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Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

Year 2020, , 889 - 898, 15.06.2020
https://doi.org/10.24012/dumf.650657

Abstract

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.

References

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  • 2. Losada, A.M; Benedicto, M.I. Target design levels for maritime structures. J Waterw Port Coast 2005, 131, 171-180.
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  • 7. Bleninger, T, Perez, L.M, Milli, H, Jirka, G.H, Internal Hydraulic Design of a Long Diffuser in Shallow Water: Buenos Aires Sewage Disposal in Rio De La Plata Estuary, proceedings of XXXI IAHR Congresses, Seoul, S.Korea, 2005, pp 1-11.
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  • 13. Erdem, R.T; Prediction of acceleration and impact force values of a reinforced concrete slab. Comput Concrete 2014, 14(5), 563-575.
  • 14. Edincliler, A; Cabalar, A.F, Cevik, A, Modelling dynamic behaviour of sand–waste tires mixtures using Neural Networks and Neuro-Fuzzy. Eur J Environ Civ En 2013, 17(8), 720–741.
  • 15. Zhao, B; Wang, Y, Chen, H, Qiu, J, Hou, D, Hydraulic optimization of a double-channel pump’s impeller based on multi-objective genetic algorithm, Chin J Mech Eng-En 2015, 28(3), 634-640.
  • 16. El-Abbasy, M.S; Senouci, A, Zayed, T, Mirahadi, F, Parvizsedghy, L, Artificial neural network models for predicting condition of offshore oil and gas pipelines, Automat Constr, 2014, 45, 50–65.
  • 17. Abaqus/CAE 6.10, 2010.
  • 18. Özturk, İ. Deniz deşarj tesisleri tasarımı; Su Vakfı Yayınları, Turkey, 2011, pp 458.
  • 19. Fischer, H.B, List, E.J, Koh, R.C.Y. Imberger, J, Brooks, N.H, Mixing in inland and coastal waters; Academic Press, USA, 1979, pp 497.
  • 20. Singh, V, Hager, W.H,. Environmental hydraulics; Springer, 1996, pp 397.
  • 21. S Chiban, A Terfous, A Ghenaim, Salman, H, Sabat, M, Sedimentation in the submarine outfall and in the mixing zones (Avoiding, diagnosis and remediation), Journal of Shipping and Ocean Engineering 2011, 1, 124-132.
  • 22. Ludwig R.G, Environmental impact assessment, sitting and design of submarine outfalls; Monitoring and Assessment Research Centre, England, 1988, pp 64.
  • 23. Agudo, E.G, Amaral, R, Berzin, G, Evaluation of the efficiency of santo/sao vicente preconditioning station for an oceanic submarine outfall, Water Sci Technol 1986, 18(11), 83-91.
  • 24. Roberts, P.J.W, H. Salas, J Reiff, F.M, Libhaber, M, Labbe, A, Thomson, J,C, Marine wastewater outfalls and treatment systems; IWA Publishing, 2010, England, pp 528.
  • 25. Matlab V 6.5. 2002. The Math Works, Inc.
Year 2020, , 889 - 898, 15.06.2020
https://doi.org/10.24012/dumf.650657

