Abstract
The study of naturally occurring turbulent flows requires the ability to collect empirical data down to the fine scales. While hotwire anemometry offers such ability, the open field studies are uncommon due to the cumbersome calibration procedure and operational requirements of hotwire anemometry, e.g., constant ambient properties and steady flow conditions. The combo probe—the combined sonic-hotfilm anemometer—developed and tested over the last decade has demonstrated its ability to overcome this hurdle. The older generation had a limited wind alignment range of 120° and the in situ calibration procedure was human decision based. This study presents the next generation of the combo probe design, and the new fully automated in situ calibration procedure implementing deep learning. The new design now enables measurements of the incoming wind flow in a 360° range. The improved calibration procedure is shown to have the robustness necessary for operation in everchanging open field flow and environmental conditions. This is especially useful with diurnally changing environments and possibly non-stationary measuring stations, i.e., probes placed on moving platforms like boats, drones, and weather balloons. Together, the updated design and the new calibration procedure, allow for continuous field measurements with minimal to no human interaction, enabling near real-time monitoring of fine-scale turbulent fluctuations. Integration of these probes will contribute toward generation of a large pool of field data to be collected to unravel the intricacies of all scales of turbulent flows occurring in natural setups.
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Acknowledgements
We express our warmest gratitude to Ran Soffer for helping us with the wind tunnel experiments and Eva Chetrit for helping with the assembly of the new combo design. The water waves portion of the experimental setup discussed in Section 3.2 was conducted in collaboration with Almog Shani-Zerbib. The authors acknowledge the support of the Israel Science Foundation grant No 2063/19.
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R.G. and D.L. conceptualized the research; R.G., D.L., and E.W. designed the experiments; R.G and E.W. performed the experiments; R.G. analyzed the data; R.G. and D.L. contributed to data collection and analysis tools and software; D.L. was responsible for funding acquisition; writing-review and editing was performed by R.G and D.L.
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Appendix
Appendix
The curves in the figure below indicate the mean square error (MSE) evolution in training of an example NN model while using the \({\text{DA}}_{3}\) method. The low training, validation, and test set errors indicate that no bias or variance is captured in the model (Fig.
Training evolution of the mean square error in a sample model
12).
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Goldshmid, R.H., Winiarska, E. & Liberzon, D. Next generation combined sonic-hotfilm anemometer: wind alignment and automated calibration procedure using deep learning. Exp Fluids 63, 30 (2022). https://doi.org/10.1007/s00348-022-03381-1
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DOI: https://doi.org/10.1007/s00348-022-03381-1