Effect of Geometrical and Flow Parameters on the Heat Transfer of Impinging Jet on the Concave Surface
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Impinging jets have been applied to transfer heat in various applications, such as the drying, the cooling of turbine blades and electronic devices, foods industries and all industries which the localized heat transfer is needed [1,2]. Pulsating the impinging jet is an effective method to increase the heat transfer performance [3,4]. The main aim of this research is to investigate the effect of nozzle geometry on the flow and heat transfer from a pulsed jet into a concave surface. Numerical simulation was performed for the frequency, Reynods number and dimensionless diameter of 25~100 Hz, 8000~16000 and 2~6, respectively. As for the validation part, numerical results showed a consistent agreement with experimental results and previous works. The geometry of nozzle directly affects the air entrainment ratio. With the increase of aspect ratio of the nozzle, averaged Nusselt number decreases. The Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) [5] is a variant of the deep learning approach that includes the memory effect in its network. Therefore, it is very convenient for developing a surrogate model for unsteady physical phenomena. In this current work, an LSTM network based nonlinear non-intrusive reduced order model is proposed for the prediction of unsteady Nusselt number distribution due to variations of the input parameters.
