用机器学习快速预测室内气流与温度分布，替代耗时的仿真计算。
Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
- 分组件建模：用三个神经网络分别提取、映射和聚合气流特征。
- 预测准确率高：在双进风口场景下，速度与温度场预测误差小。
- 适合建筑能耗优化与实时控制，可替代传统仿真流程。
精准高效地预测室内气流与温度分布对建筑节能与居住舒适性至关重要。然而，传统计算流体动力学（CFD）模拟计算成本高，难以融入实时或设计迭代流程。本文提出一种基于组件的机器学习（CBML）代理模型，以替代传统CFD模拟，实现室内速度场与温度场的快速预测。该模型包含三个神经网络：带残差连接的卷积自编码器（CAER）用于提取并压缩流场特征，多层感知机（MLP）将进风口速度映射为潜在表示，卷积神经网络（CNN）作为聚合器将单进风口特征组合成双进风口情景。以二维房间在不同左右进风速度下的案例为基准，采用CFD模拟生成训练与测试数据。结果表明，CBML模型在训练集与测试集上均能准确、快速预测双组件叠加的速度场与温度场。
原文摘要 · Abstract (English)
Accurate and efficient prediction of indoor airflow and temperature distributions is essential for building energy optimization and occupant comfort control. However, traditional CFD simulations are computationally intensive, limiting their integration into real-time or design-iterative workflows. This study proposes a component-based machine learning (CBML) surrogate modeling approach to replace conventional CFD simulation for fast prediction of indoor velocity and temperature fields. The model consists of three neural networks: a convolutional autoencoder with residual connections (CAER) to extract and compress flow features, a multilayer perceptron (MLP) to map inlet velocities to latent representations, and a convolutional neural network (CNN) as an aggregator to combine single-inlet features into dual-inlet scenarios. A two-dimensional room with varying left and right air inlet velocities is used as a benchmark case, with CFD simulations providing training and testing data. Results show that the CBML model accurately and fast predicts two-component aggregated velocity and temperature fields across both training and testing datasets.
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