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13 results for “reactive transport”
Dataset of a multiphase flow and reactive transport benchmark for radioactive waste disposal
<p>The files include the full dataset (tables and figures) of the comparion the results of a multiphase flow and reactive transport<br>benchmark for radioactive waste disposal. The codes INVERSE-FADES-CORE V2, DuMuX , TOUGHREACT and<br>iCP were benchmarked with 6 test cases of increasing complexity, starting with conservative tracer transport under variably<br>unsaturated conditions and ending with water flow, gas diffusion, minerals and cation exchange.</p>
Dataset for "Experimental and Modeling Insights into Mixing-Limited Reactive Transport in Heterogeneous Porous Media: Role of Stagnant Zones"
<p>This dataset contains the observed and simulated BTC of bimolecular transport experiment that was involved in "Yin et al., Experimental and Modeling Insights into Mixing-Limited Reactive Transport in Heterogeneous Porous Media: Role of Stagnant Zones".</p>
The impact of ice sheet geometry on meltwater ingress and reactive solute transport in sedimentary basins
<p> We investigate the effect of ice sheet geometry on groundwater flow patterns and meltwater ingress in a hypothetical sedimentary basin. The simulation results indicate that meltwater ingress is much greater in 3D domains with a relatively narrow ice sheet extent compared to a wide ice sheet, or a simplified 2D model. In high permeability units (HPUs), the simulated meltwater penetration depth can reach up to 750 m in 3D domains compared to 400 m in a comparable 2D domain. In low permeability units (LPUs), very limited meltwater penetration occurs, indicating that ice sheet geometry and model dimensionality do not substantially affect water flow in these units. A conservative tracer, with a source located at a depth of 500 meters in both HPUs and LPUs, illustrates that solutes can be transported to greater depths in HPUs for a narrow ice lobe scenario, in comparison to a wide ice sheet or the 2D approach. Tracer transport in LPUs is unaffected by ice sheet geometry. Similarly, simulation results indicate that the ingress of dissolved oxygen (O<sub>2</sub>) into HPUs is most substantial below a narrow ice lobe, while O<sub>2</sub> ingress into LPUs is not affected by ice sheet geometry. The numerical experiments indicate that 3D analysis will give more comprehensive results for flow patterns and reactive solute transport subjected to glaciation/deglaciation cycles in the case of a narrow ice lobe, but also suggest that a 2D approach might provide an adequate representation for the case of a relatively wide ice sheet.</p>
Research data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach"
<div><strong>Research Data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em></strong></div> <div> </div> <div>Dear reader,</div> <div> </div> <div>reasearch data are provided for the research article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em> (https://doi.org/10.1016/j.advwatres.2024.104763). The authors hope that the research data allows for a better understanding of the modeling workflow. The research data covers the following files:</div> <div> <ul> <li>Python scripts to create the models <ul> <li>Model scripts using FloPy (Bakker et al., 2016) are stored as .py files in './model_data/flopy_scripts/', named 'model_variant_vXYZ.py', where 'XYZ' is a wildcard for the model number. </li> <li>--> Note that model numbers correspond to the different model variants as referred to in the article, see overview below.</li> <li>The model scripts require postfix files, stored in './model_data/flopy_scripts/postfix/', a PHREEQC database file, stored in './model_data/flopy_scripts/template_database/', as well as spreadsheets that contain the initial concentrations as well as reaction rate parameters needed by PHT3D, stored as .xlsx files in './model_data/flopy_scripts/', to create the models.</li> <li>Note that the .xlsx files are used by PHT3D-FSP in the model scripts to generate relevant PHT3D input files (compare https://doi.org/10.5281/zenodo.7559750 for more details).</li> </ul> </li> <li>SEAWAT/PHT3D input files <ul> <li>Original SEAWAT and PHT3D input files, which were created with the corresponding model scripts previously (see step before).</li> <li>Input files are stored in './model_data/model_files/vXYZ/model_files/' for each model variant, where 'XYZ' is a wildcard for the model number.