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arXiv · 2203.14961

A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps

Raphael Leiteritz·Kyle Davis·Miriam Schulte·Dirk Pflüger
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Abstract

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.

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Raphael Leiteritz, Kyle Davis, Miriam Schulte, Dirk Pflüger. 2022-03-29. A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps. https://arxiv.org/abs/2203.14961

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