基于深度学习的地下水热泵热羽流预测代理模型
Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps
摘要：
地下水热泵在不依赖化石燃料的情况下满足空间加热和冷却需求的能力促使其在密集的城市环境中大规模推广。在地下水流速高的区域，从热泵的注入井产生的热羽流可向下游传播，影响周围的用户并降低其热泵效率。为了减少干扰的可能性，监管机构通常依靠简单的分析模型或高保真地下水模拟来确定热泵对地下含水层和周围热泵的影响。对于日常使用来说，这些方法要么太不准确，要么计算成本太高。在这项工作中，一个代理模型的开发，提供一个快速，高精度的预测工具的热羽流的热泵在非均质地下含水层。卷积神经网络的三个变化，接受已知的地下水达西速度作为离散的二维输入，并预测地下含水层周围的热泵的温度。由随机渗透率场和压力边界条件生成的800个数值模拟样本组成的数据集用于提供伪随机Darcy速度场作为输入场和温度场解用于训练网络。从网络输出的地下温度场提供了一个更真实的温度场，遵循达西速度流线，同时比传统的高保真解算器快几个数量级
英文摘要：
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
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