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Simulation of liquid flow with a combination artificial intelligence flow field and Adams-Bashforth method.

Meisam Babanezhad1,2, Iman Behroyan3, Ali Taghvaie Nakhjiri4

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This study introduces a novel AI framework that replaces computationally intensive fluid dynamics simulations for particle hydrodynamics. The AI-driven approach accurately predicts particle motion, accelerating industrial process optimization.

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Area of Science:

  • Computational fluid dynamics
  • Machine learning applications
  • Multiphase flow simulation

Background:

  • Direct numerical simulation (DNS) for particle hydrodynamics is computationally expensive.
  • Optimizing industrial processes requires accurate simulation of particle movement and phase interactions.
  • Traditional Eulerian flow field calculations are time-consuming.

Purpose of the Study:

  • To develop a computationally efficient AI framework for simulating particle hydrodynamics.
  • To replace conventional computational fluid dynamics (CFD) methods with machine learning for flow field generation.
  • To couple an AI-generated flow field with a Lagrangian framework for particle motion simulation.

Main Methods:

  • Trained an AI model (ANFIS) on Eulerian flow field data generated by the Adams-Bashforth finite element method.
  • Replaced the Eulerian framework with the trained AI model to create an AI flow field.
  • Coupled the AI flow field with the Lagrangian framework to simulate particle migration.

Main Results:

  • The AI-Lagrangian framework demonstrated excellent agreement with the conventional Euler-Lagrangian method.
  • The AI approach accurately mimicked vortex structures and velocity profiles within the cavity.
  • The combined machine learning and CFD method significantly accelerated flow field calculations.

Conclusions:

  • The proposed AI framework offers a viable and accelerated alternative to traditional CFD for particle hydrodynamics.
  • This approach enables efficient optimization of multiphase industrial processes.
  • Machine learning integration in CFD holds significant potential for scientific and industrial applications.