The present application belongs to the technical field of
geothermal energy development and utilization, and relates to a hydrothermal type buried
pipe heat exchanger optimization design method based on PINN, comprising: 1, numerical
simulation and
benchmark data set generation; 2, PINN construction and training: constructing a PINN neural network, converting the
mass conservation equation and the
energy conservation equation in the pore medium into a
partial differential equation residual, and together with the discrete
data error generated by the numerical
simulation software to form a composite
loss function; using the nonlinear fitting capability of the PINN neural network, and embedding the physical
partial differential equation of fluid flow and
heat transfer in the pore medium into the
loss function as a soft constraint; 3, data-driven optimization based on the proxy model; the present application not only fundamentally solves the problems of poor generalization ability and easy non-physical interpretation of traditional pure data-driven models, but also breaks through the
bottleneck of convergence difficulty and high training cost when solving complex
engineering problems by pure PINN.