{"dcterms:modified":"2023-12-08","dcterms:creator":"DaRUS","@type":"ore:ResourceMap","@id":"https://nfldevdataverse2.rus.uni-stuttgart.de/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.18419/darus-1851","ore:describes":{"processStep":[{"process:processStepId":"0000","process:processStepType":"Generation","process:processStepMethods":"00_periodicity_simulation","process:processStepSoftware":"ESPResSo, librepa, Python3"},{"process:processStepId":"0001","process:processStepType":"Analysis","process:processStepMethods":"00_periodicity_analysis","process:processStepSoftware":"Python3"},{"process:processStepId":"0100","process:processStepType":"Generation","process:processStepMethods":"01_homogeneous_scaling_preprocessing"},{"process:processStepId":"0101","process:processStepType":"Generation","process:processStepMethods":"01_homogeneous_scaling_simulation","process:processStepSoftware":"ESPResSo_old"},{"process:processStepId":"0102","process:processStepType":"Analysis","process:processStepMethods":"01_homogeneous_scaling_analysis","process:processStepSoftware":"Python3"},{"process:processStepId":"0200","process:processStepType":"Generation","process:processStepMethods":"02_droplets_simulation","process:processStepSoftware":"ESPResSo_old"},{"process:processStepId":"0201","process:processStepType":"Postprocessing","process:processStepMethods":"02_droplets_postprocessing","process:processStepSoftware":"Python3"},{"process:processStepId":"0202","process:processStepType":"Analysis","process:processStepMethods":"02_droplets_analysis","process:processStepSoftware":"Python3"},{"process:processStepId":"0300","process:processStepType":"Generation","process:processStepMethods":"03_lbm_md_simulation","process:processStepSoftware":"ESPResSo_old"},{"process:processStepId":"0400","process:processStepType":"Generation","process:processStepMethods":"04_adaptions_generation","process:processStepSoftware":"ESPResSo"},{"process:processStepId":"0401","process:processStepType":"Generation","process:processStepMethods":"04_adaptions_simulation","process:processStepSoftware":"ESPResSo, librepa"},{"process:processStepId":"0402","process:processStepType":"Postprocessing","process:processStepMethods":"04_adaptions_postprocessing","process:processStepSoftware":"Awk"},{"process:processStepId":"0500","process:processStepType":"Analysis","process:processStepMethods":"05_heterogeneity_calculation","process:processStepSoftware":"Python3"},{"process:processStepId":"0600","process:processStepType":"Generation","process:processStepMethods":"06_soot_generation"},{"process:processStepId":"0601","process:processStepType":"Generation","process:processStepMethods":"06_soot_simulation","process:processStepSoftware":"ESPResSo, librepa"}],"process:processMethods":[{"processMethodsName":"00_periodicity_simulation","process:processMethodsDescription":"MD simulation with ESPResSo. Simulation script: \"simulate-agglomerate.py\", input data: \"agglomerate-1954.txt\".\nResult in: \"simulation-result-data-extracted.csv\"."},{"processMethodsName":"00_periodicity_analysis","process:processMethodsDescription":"Analysis of the data with \"plot-results.py\" and \"plot-setup.py\"."},{"processMethodsName":"01_homogeneous_scaling_preprocessing","process:processMethodsDescription":"Program \"generate-grid-mpiio-data.cc\" can generate the input data as described in the referenced publication."},{"processMethodsName":"01_homogeneous_scaling_simulation","process:processMethodsDescription":"MD simulation with custom version of ESPResSo using \"measurement.tcl\".\nMeasure runtimes for different numbers of processes and store them in a file, see \"scaling.tab\"."},{"processMethodsName":"01_homogeneous_scaling_analysis","process:processMethodsDescription":"Use script \"plot-scaling.py\" to plot \"scaling.tab\""},{"processMethodsName":"02_droplets_simulation","process:processMethodsDescription":"MD simulation with custom version of ESPResSo. Simulation script: \"simulation_script.tcl\"."},{"processMethodsName":"02_droplets_postprocessing","process:processMethodsDescription":"Extract runtimes and scaling information from the raw simulation output (generated in the step before) using \"parse-output-files.py\". Input: Raw ouput; Output: Tabulated runtime data.\nProduces \"lj-default-1700ppp.dat\" and \"lj-reba-1700ppp.dat\"."},{"processMethodsName":"02_droplets_analysis","process:processMethodsDescription":"Plot the results using \"plot.py\""},{"processMethodsName":"03_lbm_md_simulation","process:processMethodsDescription":"The simulation script for the coupled LBM-MD simulation is \"lbm-md-coupled-sim.py\". All the different experiments needed for the scaling results can be run automatically with \"run.sh\"."},{"processMethodsName":"04_adaptions_generation","process:processMethodsDescription":"Use \"spinodal_decomposition.py\" with ESPResSo to generate the snapshots for a spinodal decomposition."