Dataset: Single Heat Pump Simulation - Raw, 100 Data Points
J. Pelzer. Dataset, (2023)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.
DOI: 10.18419/darus-3649
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.
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
%0 Generic
%1 pelzer2023dataset
%A Pelzer, Julia
%D 2023
%K darus mult ubs_10005 ubs_10021 ubs_20008 ubs_20019 ubs_30082 ubs_30165 ubs_40116 unibibliografie
%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 = {2023-09-11T12:41:56.000+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 = {darus mult ubs_10005 ubs_10021 ubs_20008 ubs_20019 ubs_30082 ubs_30165 ubs_40116 unibibliografie},
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 = {2024-04-08T16:51:40.000+0200},
title = {Dataset: Single Heat Pump Simulation - Raw, 100 Data Points},
year = 2023
}