Investigation of Shadow Effects in Reflective Ultrasonic Anemometers Based on Particle Image Velocimetry and Computational Fluid Dynamics

Document Type : Regular Article

Authors

1 College of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi, 830000, China

2 Country Electric Power Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, China

10.47176/jafm.19.1.3624

Abstract

To address the measurement instability of reflective ultrasonic anemometers in complex wind fields, this study systematically investigates the mechanisms by which shadow effects caused by transducers and reflector support pillars affect measurement accuracy under varying wind speeds and directions. By integrating particle image velocimetry (PIV) experiments with computational fluid dynamics (CFD) simulations, 1:1 and 1:2 scale models are employed to reveal the flow field characteristics and error mechanisms. The results indicate that at a wind direction of 0°, wall-following vortices and turbulent wakes generated by transducer structures cause systematic wind speed deviations along the measurement paths. At a 45° wind direction, flow disturbances around the support pillars become the dominant source of shadow effects. The 1:1 scale model exhibits insufficient decay of large-scale, low-frequency turbulent energy, resulting in the accumulation of turbulent kinetic energy and significant wind speed errors at 0°. In contrast, the 1:2 scale model enables efficient energy transfer through high-frequency, small-scale vortices, enhances vortex intensity uniformity, and achieves improved spatial homogeneity in cross-wind measurement errors. These findings provide an important theoretical foundation for improving the high-precision measurement performance of reflective ultrasonic anemometers in complex wind environments.

