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Computational Fluid Dynamics (CFD) is a crucial tool in engineering and applied sciences, used to simulate and analyze the behavior of fluids under various conditions. However, CFD has significant limitations, such as the need for substantial computational resources and extended computation times, which constrain its applicability in large-scale projects or those requiring real-time results. These limitations highlight the potential for integrating tools based on Convolutional Neural Networks (CNNs), which have demonstrated the ability to approximate solutions to complex problems with superior speed and efficiency. This article aims to develop a tool that enables the creation of specific databases to feed these CNNs, thereby optimizing simulation and analysis processes in fluid dynamics.

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Development of a Database for Convolutional Neural Networks Simulating CFD Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Fernando Herrera-Marín,
  • Jesús Enrique Sierra-García,
  • Matilde Santos

摘要

Computational Fluid Dynamics (CFD) is a crucial tool in engineering and applied sciences, used to simulate and analyze the behavior of fluids under various conditions. However, CFD has significant limitations, such as the need for substantial computational resources and extended computation times, which constrain its applicability in large-scale projects or those requiring real-time results. These limitations highlight the potential for integrating tools based on Convolutional Neural Networks (CNNs), which have demonstrated the ability to approximate solutions to complex problems with superior speed and efficiency. This article aims to develop a tool that enables the creation of specific databases to feed these CNNs, thereby optimizing simulation and analysis processes in fluid dynamics.