Dehghan Manshadi Mahsa, Alafchi Nima, Tat Alireza, Mousavi Milad, Mosavi Amirhosein
Institute of Information Society, National University of Public Service, 1083 Budapest, Hungary.
Department of Biomedical Engineering, Payame Noor University, Tehran 19395-4697, Iran.
Polymers (Basel). 2022 May 13;14(10):1996. doi: 10.3390/polym14101996.
This study has compared different methods to predict the simultaneous effects of conductive and radiative heat transfer in a polymethylmethacrylate (PMMA) sample. PMMA is a type of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of the governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to obtain data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For the deep neural network (DNN) method, the novel long short-term memory (LSTM) method was introduced to find accurate results in the least processing time compared to the numerical method. A recent study derived the combined heat transfer and temperature profiles for the PMMA sample. Furthermore, the transient temperature profile was validated by another study. A comparison proves the perfect agreement. It shows the temperature gradient in the primary positions, which provides a spectral amount of conductive heat transfer from the PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution, it can be understood that the conductive and radiative heat flux are similar in the case of gradient behavior, but the amount is also twice as high approximately. Hence, total heat flux has a constant value in an approximated steady-state condition. In addition to analyzing their composition, the receiver operating characteristic (ROC) curve and confusion matrix were implemented to evaluate the algorithm's performance.
本研究比较了不同方法来预测聚甲基丙烯酸甲酯（PMMA）样品中传导传热和辐射传热的同时效应。PMMA是一种用于各种传感器和致动器装置的聚合物。数值分析中考虑了一维组合传热。在离散化情况下，采用隐式有限差分法对控制方程进行数值求解以实现计算机模拟。通过考虑应用于PMMA样品边界的单色辐射强度和温度条件，利用基尔霍夫变换从传导传热的非线性方程中获取数据。对于深度神经网络（DNN）方法，引入了新颖的长短期记忆（LSTM）方法，以与数值方法相比在最少的处理时间内获得准确结果。最近的一项研究推导了PMMA样品的组合传热和温度分布。此外，另一项研究验证了瞬态温度分布。比较证明了两者完全吻合。它显示了主要位置的温度梯度，这提供了来自PMMA样品的传导热传递的光谱量。与新颖的DNN方法相比时更直观。结果表明，这种人工智能方法在预测问题时准确且快速。通过分析数值解的结果，可以理解在梯度行为情况下传导热通量和辐射热通量相似，但量大约也是两倍。因此，在近似稳态条件下总热通量具有恒定值。除了分析它们的组成外，还采用了接收者操作特征（ROC）曲线和混淆矩阵来评估算法的性能。
