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Enhancing accuracy of surface wind sensors in wind tunnel Testing: A Physics-Guided neural network calibration approach

Wang, Z., Giaralis, A. ORCID: 0000-0002-2952-1171, Daniels, S. , He, M., Margnelli, A. & Jagadeesh, C. (2024). Enhancing accuracy of surface wind sensors in wind tunnel Testing: A Physics-Guided neural network calibration approach. Measurement, 234, article number 114812. doi: 10.1016/j.measurement.2024.114812


Irwin’s surface wind sensor is widely used in the wind tunnel testing for the studies of urban and environmental aerodynamics. However, conventional physics-based calibration of this sensor leads to decreased measurement accuracy in regions with low flow velocities and high turbulence intensity. To address this issue, this study proposes a novel physics-guided neural network (PGNN) calibration approach, which couples a physics-based calibration model, derived from extended Taylor series expansions of measured wind speed, with an adaptive, data-driven general regression neural network. Sensors are calibrated within the turbulent boundary layer of an empty flat plate, considering both mean and standard deviation of wind velocity measured by high-accuracy thermal anemometry. The accuracy of calibrated sensors is then assessed using a 1:400 benchmark urban model. Experimental results show significant improvement in measurement accuracy, reducing mean absolute percentage error for wind speed standard deviation from 92.3 % with the current model to 9.8 % using PGNN.

Publication Type: Article
Publisher Keywords: Skin friction sensors, Pedestrian wind comfort, Physics-guided neural networks, Adaptive general regression neural networks, Irwin sensor, Wind tunnel testing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Departments: School of Science & Technology
School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of Wang et al_ MEAS_2024_Authors version.pdf] Text - Accepted Version
This document is not freely accessible due to copyright restrictions.
Available under License Creative Commons: Attribution International Public License 4.0.


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