},{"processMethodsName":"04_adaptions_simulation","process:processMethodsDescription":"Use \"spinodal-load-balancing.py\" to load the snapshots and run load-balancing tests on them."},{"processMethodsName":"04_adaptions_postprocessing","process:processMethodsDescription":"The output from \"spinodal-load-balancing.py\" can be parsed with \"extract-all.awk\"."},{"processMethodsName":"05_heterogeneity_calculation","process:processMethodsDescription":"Use the script \"calculate_heterogeneity.py\" to calculate the heterogeneity of snapshots. In the publication, we use this on the spinodal decomposition scenario (0400) and the soot particle agglomeration (0601).\n\nThe referenced publication (\"Related Publication\") uses the following parameters, where \"X,Y,Z\" is the box size, e.g. \"2600.0,2600.0,2600.0\": --move-mode --fold-particles --box \"X,Y,Z\" --sample-size 10000 --num-kde-samples 100"},{"processMethodsName":"06_soot_generation","process:processMethodsDescription":"Use program \"generate-uniform-random-mpiio-data.cc\" to generate a snapshot with initial conditions as used in the publication."},{"processMethodsName":"06_soot_simulation","process:processMethodsDescription":"Simulation of the soot particle agglomeration with ESPResSo simulation script \"case6-100mio.py\". Use input from step 0600."}],"processSoftware":[{"processSoftwareName":"librepa","processSoftwareVersion":"v1.1.3","processSoftwareURL":"https://github.com/SC-SGS/repa","processSoftwareLicence":"GPLv3"},{"processSoftwareName":"ESPResSo","processSoftwareVersion":"4.1.2+generic_dd","processSoftwareURL":"https://github.com/espressomd/espresso/pull/3662","processSoftwareLicence":"GPLv3"},{"processSoftwareName":"ESPResSo_old","processSoftwareVersion":"custom","processSoftwareURL":"https://github.com/hirschsn/espresso/commits/merge_py","processSoftwareLicence":"GPLv3"},{"processSoftwareName":"Python3","processSoftwareVersion":"3.6+","processSoftwareURL":"https://www.python.org/","processSoftwareLicence":"Python Software Foundation License"},{"processSoftwareName":"Awk","processSoftwareVersion":"GNU awk 5.0.1","processSoftwareURL":"https://www.gnu.org/software/gawk/","processSoftwareLicence":"GPLv3"},{"processSoftwareName":"Numpy","processSoftwareVersion":"1.17.4","processSoftwareURL":"https://numpy.org/","processSoftwareLicence":"BSD 3-clause"},{"processSoftwareName":"Matplotlib","processSoftwareVersion":"3.1.2","processSoftwareURL":"https://matplotlib.org/","processSoftwareLicence":"Matplotlib license"}],"engMetaMeasuredVar":[{"engMetaMeasuredVarName":"Runtime","engMetaMeasuredVarUnit":"seconds"},{"engMetaMeasuredVarName":"Imbalance","engMetaMeasuredVarSymbol":"I","engMetaMeasuredVarUnit":"1"},{"engMetaMeasuredVarName":"Radius of Gyration","engMetaMeasuredVarSymbol":"r_g","engMetaMeasuredVarUnit":"sigma"},{"engMetaMeasuredVarName":"Fractal dimension","engMetaMeasuredVarSymbol":"Df","engMetaMeasuredVarUnit":"1"}],"EngMeta:engMetaControlledVar":[{"EngMeta:engMetaControlledVarName":"Particle diameter","EngMeta:engMetaControlledVarSymbol":"sigma"},{"EngMeta:engMetaControlledVarName":"Dispersion energy","EngMeta:engMetaControlledVarSymbol":"epsilon"},{"EngMeta:engMetaControlledVarName":"Time step","EngMeta:engMetaControlledVarSymbol":"delta t"},{"EngMeta:engMetaControlledVarName":"Particle density","EngMeta:engMetaControlledVarSymbol":"rho"},{"EngMeta:engMetaControlledVarName":"Temperature","EngMeta:engMetaControlledVarSymbol":"T"}],"citation:topicClassification":[{"citation:topicClassValue":"Parallel algorithms in computer science (68W10)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A68W10"},{"citation:topicClassValue":"Distributed algorithms (68W15)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A68W15"},{"citation:topicClassValue":"Approximation algorithms (68W25)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A68W25"},{"citation:topicClassValue":"Parallel numerical computation (65Y05)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A65Y05"},{"citation:topicClassValue":"Applications to the sciences (65Z05)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A65Z05"},{"citation:topicClassValue":"Performance evaluation, queueing, and scheduling in the context of computer systems (68M20)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A68M20"},{"citation:topicClassValue":"Turbulent combustion; reactive turbulence (76F80)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A76F80"},{"citation:topicClassValue":"Combustion (80A25)","citation:topicClassVocab":"MSC2020","citation:topicClassVocabURI":"https://zbmath.org/classification/?q=cc%3A80A25"}],"citation:dsDescription":{"citation:dsDescriptionValue":"This dataset contains input data, scripts, etc. for replicating the numerical experiments in Steffen Hirschmann's dissertation.
\n\nThe data is prefixed with folders indicating the specific experiment as is the processing metadata.
\n\nThe experiments are:\n\n