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.

翻译：本文旨在全面研究各种模型降阶（MOR）与深度学习技术在预测脉冲射流冲击凹面换热问题中的有效性。在先前关于脉冲圆形射流的实验与数值研究基础上，本研究进一步评估了面向不同射流特性的换热预测替代模型（PSM）。为此，本文提出两种预测方法：其一采用快速傅里叶变换增强型人工神经网络（FFT-ANN），用于预测恒定频率工况下的平均努塞尔数；其二引入本征正交分解与长短期记忆网络（POD-LSTM）方法，适用于随机频率冲击射流。POD-LSTM方法能够捕捉时间模态的趋势与数值，被证明是预测随机频率冲击工况下局部换热率的稳健方案。两种方法的对比凸显了先进机器学习技术在模拟复杂换热现象中的通用性与有效性。