Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control

TL;DR

This paper develops a CNN-based surrogate model to predict heat transfer in impinging jet arrays, enabling real-time thermal management by approximating CFD results with high accuracy and scalability.

Contribution

The study introduces a novel CNN surrogate model for rapid heat transfer prediction in complex jet array configurations, including flow control transformations and Reynolds number extrapolation.

Findings

Surrogate models achieved less than 2% error on validation data.

Model accurately predicts Nusselt distribution in real time.

Experimental validation confirms high predictive accuracy.

Abstract

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.