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  A stochastic nonlinear differential propagation model for underwater acoustic propagation: Theory and solution

Haiyang, Y., Haiyan, W., Zhichen, Z., Yong, X., Kurths, J. (2021): A stochastic nonlinear differential propagation model for underwater acoustic propagation: Theory and solution. - Chaos, Solitons and Fractals, 150, 111105.
https://doi.org/10.1016/j.chaos.2021.111105

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 Creators:
Haiyang, Yao1, Author
Haiyan, Wang1, Author
Zhichen, Zhang1, Author
Yong, Xu1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The principle of underwater acoustic signal propagation is of vital importance to realize the “digital ocean”. However, underwater circumstances are becoming more complex and multi-factorial because of raising human activities, changing climate, to name a few. For this study, we formulate a mathematical model to describe the complex variation of underwater propagating acoustic signals, and the solving method are presented. Firstly, the perturb-coefficient nonlinear propagation equation is derived based on hydrodynamics and the adiabatic relation between pressure and density. Secondly, physical elements are divided into two types, intrinsic and extrinsic. The expression of the two types are combined with the perturb-coefficient nonlinear propagation equation by location and stochastic parameters to obtain the stochastic nonlinear differential propagation model. Thirdly, initial and boundary conditions are analyzed. The existence theorem for solutions is proved. Finally, the operator splitting procedure is proposed to obtain the solution of the model. Two simulations demonstrate that this model is effective and can be used in multiple circumstances.

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 Dates: 2021-06-162021-06-162021-09
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.chaos.2021.111105
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Oceans
Research topic keyword: Weather
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Chaos, Solitons and Fractals
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 150 Sequence Number: 111105 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190702
Publisher: Elsevier