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Coastal Engineering

ISSN: 0378-3839

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Datasets associated with articles published in Coastal Engineering

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1970
2024
1970 2024
18 results
  • Data for: Shoaling of bound infragravity waves on plane slopes for bichromatic wave conditions
    In the code, the semi-analytical solution to compute the infragravity waves for bichromatic primary waves for non-flat bottom based on (Schaffer 1993) has been computed.
    • Dataset
  • Data for: Characterization and prediction of tropical cyclone forerunner surge
    Alldelta_p.txt contains the pressure deficit information in hPa for storm1 to storm80. Allrmax.txt contains the radii to maximum wind information in km for storm1 to storm80. Allvf.txt contains the forward speed information in km/h for storm1 to storm 80. loc_TX-1_storm1.txt contains the surge times series data for storm1 at location TX-1 in Figure 1 in the manuscript. loc_TX-2_storm1.txt contains the surge times series data for storm1 at location TX-2 in Figure 1 in the manuscript.
    • Dataset
  • Data for: Empirical stochastic model to predict the stability of rocks on flat beds under waves and currents
    Results of the wave flume and circulating water channel experiments
    • Dataset
  • Source code for: Coupled finite particle method for simulations of wave and structure interaction
    This is a source code for coupled finite particle method and smooth particle hydrodynamics. The source code can be used to test 2D regular waves contained in the paper "Coupled finite particle method for simulations of wave and structure interaction. Coastal Engineering, 2018". The code is compiled by 2013 Visual Studio with the C language.
    • Dataset
  • SFINCS: Super-Fast INundation of CoastS model
    What is SFINCS? SFINCS is Deltares' new open-source, open-access reduced-complexity model designed for super-fast modelling of compound flooding events in a dynamic way. This DOI reference is complementary to our open-source software releases, as managed on https://github.com/Deltares/SFINCS. Why SFINCS? Compound flooding during extreme events can result in tremendous amounts of property damage and loss of life. Early warning systems and multi-hazard risk analysis can reduce these impacts. However, traditional approaches either do not involve relevant physics or are too computationally expensive to do so for large stretches of coastline. The SFINCS model is a new reduced-complexity engine recently developed by Deltares, that is capable of simulating compound flooding including a high computational efficiency balanced with good accuracy. Where do I find more information about SFINCS? For general information see: https://www.deltares.nl/en/software/sfincs/ Find the user manual and more information on: https://sfincs.readthedocs.io/en/latest/ How do I get SFINCS? Download the latest windows executable here: https://download.deltares.nl/en/download/sfincs/ Get the Docker of version of SFINCS to run on Mac, Linux or HPC here: https://hub.docker.com/r/deltares/sfincs-cpu How to cite? For the introduction journal paper one can refer to Leijnse et al. (2021) - https://doi.org/10.1016/j.coastaleng.2020.103796. For use of a specific release of SFINCS, one can refer to this Zenodo DOI. How to contribute? If you find any issues in the code or documentation feel free to leave an issue on the github issue tracker. You can find information about how to contribute to the SFINCS model at our contributing page. SFINCS seeks active contribution from the hydro modelling community, so feel free to add something to our docs or model code, or reach out to 'sfincs@deltares.nl'!
    • Software/Code
  • NEESI: Numerical Experiments of Estuarine Salt Intrusion dataset
    The dataset contains the processed data of 1252 simulations using Delft3D Flexible Mesh (DFM) in which estuaries were designed using a parametric design. Every estuary design is based on thirteen (13) input parameters: three (3) boundary conditions, and ten (10) geomorphological characteristics. The output is represented by two (2) variables: (1) the salt intrusion length, 'L'; and (2) the salt variability, 'V'. Simulations are carried out over a span of nine (9) days of which the first eight (8) are considered spin-up; i.e., one (1) day of the simulation is used for further post-processing. The salt intrusion length is a depth- and tide-averaged estimation of the salt intrusion of this last day; and the salt variability an estimate of the difference between the maximum salinity and the minimum salinity over the tide, depth- and spatially- averaged. The various settings of the simulations are drawn using machine learning techniques.
    • Dataset
  • NEESI: Numerical Experiments of Estuarine Salt Intrusion dataset
