  In the whole aircraft structural optimization loop, thermal analysis plays a
very important role. But it faces a severe computational burden when directly
applying traditional numerical analysis tools, especially when each
optimization involves repetitive parameter modification and thermal analysis
followed. Recently, with the fast development of deep learning, several
Convolutional Neural Network (CNN) surrogate models have been introduced to
overcome this obstacle. However, for temperature field prediction on irregular
geometric domains (TFP-IGD), CNN can hardly be competent since most of them
stem from processing for regular images. To alleviate this difficulty, we
propose a novel physics and data co-driven surrogate modeling method. First,
after adapting the Bezier curve in geometric parameterization, a body-fitted
coordinate mapping is introduced to generate coordinate transforms between the
irregular physical plane and regular computational plane. Second, a
physics-driven CNN surrogate with partial differential equation (PDE) residuals
as a loss function is utilized for fast meshing (meshing surrogate); then, we
present a data-driven surrogate model based on the multi-level reduced-order
method, aiming to learn solutions of temperature field in the above regular
computational plane (thermal surrogate). Finally, combining the grid position
information provided by the meshing surrogate with the scalar temperature field
information provided by the thermal surrogate (combined model), we reach an
end-to-end surrogate model from geometric parameters to temperature field
prediction on an irregular geometric domain. Numerical results demonstrate that
our method can significantly improve accuracy prediction on a smaller dataset
while reducing the training time when compared with other CNN methods.
