论文标题
一种深度学习方法，用于地下水热泵预测
A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps
论文作者
论文摘要
建筑物的气候控制占全球能源消耗的很大一部分，地下水热泵提供了合适的选择。为了防止整个城市的热泵之间可能进行负相互作用，城市规划人员将来必须优化其布局。我们开发了一种新型的数据驱动方法，用于构建小规模替代物，以建模周围地下水中地下水热泵产生的热羽流。在2D数值模拟生成的数据集的基础上，我们训练一个卷积神经网络，用于从给定的地下速度字段预测稳态地下温度场。我们表明，与现有模型相比，我们的模型可以捕获更复杂的动态，同时仍然快速计算。因此，由城市规划人员非常适合互动设计工具。
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.
