A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface
This work addresses heat transfer prediction in complex engineering systems like impinging jets, but it is incremental as it extends existing methods to new scenarios.
The paper tackled predicting heat transfer for a pulsed jet impinging on a concave surface by evaluating Model Order Reduction and deep learning techniques, introducing FFT-ANN for constant-frequency scenarios and POD-LSTM for random-frequency ones, with POD-LSTM proving robust in capturing temporal modes.
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.