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KWRz2v$NwL6ra|)n literal 0 HcmV?d00001 From 8e613a5e082689ffed26755626e0941f57322845 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Wed, 18 Dec 2024 21:02:52 +0000 Subject: [PATCH 2/5] Creating 10.21105.joss.05360.crossref.xml --- joss.05360/10.21105.joss.05360.crossref.xml | 377 ++++++++++++++++++++ 1 file changed, 377 insertions(+) create mode 100644 joss.05360/10.21105.joss.05360.crossref.xml diff --git a/joss.05360/10.21105.joss.05360.crossref.xml b/joss.05360/10.21105.joss.05360.crossref.xml new file mode 100644 index 000000000..491122755 --- /dev/null +++ b/joss.05360/10.21105.joss.05360.crossref.xml @@ -0,0 +1,377 @@ + + + + 20241218210157-e2f021645fb75707077372100eb9c69e95aa6093 + 20241218210157 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 12 + 2024 + + + 9 + + 104 + + + + r2ogs6: An R wrapper of the OpenGeoSys 6 Multiphysics Simulator + + + + Ruben + Heinrich + + Leipzig University of Applied Sciences, Karl-Liebknecht-Strasse 132, 04277 Leipzig, Germany + + + + Johannes + Boog + + Helmholtz Centre for Environmental Research, Department Environmental Informatics, Permoser Str. 15, 04318 Leipzig, Germany + + https://orcid.org/0000-0003-0872-7098 + + + Philipp + Schad + + Helmholtz Centre for Environmental Research, Department Environmental Informatics, Permoser Str. 15, 04318 Leipzig, Germany + + https://orcid.org/0000-0003-3332-5867 + + + Thomas + Kalbacher + + Helmholtz Centre for Environmental Research, Department Environmental Informatics, Permoser Str. 15, 04318 Leipzig, Germany + + https://orcid.org/0000-0002-7866-5702 + + + + 12 + 18 + 2024 + + + 5360 + + + 10.21105/joss.05360 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.14313641 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5360 + + + + 10.21105/joss.05360 + https://joss.theoj.org/papers/10.21105/joss.05360 + + + https://joss.theoj.org/papers/10.21105/joss.05360.pdf + + + + + + Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change + Field + 10.1017/CBO9781139177245 + 2012 + Field, C., Barros, V., Stocker, T., & Dahe, Q. 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Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers. Nature Communications, 11(1), 3710. https://doi.org/10.1038/s41467-020-17581-y + + + Big data in earth system science and progress towards a digital twin + Li + Nature Reviews Earth & Environment + 10.1038/s43017-023-00409-w + 2023 + Li, X., Feng, M., Ran, Y., Su, Y., Liu, F., Huang, C., Shen, H., Xiao, Q., Su, J., Yuan, S., & others. (2023). Big data in earth system science and progress towards a digital twin. Nature Reviews Earth & Environment, 1–14. https://doi.org/10.1038/s43017-023-00409-w + + + Reactive transport codes for subsurface environmental simulation + Steefel + Computational geosciences + 3 + 19 + 10.1007/s10596-014-9443-x + 2015 + Steefel, C. I., Appelo, C. A. J., Arora, B., Kalbacher, D., Kolditz, O., Lagneau, V., Lichtner, P. C., Mayer, K. U., Meeussen, J. C. L., Molins, S., Moulton, D., Shao, D., Simunek, J., Spycher, N., Yabusaki, S. B., & Yeh, G. T. (2015). Reactive transport codes for subsurface environmental simulation. Computational Geosciences, 19(3), 445–478. https://doi.org/10.1007/s10596-014-9443-x + + + Development of open-source porous media simulators: Principles and experiences + Bilke + Transport in Porous Media + 130 + 10.1007/s11242-019-01310-1 + 2019 + Bilke, L., Flemisch, B., Kalbacher, T., Kolditz, O., Helmig, R., & Nagel, T. (2019). Development of open-source porous media simulators: Principles and experiences. Transport in Porous Media, 130, 337–361. https://doi.org/10.1007/s11242-019-01310-1 + + + Thermo-hydro-mechanical-chemical processes in porous media: Benchmarks and examples + 10.1007/978-3-642-27177-9 + 978-3-642-27176-2 + 2012 + Kolditz, O., Görke, U.-J., Shao, H., & Wang, W. (Eds.). (2012). Thermo-hydro-mechanical-chemical processes in porous media: Benchmarks and examples (1. ed.). