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Surrogate model for heat transfer prediction in impinging jet arrays using dynamic inlet/outlet and flow rate control
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DOI：10.1016/j.icheatmasstransfer.2026.111046.png)
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Abstract
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This study presents a surrogate model designed to predict the Nusselt number distribution in enclosed impinging jet arrays, where each jet functions independently and where jets can be transformed from inlets to outlets. While computational fluid dynamics (CFD) simulations can predict 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 predicts the Nusselt distribution in real time. We train it with data from implicit large-eddy simulations (Re < 2000). 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 jets surrogate model and 0.6% for the three by three jets surrogate model. Their predictions are validated experimentally using temperature measurements. This work provides a foundation for model-based control strategies in advanced thermal management applications.
Keywords:
Impinging jets
Large-eddy simulation (LES)
Surrogate model
Heat and mass transfer
Computational fluid dynamics
Convolutional neural network
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