DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Networks Ensembles for Turbocompressors on Gas-Bearings
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Date
2024
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Conference Paper
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Abstract
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.
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published
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Book title
Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2024). Volume 3B: 50th Design Automation Conference (DAC)
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Volume
Pages / Article No.
Publisher
American Society of Mechanical Engineers
Event
50th Design Automation Conference (DAC 2024) part of ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE 2024)
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Software
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Subject
Artificial neural networks; Computer-aided design; Design; Design automation; Gas bearings; Simulation
Organisational unit
09828 - Fuge, Mark / Fuge, Mark
Notes
Paper No: DETC2024-143135