</li> <li>SEAWAT/PHT3D executables can directly run the model files files. Thus, the files don't need to be re-created via the previous step.</li> </ul> </li> <li>Model outputs <ul> <li>Model output data is stored as NumPy arrays in './model_data/model_files/vXYZ/npy_arrays/', where 'XYZ' is a wildcard for the model number.</li> <li>The script './model_data/flopy_scripts/template_output/pht3d_output_hpc_v006.py' was used to generate the output files.</li> <li>2-D species concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/species/', where 'XYZ' is a wildcard for the model number.</li> <li>Species min./max. concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/min_max/', where 'XYZ' is a wildcard for the model number.</li> <li>2-D water budget arrays (CH & WEL boundaries) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/budgets/', where 'XYZ' is a wildcard for the model number.</li> <li>Model discretization information (ncol, nrow, nlay etc.) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/discretization/', where 'XYZ' is a wildcard for the model number.</li> </ul> </li> <li>Figure files <ul> <li>Original figure files as well as the corresponding Python scripts to create the figures are stored in the subfolder'./figures'.</li> </ul> </li> </ul> <p>Numbering of the model variants is as follows:<br><br>v401 --> VAR-conservative<br>v402 --> VAR-OM<br>v403 --> VAR-C/I<br>v404 --> VAR-C/I/S<br>v405 --> VAR-C/I/P<br>v406 --> VAR-C/I/P/H<br>v407 --> VAR-C/I/P/V<br>v408 --> VAR-C/I/P-Co<br>v409 --> VAR-all<br>v410 --> VAR-all (no C)</p> </div> <div> </div> <div>Literature:</div> <div> </div> <div>Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J.J. and Fienen, M.N., 2016. Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), pp.733-739. https://doi.org/10.1111/gwat.12413</div> <div> </div> <div>Seibert, S.L., Massmann, G., Meyer, R., Post, V.E.A., Greskowiak, J., 2024. Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2024.104763</div> <div> </div> <div><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Vincent E.A. Post (vincent@edinsi.nl), Rena Meyer (rena.meyer@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</div>
Supplemental data of publication "Reactive Transport Model of Kinetically Controlled Celestite to Barite Replacement".
<p>Supplemental data of publication "Reactive Transport Model of Kinetically Controlled Celestite to Barite Replacement".</p> <p>Authors: Morgan Tranter, Maria Wetzel, Marco De Lucia, Michael Kühn</p> <p>Contact: mtranter@gfz-potsdam.de</p> <p>Submitted to Advances in Geosciences (31.06.2021).<br> Special Issue: European Geosciences Union General Assembly 2021, EGU Division Energy, Resources & Environment (ERE)</p> <p>EGU21 Abstract:<br> https://doi.org/10.5194/egusphere-egu21-9832</p> <p>Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</p>
The impact of ice sheet geometry on meltwater ingress and reactive solute transport in sedimentary basins
Open the record for dataset details and reuse information.
Input files and simulation results for reactive two-phase transport calculations in a waste package with OGS6-MP-LT-Drum
<p>The folders contain the original input and output files for reactive two-phase flow calculations with OpenGeoSys6-MP-LT-drum (doi:10.5281/zenodo.4060885), as well as the GEM-Selektor V3.3 project for look-up table generation, state<br> files for Paraview, R-script and jupyter notebook files used for post-processing the simulation output, which were used for creating the results in:</p> <p>Huang, Y., Shao, H., Wieland, E. <em>et al.</em> Two-phase transport in a cemented waste package considering spatio-temporal evolution of chemical conditions. <em>npj Mater Degrad</em> <strong>5, </strong>4 (2021). https://doi.org/10.1038/s41529-021-00150-z</p>
An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
<p>This data includes the training and testing dataset for DNN design and the observation data of synthetic example for validation. </p> <p>The code of TNNA-AUS inversion method.</p>
Micro-macro model of multicomponent reactive transport in a multimineral system
<p>This SEM image is from this article "Quantification of mineral accessible surface area and flow-dependent fluid-mineral reactivity at the pore scale". https://doi.org/10.1016/j.chemgeo.2020.120042</p>
Output Files from Reactive Transport Models (supplement to Solid Earth Manuscript 'se-2020-99')