Abstract

References

  • 1. Mendonça, A.; Losada, M.A, Reis, M.T, Neves, M.G. Risk assessment in submarine outfall projects: The case of Portugal. J Environ Manage 2013, 116, 186-195.
  • 2. Losada, A.M; Benedicto, M.I. Target design levels for maritime structures. J Waterw Port Coast 2005, 131, 171-180.
  • 3. Bleninger, T, Jirka, G.H, User's Manual for Corhyd: An internal diffuser hydraulics model, Version 1.0; University of Karlsruhe, Germany, 2005; pp 84.
  • 4. Aksenov, A, Dyadkin, A, Luniewski, T, Pokhilko, V, Fluid Structure Interaction Analysis Using Abaqus and Flowvision, proceedings of the Comsol Conference, Belgium, 2004, pp 39-47.
  • 5. Gücüyen, E; Analysis of offshore wind turbine tower under environmental loads. Ships Offshore Struc 2017, 12(4), 513-520.
  • 6. Gücüyen, E; Analysis of submarine outfalls subjected to wave load. Građevinar 2015, 67(8), 1-10.
  • 7. Bleninger, T, Perez, L.M, Milli, H, Jirka, G.H, Internal Hydraulic Design of a Long Diffuser in Shallow Water: Buenos Aires Sewage Disposal in Rio De La Plata Estuary, proceedings of XXXI IAHR Congresses, Seoul, S.Korea, 2005, pp 1-11.
  • 8. Yan, Y; Wang, L, Wang, T, Wang, X, H, Yonghui, Duan, Q. Application of soft computing techniques to multiphase flow measurement: A review. Flow Meas Instrum 2018, 60, 30-43.
  • 9. Mrzygłód, B; Hawryluk, M, Gronostajski, Z, Opaliński, A, Kaszuba, M, Polak, S, Widomski, P, Ziemb, J. Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks. Arch Cıv Mech Eng 2018, 18(4), 1079-1091.
  • 10. Rafiq, M.Y, Bugmann, G, Easterbrook, D.J, Neural network design for engineering applications. Comput Struct 2001, 79, 1541–1552.
  • 11. Zurada, J.M. Introduction to artificial neural networks; West Publishing, St. Paul, USA, 1992; pp 683.
  • 12. Vazirizade, S.M; Nozhati, S, Zadeh, M.A. Seismic reliability assessment of structures using artificial neural network. J. Build. Eng 2017, 11, 230-235.
  • 13. Erdem, R.T; Prediction of acceleration and impact force values of a reinforced concrete slab. Comput Concrete 2014, 14(5), 563-575.
  • 14. Edincliler, A; Cabalar, A.F, Cevik, A, Modelling dynamic behaviour of sand–waste tires mixtures using Neural Networks and Neuro-Fuzzy. Eur J Environ Civ En 2013, 17(8), 720–741.
  • 15. Zhao, B; Wang, Y, Chen, H, Qiu, J, Hou, D, Hydraulic optimization of a double-channel pump’s impeller based on multi-objective genetic algorithm, Chin J Mech Eng-En 2015, 28(3), 634-640.
  • 16. El-Abbasy, M.S; Senouci, A, Zayed, T, Mirahadi, F, Parvizsedghy, L, Artificial neural network models for predicting condition of offshore oil and gas pipelines, Automat Constr, 2014, 45, 50–65.
  • 17. Abaqus/CAE 6.10, 2010.
  • 18. Özturk, İ. Deniz deşarj tesisleri tasarımı; Su Vakfı Yayınları, Turkey, 2011, pp 458.
  • 19. Fischer, H.B, List, E.J, Koh, R.C.Y. Imberger, J, Brooks, N.H, Mixing in inland and coastal waters; Academic Press, USA, 1979, pp 497.
  • 20. Singh, V, Hager, W.H,. Environmental hydraulics; Springer, 1996, pp 397.
  • 21. S Chiban, A Terfous, A Ghenaim, Salman, H, Sabat, M, Sedimentation in the submarine outfall and in the mixing zones (Avoiding, diagnosis and remediation), Journal of Shipping and Ocean Engineering 2011, 1, 124-132.
  • 22. Ludwig R.G, Environmental impact assessment, sitting and design of submarine outfalls; Monitoring and Assessment Research Centre, England, 1988, pp 64.
  • 23. Agudo, E.G, Amaral, R, Berzin, G, Evaluation of the efficiency of santo/sao vicente preconditioning station for an oceanic submarine outfall, Water Sci Technol 1986, 18(11), 83-91.
  • 24. Roberts, P.J.W, H. Salas, J Reiff, F.M, Libhaber, M, Labbe, A, Thomson, J,C, Marine wastewater outfalls and treatment systems; IWA Publishing, 2010, England, pp 528.
  • 25. Matlab V 6.5. 2002. The Math Works, Inc.
There are 25 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Engin Gücüyen This is me

Recep Tuğrul Erdem

Publication Date June 15, 2020
Submission Date November 25, 2019
Published in Issue Year 2020

Cite

IEEE E. Gücüyen and R. T. Erdem, “Estimating Fluid Parameters of Submarine Outfall Using Neural Networks”, DÜMF MD, vol. 11, no. 2, pp. 889–898, 2020, doi: 10.24012/dumf.650657.
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