Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

Engin Gücüyen; Recep Tuğrul Erdem

doi:10.24012/dumf.650657

Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

Öz

Disposal of the urban and industrial liquid waste has become important by paying attention to environmental and human health recently. Submarine outfall diffusers are the major parts of the marine disposal systems. Pipe of the diffuser, risers and ports, internal and external flows which form the discharge system are modelled and fluid-structure interaction (FSI) method is utilized by ABAQUS finite elements program. Coupled CFD & Explicit technique is performed in FSI analysis. Method of bidirectional fluid-structure interaction (FSI) is used in finite elements method (FEM). Internal and external flows constitute fluid domain and diffuser constitutes the structure domain. While internal velocity and pressure values are obtained from the program, predictions of these results are performed by Artificial Neural Network (ANN) analysis. The average discharge velocities provide to avoid water intrusion into the ports. According to results obtained by FEM it can be said that the discharge system works efficiently. Numerical and estimated values are compared and the relationship between these values is investigated. The correlation coefficients are calculated by using numerical and estimated values and it is observed that a strong relationship is obtained between them.

Anahtar Kelimeler

Numerical Analysis,Submarine Outfalls,Finite Element,FSI,ANN

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yazarlar

Engin Gücüyen Bu kişi benim
Türkiye

Recep Tuğrul Erdem ^*
Türkiye

Yayımlanma Tarihi

15 Haziran 2020

Gönderilme Tarihi

25 Kasım 2019

Kabul Tarihi

3 Ocak 2020

Yayımlandığı Sayı

Yıl 2020 Cilt: 11 Sayı: 2

DOI

https://doi.org/10.24012/dumf.650657

IZ

https://izlik.org/JA44DN67RW

Kaynak Göster

RIS / Bibtex

IEEE

[1]E. Gücüyen ve R. T. Erdem, “Estimating Fluid Parameters of Submarine Outfall Using Neural Networks”, DÜMF MD, c. 11, sy 2, ss. 889–898, Haz. 2020, doi: 10.24012/dumf.650657.