scholarly journals Geochemical and reactive transport modelling in R with the RedModRphree package

2021 ◽  
Vol 56 ◽  
pp. 33-43
Author(s):  
Marco De Lucia ◽  
Michael Kühn
Keyword(s):  
Reactive Transport ◽  
Data Science ◽  
Laboratory Data ◽  
Third Party ◽  
Ecological Modelling ◽  
Geochemical Data ◽  
Calibration Error ◽  
Transport Modelling ◽  
Analysis Experiment ◽  
Geochemical Models

Abstract. Advances in computing and experimental capabilities in the research of water-rock-interactions require geoscientists to routinely combine laboratory data and models to produce new knowledge. Data science is hence a more and more pervasive instrument for geochemists, which in turn demands flexible and easy to learn software adaptable to their specific needs. The GNU R language and programming environment has established itself as de facto standard language for statistics and machine learning, enjoying increasing diffusion in many applied scientific fields such as bioinformatics, chemometrics and ecological modelling. The availability of excellent third party extensions as well as its advanced graphical and numerical capabilities make R an ideal platform for comprehensive geochemical data analysis, experiment evaluation and modelling. We introduce the open source RedModRphree extension package, which leverages the R interface to the established PHREEQC geochemical simulator. The aim of RedModRphree is to provide the user with an easy-to-use, high-level interface to program algorithms involving geochemical models: parameter calibration, error and sensitivity analysis, thermodynamical database manipulation, up to CPU-intensive parallel coupled reactive transport models. Among the out-of-the-box features included in RedModRphree, we highlight the computation and visualization of Pourbaix (Eh-pH) diagrams using full speciation as computed by PHREEQC and the implementation of 1D advective reactive transport supporting the use of surrogate models replacing expensive equation-based calculations.

scholarly journals Geochemical and reactive transport modelling in R with the RedModRphree package

2021 ◽  
Author(s):  
Marco De Lucia ◽  
Michael Kühn
Keyword(s):  
Reactive Transport ◽  
Data Science ◽  
Laboratory Data ◽  
Ecological Modelling ◽  
Geochemical Data ◽  
Calibration Error ◽  
Reactive Transport Modelling ◽  
Transport Modelling ◽  
Analysis Experiment ◽  
Geochemical Models

<p>The modern advances in computing and experimental capabilities in the research of water-rock-interactions require geoscientists to routinely combine laboratory data and models to produce knowledge in order to solve pressing societal challenges connected to subsurface utilization. Data science is hence a more and more pervasive instrument also for  geochemists, which in turn demands flexible and easy to learn software adaptable to their specific needs. <br>In this contribution we showcase geochemical and reactive transport modelling with our RedModRphree [1] extension package for the GNU R environment and programming language. The new version of the package leverages the R interface to the established PHREEQC geochemical simulator maintained by its original authors [2]. R has established itself as de facto standard language for statistics and machine learning. It enjoys increasing diffusion in many applied scientific fields such as bioinformatics, chemometrics and ecological modelling. The availability of excellent third party extensions such as the thermodynamic package CHNOSZ [3], which extends the functionalities of SUPCRT92, as well as its advanced graphical and numerical capabilities, make R an attractive platform for comprehensive geochemical data analysis, experiment evaluation and modelling. <br>The aim of RedModRphree is to provide the user with an easy-to-use, high-level interface to program algorithms involving geochemical models, which are then solved using the PHREEQC engine: parameter calibration, error and sensitivity analysis, visualization, up to CPU-intensive parallel coupled reactive transport models. Among the out-of-the-box features included in RedModRphree, we highlight the computation and visualization of Pourbaix (Eh-pH) diagrams and the implementation of 1D advective reactive transport supporting the use of surrogate models replacing expensive PHREEQC calculations [4]. RedModRphree is open source and can be installed from https://git.gfz-potsdam.de/delucia/RedModRphree.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.72552df75cff57113730161/sdaolpUECMynit/12UGE&app=m&a=0&c=afa6bef86f4c5523b81a86ccdd579fc9&ct=x&pn=gnp.elif&d=1" alt=""></p><p>[1] De Lucia, M. and Kühn, M.: Coupling R and PHREEQC: Efficient Programming of Geochemical Models, Energy Procedia, 40, 464–471, doi.org/10.1016/j.egypro.2013.08.053, 2013.</p><p>[2] Charlton, S.R. and Parkhurst, D.L.: Modules based on the geochemical model PHREEQC for use in scripting and programming languages, Computers & Geosciences 37, 10, 1653–1663, doi.org/10.1016/j.cageo.2011.02.005, 2011.</p><p>[3] Dick, J.M.: CHNOSZ: Thermodynamic Calculations and Diagrams for Geochemistry, Frontiers in Earth Science, 7, https://doi.org/10.3389/feart.2019.00180, 2019.</p><p>[4] Jatnieks, J., De Lucia, M., Dransch, D., and Sips, M.: Data-driven Surrogate Model Approach for Improving the Performance of Reactive Transport Simulations, Energy Procedia, 97, 447–453, doi.org/10.1016/j.egypro.2016.10.047, 2016.</p><p> </p>

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

scholarly journals Reactive transport modelling of a high-pH infiltration test in concrete

2017 ◽  
Vol 99 ◽  
pp. 131-141 ◽  
Author(s):  
M. Carme Chaparro ◽  
Josep M. Soler ◽  
Maarten W. Saaltink ◽  
Urs K. Mäder
Keyword(s):  
Reactive Transport ◽  
Reactive Transport Modelling ◽  
Transport Modelling ◽  
High Ph ◽  
Infiltration Test

Epistemic and Aleatory Uncertainty in Recommended, Generic Rock Kd Values used in Performance Assessment Studies

2006 ◽  
Vol 932 ◽  
Author(s):  
James Crawford ◽  
Ivars Neretnieks ◽  
Luis Moreno
Keyword(s):  
Performance Assessment ◽  
Laboratory Data ◽  
Scientific Basis ◽  
Performance Assessments ◽  
Aleatory Uncertainty ◽  
Transport Modelling ◽  
Natural Systems ◽  
Rock Types ◽  
Kd Values ◽  
Experimental Laboratory

ABSTRACTOver the past decade or so there has been an explosion in the number of sorption modelling approaches and applications of sorption modelling for understanding and predicting solute transport in natural systems. The most widely used and simplest of all models, however, is that employing a constant distribution coefficient (Kd) relating the sorbed concentration of a solute on a mineral surface and its aqueous concentration.There are a number of reasons why a constant partitioning coefficient is attractive to environmental modellers for predicting radionuclide retardation, and in spite of all the shortcomings and pitfalls associated with such an approach, it remains the leitmotif of most performance assessment transport modelling.This paper examines the scientific basis underpinning the Kd-approach and its broad defensibility in a performance assessment framework. It also examines sources of epistemic and aleatory uncertainty that undermine confidence in Kd-values reported in the open literature. The paper focuses particularly upon the use of so-called “generic” data for generalised rock types that may not necessarily capture the full material property characteristics of site-specific materials.From the examination of recent literature data, it appears that there are still a number of outstanding issues concerning interpretation of experimental laboratory data that need to be considered in greater detail before concluding that the recommended values used in performance assessments are indeed conservative.

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