Physics-Informed Neural Networks (PINNs) are an emerging and powerful approach for developing surrogate models. Our company has previously released demos for applications such as cantilever beam models and distributed load models for shell elements.
As long as applicable physical equations exist, PINNs can be extended beyond structural analysis to various fields, including fluid dynamics and electromagnetism. This time, we have developed an AI model for fluid analysis. The model simulates pressure and flow velocity changes when an elliptical valve is placed inside a uniformly sized pipe, with varying dimensions and angles.
Access the demo site from the link below.
To use the demo, enter values for the parameters a, b, and theta, then click the Execute button.
The results will be displayed instantly—select the desired result from Contour Name to switch the display.
For more information on the model, please refer to the PDF file below.
