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Automated large-scale and terrain-induced turbulence modulation of atmospheric surface layer flows in a large wind tunnel

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Abstract

This study leverages a multi-fan flow control instrument and a mechanized roughness element grid to simulate large- and small-scale turbulent features of atmospheric flows in a large boundary layer wind tunnel (BLWT). The flow control instrument termed the flow field modulator (FFM), is a computer-controlled 3 m \(\times\) 6 m (2D) fan array located at the University of Florida (UF) Natural Hazard Engineering Research Infrastructure (NHERI) Experimental Facility. The system comprises 319 modular hexagonal aluminum cells, each equipped with shrouded three-blade corotating propellers. The FFM enables the active generation of large-scale turbulent structures by replicating user-specified velocity time signals to inject low-frequency fluctuations into BLWT flows. In the present work, the FFM operated in conjunction with a mechanized roughness element grid, called the Terraformer, located downstream of the FFM array. The Terraformer aided in the production of near-wall turbulent mixing through precise adjustment of roughness element heights. A series of BLWT velocity profile measurements were carried out at the UF BLWT test section for a range of turbulence intensity and integral length scale regimes. Input commands to the FFM and Terraformer were iteratively updated via a governing convergence algorithm (GCA) to achieve user-specified mean and turbulent flow statistics. Results demonstrate the capabilities of the FFM for significantly increasing the longitudinal integral length scales compared to conventional BLWT approaches (i.e., no active large-scale turbulence generation). The study also highlights the efficacy of the GCA scheme for attaining prescribed target mean and turbulent flow conditions at the measurement location.

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Availability of data and materials

The curated dataset of flow velocity time series and relevant metadata is publicly available and can be accessed in the DesignSafe-CI Data Depot repository (see Mokhtar et al. 2023).

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Acknowledgements

The authors would like to acknowledge the DesignSafe-CI web-based research platform for the storage and curation of experimental data (Rathje et al. 2017). The authors also wish to recognize the Powell Structures and Materials Laboratory staff, with special thanks to Scott Powell, Tai-An Chen, Ian Van Voris, Justin Davis, and Rudy Wilder for their contribution in wind tunnel testing and assisting in the development of MATLAB and LabVIEW code for data acquisition and analysis.

Funding

This work is supported by the US National Science Foundation (NSF) under Grant No. 2317176. The authors also acknowledge the NSF NHERI EF awardees under Grant No. 2037725. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

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Authors and Affiliations

  1. Florida A&M University–Florida State University (FAMU-FSU) College of Engineering, Tallahassee, FL, USA

    Nasreldin O. Mokhtar & Pedro L. Fernández-Cabán

  2. University of Florida, Gainesville, FL, USA

    Ryan A. Catarelli

Authors
  1. Nasreldin O. Mokhtar
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  2. Pedro L. Fernández-Cabán
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  3. Ryan A. Catarelli
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Contributions

NM and PFC wrote the main manuscript text, NM prepared Figs. 5, 6, 7, 8, 9, and 10, and PFC created the remaining figures (including the graphical abstract). RC developed the BLWT control algorithms and a portion of the data processing and analysis scripts. All authors reviewed the manuscript.

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Correspondence to Pedro L. Fernández-Cabán.

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Mokhtar, N.O., Fernández-Cabán, P.L. & Catarelli, R.A. Automated large-scale and terrain-induced turbulence modulation of atmospheric surface layer flows in a large wind tunnel. Exp Fluids 65, 5 (2024). https://doi.org/10.1007/s00348-023-03739-z

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