A general Physics-Informed neural network approach for deriving fluid flow fields from temperature distribution

Abstract

One attractive application of Physics-Informed Neural Network (PINN) is deriving fluid flow information such as velocity and pressure from temperature field. However, this requires developing a suitable loss function for a given flow condition. In this paper we implemented a general loss function in PINN to directly obtain fluid velocity and pressure from temperature information. We first validated this method through cases of natural convection in a square cavity, flow around a cylinder in a square cavity, and two-dimensional channel flow. Additionally, we examined forced convection heat transfer around a cylinder in depth. Results show that different Reynolds numbers (2, 10, 40, and 140) can all be effectively resolved using temperature data. Furthermore, super-resolution predictions of temperature, velocity and pressure are achievable even far outside the training area, suggesting that the proposed PINN method could be used to measure fluid flow within confined spaces via finite visualization windows without tracers.