Abstract

A numerical model was used to simulate the propagation of internal waves (IW) along the surface layer. The results show that strong water exchange during IW propagation results in strong free surface flow and produces small but distinct free surface waves. We found a close relationship between the internal and ocean surface waves. Our intuitive reaction is that by training the relationship between the water surface wave height and the internal wave waveform, the internal wave waveform can be reversed from the water surface wave height value. This paper intends to validate our intuition. The artificial neural network (ANN) method was used to train the Fluent simulated results, and then the trained ANN model was used to predict the inner waves below by the free surface wave signal. In addition, two linear internal wave equations (I and II) were derived, one based on the Archimedes principle and the other based on the long wave and Boussinesq approximation. The prediction by equation (II) was superior to the prediction of equation (I), which is independent of depth. The predicted IW of the proposed ANN method was in good agreement with the simulated results, and the predicted quality was much better than the two linear wave formulas. The proposed simple method can help researchers infer the magnitude of IW from the free surface wave signal. In the future, the spatial distribution of IW below the sea surface might be obtained by the proposed method without costly field investigation.

Highlights

  • Internal waves (IW) occur along the boundaries of different seawater densities, which will result in changes in the pressure gradient

  • We firstly presented the simulation of an IW passing a planar sea Discussion bottom and delineated and confirmed the relationship between the evolution of an IW and

  • The simulated results show that the propagation of the trough or peak of IW will trigger the transient free surface flow, and that the free surface displacement was affected by the rotating flow in the upper water layer

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Summary

Introduction

Internal waves (IW) occur along the boundaries of different seawater densities, which will result in changes in the pressure gradient. The researchers use high-resolution remote sensing instruments such as synthetic aperture radar (SAR) to survey internal wave images of relatively larger areas. The remote sensing images could not deduce the amplitudes and the types of the internal waves traveling below, the free surface wave characteristics can be deduced by SAR [17]. Once the free surface heights can be obtained by SAR image, the proposed trained ANN model may possibly produce the hindcasting of the internal wave below. The ANN method is used to train the simulated results of free surface waves and internal waves. The trained ANN model is, used to predict internal wave below by the simulated free surface wave (as input signals).

Fluent Simulation Method
Artificial
Results
IW Propagation
Correlation between the Simulated and ANN Predicted IWs
The simulated and ANN predicted timeof history of amplitude of depression
Linear Internal Wave Theory
Linear
The Layers Thickness
Conclusions
Full Text
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