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-27177-9 + + + ogs6py and VTUinterface: Streamlining OpenGeoSys workflows in Python + Buchwald + Journal of Open Source Software + 67 + 6 + 10.21105/joss.03673 + 2021 + Buchwald, J., Kolditz, O., & Nagel, T. (2021). ogs6py and VTUinterface: Streamlining OpenGeoSys workflows in Python. Journal of Open Source Software, 6(67), 3673. https://doi.org/10.21105/joss.03673 + + + Non-iterative phase-equilibrium model of the H2O-CO2-NaCl-system for large-scale numerical simulations + Grunwald + Mathematics and Computers in Simulation + 178 + 10.1016/j.matcom.2020.05.024 + 0378-4754 + 2020 + Grunwald, N., Maßmann, J., Kolditz, O., & Nagel, T. (2020). Non-iterative phase-equilibrium model of the H2O-CO2-NaCl-system for large-scale numerical simulations. Mathematics and Computers in Simulation, 178, 46–61. https://doi.org/10.1016/j.matcom.2020.05.024 + + + Towards the construction of representative regional hydro (geo) logical numerical models: Modelling the upper Danube basin as a starting point + Pujades + Frontiers in Earth Science + 11 + 10.3389/feart.2023.1061420 + 2023 + Pujades, E., Kumar, R., Houben, T., Jing, M., Rakovec, O., Kalbacher, T., & Attinger, S. (2023). Towards the construction of representative regional hydro (geo) logical numerical models: Modelling the upper Danube basin as a starting point. Frontiers in Earth Science, 11, 1061420. https://doi.org/10.3389/feart.2023.1061420 + + + OpenGeoSys-ChemApp: A coupled simulator for reactive transport in multiphase systems and application to CO 2 storage formation in northern Germany + Li + Acta Geotechnica + 9 + 10.1007/s11440-013-0234-7 + 2014 + Li, D., Bauer, S., Benisch, K., Graupner, B., & Beyer, C. (2014). OpenGeoSys-ChemApp: A coupled simulator for reactive transport in multiphase systems and application to CO 2 storage formation in northern Germany. Acta Geotechnica, 9, 67–79. https://doi.org/10.1007/s11440-013-0234-7 + + + Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5. 7) to the groundwater model OpenGeoSys (OGS) + Jing + Geoscientific Model Development + 5 + 11 + 10.5194/gmd-11-1989-2018 + 2018 + Jing, M., Heße, F., Kumar, R., Wang, W., Fischer, T., Walther, M., Zink, M., Zech, A., Samaniego, L., Kolditz, O., & others. (2018). Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5. 7) to the groundwater model OpenGeoSys (OGS). Geoscientific Model Development, 11(5), 1989–2007. https://doi.org/10.5194/gmd-11-1989-2018 + + + Benchmarking a new TH2M implementation in OGS-6 with regard to processes relevant for nuclear waste disposal + Pitz + Environmental Earth Sciences + 13 + 82 + 10.1007/s12665-023-10971-7 + 2023 + Pitz, M., Grunwald, N., Graupner, B., Radeisen, E., Maßmann, J., Ziefle, G., Thiedau, J., & Nagel, T. (2023). Benchmarking a new TH2M implementation in OGS-6 with regard to processes relevant for nuclear waste disposal. Environmental Earth Sciences, 82(13), 1–24. https://doi.org/10.1007/s12665-023-10971-7 + + + A review of surrogate models and their application to groundwater modeling + Asher + Water Resources Research + 8 + 51 + 10.1002/2015WR016967 + 2015 + Asher, M. J., Croke, B. F., Jakeman, A. J., & Peeters, L. J. (2015). A review of surrogate models and their application to groundwater modeling. Water Resources Research, 51(8), 5957–5973. https://doi.org/10.1002/2015WR016967 + + + ggplot2: Create elegant data visualisations using the grammar of graphics + Wickham + 10.32614/cran.package.ggplot2 + 2021 + Wickham, H., Chang, W., Henry, L., Pedersen, T., Takahashi, K., Wilke, C., Woo, K., Yutani, H., & Dunnington, D. (2021). ggplot2: Create elegant data visualisations using the grammar of graphics. https://doi.org/10.32614/cran.package.ggplot2 + + + dplyr: A grammar of data manipulation + Wickham + 10.32614/CRAN.package.dplyr + 2021 + Wickham, H., François, R., Henry, L., & Müller, K. (2021). dplyr: A grammar of data manipulation. https://doi.org/10.32614/CRAN.package.dplyr + + + mlrMBO: A modular framework for model-based optimization of expensive black-box functions + Bischl + 10.32614/CRAN.package.mlrMBO + 2017 + Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (2017). mlrMBO: A modular framework for model-based optimization of expensive black-box functions. https://doi.org/10.32614/CRAN.package.mlrMBO + + + lhs: Latin hypercube samples + Carnell + 10.32614/cran.package.lhs + 2021 + Carnell, R. (2021). lhs: Latin hypercube samples. https://doi.org/10.32614/cran.package.lhs + + + Theis’ problem + Wang + 2020 + Wang, W. (2020). Theis’ problem. https://www.opengeosys.org/docs/benchmarks/liquid-flow/liquid-flow-theis-problem/ + + + Theis solution for well pumping + Walther + 2020 + Walther, M. (2020). Theis solution for well pumping. https://www.opengeosys.org/docs/benchmarks/hydro-component/theis/hc_theis/ + + + Entwicklung und Implementierung der Schnittstelle R2OpenGeoSys zur Workflow-Optimierung am Beispiel der Modellierung eines Stofftransportproblems + Heinrich + 2021 + Heinrich, R. (2021). Entwicklung und Implementierung der Schnittstelle R2OpenGeoSys zur Workflow-Optimierung am Beispiel der Modellierung eines Stofftransportproblems [Master’s thesis]. HTWK Leipzig. + + + DATA-driven CALibration of large-scale physical groundwater flow models using meta-modeling and visual analytics + Boog + AGU fall meeting abstracts + 2021 + 2021 + Boog, J., Sips, M., De Lucia, M., Eggert, D., & Kalbacher, T. (2021). DATA-driven CALibration of large-scale physical groundwater flow models using meta-modeling and visual analytics. AGU Fall Meeting Abstracts, 2021, H34D–04. + + + Scripting MODFLOW model development using Python and FloPy + Bakker + Groundwater + 5 + 54 + 10.1111/gwat.12413 + 2016 + Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J. T., Starn, J. J., & Fienen, M. N. (2016). Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), 733–739. https://doi.org/10.1111/gwat.12413 + + + Geochemical and reactive transport modelling in R with the RedModRphree package + De Lucia + Advances in Geoscience + 56 + 10.5194/adgeo-56-33-2021 + 2021 + De Lucia, M., & Kühn, M. (2021). Geochemical and reactive transport modelling in R with the RedModRphree package. Advances in Geoscience, 56, 33–43. https://doi.org/10.5194/adgeo-56-33-2021 + + + ogs5py: A Python-API for the OpenGeoSys 5 scientific modeling package + Müller + Groundwater + 1 + 59 + 10.1111/gwat.13017 + 2021 + Müller, S., Zech, A., & Heße, F. (2021). ogs5py: A Python-API for the OpenGeoSys 5 scientific modeling package. Groundwater, 59(1), 117–122. https://doi.org/10.1111/gwat.13017 + + + toughio: Pre- and post-processing Python library for TOUGH + Luu + Journal of Open Source Software + 51 + 5 + 10.21105/joss.02412 + 2020 + Luu, K. (2020). toughio: Pre- and post-processing Python library for TOUGH. Journal of Open Source Software, 5(51), 2412. https://doi.org/10.21105/joss.02412 + + + r2ogs5: Calibration of numerical groundwater flow models with bayesian optimization in R + Schad + Groundwater + 1 + 61 + 10.1111/gwat.13221 + 2023 + Schad, P., Boog, J., & Kalbacher, T. (2023). r2ogs5: Calibration of numerical groundwater flow models with bayesian optimization in R. Groundwater, 61(1), 119–130. https://doi.org/10.1111/gwat.13221 + + + MODFLOW 6 modular hydrologic model version 6.2. 2 + Langevin + US Geological Survey Software Release + 10 + 2021 + Langevin, C., Hughes, J., Banta, E., Provost, A., Niswonger, R., & Panday, S. (2021). MODFLOW 6 modular hydrologic model version 6.2. 2. US Geological Survey Software Release, 10, F76Q1VQV. + + + The TOUGH codes—A family of simulation tools for multiphase flow and transport processes in permeable media + Pruess + Vadose zone journal + 3 + 3 + 10.2113/3.3.738 + 2004 + Pruess, K. (2004). The TOUGH codes—A family of simulation tools for multiphase flow and transport processes in permeable media. Vadose Zone Journal, 3(3), 738–746. https://doi.org/10.2113/3.3.738 + + + Modules based on the geochemical model PHREEQC for use in scripting and programming languages + Charlton + Computers & Geosciences + 10 + 37 + 10.1016/j.cageo.2011.02.005 + 2011 + Charlton, S. R., & Parkhurst, D. L. (2011). Modules based on the geochemical model PHREEQC for use in scripting and programming languages. Computers & Geosciences, 37(10), 1653–1663. https://doi.org/10.1016/j.cageo.2011.02.005 + + + + + + From 13a8901e5c1bae34fb8d2d3b8fb84bc8af72b34a Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Wed, 18 Dec 2024 21:02:52 +0000 Subject: [PATCH 3/5] Creating 10.21105.joss.05360.jats --- .../paper.jats/10.21105.joss.05360.jats | 1080 +++++++++++++++++ 1 file changed, 1080 insertions(+) create mode 100644 joss.05360/paper.jats/10.21105.joss.05360.jats diff --git a/joss.05360/paper.jats/10.21105.joss.05360.jats b/joss.05360/paper.jats/10.21105.joss.05360.jats new file mode 100644 index 000000000..bb5710c59 --- /dev/null +++ b/joss.05360/paper.jats/10.21105.joss.05360.jats @@ -0,0 +1,1080 @@ + + +
    + + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5360 +10.21105/joss.05360 + +r2ogs6: An R wrapper of the OpenGeoSys 6 Multiphysics +Simulator + + + + +Heinrich +Ruben + + + + +https://orcid.org/0000-0003-0872-7098 + +Boog +Johannes + + + + +https://orcid.org/0000-0003-3332-5867 + +Schad +Philipp + + + + +https://orcid.org/0000-0002-7866-5702 + +Kalbacher +Thomas + + + + + +Leipzig University of Applied Sciences, +Karl-Liebknecht-Strasse 132, 04277 Leipzig, Germany + + + + +Helmholtz Centre for Environmental Research, Department +Environmental Informatics, Permoser Str. 15, 04318 Leipzig, +Germany + + + + +19 +12 +2022 + +9 +104 +5360 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2024 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +R +OpenGeoSys +finite element method +physics based modeling +groundwater flow modeling + + + + + + Summary +

