Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements Articles uri icon

publication date

  • August 2024

issue

  • 991

volume

  • 991

International Standard Serial Number (ISSN)

  • 0022-1120

Electronic International Standard Serial Number (EISSN)

  • 1469-7645

abstract

  • Different types of neural networks have been used to solve the flow sensing problem
    in turbulent flows, namely to estimate velocity in wall-parallel planes from wall
    measurements. Generative adversarial networks (GANs) are among the most promising
    methodologies, due to their more accurate estimations and better perceptual quality. This
    work tackles this flow sensing problem in the vicinity of the wall, addressing for the first
    time the reconstruction of the entire three-dimensional (3-D) field with a single network,
    i.e. a 3-D GAN.With this methodology, a single training and prediction process overcomes
    the limitation presented by the former approaches based on the independent estimation
    of wall-parallel planes. The network is capable of estimating the 3-D flow field with a
    level of error at each wall-normal distance comparable to that reported from wall-parallel
    plane estimations and at a lower training cost in terms of computational resources. The
    direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent
    structures. It is shown that the accuracy of the network depends directly on the wall
    footprint of each individual turbulent structure. It is observed that wall-attached structures
    are predicted more accurately than wall-detached ones, especially at larger distances from
    the wall. Among wall-attached structures, smaller sweeps are reconstructed better than
    small ejections, while large ejections are reconstructed better than large sweeps as a
    consequence of their more intense footprint.

subjects

  • Aeronautics
  • Mechanical Engineering

keywords

  • turbulent boundary layers; channel flow; machine learning