A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades

TL;DR

This paper introduces a novel progressive Euler-PINN framework that efficiently predicts 2D turbomachinery blade aerodynamics, matching CFD accuracy while significantly reducing computational costs for large-scale blade screening.

Contribution

It is the first to use a single PINN workflow for large-scale, multi-condition turbomachinery blade screening, improving efficiency and accuracy over traditional methods.

Findings

Achieved CFD-comparable accuracy for pressure and velocity fields.

Reduced computational cost for family-wide blade screening.

Validated on ten NACA6 variants across 30 operating points.

Abstract

Rapid aerodynamic screening of turbomachinery blades across wide operating envelopes remains a major computational bottleneck in preliminary design, particularly for energy-conversion and storage systems such as emerging Carnot batteries. Physics-informed neural networks (PINNs) offer a mesh-free alternative to conventional CFD, yet convergence and accuracy often deteriorate for complex blade geometries and off-design flows. We propose a progressive Euler-PINN framework that (i) gradually relaxes boundary conditions from tunnel flow without a blade to full outlet static pressure, and (ii) employs a geometry-aware dynamic loss-weighting scheme that intensifies residual penalties near highly curved boundaries. To the best of our knowledge, this is the first study to deploy a single PINN workflow for large-scale, engineering-grade screening of turbomachinery blade families across multiple operating conditions, covering ten NACA6 variants and 30 subsonic operating points. The proposed framework achieves CFD-comparable accuracy for pressure and velocity fields while reducing the computational cost required for family-wide blade screening. These results establish the method as a practical surrogate for two-dimensional turbomachinery blade pre-design and optimisation.