Machine-learned flow estimation with sparse data--Exemplified for the rooftop of an unmanned aerial vehicle vertiport Articles uri icon

publication date

  • December 2024

issue

  • 12, 125198

volume

  • 36

International Standard Serial Number (ISSN)

  • 1070-6631

Electronic International Standard Serial Number (EISSN)

  • 1089-7666

abstract

  • We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various working conditions, thus allowing the extrapolation for wind conditions far beyond the training data. Another key enabler is a machine-learned non-dimensionalized manifold from snapshot data. The velocity field is modeled using a double encoder–decoder approach. The first encoder normalizes data using the oncoming wind speed, while the second encoder projects this normalized data onto the isometric feature mapping manifold. The decoders reverse this process, with k-nearest neighbor performing the first decoding and the second undoing the normalization. The manifold is coarse-grained by clustering to reduce the computational load for de- and encoding. The sensor-based flow estimation is based on the estimate of the oncoming wind speed and a mapping from sensor signal to the manifold latent variables. The proposed machine-learned flow estimation framework is exemplified for the flow above an unmanned aerial vehicle vertiport. The wind estimation is shown to generalize well for rare wind conditions, not included in the original database.

subjects

  • Mechanical Engineering
  • Physics

keywords

  • wind-tunnel; cfd simulation; urban; ventilation; field; pod