用深度学习模型实时预测喷流阵列的传热分布，支持动态调控。
Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control
- 基于CNN构建代理模型，实现喷流阵列传热分布的实时预测。
- 五排一列与三乘三阵列模型误差分别低于2%和0.6%，精度高。
- 可外推至更高雷诺数，适合先进温控系统中的模型控制应用。
本研究提出一种代理模型，用于预测封闭式喷流阵列中的努塞尔数分布。每个喷流可独立工作，并能动态切换为进气口或出气口，导致大量可能的流动配置。尽管计算流体动力学（CFD）能高保真模拟传热，但其计算成本高，难以实现实时应用，如基于模型的温度控制。为此，我们基于隐式大涡模拟（Re < 2,000）数据，训练了两种基于卷积神经网络的代理模型：一种用于五排一列喷流阵列（83次模拟），另一种用于三乘三阵列（100次模拟）。引入基于相关性的缩放方法，将预测能力外推至更高雷诺数（Re < 10,000）。验证数据显示，五排一列模型的归一化平均绝对误差低于2%，三乘三模型低于0.6%。实验验证确认了模型的预测能力。该工作为先进热管理中的模型驱动控制策略提供了基础。
原文摘要 · Abstract (English)
This study presents a surrogate model designed to predict the Nusselt number distribution in an enclosed impinging jet arrays, where each jet function independently and where jets can be transformed from inlets to outlets, leading to a vast number of possible flow arrangements. While computational fluid dynamics (CFD) simulations can model heat transfer with high fidelity, their cost prohibits real-time application such as model-based temperature control. To address this, we generate a CNN-based surrogate model that can predict the Nusselt distribution in real time. We train it with data from implicit large eddy computational fluid dynamics simulations (Re < 2,000). We train two distinct models, one for a five by one array of jets (83 simulations) and one for a three by three array of jets (100 simulations). We introduce a method to extrapolate predictions to higher Reynolds numbers (Re < 10,000) using a correlation-based scaling. The surrogate models achieve high accuracy, with a normalized mean average error below 2% on validation data for the five by one surrogate model and 0.6% for the three by three surrogate model. Experimental validation confirms the model's predictive capabilities. This work provides a foundation for model-based control strategies in advanced thermal management applications.
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