizontal water movement. The wave generates an erosive character that causes the bottom substrate to be unstable and highly turbid. Large waves in oceanic coastal zones or in large lakes influence the bottom down to great depths; however, the velocities resulting from the waves usually die out at a depth of approximately λ/2 below the water surface. In small lakes the wave action is limited, but even the leeward sides of large lakes are often shallow and heavily vegetated, whereas the windward sides are deeper and biologically bare, which is a result of the stronger wave action on the windward side.
Tsunami hazard analysis for Chinese coast from potential earthquakes in the western North Pacific
Published in Geomatics, Natural Hazards and Risk, 2020
Jingming Hou, Ye Yuan, Tao Li, Zhiyuan Ren
During the past 100 years, there have been 7 tsunamis with tsunami amplitude over 20 m in the western North Pacific. The maximum wave height was 38.9 m, which occurred during Japan’s tsunami in 2011. The most deadly tsunami was the 7.9 earthquake tsunami of Japan in 1923, which killed about 140,000 people. The biggest magnitude of earthquake which trigger tsunami was 9.1 of Japan earthquake in 2011. The Japan tsunami in 2011 killed at least 15641 people and 5007 people disappeared (Mori et al. 2011), which is the deadliest natural disaster of Japan after World War II. The post-disaster survey showed that the tsunami runup heights in many areas were over 10 m, even 40 m (Mori and Takahashi 2012). The coast areas of Fukushima, Iwate and Miyagi County were devastated by the tsunami after the earthquake. Most of the runway at Sendai airport was inundated, and a nuclear leakage accident occurred at the Fukushima nuclear power plant.
An empirical examination of the use of non-dimensional wave parameters
Published in Waves in Random and Complex Media, 2020
Waves grow under the influence of wind such that the significant wave height and the dominant wavelength increase with both the wind speed and the fetch. This continues until the waves are traveling at the same velocity as the wind. Snyder et al. [18] and Plant [19] have proposed different wind pumping laws that are believed to be applicable at different fetch regimes.
Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines
Published in Engineering Applications of Computational Fluid Mechanics, 2020
Shahaboddin Shamshirband, Amir Mosavi, Timon Rabczuk, Narjes Nabipour, Kwok-wing Chau
Machine learning (ML) based models extract mathematical expressions or find empirical relationships between input and target variables from analysis of available time series (Solomatine & Ostfeld, 2008). They have been widely used for real-life applications of different fields such as discharge and river flow prediction (Cheng et al., 2005; Yaseen et al., 2018), evaporation estimation and flood management (Fotovatikhah et al., 2018; Moazenzadeh et al., 2018) and for wind speed prediction (Samadianfardet al., 2020). Artificial neural network (ANN), as a common type model, was widely used for different forecasting applications. Apart from the ANN, other types of soft computing techniques including SVR and ANFIS were also employed for time series simulation and prediction. Malekmohamadi et al. (2011) and James et al. (2018) applied different ML models to predict wave conditions in coastal waters indicating the efficiency of the employed models. Moreover, recently, Anitescu et al. (2019) presented a successful application of ANN to solve the second-order boundary value problem and the results showed that using ANN can lead to remarkable computational savings especially for the non-smooth solutions. Due to inherent simplicity, easily implementation and suitable performance, they are being increasingly in use and attracting more popularities than traditional conceptual models. Extreme learning machines as a more recent version of the ANN have been successfully applied and examined for the forecasting time series in many different applications in hydrology and environmental studies among others. On the other hand, ML-based models can be developed by employing only a limited number of input variables, which are of great importance as the conceptual models require to introduce a large number of variables affecting the target parameter. In this regard, different types of ML-based models based on their performance, complexity, and computational cost have been designed and developed to simulate and predict several different atmospheric, oceanic, environmental and hydrological processes (Alizadeh, Nourani, et al., 2017; Taormina & Chau, 2015; Wang et al., 2009). Dealing with such models there is no need to introduce exact mathematical relationships between input and outputs. The models recognize the relationship through a training procedure and assign appropriate weights indicating the strength of the input variables. For wave modeling purpose, there are two usual approaches which include predicting the wave height using wave height in previous time steps or estimating wave height using wind data as the main driven force.