Sponsor
The authors thank Amélie Ferran for providing the hot wire measurements. The authors acknowledge funding by ECOS-Sud project No. A18ST04. PCDL, PJC, and PDM financial support from UBACyT Grant No. 20020170100508BA and Redes Federales de Alto Impacto REMATE, Argentina.
Published In
Results in Engineering
Document Type
Article
Publication Date
11-5-2024
Subjects
Environmental flows -- study of
Abstract
Active grids operated with random protocols are a standard way to generate large Reynolds number turbulence in wind and water tunnels. But anomalies in the decay and third-order scaling of active-grid turbulence have been reported. We combine Laser Doppler Velocimetry and hot-wire anemometry measurements in a wind tunnel, with machine learning techniques and numerical simulations, to gain further understanding on the reasons behind these anomalies. Numerical simulations that incorporate the statistical anomalies observed in the experimental velocity field near the active grid can reproduce the experimental anomalies observed later in the decay. The results indicate that anomalies in experiments near the active grid introduce correlations in the flow that can persist for long times.
Rights
Copyright (c) 2024 The Authors Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1016/j.rineng.2024.103265
Persistent Identifier
https://archives.pdx.edu/ds/psu/42705
Citation Details
Angriman, S., Smith, S. E., di Leoni, P. C., Cobelli, P. J., Mininni, P. D., & Obligado, M. (2024). Active grid turbulence anomalies through the lens of physics informed neural networks. Results in Engineering, 24, 103265.