    The dataset contains the processed data of 1252 simulations using Delft3D Flexible Mesh (DFM) in which estuaries were designed using a parametric design. Every estuary design is based on thirteen (13) input parameters: three (3) boundary conditions, and ten (10) geomorphological characteristics. The output is represented by two (2) variables: (1) the salt intrusion length, 'L'; and (2) the salt variability, 'V'. Simulations are carried out over a span of nine (9) days of which the first eight (8) are considered spin-up; i.e., one (1) day of the simulation is used for further post-processing. The salt intrusion length is a depth- and tide-averaged estimation of the salt intrusion of this last day; and the salt variability an estimate of the difference between the maximum salinity and the minimum salinity over the tide, depth- and spatially- averaged. The various settings of the simulations are drawn using machine learning techniques.
    • Dataset
  • SFINCS: Super-Fast INundation of CoastS model
    What is SFINCS? SFINCS is Deltares' new open-source, open-access reduced-complexity model designed for super-fast modelling of compound flooding events in a dynamic way. This DOI reference is complementary to our open-source software releases, as managed on https://github.com/Deltares/SFINCS. Why SFINCS? Compound flooding during extreme events can result in tremendous amounts of property damage and loss of life. Early warning systems and multi-hazard risk analysis can reduce these impacts. However, traditional approaches either do not involve relevant physics or are too computationally expensive to do so for large stretches of coastline. The SFINCS model is a new reduced-complexity engine recently developed by Deltares, that is capable of simulating compound flooding including a high computational efficiency balanced with good accuracy. Where do I find more information about SFINCS? For general information see: https://www.deltares.nl/en/software/sfincs/ Find the user manual and more information on: https://sfincs.readthedocs.io/en/latest/ How do I get SFINCS? Download the latest windows executable here: https://download.deltares.nl/en/download/sfincs/ Get the Docker of version of SFINCS to run on Mac, Linux or HPC here: https://hub.docker.com/r/deltares/sfincs-cpu How to cite? For the introduction journal paper one can refer to Leijnse et al. (2021) - https://doi.org/10.1016/j.coastaleng.2020.103796. For use of a specific release of SFINCS, one can refer to this Zenodo DOI. How to contribute? If you find any issues in the code or documentation feel free to leave an issue on the github issue tracker. You can find information about how to contribute to the SFINCS model at our contributing page. SFINCS seeks active contribution from the hydro modelling community, so feel free to add something to our docs or model code, or reach out to 'sfincs@deltares.nl'!
    • Software/Code
  • NEESI: Numerical Experiments of Estuarine Salt Intrusion dataset
    The dataset contains the processed data of 1252 simulations using Delft3D Flexible Mesh (DFM) in which estuaries were designed using a parametric design. Every estuary design is based on thirteen (13) input parameters: three (3) boundary conditions, and ten (10) geomorphological characteristics. The output is represented by two (2) variables: (1) the salt intrusion length, 'L'; and (2) the salt variability, 'V'. Simulations are carried out over a span of nine (9) days of which the first eight (8) are considered spin-up; i.e., one (1) day of the simulation is used for further post-processing. The salt intrusion length is a depth- and tide-averaged estimation of the salt intrusion of this last day; and the salt variability an estimate of the difference between the maximum salinity and the minimum salinity over the tide, depth- and spatially- averaged. The various settings of the simulations are drawn using machine learning techniques.
    • Dataset
  • NEESI: Numerical Experiments of Estuarine Salt Intrusion dataset
    The dataset contains the processed data of 1252 simulations using Delft3D Flexible Mesh (DFM) in which estuaries were designed using a parametric design. Every estuary design is based on thirteen (13) input parameters: three (3) boundary conditions, and ten (10) geomorphological characteristics. The output is represented by two (2) variables: (1) the salt intrusion length, 'L'; and (2) the salt variability, 'V'. Simulations are carried out over a span of nine (9) days of which the first eight (8) are considered spin-up; i.e., one (1) day of the simulation is used for further post-processing. The salt intrusion length is a depth- and tide-averaged estimation of the salt intrusion of this last day; and the salt variability an estimate of the difference between the maximum salinity and the minimum salinity over the tide, depth- and spatially- averaged. The various settings of the simulations are drawn using machine learning techniques.
    • Dataset
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