DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Network Ensembles for Turbocompressors on Gas-Bearings

Abstract

DARTS-NETGAB is a unified framework for real-time simulation and automated design of gas-bearing supported turbocompressors, facilitating efficient transition from optimization to manufacturable designs. The framework integrates ensemble artificial neural networks (EANNs) trained on high-fidelity simulation data to predict performance metrics—including isentropic efficiency, pressure ratio, and rotordynamic stability—across various operating conditions and manufacturing tolerances. A user-friendly interface using Panel-Bokeh libraries allows dynamic design modifications and immediate visualization. The ParaturboCAD library automates the generation of detailed 3D computer-aided design (CAD) models from optimized design parameters. The surrogate models maintained prediction errors below 5% for isentropic efficiency and pressure ratio in most conditions, with errors up to 11% near choke limits. Real-time simulations were efficient, averaging 1 s for coarse discretization (6195 points) and 8.5 s for fine discretization (311,250 points). Automated CAD generation produced manufacturable 3D models in approximately 7 min per model, successfully translating optimized designs into detailed geometries suitable for production. DARTS-NETGAB enhances the efficiency and accuracy of the turbocompressor design process by unifying rapid performance prediction with automated CAD model generation. This integration enables rapid iterations and robust assessments of design sensitivity to manufacturing imperfections, addressing a critical gap in transitioning from optimization to practical, manufacturable designs.