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 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 second for coarse discretization (6,195 points) and 8.5 seconds for fine discretization (311,250 points). Automated CAD generation produced manufacturable 3D models in approximately 7 minutes 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.

This research introduces an automated methodology for visualizing gas-bearing supported turbocompressor designs utilizing CadQuery 2, a scripting Computer-Aided Design (CAD) platform. This innovative approach simplifies the traditional design process, which often involves the use of multiple software tools and extensive manual data manipulation, by consolidating it into a singular, efficient, and robust workflow. Moreover, CadQuery 2 enables the rapid visualization of abstract optimization results, enhancing the design phase’s efficiency. A pivotal element of this study is the adept transformation of design variables related to key components such as the rotor, compressor, and bearings into precise three-dimensional turbocompressor models. This intricate procedure is expedited by employing a structured Python dictionary, which serves to encapsulate the geometric parameters of each component comprehensively. The utility of this framework is demonstrated through the creation of turbocompressors featuring diverse geometries, highlighting the methodology’s capacity to produce models with high accuracy and within a reasonable generation timeframe of approximately seven minutes per turbocompressor. While there are minor constraints, notably in parametrization choices and the time efficiency of modeling with CadQuery 2, these do not significantly detract from the method’s overall value. Indeed, this approach represents a substantial advancement in the field of design and manufacturing, promising to refine and expedite the development process of these complex systems.

In the domain of engineering design, where efficiency in simulation and precision in modeling are paramount, this study introduces DARTS-NETGAB, a pioneering platform uniquely designed for real-time simulation and automated design. Specifically tailored for gas-bearing supported turbocompressors, DARTS-NETGAB integrates neural network ensembles with a parametric CAD construction library to deliver unprecedented prediction speeds and modeling precision across various engineering systems. This integration allows for seamless, real-time performance evaluations of complex, multidisciplinary systems and automated CAD model generation. This framework streamlines the design process, reduces cycles times and enhances adaptability to manufacturing imperfection. DARTS-NETGAB features a user-centric interface developed using the advanced Panel-Bokeh Python libraries, facilitating dynamic and interactive design modifications directly within a web browser. This capability enables immediate visualization and adjustment of a comprehensive turbocompressor model, thereby streamlining the transition from theoretical design to practical application. The paper details how the combination of DARTS-NETGAB’s rapid, accurate predictive capabilities with its robust design tools not only advances micro-turbocompressor design but also revolutionizes engineering processes across diverse systems. By merging cutting-edge computational techniques with practical, user-friendly tools, DARTS-NETGAB offers a significant improvement over traditional methods, fostering more efficient and innovative engineering solutions.