    Understanding the impacts of climate change and hydrologic extreme + events on our sub–surface earth system is even in temperate zones of + utmost importance + (Felfelani + et al., 2017; + Field + et al., 2012; + Wu et + al., 2020). Key tools to develop such understanding are + physics–based simulation models that describe the manifold + interactions of involved natural phenomena across time and space + (X. Li + et al., 2023; + Steefel + et al., 2015).

    +

    Our package r2ogs6 provides a file–based + interface of the multi–physics simulation code + OpenGeoSys 6 + (Bilke + et al., 2019; + Kolditz + et al., 2012) to the + R + programing and statistical computing environment. + r2ogs6 enables R users + to perform and analyze simulation models of the sub–surface earth + system in R. It allows to access the + capabilities of OpenGeoSys 6 to simulate + thermo-hydro-mechanical-chemical and biological (THMC/B) processes in + porous and fractured media within R. In this + way, r2ogs6 enables R + users to model sub–surface phenomena and technologies such as + groundwater flow, reactive transport, geothermal energy usage and/or + nuclear waste repositories as well as to analyze and further process + simulation output. r2ogs6 enables users to + prepare and manipulate OpenGeoSys 6 simulation + models, run the simulations and retrieve corresponding output, all + within an R session or simple + R scripts. Therefore, R + classes and functions were designed to communicate with the respective + OpenGeoSys 6 input and output files as well as + executables. In addition to single-simulation runs, + r2ogs6 supports ensemble runs that can be used + to set up uncertainty and sensitivity analyses as well as parameter + studies. Furthermore, r2ogs6 allows conducting + and documenting OpenGeoSys 6 simulations in + reproducible R scripts or notebooks. As + OpenGeoSys 6 is continuously being developed + further, code generation functions for r2ogs6 + developers were included to speed up the package updating process in + case of future changes to OpenGeoSys 6.

