Wiener chaos expansions of ocean waves

In this chapter the wide array of engineering devices, from the kitchen tap (a valve) to supersonic aircraft, the basic design of which depends upon considerations of the flow of gases and liquids, is shown. Much the same is true of most natural phenomena from the atmosphere and our weather to ocean waves, and the movement of sperm and other bodily fluids. In this textbook a number of the concepts, principles, and procedures which underlie the analysis of any problem involving fluid flow or a fluid at rest are introduced. In this Introduction, examples have been selected for which, by the end of the book, the student should be in a position to make practically useful engineering-design calculations. These include a dam, a rocket motor, a supersonic aerofoil with shock and expansion waves, a turbojet engine, a turbofan engine, and the blading of a gas turbine.

Download Full-text

On the generation of ocean waves by wind

The Interaction of Ocean Waves and Wind ◽

10.1017/cbo9780511525018.004 ◽

2004 ◽

pp. 56-128 ◽

Cited By ~ 1

Keyword(s):

Ocean Waves

Download Full-text

Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

Remote Sensing ◽

10.3390/rs13020195 ◽

2021 ◽

Vol 13 (2) ◽

pp. 195

Author(s):

He Wang ◽

Jingsong Yang ◽

Jianhua Zhu ◽

Lin Ren ◽

Yahao Liu ◽

...

Keyword(s):

Deep Learning ◽

Wave Height ◽

Significant Wave Height ◽

Incidence Angle ◽

Ocean Waves ◽

Global Scale ◽

Learning Technology ◽

Sea State ◽

Significant Wave ◽

Wave Heights

Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.

Download Full-text