DARTS-NetGAB

Published:

DARTS-NetGAB

DARTS‑NetGAB (Design Automation and Real-Time Simulation using Neural Network Ensembles for Gas-Bearing Supported Turbocompressors) is an integrated computational framework that couples Panel/Bokeh interfaces, ParaturboCAD, and ensemble artificial neural networks for fast iterative modification and computation of performance for gas bearing supported turbocompressors.

Key Features

  • Ensemble surrogate models of high‑fidelity data with < 5 % error on isentropic efficiency and pressure ratio (up to 11 % near choke limits)
  • Real‑time simulation: ~1 s for coarse (6 195 pts) and ~8.5 s for fine (311 250 pts) performance maps
  • Interactive Panel/Bokeh GUI for on‑the‑fly parameter tweaks and instant visualization
  • Automated CAD generation via ParaturboCAD/CadQuery: ~7 min per STEP/STL model

Tech Stack

  • Python 3.9.17
  • Panel 1.3.4 & Bokeh 3.3.2
  • TensorFlow 2.10 (EANNs)
  • ParaturboCAD (CadQuery 2.3.1)
  • pandas 2.1.4, NumPy 1.26.2, cqMore 0.1, VTK/PyVista, trimesh 4.0.5, gmsh 4.11.1

DARTS‑NetGAB: Design Automation and Real‑Time Simulation Using Neural Network Ensembles for Gas‑Bearing Supported Turbocompressors

This framework enables engineers to rapidly explore design spaces and optimize turbocompressor performance through an integrated computational approach.