    +

    r2ogs6 was designed to be used by domain + researchers, data scientists and students working with + OpenGeoSys 6. Moreover, + r2ogs6 was intended to include + OpenGeoSys 6 into R + based scientific workflows and aims to bridge the gap between data + produced by a scientific simulation code and data science.

    +
    + + Statement of need +

    Major challenges humanity has to face in the coming decades are + climate change and hydrologic extremes + (Field + et al., 2012). Understanding the impacts of climate change and + hydrologic extreme events on our sub–surface earth system is even in + temperate zones of utmost importance for ensuring adequate domestic + and drinking water supplies, together with functioning lake and river + systems with healthy aquatic ecosystems and ecosystem services + (Felfelani + et al., 2017; + Wu et + al., 2020). Of course, the needs of the population and the + needs of nature are often in conflict, which increases the necessity + to study the complex interaction of both within different scenarios. + The core of such studies is most often the system and scenario + analysis of individual or coupled earth systems compartments through + physics simulations + (X. Li + et al., 2023; + Steefel + et al., 2015). In physics simulation models, multiple coupled + natural processes are implemented, which are usually described with + partial differential equations. Solving these equations requires + appropriate numerical methods such as the finite element method (FEM). + For reasons of performance, (multi) physics simulators are mostly + implemented in languages such as FORTRAN, + C or C++ + (Steefel + et al., 2015).

    +

    One of these simulators is OpenGeoSys (OGS) + (https://www.opengeosys.org/), a scientific open source project for + the development of numerical methods to simulate + thermo-hydro-mechanical-chemical and biological (THMC/B) processes in + porous and fractured media + (Bilke + et al., 2019; + Kolditz + et al., 2012). OGS has applications ranging from small-scale + geotechnical investigations + (Grunwald + et al., 2020), to reservoir studies + (D. Li + et al., 2014), nuclear waste repositories + (Pitz + et al., 2023) and even groundwater management of entire + landscapes + (Jing + et al., 2018; + Pujades + et al., 2023). + To identify the right action needs and to derive further decision + support for the above described problems it often requires to explore + several problem scenarios through meaningful model ensembles. This + translates to more and more complex and larger model setups + (Asher + et al., 2015). Thus, faster and more efficient model setup and + parametrization procedures are needed. But while + OpenGeoSys is a powerful FEM code, setting up, + running and evaluating multiple simulations can prove complicated.

    +

    Here is where languages such as R and + Python can prove useful. Via an interface that + adds a layer on top of OpenGeoSys 6, the user + can access preprocessing tools, the solver itself and postprocessing + tools alike, thus increasing usability and accessibility. The + development and application of high–level programming and/or scripting + languages interfaces to geoscientific simulators has been gaining + increasing attention in recent years. Examples are + FloPy: a Python + interface to the groundwater simulator Modflow + Langevin et al. + (2021), + RedModRPhree: an R + package with utility functions to the geo-chemical solver + Phreeqc Charlton & Parkhurst + (2011), + the toughio Python interface to the multi-phase + flow simulator TOUGH Pruess + (2004), + the Python interfaces to OpenGeoSys 5: + ogs5py + (Müller + et al., 2021) and OpenGeoSys 6: + ogs6py + (Buchwald + et al., 2021) as well as the R interface + to OpenGeoSys 5: r2ogs5 + (Schad + et al., 2023). FloPy, + ogs5py, ogs6py and + r2ogs5 provide object-oriented frameworks to + set-up, call the executables and import the output of the associated + simulators. The idea of these packages is to provide + Python and R functions + to pre- and process simulation data but to just run simulations via a + call to the the operating system to execute the simulators with the + prepared input. RedModRPhree and + toughio rather focus on additional pre- and + post processing functions to simplify the set-up of simulations with + R and Python. These + packages still rely on external execution of the associated simulation + program.

    +

    Altough there already exist the OpenGeoSys 6 + Python interface ogs6py, + we consider an R interface to be just as + important, as R is a well known language in the + environmental and geosciences. Furthermore, since + R is a popular language in the field of data + science with many powerful packages for data analysis and + vizualisation, e. g. dplyr + (Wickham, + François, et al., 2021) and ggplot2 + (Wickham, + Chang, et al., 2021), it’s a natural choice for processing data + generated by simulation tools such as + OpenGeoSys 6. r2ogs6 + follows a similar design approach compared to + FloPy, ogs5py, + ogs6py and r2ogs5: it + provides an object-oriented approach with R + classes and functions to set-up an OpenGeoSys 6 + simulation, call the respective executable and import the output of + finished simulations (see section Package Structure + for more details).

