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Advances in Coastal Hydrodynamics and Water Quality Modeling
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Advances in Coastal Hydrodynamics and Water Quality Modeling

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Environmental Science".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 7040

Special Issue Editors

Dr. Xiaohui Yan

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N6N5, Canada
Interests: CFD coding; turbulence; turbulence modeling; turbulent flow; computational fluid dynamics; CFD Simulation; numerical simulation; computational fluid mechanics; numerical modeling; fluent
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Majid Mohammadian

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada
Interests: computational fluid dynamics; numerical modeling of riverine and coastal waters; artificial intelligence application; environmental sustainability; sediment transport; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The growth of population and urbanization in coastal areas has led to a growing amount of liquid and solid waste. The inadequate disposal of these pollutants in water bodies can significantly jeopardize the coastal environment and ecology; thus, a clear understanding of the hydrodynamics and water quality in coastal environments is important.

Coastal hydrodynamics and water quality has been a topic of significant research interest, and significant advances have been achieved. However, further investigations are needed to improve modeling accuracy and efficiency, and to develop new candidate modeling tools.

This Special Issue welcomes review articles, original research, and case studies with respect to the modeling of coastal hydrodynamics and water quality and related topics. Any other types of works (e.g., experiments, theoretical analyses, and field observations) that can provide contributions to improve the practice of modeling hydrodynamics and water quality are also welcome.

Assoc. Prof. Dr. Xiaohui Yan
Prof. Dr. Majid Mohammadian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Hydrodynamics
  • Water quality
  • Modeling
  • Seawater
  • Wastewater
  • Marine
  • Environment
  • Physical
  • Numerical

Published Papers (4 papers)

