A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a
  Concave Surface
Sajad Salavatidezfouli Saeid Rakhsha Armin Sheidani Giovanni Stabile Gianluigi Rozza
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.