The ability for groundwater heat pumps to meet space heating and cooling demands without relying on fossil fuels, has prompted their mass roll out in dense urban environments. In regions with high subsurface groundwater flow rates, the thermal plume generated from a heat pump's injection well can propagate downstream, affecting surrounding users and reducing their heat pump efficiency. To reduce the probability of interference, regulators often rely on simple analytical models or high fidelity groundwater simulations to determine the impact that a heat pump has on the subsurface aquifer and surrounding heat pumps. These are either too inaccurate or too computationally expensive for everyday use. In this work, a surrogate model was developed to provide a quick, high accuracy prediction tool of the thermal plume generated by a heat pump within heterogeneous subsurface aquifers. Three variations of a convolutional neural network were developed that accepts the known groundwater Darcy velocities as discrete two-dimensional inputs and predicts the temperature within the subsurface aquifer around the heat pump. A data set consisting of 800 numerical simulation samples, generated from random permeability fields and pressure boundary conditions, was used to provide pseudo-randomized Darcy velocity fields as input fields and the temperature field solution for training the network. The subsurface temperature field output from the network provides a more realistic temperature field that follows the Darcy velocity streamlines, while being orders of magnitude faster than conventional high fidelity solvers

翻译：地下水源热泵满足建筑供暖制冷需求且不依赖化石燃料的能力，促使其在密集城市环境中大规模推广应用。在地下水流速较高的区域，热泵注水井产生的热羽会向下游扩散，影响周边用户并降低其热泵效率。为降低干扰概率，监管机构通常依赖简单解析模型或高保真地下水模拟来确定热泵对地下含水层及周边热泵的影响。但这些方法要么精度不足，要么计算成本过高，难以日常使用。本研究开发了一种代理模型，可针对非均质地下含水层中热泵产生的热羽提供快速高精度预测工具。我们构建了三种卷积神经网络变体，将已知地下水达西速度作为离散二维输入，预测热泵周围含水层的温度分布。采用由随机渗透率场和压力边界条件生成的800个数值模拟样本构成数据集，提供伪随机达西速度场作为输入场，温度场解作为训练数据。网络输出的地下水温度场能够更真实地反映沿达西流速流线的温度分布，同时其计算速度比传统高保真求解器快数个数量级。