%0 Generic %1 pelzer2024datasets %A Pelzer, Julia %D 2024 %K %R 10.18419/darus-4533 %T Datasets: First Stage 3D Simulation - Raw, 100 and 1000 Data Points %X These data sets serve as training data for modelling the temperature field emanating from a groundwater heat pump. They are simulated with Pflotran and saved in h5 format. They contain 100 and 1000 data points, each consisting of one simulation run until a near steady state is reached. Each datapoint measures 320 m x 1280 m x 320 m with 64 x 256 x 64 cells. The varying parameters of the data sets are pressure and permeability. Both are constant within a data point, but vary across the data sets. Other parameters that define the data sets, such as porosity, are chosen to be as close as possible to reality.Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.Generated with scripts from Dataset generation with Pflotran with arguments given in inputs/args.yaml.

@misc{pelzer2024datasets, abstract = {These data sets serve as training data for modelling the temperature field emanating from a groundwater heat pump. They are simulated with Pflotran and saved in h5 format. They contain 100 and 1000 data points, each consisting of one simulation run until a near steady state is reached. Each datapoint measures 320 m x 1280 m x 320 m with 64 x 256 x 64 cells. The varying parameters of the data sets are pressure and permeability. Both are constant within a data point, but vary across the data sets. Other parameters that define the data sets, such as porosity, are chosen to be as close as possible to reality.Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.Generated with scripts from Dataset generation with Pflotran with arguments given in inputs/args.yaml. }, added-at = {2025-03-11T10:46:21.000+0100}, affiliation = {Pelzer, Julia/University of Stuttgart}, author = {Pelzer, Julia}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24c79e2aa1de71f2f6e5aa16149ed4ed0/unibiblio}, doi = {10.18419/darus-4533}, howpublished = {Dataset}, interhash = {37bc338fc68236887c952775706f297b}, intrahash = {4c79e2aa1de71f2f6e5aa16149ed4ed0}, keywords = {}, note = {Related to: Johanna Trick. "Erweiterung eines Convolutional Neural Network auf 3D zur Modellierung von Grundwasserwärmepumpen". Bachelor Thesis (2024). (unpublished)}, orcid-numbers = {Pelzer, Julia/0009-0003-1727-9626}, timestamp = {2025-03-11T09:46:21.000+0100}, title = {Datasets: First Stage 3D Simulation - Raw, 100 and 1000 Data Points}, year = 2024 }