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Main Subjects


Andrianaki, G., Grigoriadis, A., Skoulakis, A., Tazes, I., Mancelli, D., Fitilis, I., Dimitriou, V., Benis, E. P., Papadogiannis, N. A., Tatarakis, M., Nikolos, I. K. (2023). Design, manufacturing, evaluation, and performance of a 3D-printed, custom-made nozzle for laser wakefield acceleration experiments. Review of Scientific Instruments, 94(10), 103309. https://doi.org/10.1063/5.0169623
Blocken, B., & Stathopoulos, T. (2013). CFD simulation of pedestrian-level wind conditions around buildings: Past achievements and prospects. Journal of Wind Engineering and Industrial Aerodynamics, 121, 138-145. https://doi.org/10.1016/j.jweia.2013.08.008
Chen, X., & Zhan, W. (2021). Effect of Transducer Shadowing of Ultrasonic Anemometers on Wind Velocity Measurement. IEEE Sensors Journal, 21(4), 4731-4738. https://doi.org/10.1109/jsen.2020.3030634
Cuerva, A., & Sanz-Andrés, A. (2000). On sonic anemometer measurement theory. Journal of Wind Engineering and Industrial Aerodynamics, 88(1), 25-55. https://doi.org/10.1016/s0167-6105(00)00023-4
Franchina, N., Kouaissah, O., Persico, G., & Savini, M. (2022). Three-dimensional modeling and investigation of the flow around a vertical axis wind turbine at different conditions. Renewable Energy, 199, 368-381. https://doi.org/10.1016/j.renene.2022.08.130
Frank, J. M., Massman, W. J., Swiatek, E., Zimmerman, H. A., & Ewers, B. E. (2016). All Sonic Anemometers Need to Correct for Transducer and Structural Shadowing in Their Velocity Measurements. Journal of Atmospheric and Oceanic Technology, 33(1), 149-167. https://doi.org/10.1175/jtech-d-15-0171.1
Ghaemi-Nasab, M., Franchini, S., Sorribes-Palmer, F., & Davari, A. R. (2018). A calibration procedure to correct the shadow effect in ultrasonic wind sensors. Journal of Wind Engineering and Industrial Aerodynamics, 179, 475-482. https://doi.org/10.1016/j.jweia.2018.07.005
Han, D., Kim, S., & Park, S. (2008). Two-dimensional ultrasonic anemometer using the directivity angle of an ultrasonic sensor. Microelectronics Journal, 39(10), 1195-1199. https://doi.org/10.1016/j.mejo.2008.01.090
Han, D., & Park, S. (2011). Measurement range expansion of continuous wave ultrasonic anemometer. Measurement, 44(10), 1909-1914. https://doi.org/10.1016/j.measurement.2011.08.030
Hangan, H., Refan, M., Jubayer, C., Romanic, D., Parvu, D., LoTufo, J., & Costache, A. (2017). Novel techniques in wind engineering. Journal of Wind Engineering and Industrial Aerodynamics, 171, 12-33. https://doi.org/10.1016/j.jweia.2017.09.010
Horst, T. W., Semmer, S. R., & Maclean, G. (2015). Correction of a Non-orthogonal, Three-Component Sonic Anemometer for Flow Distortion by Transducer Shadowing. Boundary-Layer Meteorology, 155(3), 371-395. https://doi.org/10.1007/s10546-015-0010-3
Jeong, H., Lee, S., & Kwon, S. D. (2018). Blockage corrections for wind tunnel tests conducted on a Darrieus wind turbine. Journal of Wind Engineering and Industrial Aerodynamics, 179, 229-239. https://doi.org/10.1016/j.jweia.2018.06.002
Kaimal, J. C., Gaynor, J. E., Zimmerman, H. A., & Zimmerman, G. A. (1990). Minimizing flow distortion errors in a sonic anemometer. Boundary-Layer Meteorology, 53(1), 103-115. https://doi.org/10.1007/BF00122466
Keller, J., Blanco, E., Barrio, R., & Parrondo, J. (2014). PIV measurements of the unsteady flow structures in a volute centrifugal pump at a high flow rate. Experiments in Fluids, 55(10), 1820. https://doi.org/10.1007/s00348-014-1820-7
Li, H., Xing, H., Wang, S., & Hou, T. (2023). Multi-reflection ultrasonic wind measuring model and algorithm research. Journal of Electronic Measurement and Instrumentation, 37(09), 110-118. https://doi.org/10.13382/j.jemi.B2306320
Li, H., Yan, G., Zhu, H., Feng, Y., & He, J. (2025). Particle displacement refinement based on hybrid cross-correlation optical flow method with gradient constancy assumption. Review of Scientific Instruments, 96(4), 043001. https://doi.org/10.1063/5.0238355
Lopes, G. M. G., da Silva, D. P., de França, J. A., França, M. B. D., Ribeiro, L. D., Moreira, M., & Elias, P. (2017). Development of 3-D Ultrasonic Anemometer With Nonorthogonal Geometry for the Determination of High-Intensity Winds. Ieee Transactions on Instrumentation and Measurement, 66(11), 2836-2844. https://doi.org/10.1109/tim.2017.2714438
Mane, R., Bhatia, K., Sharma, S., & Prasad, R. K. (2025). A CFD Analysis of the Geometrical Optimization of Slotted Airfoils by Using the RANS k-ω SST Turbulence Model. Journal of Aerospace Engineering, 38(2), 04024118. https://doi.org/10.1061/JAEEEZ.ASENG-5906
Mehmood, Z., Wang, Z. Y., Zhang, X., & Shen, G. Y. (2024). Aerodynamic Performance and Numerical Validation Study of a Scaled-Down and Full-Scale Wind Turbine Models. Energies, 17(21), 5449. https://doi.org/10.3390/en17215449
Qiu, S., Sun, Y., Chen, X., Bian, Z., Bai, Y., & Ren, Y. (2024). Study on obstructing influence and correction of method of wind tunnel calibration for anemometer. Journal of Electronic Measurement and Instrument, 38(1), 34-42. https://doi.org/10.13382/j.jemi.B2306860
Rida, Z., Cazin, S., Lamadie, F., Dherbécourt, D., Charton, S., & Climent, E. (2019). Experimental investigation of mixing efficiency in particle-laden Taylor-Couette flows. Experiments in Fluids, 60(4), 61. https://doi.org/10.1007/s00348-019-2710-9
Sciacchitano, A., Arpino, F., & Cortellessa, G. (2021). Benchmark PIV database for the validation of CFD simulations in a transitional cavity flow. International Journal of Heat and Fluid Flow, 90, 108831. https://doi.org/10.1016/j.ijheatfluidflow.2021.108831
Sciacchitano, A., Wieneke, B., & Scarano, F. (2013). PIV uncertainty quantification by image matching. Measurement Science and Technology, 24(4), 045302. https://doi.org/10.1088/0957-0233/24/4/045302
Shan, Z., Xie, X., & Liu, X. (2023). Wind Speed and Direction Measurement Based on Three Mutually Transmitting Ultrasonic Sensors. IEEE Geoscience and Remote Sensing Letters, 20, 8000205. https://doi.org/10.1109/LGRS.2023.3236005
Smits, A. J., McKeon, B. J., & Marusic, I. (2011). High–Reynolds Number Wall Turbulence. Annual Review of Fluid Mechanics, 43, 353-375. https://doi.org/10.1146/annurev-fluid-122109-160753
Westerweel, J. (1997). Fundamentals of digital particle image velocimetry. Measurement Science and Technology, 8(12), 1379. https://doi.org/10.1088/0957-0233/8/12/002
Westerweel, J., Elsinga, G. E., & Adrian, R. J. (2013). Particle Image Velocimetry for Complex and Turbulent Flows. Annual Review of Fluid Mechanics, 45, 409-436. https://doi.org/10.1146/annurev-fluid-120710-101204
Wieneke, B. (2015). PIV uncertainty quantification from correlation statistics. Measurement Science and Technology, 26(7), 074002. https://doi.org/10.1088/0957-0233/26/7/074002
Wolf, E., Kähler, C. J., Troolin, D. R., Kykal, C., & Lai, W. (2011). Time-resolved volumetric particle tracking velocimetry of large-scale vortex structures from the reattachment region of a laminar separation bubble to the wake. Experiments in Fluids, 50(4), 977-988. https://doi.org/10.1007/s00348-010-0973-2
Xing, H., Wei, J., Xu, W., Yu, X., & Zhou, H. (2017). Improved design of ultrasonic transducer array for wind measurement. Chinese Journal of Scientific Instrument, 38(08), 1988-1995. https://doi.org/10.19650/j.cnki.cjsi.2017.08.018
Yuan, D. D., Deng, J., Zhang, X. X., Wang, Z. H., Qiao, S. X., Tan, S. C., & Li, D. Y. (2021). Experimental investigation of turbulent flow under different Reynolds numbers and blockage ratios in a heated rectangular channel. Annals of Nuclear Energy, 164, 108608. https://doi.org/10.1016/j.anucene.2021.108608