  This study compares different methods to predict the simultaneous effects of
conductive and radiative heat transfer in a Polymethylmethacrylate (PMMA)
sample. PMMA is a kind 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
governing equation with the implicit finite difference method in the case of
discretization. Kirchhoff transformation was used to get 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
Deep Neural Network (DNN) method, the novel Long Short Term Memory (LSTM)
method was introduced to find accurate results in the least processing time
than the numerical method. A recent study derived the combined heat transfers
and their temperature profiles for the PMMA sample. Furthermore, the transient
temperature profile is validated by another study. A comparison proves a
perfect agreement. It shows the temperature gradient in the primary positions
that makes a spectral amount of conductive heat transfer from a 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 is
similar in the case of gradient behavior, but it is also twice in its amount
approximately. Hence, total heat flux has a constant value in an approximated
steady state condition. In addition to analyzing their composition, ROC curve
and confusion matrix were implemented to evaluate the algorithm performance.
