A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface

TL;DR

This paper evaluates machine learning-based surrogate models, including FFT-ANN and POD-LSTM, for predicting heat transfer in pulsed jet impingement on concave surfaces, demonstrating their effectiveness across different jet conditions.

Contribution

It introduces and compares two novel predictive surrogate models, FFT-ANN and POD-LSTM, for heat transfer prediction in pulsed jet impingement scenarios.

Findings

POD-LSTM effectively predicts local heat transfer rates under random-frequency impingement.

FFT-ANN accurately predicts average Nusselt number for constant-frequency jets.

Advanced machine learning techniques enhance modeling of complex heat transfer phenomena.

Abstract

This paper aims to comprehensively investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface. Expanding on the previous experimental and numerical research involving pulsed circular jets, this investigation extends to evaluate Predictive Surrogate Models (PSM) for heat transfer across various jet characteristics. To this end, this work introduces two predictive approaches, one employing a Fast Fourier Transformation augmented Artificial Neural Network (FFT-ANN) for predicting the average Nusselt number under constant-frequency scenarios. Moreover, the investigation introduces the Proper Orthogonal Decomposition and Long Short-Term Memory (POD-LSTM) approach for random-frequency impingement jets. The POD-LSTM method proves to be a robust solution for predicting the local heat transfer rate under random-frequency impingement scenarios, capturing both the trend and value of temporal modes. The comparison of these approaches highlights the versatility and efficacy of advanced machine learning techniques in modelling complex heat transfer phenomena.