Solving ultra-high-load, low-pressure turbine cascade flow using physics-informed neural networks with boundary identification strategy

This paper constructs a physics-informed neural network with boundary identification strategy (BI-PINN) to reconstruct the flow field of an ultra-high-load, low-pressure turbine cascade using sparse data. Since the boundary layer position is unknown before training, an iterative method is required to determine the boundary layer range. The BI-PINN trains the network based on a dual convergence criterion: achieving flow field reconstruction convergence in both the boundary layer and mainstream regions to extract the current boundary layer range and then ensuring the convergence of the boundary layer range. To improve neural network convergence while maintaining high training speed, this study assigns different weights to the governing equation terms in the loss function for the boundary layer and mainstream regions. The BI-PINN method demonstrates computational accuracy comparable to computational fluid dynamics in inverse problems with incomplete boundary conditions. This study finds that for high-load, low-pressure turbine cascades, selecting pressure observations only on the blade surface is sufficient for high-accuracy flow field reconstruction. However, for ultra-high-load, low-pressure turbine cascades, this paper proposes a method of selecting multiple normal-direction pressure observations along the blade surface to achieve high-accuracy flow field reconstruction with a small observation set. The BI-PINN method balances the conflict between the low-gradient mainstream region and the high-gradient boundary layer region in flow field reconstruction. By leveraging both physical governing equations and observation data, it enables precise full flow field reconstruction of ultra-high-load, low-pressure turbine cascades using a small dataset and a neural network with a limited number of parameters.