In this paper, a computational method for solving for the one-dimensional heat
conduction temperature field is proposed based on a data-driven approach. The
traditional numerical solution requires algebraic processing of the heat
conduction differential equations, and necessitates the use of a complex
mathematical derivation process to solve for the temperature field. In this
paper, a temperature field solution model called HTM (Hidden Temperature
Method) is proposed. This model uses an artificial neural network to
establish the correspondence relationship of the node temperature values
during the iterative process, so as to obtain the "Data to Data" solution.
In this work, one example of one-dimensional steady state and
three examples of one-dimensional transient state are selected, and the
calculated values are compared to those obtained by traditional numerical
methods. The mean-absolute error(MAE)of the steady state is only 0.2508, and
among the three transient cases, the maximum mean-square error(MSE) is only
2.6875, indicating that the model is highly accurate in both steady-state and
transient conditions. This shows that the HTM simulation can be applied to
the solution of the heat conduction temperature field. This study provides a
basis for the further optimization of the HTM algorithm.
A computational method to solve for the heat conduction temperature field based on data-driven approach