<p>This repository includes the output files (in Tecplot format) of the numerical models in the paper entitled "<em><strong>Understanding controls on hydrothermal dolomitisation: insights from 3D Reactive Transport Modelling of geothermal convection</strong></em>".</p> <p>A key to the output data provided here is summarised in Data_Benjakul_et_al_2020.xlsx.</p> <p> </p> <p>We used <strong>TOUGHREACT</strong>, a numerical simulation program developed by the Lawrence Berkeley National Laboratory (LBNL) which is available at: <a href="https://tough.lbl.gov/licensing-download/toughreact-licensing-download/">https://tough.lbl.gov/licensing-download/toughreact-licensing-download/</a> to numerically model the convection of seawater in a single fault within carbonate-hosted system. The parameters used in the numerical models are given in the paper.</p> <p><a href="https://doi.org/10.5194/se-11-2439-2020">https://doi.org/10.5194/se-11-2439-2020</a></p> <p>The output files from TOUGHREACT (in Tecplot format) are visualised using <strong>trexplot</strong>, a python script developed by Hamish Robertson which can be found at: <a href="https://github.com/hammytheham/trexplot">https://github.com/hammytheham/trexplot</a> (last access: 5 December 2020).</p> <p> </p> <p>Please contact me via the following e-mail addresses in case you have any questions: <a href="mailto:rb17526@bristol.ac.uk">rb17526@bristol.ac.uk</a> or <a href="mailto:rbenjaku@gmail.com">rbenjaku@gmail.com</a></p>
Observation of Reactive Transport in Soil Columns with Positron Emission Tomography (GeoPET)
<p>Here we investigate reactive transport in soils with GeoPET as quantitative spatiotemporal molecular imaging method. As PET directly yields tracer concentrations, the data can be utilised for parameterization and validation of reactive transport computer models.</p> <p>Artificial soil columns (sand/silt/clay/Goethit, length 90 mm, diameter 40 mm) have been prepared under CO<sub>2</sub>-atmosphere. Four different experiments on one and the same soil column are shown as motion pictures from the GeoPET-observations:</p> <p>1) Injection of water, labelled with [<sup>18</sup>F]KF, into the CO<sub>2</sub>-saturated column</p> <p>2) Conservative transport of [<sup>18</sup>F]KF solution through the water-saturated column</p> <p>3) Injection of the reactive tracer [<sup>64</sup>Cu]Cu(MCPA)<sub>2</sub> into the unconditioned water-saturated column</p> <p>4) Injection of the reactive tracer [<sup>64</sup>Cu]Cu(MCPA)<sub>2</sub> into the preconditioned column.</p> <p> </p> <p>The study was supported within the framework of the priority program “Biogeochemical Interfaces in Soil” by the German Science Foundation (DFG SPP 1315: KE508/19 and LI872/5).</p> <p> </p> <p>References:</p> <p>Kulenkampff, J., Zakhnini, A., Gründig, M., and Lippmann-Pipke, J.: Quantitative experimental monitoring of molecular diffusion in clay with positron emission tomography, Solid Earth, <strong>7</strong>, 1207-1215, 2016.</p> <p>Kulenkampff, J., Gründig, M., Zakhnini, A., Gerasch, R., and Lippmann-Pipke, J.: Process tomography of diffusion, using PET, to evaluate anisotropy and heterogeneity, Clay Miner., <strong>50</strong>, 369–375, 2015.</p> <p>Kulenkampff, J., Gründig, M., Zakhnini, A., and Lippmann-Pipke, J.: Geoscientific process monitoring with positron emission tomography (GeoPET), Solid Earth, <strong>7</strong>, 1217–1231, 2016.</p> <p>Zakhnini, A., Kulenkampff, J., Sauerzapf, S., Pietrzyk, U., and Lippmann-Pipke, J.: Monte Carlo simulations of GeoPET experiments: 3D images of tracer distributions (<sup>18</sup>F, <sup>124</sup>I and <sup>58</sup>Co) in Opalinus Clay, anhydrite and quartz, Comput. Geosci., 57, 183–196, 2013.</p> <p>Lippmann-Pipke, J., Gerasch, R., Schikora, J., and Kulenkampff, J.: Benchmarking PET for geoscientific applications: 3D quantitative diffusion coefficient estimation in clay rock, Comput. Geosci. <strong>101</strong>, 21-27, 2017.</p> <p>Stoll, M., Kulenkampff, J., Gründig, M., Lippmann-Pipke, J., and Kersten, M.: Molecular positron emission tomography imaging of Cu mobility enhanced by the herbicide 4-chloro-2-methylphenoxy-acetic acid in a soil column, <em>submitted</em></p> <p>Lippold, H., Karimzadeh L., Kulenkampff, J. Wissmeier, L., Stuhlfauth, C., Stoll, M., Lippmann-Pipke, L.: Effect of pH on the mobility of the herbicide MCPA in a goethite-sand matrix: 1D and 2D reactive transport modelling, <em>to be submitted</em></p> <p> </p>
The dataset for the manuscript entitled "SUPHRE: A reactive transport model with unsaturated and density-dependent flow"
<p>This is the dataset for the manuscript entitled "SUPHRE: A reactive transport model with unsaturated and density-dependent flow".</p>
Reactive Transport in Heterogeneous Porous Media Under Different Peclet Numbers
<p>In our recent paper “Reactive Transport in Heterogeneous Porous Media Under Different Peclet Numbers“, we study the synergistic effects of the Peclet number and the length scale of medium heterogeneity on the evolution of bimolecular reactive transport between mobile and immobile species. We performed a suite of numerical simulations at the Darcy scale that quantify the instantaneous, irreversible bimolecular reaction , under various transport conditions (Peclet numbers) and porous media configurations (correlation lengths).</p> <p> </p> <p><strong>Numerical Methods</strong></p> <p>We used the following steps to obtain the results:</p> <p>1. Generation of the hydraulic conductivity fields – we used a widely tested sequential Gaussian simulator (Gómez-Hernández & Journel, 1993). The software is written in C and available to download via <a href="https://wiki.52north.org/AI_GEOSTATS/SWGCOSIM3D">https://wiki.52north.org/AI_GEOSTATS/SWGCOSIM3D</a>. In the file ‘real20.mat’, we show an example of the generated 20 realizations, from the software, for <span class="math-tex">\(\ell\)</span>=1 ,<span class="math-tex">\(\sigma^2\)</span>=1. </p> <p>2. Determination of the flow field – we solve the Darcy equation (for each of the hydraulic conductivity fields), using an open-source code MRST (Lie, 2016). The software is available to download via <a href="https://www.sintef.no/projectweb/mrst/">https://www.sintef.no/projectweb/mrst/</a>. In the file ‘real20_solutions.mat’, we show the flow field solutions (for the different realizations from ‘real20.mat’) under different Peclet numbers. For each realization and Peclet, we store the solution of the velocity components (first column), the velocity magnitude (second column) and the hydraulic conductivity field (third column).</p> <p>3. Chemical transport – was modeled through the Langevin equation, where movement by advection and diffusion were taken into account. Chemical reaction between the different chemical species was modeled via the reaction-radius approach (Edery, Porta, Guadagnini, Scher, & Berkowitz, 2016).</p> <p><br> <strong>Results</strong></p> <p>The data of the published figures can be download from the following files:</p> <p><strong>Fig2.txt</strong> -- processed simulation results shown in Figure 2.</p> <p><strong>Fig3.txt</strong> -- processed simulation results shown in Figure 3a,b.</p> <p><strong>Fig4.txt </strong>-- processed simulation results shown in Figure 4.</p> <p><strong>Fig5.txt </strong>-- processed simulation results shown in Figure 5.</p> <p><strong>FigS1.png </strong>-- First and second statistical moments of the generated conductivity fields, for different correlation lengths. The left axis displays the first moment of the generated fields (ln(K)), and the right axis shows the second moment (ln)). The small circles represent individual realizations, and the large circles indicate the mean value among realizations.</p> <p><strong>FigS2.png </strong>-- Sensitivity analysis of the power law parameters (a) and (b) (see equation (1) in the main text), among realizations; mean values are represented by gray circles, and the standard deviations by the vertical black lines. (c) and (d) show the standard deviations of (a) and (b), respectively, in percentage. Note that among the different realizations, the standard deviation does not exceed 3%. </p> <p> </p> <p><strong>References</strong></p> <p>[1] Edery, Y., Porta, G. M., Guadagnini, A., Scher, H., & Berkowitz, B. (2016). Characterization of Bimolecular Reactive Transport in Heterogeneous Porous Media. Transport in Porous Media. https://doi.org/10.1007/s11242-016-0684-0</p> <p>[2] Gómez-Hernández, J. J., & Journel, A. G. (1993). Joint Sequential Simulation of MultiGaussian Fields. https://doi.org/10.1007/978-94-011-1739-5_8</p> <p>[3] Lie, K.-A. (2016). User Guide for the MATLAB Reservoir Simulation Toolbox (MRST). In A. Soares (Ed.), An Introduction to Reservoir Simulation Using MATLAB. Oslo, Norway: SINTEF ICT, Department of Applied Mathematics. </p> <p> </p> <p> </p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.