DATA-DRIVEN AI SURROGATE MODEL FOR RAPID 3D FLOW APPROXIMATION IN AXIAL FANS

Abstract

Computational fluid dynamics (CFD) simulations are crucial for optimizing engineering designs, but are often computationally expensive and time-consuming. This paper introduces a data-driven AI surrogate model for axial fans that provides rapid approximations of 3D CFD results, significantly reducing the computational resources required for 3D flow solutions. The AI model leverages existing CFD data to learn the complex relationships between geometry, boundary conditions, and flow solutions, allowing it to predict flow fields for new, unseen geometries. This research extends the application of AI surrogate modeling to unstructured data for axial fans in industrial settings, based on previous work with structured data for aircraft compressors. It demonstrates the model’s ability to predict low Mach number, incompressible flows relevant to industrial axial fans, expanding the scope of AI in turbomachinery beyond traditional high-speed aerospace use cases. The flexible model architecture accommodates both structured and unstructured CFD data from a variety of turbomachinery flows. Trained on a design of experiments (DOE) database using XYZ coordinates of geometric surface points, rotational speed, and boundary conditions, the model accurately predicts flow variables such as velocities, pressure, and density. Validation against optimized fan designs shows promising agreement with CFD results, confirming the model’s effectiveness in capturing essential flow features for designs not included in the training.