A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps
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Abstract (arXiv)
Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative interactions between heat pumps throughout a city, city planners have to optimize their layouts in the future. We develop a novel data-driven approach for building small-scale surrogates for modelling the thermal plumes generated by groundwater heat pumps in the surrounding subsurface water. Building on a data set generated from 2D numerical simulations, we train a convolutional neural network for predicting steady-state subsurface temperature fields from a given subsurface velocity field. We show that compared to existing models ours can capture more complex dynamics while still being quick to compute. The resulting surrogate is thus well-suited for interactive design tools by city planners.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 29 Mar 2022.
Builds on
9 of 29 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Datacited by 262
- 2018Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flowscited by 150
- 2020Understanding and mitigating gradient pathologies in physics-informed neural networkscited by 135
- 2016Accelerating Eulerian Fluid Simulation With Convolutional Networkscited by 130
- 2020CFDNet: a deep learning-based accelerator for fluid simulationscited by 40
- 2018Reduced order modeling of subsurface multiphase flow models using deep residual recurrent neural networkscited by 9
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Citing papers per year: 2026: 1.
1 paper.