Predicting Velocity and Pressure Distribution in Cyclone Systems: A Novel Combined CFD-ANN Approach

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Authors

  • Ahmad Indra SISWANTARA Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, West Java, Indonesia
  • Illa RIZIANIZA Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, West Java / Mechanical Engineering, Faculty of Engineering and Industrial Technology, Institut Teknologi Kalimantan, Balikpapan, Kalimantan Timur, Indonesia
  • Ridho IRWANSYAH Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, West Java, Indonesia
  • Sulaksana PERMANA Mechanical Engineering, Faculty of Industrial Technology, Universitas Gunadarma, Depok, West Java, Indonesia
  • Fadil Naufal WAHAS Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, West Java, Indonesia
  • Adi SYURIADI Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, West Java / Mechanical Engineering, Politeknik Negeri Jakarta, Depok, West Java, Indonesia
  • M Hilman Gumelar SYAFEI Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, West Java / Mechanical Engineering, Faculty of Engineering, Universitas Negeri Semarang, Semarang, Jawa Tengah, Indonesia

Abstract

This study uses computational fluid dynamics (CFD) with the k-ε turbulence model and an artificial neural network (ANN) to analyze cyclone flow. The results show that the pressure drop rises from 0.90 kPa to 6.54 kPa for inlet velocities of 7 m/s to 20 m/s. The ANN predicts the pressure drop with a 4.3 % error. The CFD-ANN approach improves insight into cyclone design.

Keywords:

artificial neural network, cyclone, computational fluid dynamics, pressure drop, prediction

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