    +

    Moreover, r2ogs6 can facilitate the + calibration of OpenGeoSys 6 models. One + possibility is to use implemented functions to design ensemble runs. A + second possibility is to use available R + packages for model calibration such as lhs + (Carnell, + 2021), mlrBO + (Bischl + et al., 2017). For R users who do not + have a lot of (or any) experience with environmental and geoscientific + sub–surface simulations, yet an interest in such, + r2ogs6 provides a good starting point. + Utilizing r2ogs6, users can easily set up their + first OpenGeoSys 6 simulations by choosing one + of the provided benchmark files. Moreover, with + R scripts and + R--Markdown or JupyteR + notebooks, modeling workflows can easily be documented, published and + shared with peers.

    +

    r2ogs6 has already been applied in + (Heinrich, + 2021), where its ensemble functionality was tested utilizing + the OpenGeoSys 6 Theis’ problem benchmark files + (Walther, + 2020; + Wang, + 2020); or to calibrate groundwater flow models + (Boog + et al., 2021).

    +
    + + Package Structure +

    r2ogs6 is thought to set up an + OpenGeoSys 6 simulation inside an + R session by executing specified model creation + functions or by reading existing OpenGeoSys 6 + input files. With further functions, the simulation can be executed + and corresponding output can be read into the R + session again. + [fig:structure] + highlights the structure of r2ogs6. The central + element that represents an OpenGeoSys 6 + simulation is the OGS6 object, which is an + instance of a R6 class. This object represents + a single simulation; multiple simulations can be defined with the + OGS6_Ensemble class. An + OGS6 object contains several child objects that + represent the simulation input and output. The main + OpenGeoSys 6 input files are the project file + *.prj, geometry file + *.gml and input FEM mesh file(s) + *.vtu. These are read in or written via + S3 class based functions (block + read_in* / export* in + [fig:structure]). + When reading in, the XML–based *.prj input file + is parsed. Individual tags are represented as + S3 class objects which are available via active + fields in the OGS6 object. Individual + *.prj tags may change due to ongoing + development activities in OpenGeoSys 6, + therefore, future updates of the related classes may be necessary. To + simplify updates like this, helper functions for analyzing + *.prj files as well as suggesting and creating + classes were implemented.

    +

    As the *.gml and the + *.vtu files are less complex and less likely to + change, these files are represented as R6 class + objects and also available as active fields inside the + OGS6 object. To execute simulations, functions + for writing the OpenGeoSys 6 input + (ogs6_export_simfiles()) and call the + OpenGeoSys 6 executable + (ogs6_run_simulation()) were implemented. Note + that an OpenGeoSys 6 executable or singularity + container needs to be present. Default executables and paths can be + defined in a configuration file.

    +

    During execution OpenGeoSys 6 generates + output data as *.vtu files. These files are + produced at user defined timesteps of the simulation and are + referenced in a *.pvd file. The function + ogs6_read_output_files() then attaches the + output files to the OGS6 object as + OGS6_pvd objects (which in turn reference + OGS6_vtu objects). In this way, all data + required for and produced by OpenGeoSys 6 can + be represented as R native objects and results + can be processed further using R functions.

    + +

    Schematic of the r2ogs6 + structure.

    + +
    +
    + + Examples + + Quick Start with Theis’ Problem +

    Theis’ problem examines the lowering of the water level around a + pumping well. Water is pumped from a well, which induces a lowering + of the water level over time. The Theis’ problem is a common + benchmark for sub–surface flow simulators. See the respective + desription on the OpenGeoSys 6 page + here. + The required input files are already present in the installed + r2ogs6 package.

    +

    You can just create an .R script with the + r2ogs6 commands to set-up a simulation from a + benchmark file directly. Just load the package and get the path to + the Theis’ problem benchmark .prj file.

    + library(r2ogs6) + +# Modify the prj_path depending on where you saved the benchmark file. +prj_path <- system.file("extdata/benchmarks/AxiSymTheis/", + "axisym_theis.prj", package = "r2ogs6") +

    Then define the path were the generated script is to be save + (script_path) and were the simulation is to + be run (sim_path). Note, that the folders + should already exist. Finally, call the respective function to + generate the script; the script will be named according to the name + of the project file you specified but with the extension + .R (here: + axisym_theis.R).

