%0 Generic %1 pelzer2023dataset %A Pelzer, Julia %D 2023 %K %R 10.18419/darus-3651 %T Dataset: Two Heat Pumps Simulation - Raw, 100 Data Points %X This data set serves as training data for modelling the temperature field emanating from two groundwater heat pumps (one fixed, one randomly placed). 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 1250 m x 2560 m with 250 x 512 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 two groundwater heat pumps (one fixed, one randomly placed). 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 1250 m x 2560 m with 250 x 512 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 = {2024-04-08T16:52:29.000+0200}, affiliation = {Pelzer, Julia/Universität Stuttgart}, author = {Pelzer, Julia}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2cae8a55c6796caa51cbd8486bb4069c5/unibiblio}, doi = {10.18419/darus-3651}, howpublished = {Dataset}, interhash = {a55a9423753dbfaa3c2ec975b35de96c}, intrahash = {cae8a55c6796caa51cbd8486bb4069c5}, 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 = {2024-04-08T14:52:29.000+0200}, title = {Dataset: Two Heat Pumps Simulation - Raw, 100 Data Points}, year = 2023 }