A physics-informed neural network method for pressure-driven turbine-blade optimization
Abstract
We develop a physics-informed method for aerodynamic shape optimisation of turbine blades, treating the flow field and blade geometry as coupled unknowns. The method translates prescribed surface-pressure distributions into geometric modifications through pressure-target continuation and alternating flow-geometry updates. A differentiable representation of the blade surfaces allows their positions and orientations to be optimised within the current flow field, followed by flow re-adaptation to the modified boundaries. The inverse formulation uses the two-dimensional steady compressible Euler equations as physics constraints and is evaluated on the LS89 turbine cascade using 28 pressure-target points derived from experimental data. Starting from a modified profile and a warm-started flow network, the final pressure-side and suction-side root-mean-square fitting errors are 1.13% and 2.21% of inlet total pressure, respectively. Independent forward verification with SharC reproduces the principal surface-pressure trends of the experimental measurements, with remaining differences near the trailing edge. These results demonstrate the translation of experimental-origin loading data into geometric blade modifications through coordinated optimisation of the flow field and moving boundaries.
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BibTeX
@misc{huang2026physicsinformed,
title = {{A physics-informed neural network method for pressure-driven turbine-blade optimization}},
author = {Yuan Huang and Francesca di Mare},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.05691},
eprint = {2611.05691},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.05691},
}
RIS
TY - UNPB
TI - A physics-informed neural network method for pressure-driven turbine-blade optimization
AU - Yuan Huang
AU - Francesca di Mare
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.05691
UR - https://arxiv.org/abs/2611.05691
AB - We develop a physics-informed method for aerodynamic shape optimisation of turbine blades, treating the flow field and blade geometry as coupled unknowns. The method translates prescribed surface-pressure distributions into geometric modifications through pressure-target continuation and alternating flow-geometry updates. A differentiable representation of the blade surfaces allows their positions and orientations to be optimised within the current flow field, followed by flow re-adaptation to the modified boundaries. The inverse formulation uses the two-dimensional steady compressible Euler equations as physics constraints and is evaluated on the LS89 turbine cascade using 28 pressure-target points derived from experimental data. Starting from a modified profile and a warm-started flow network, the final pressure-side and suction-side root-mean-square fitting errors are 1.13% and 2.21% of inlet total pressure, respectively. Independent forward verification with SharC reproduces the principal surface-pressure trends of the experimental measurements, with remaining differences near the trailing edge. These results demonstrate the translation of experimental-origin loading data into geometric blade modifications through coordinated optimisation of the flow field and moving boundaries.
ER -