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May 26, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling
Pelzer, Julia; Böttcher, Fabian, 2026, "Step 2: Two Interacting Heat Plumes", https://doi.org/10.18419/DARUS-5807, DaRUS, V2
This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt...
ZIP Archive - 821.1 KB - MD5: f74441389aa474813e1f09d508bbfd73
ZIP Archive - 2.3 GB - MD5: 6529243b15ba5246f651068be6e66812
ZIP Archive - 36.1 GB - MD5: ea54c69a2ae0c998ac9a7568fc3a5c1d
Data
May 26, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling
Pelzer, Julia; Böttcher, Fabian, 2026, "Step 1: Single Heat Plume", https://doi.org/10.18419/DARUS-5806, DaRUS, V2
This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt...
ZIP Archive - 168.4 KB - MD5: ed980d99361de108a02b7bf4fa6eefa2
ZIP Archive - 1.2 GB - MD5: b9047c2a6212aead5cb4b7508df8e1e5
ZIP Archive - 37.5 GB - MD5: 53723531c452f848a2cf9de8b621d66b
Data
May 22, 2026 - Surrogate models for groundwater flow simulations
Baratto, Thomas, 2026, "Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics", https://doi.org/10.18419/DARUS-5815, DaRUS, V3, UNF:6:dsogzGIJc3jbogD4D0lTEA== [fileUNF]
Inference package for thermal plume prediction (v1.0.0). Contains pre-trained MLP and randomized neural network models, the ba-predict CLI, the CSV data used to train the models obtained via simulation by Fabian Böttcher, sample input files, and a Dockerfile. CPU-only (no GPU required). Install with: pip install ./code. Full source code (training s...
Tabular Data - 7.7 MB - 13 Variables, 85531 Observations - UNF:6:6k+73yoVIjlpTOs2PFnb6w==
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