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Research

19 pages, 3820 KiB  
Article
Experimental and Numerical Investigation of Beach Slope Effects on the Hydrodynamic Loading of Tsunami-like Surges on a Vertical Wall
by Shilong Liu, Ioan Nistor, Abdolmajid Mohammadian and Amir H. Azimi
J. Mar. Sci. Eng. 2022, 10(11), 1580; https://doi.org/10.3390/jmse10111580 - 26 Oct 2022
Cited by 2 | Viewed by 1396
Abstract
Over the past decades, hydraulic surge generated by dam-break waves has been used to simulate the effects of tsunamis on coastal infrastructure. This study investigates the slope effects on hydrodynamic loading of dam-break waves on structure when propagating over four different inclined beds [...] Read more.
Over the past decades, hydraulic surge generated by dam-break waves has been used to simulate the effects of tsunamis on coastal infrastructure. This study investigates the slope effects on hydrodynamic loading of dam-break waves on structure when propagating over four different inclined beds (0-, 5-, 10-, 15-degree) by experiment and numerical simulation using OpenFOAM and DualSPHysics. Except for small discrepancies in the pressure time-history, numerical results obtained with both OpenFOAM and DualSPHysics agreed closely with the experimental dynamic pressures. The results revealed that the hydrodynamic pressure decreased after an initial impact peak from the lowest transducers in the 5-, 10-, and 15-degree cases when compared with the horizontal case. However, the dynamic pressure of transducers at same corresponding level increased with an increase in the slope. The integrated experimental hydrodynamic forces were similar to the numerical results for the 0- and 5-degree cases, while they were higher for the 10- and 15-degree cases due to insufficient pressure data. By investigating the relation between the force decrease and slopes, a non-dimensional reduction factor was proposed from the linear fitness for slope effects estimation. This experimental and numerical study can provide novel insight on the hydrodynamic force calculation of tsunami-like surges on coastal infrastructures when considering beach slope. Full article
(This article belongs to the Special Issue Advances in Coastal Hydrodynamics and Water Quality Modeling)
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15 pages, 3612 KiB  
Article
CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets
by Xiaohui Yan, Yan Wang, Abdolmajid Mohammadian, Jianwei Liu and Xiaoqiang Chen
J. Mar. Sci. Eng. 2022, 10(10), 1383; https://doi.org/10.3390/jmse10101383 - 27 Sep 2022
Cited by 3 | Viewed by 1150
Abstract
At present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater [...] Read more.
At present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater discharge for the benefit of environmental protection, but because the multiport discharge of the wastewater concentration field is greatly affected by the mixing and interacting functions of wastewater, the traditional research methods on single-port discharge are invalid. This study takes the rosette multiport jet as a research subject to develop a new technology of computational fluid dynamics (CFD) modeling and carry out convolutional neural network (CNN) simulation of the concentration field of a multiport buoyant jet. This study takes advantage of CFD technology to simulate the mixing process of a rosette multiport buoyant jet, uses CNNs to construct the machine learning model, and applies RSME, R2 to conduct evaluations of the models. This work also makes comparisons with the machine learning approach based on multi-gene genetic programming, to assess the performance of the proposed approach. The experimental results show that the models constructed based on the proposed approach meet the accuracy requirement and possess better performance compared with the traditional machine learning method, and they can provide reasonable predictions. Full article
(This article belongs to the Special Issue Advances in Coastal Hydrodynamics and Water Quality Modeling)
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13 pages, 1775 KiB  
Article
Using an Adaptive Neuro-Fuzzy Inference System to Predict Dilution Characteristics of Vertical Buoyant Jets Subjected to Lateral Confinement
by Yufeng Zhao, Junshi He, Xiaohui Yan and Jianwei Liu
J. Mar. Sci. Eng. 2022, 10(3), 439; https://doi.org/10.3390/jmse10030439 - 18 Mar 2022
Cited by 1 | Viewed by 1758
Abstract
In order to predict the dilution characteristics of vertical buoyant jets constrained by lateral obstructions, we propose a new method based on a commonly used machine learning algorithm: the adaptive neuro-fuzzy inference system (ANFIS). By using experimental data to train and test the [...] Read more.
In order to predict the dilution characteristics of vertical buoyant jets constrained by lateral obstructions, we propose a new method based on a commonly used machine learning algorithm: the adaptive neuro-fuzzy inference system (ANFIS). By using experimental data to train and test the ANFIS model, this study shows that it had better performance than commonly used empirical equations for laterally confined jets and another artificial intelligence technique—genetic programming. The RMSE values of the ANFIS-based model were lower, and the R2 values were higher, compared with those of the empirical equation and genetic programming models. The reduction in RMSE achieved by using ANFIS to replace the empirical equations or genetic programming algorithm exceeded 20%. This research confirms that the ANFIS technique has real potential in the development of effective and accurate models that can be used to estimate the dilution characteristics of a vertical buoyant jet subjected to lateral confinement, providing a new avenue for the prediction of dilution characteristics using artificial intelligence techniques, which can also be utilized for other effluent mixing problems in marine systems. Full article
(This article belongs to the Special Issue Advances in Coastal Hydrodynamics and Water Quality Modeling)
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13 pages, 48474 KiB  
Article
Simulations of the Concentration Fields of Rosette-Type Multiport Buoyant Discharges Using Combined CFD and Multigene Genetic Programming Techniques
by Xiaohui Yan, Yan Wang, Abdolmajid Mohammadian and Jianwei Liu
J. Mar. Sci. Eng. 2021, 9(11), 1311; https://doi.org/10.3390/jmse9111311 - 22 Nov 2021
Cited by 5 | Viewed by 1501
Abstract
Rosette-type diffusers are becoming popular nowadays for discharging wastewater effluents. Effluents are known as buoyant jets if they have a lower density than the receiving water, and they are often used for municipal and desalination purposes. These buoyant effluents discharged from rosette-type diffusers [...] Read more.
Rosette-type diffusers are becoming popular nowadays for discharging wastewater effluents. Effluents are known as buoyant jets if they have a lower density than the receiving water, and they are often used for municipal and desalination purposes. These buoyant effluents discharged from rosette-type diffusers are known as rosette-type multiport buoyant discharges. Investigating the mixing properties of these effluents is important for environmental impact assessment and optimal design of the diffusers. Due to the complex mixing and interacting processes, most of the traditional simple methods for studying free single jets become invalid for rosette-type multiport buoyant discharges. Three-dimensional computational fluid dynamics (3D CFD) techniques can satisfactorily model the concentration fields of rosette-type multiport buoyant discharges, but these techniques are typically computationally expensive. In this study, a new technique of simulating rosette-type multiport buoyant discharges using combined 3D CFD and multigene genetic programming (MGGP) techniques is developed. Modeling the concentration fields of rosette-type multiport buoyant discharges using the proposed approach has rarely been reported previously. A validated numerical model is used to carry out extensive simulations, and the generated dataset is used to train and test MGGP-based models. The study demonstrates that the proposed method can provide reasonable predictions and can significantly improve the prediction efficiency. Full article
(This article belongs to the Special Issue Advances in Coastal Hydrodynamics and Water Quality Modeling)
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