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This study compares different methods to predict the simultaneous effects ofconductive and radiative heat transfer in a Polymethylmethacrylate (PMMA)sample. PMMA is a kind of polymer utilized in various sensors and actuatordevices. One-dimensional combined heat transfer is considered in numericalanalysis. Computer implementation was obtained for the numerical solution ofgoverning equation with the implicit finite difference method in the case ofdiscretization. Kirchhoff transformation was used to get data from a non-linearequation of conductive heat transfer by considering monochromatic radiationintensity and temperature conditions applied to the PMMA sample boundaries. ForDeep Neural Network (DNN) method, the novel Long Short Term Memory (LSTM)method was introduced to find accurate results in the least processing timethan the numerical method. A recent study derived the combined heat transfersand their temperature profiles for the PMMA sample. Furthermore, the transienttemperature profile is validated by another study. A comparison proves aperfect agreement. It shows the temperature gradient in the primary positionsthat makes a spectral amount of conductive heat transfer from a PMMA sample. Itis more straightforward when they are compared with the novel DNN method.Results demonstrate that this artificial intelligence method is accurate andfast in predicting problems. By analyzing the results from the numericalsolution it can be understood that the conductive and radiative heat flux issimilar in the case of gradient behavior, but it is also twice in its amountapproximately. Hence, total heat flux has a constant value in an approximatedsteady state condition. In addition to analyzing their composition, ROC curveand confusion matrix were implemented to evaluate the algorithm performance.

Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate