Data-Driven Inverse Design of Turbine Blade Passages

To overcome the computational bottlenecks of iterative Computational Fluid Dynamics (CFD) in turbomachinery design, this study introduces a real-time, data-driven inverse design framework for 2D uncooled, high-Reynolds turbine blades. The novelty of this work lies in the application of Kolmogorov–Arnold Networks (KAN), a distinct deep-learning architecture, to predict blade geometry and performance metrics from aerodynamic loading inputs. The foundation of the model is a comprehensive database of approximately 30,000 blade profiles, generated through an automated optimization pipeline coupled with the MISES solver. This dataset explores an extensive design space, covering inlet flow angles from −50∘ to 0∘ and outlet angles from 50∘ to 75∘, with flow turning up to 125∘. A rigorous benchmarking campaign compares KAN against Multi-Layer Perceptrons (MLPs) and Gaussian Process Regression (GPR), highlighting KAN’s capability to overcome the scalability bottlenecks of Gaussian Process Regression to enable real-time performance while achieving MLP-level accuracy with significantly fewer parameters. A further analysis regarding the trade-off between database size and filtration of unfeasible designs indicates that an optimal data filtration threshold exists, balancing noise reduction with model robustness. The final KAN tool achieves real-time inference speeds (∼0.1 s), reducing the design cycle by four orders of magnitude compared to traditional solvers, while maintaining high accuracy (mean outlet angle error of 0.086∘ and Mach profile RMS error of 0.004). Furthermore, the model’s predicted RMS error is exploited as a quantitative proxy for aerodynamic feasibility, identifying ill-posed inverse problems where the target loading cannot be physically realized. This metric enables the generation of comprehensive maps that rigorously delineate the boundaries of the viable design space across arbitrary aerodynamic loading styles, providing physics-aware guidelines for preliminary design.