Intelligent Design and Optimization of a 3 mm Micro-Turbine Blade Profile Using Physics-Informed Neural Networks and Active Learning

The design of millimeter-scale micro-turbine blades is challenging due to conflicting requirements: achieving aerodynamic performance while remaining compatible with microfabrication, and exploring high-dimensional morphological design spaces without prohibitive computational cost. To address these challenges, this study proposes an intelligent framework for the design and optimization of the three-dimensional blade profile of a 3 mm diameter micro-turbine. The blade morphology is parameterized using 22 variables, ensuring geometric feasibility for micro-EDM (Electrical Discharge Machining) fabrication. A physics-informed neural network (PINN) surrogate model, efficiently trained through a two-stage active learning strategy combining KD-tree exploration and residual-based sampling, provides accurate predictions of flow fields. Multi-objective optimization using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then performed to maximize torque and thrust. Experimental results show that the optimized blade achieves a 38.6% increase in rotational speed while retaining 75.1% of thrust at 0.2 MPa inlet pressure, validating the framework’s effectiveness. This methodology offers a systematic solution for designing microfluidic devices characterized by high-dimensional parameters and high-fidelity simulation requirements.