    + script_path <- "path/to/scripts" +sim_path <- "path/to/sim" + +ogs6_generate_benchmark_script(prj_path, + sim_path = sim_path, + script_path = script_path) +

    You can now open the generated script + axisym_theis.R and have a all the commands to + generate and run a simulation object ready to explore.

    +
    + + Sensitivity Analysis of the Theis’ Problem +

    Here, we will set up an ensemble of models to visualize the + sensitivity of the water level lowering to changes in the material + specific parameter called storage.

    +

    The variable that corresponds to the water level is the + pressure in the water, which increases with + depth. So changes in storage will induce + changes in the evolution of the pressure + gradient around the well.

    +

    At first, load the required libraries.

    + library(r2ogs6) +library(ggplot2) +library(dplyr) +

    Then, create a simulation object to base the ensemble on and read + in the .prj file.

    + # Change this to fit your system +testdir_path <- tempdir() +sim_path <- paste0(testdir_path, "/axisym_theis_sim") + +# Create the simulation object +ogs6_obj <- OGS6$new(sim_name = "axisym_theis", + sim_path = sim_path) + +# The input files should be present in your r2ogs6 installation directory +prj_path <- system.file("extdata/benchmarks/AxiSymTheis/", + "axisym_theis.prj", package = "r2ogs6") + +# Now read in the input files +read_in_prj(ogs6_obj, prj_path, read_in_gml = T) +

    To examine the effects of storage we + change it by 1%, 10% and 50%. We can use the + percentages_mode parameter of + OGS6_Ensemble for this.

    + # Assign percentages +percentages <- c(-50, -10, -1, 0, 1, 10, 50) + +# Define an ensemble object +ogs6_ens <- + OGS6_Ensemble$new( + ogs6_obj = ogs6_obj, + parameters = list(list(ogs6_obj$media[[1]]$properties[[4]]$value, + percentages)), + percentages_mode = TRUE) +

    Now start the simulation, read and visualize the output.

    + # Start the simulation +ogs6_ens$run_simulation() + +# Attach the output files to the ensemble object. +lapply(ogs6_ens$ensemble, ogs6_read_output_files) + +# Extract point specific data of the `pressure` variable from the output files +storage_tbl <- + ogs6_ens$get_point_data(point_ids = c(0, 1, 2), + keys = c("pressure")) + +# Compute the spatial average of the pressure for all simulations +avg_pr_df <- storage_tbl %>% + group_by(sim_id, timestep) %>% + summarise(avg_pressure = mean(pressure)) + +# Plot the spatially averaged pressure over time for all simulations +ggplot(avg_pr_df, aes(x = as.numeric(as.factor(timestep)), + y = avg_pressure, + group = sim_id)) + + geom_point(aes(color = as.factor(sim_id))) + + geom_line(aes(color = as.factor(sim_id))) + + labs(color = "sim id") + + xlab("Timestep") +

    The plot then shows the development of the average + pressure in each simulation of the ensemble + over time.

    + +

    plot of chunk p-t-all-combined-plot

    + +
    +

    Check the following package vignettes for more information: - a + guide to set up a single simulation of a hydro-mechanics benchmark + (link) + - a guide to create ensemble runs + (link) + - a development guide + (link)

    +
    +
    + + Acknowledgements +

    We acknowledge funding from the Initiative and Networking Fund of + the Helmholtz Association through the project Digital + Earth (funding code ZT-0025). Furthermore, we acknowledge the + Helmholtz Centre for Environmental Research–UFZ for additional funding + and support. We would like to express our gratitude to the + OpenGeoSys Community for technical support and for + hosting the GitLab server (https://gitlab.opengeosys.org) for our + development. The package has in part been developed on the + High-Performance Computing (HPC) Cluster EVE, a joint effort of both + the Helmholtz Centre for Environmental Research - UFZ + (http://www.ufz.de/) and the German Centre for Integrative + Biodiversity Research (iDiv) Halle-Jena-Leipzig + (http://www.idiv-biodiversity.de/). We would like to thank the + administration and support staff of EVE who keep the system running + and support us with our scientific computing needs: Thomas Schnicke, + Ben Langenberg, Guido Schramm, Toni Harzendorf and Tom Strempel from + the UFZ, and Christian Krause from iDiv.

    +
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