%0 Generic %1 pelzer2023dataset %A Pelzer, Julia %D 2023 %K %R 10.18419/darus-3649 %T Dataset: Single Heat Pump Simulation - Raw, 100 Data Points %X This data set serves as training data for modelling the temperature field emanating from a groundwater heat pump. It is simulated with Pflotran and saved in h5 format. It contains 100 data points, each consisting of one simulation run until a near steady state is reached. Each datapoint measures 100 m x 1280 m with 20 x 256 cells. The varying parameters of the data set are pressure and permeability. Both are constant within a data point, but vary across the data set. Other parameters that define the data set, 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 https://github.com/JuliaPelzer/Phd_simulation_groundtruth (commit 94daf52) with arguments given in inputs/args.yaml.

@misc{pelzer2023dataset, abstract = {This data set serves as training data for modelling the temperature field emanating from a groundwater heat pump. It is simulated with Pflotran and saved in h5 format. It contains 100 data points, each consisting of one simulation run until a near steady state is reached. Each datapoint measures 100 m x 1280 m with 20 x 256 cells. The varying parameters of the data set are pressure and permeability. Both are constant within a data point, but vary across the data set. Other parameters that define the data set, 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 https://github.com/JuliaPelzer/Phd_simulation_groundtruth (commit 94daf52) with arguments given in inputs/args.yaml. }, added-at = {2024-04-08T16:51:40.000+0200}, affiliation = {Pelzer, Julia/Universität Stuttgart}, author = {Pelzer, Julia}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24ced2c2c5e38a07f4e4280f635f190c0/unibiblio}, doi = {10.18419/darus-3649}, howpublished = {Dataset}, interhash = {1be32ce4d5960ae2a50981c01452967a}, intrahash = {4ced2c2c5e38a07f4e4280f635f190c0}, keywords = {}, note = {Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788}, orcid-numbers = {Pelzer, Julia/0009-0003-1727-9626}, timestamp = {2024-04-08T14:51:40.000+0200}, title = {Dataset: Single Heat Pump Simulation - Raw, 100 Data Points}, year = 2023 }