Using computational fluid dynamics and field experiments to improve vehicle-based wind measurements for environmental monitoring

Abstract

Abstract. Vehicle-based measurements of wind speed and direction are presently used for a range of applications, including gas plume detection. Many applications use mobile wind measurements without knowledge of the limitations and accuracy of the mobile measurement system. Our research objective for this field-simulation study was to understand how anemometer placement and the vehicle's external airflow field affect measurement accuracy of vehicle-mounted anemometers. Computational fluid dynamic (CFD) simulations were generated in ANSYS Fluent to model the external flow field of a research truck under varying vehicle speed and wind yaw angle. The CFD simulations provided a quantitative description of fluid flow surrounding the vehicle and demonstrated that the change in wind speed magnitude from the inlet increased as the wind yaw angle between the inlet and the vehicle's longitudinal axis increased. The CFD results were used to develop empirical speed correction factors at specified yaw angles and to derive an aerodynamics-based correction function calibrated for wind yaw angle and anemometer placement. For comparison with CFD, we designed field tests on a square, 12.8 km route in flat, treeless terrain with stationary sonic anemometers positioned at each corner. The route was driven in replicate under varying wind conditions and vehicle speeds. The vehicle-based anemometer measurements were corrected to remove the vehicle speed and course vector. From the field trials, we observed that vehicle-based wind speed measurements differed in average magnitude in each of the upwind, downwind, and crosswind directions. The difference from stationary anemometers increased as the yaw angle between the wind direction and the truck's longitudinal axis increased, confirming the vehicle's impact on the surrounding flow field and validating the trends in CFD. To further explore the accuracy of CFD, we applied the function derived from the simulations to the field data and again compared these with stationary measurements. From this study, we were able to make recommendations for anemometer placement, demonstrate the importance of applying aerodynamics-based correction factors to vehicle-based wind measurements, and identify ways to improve the empirical aerodynamic-based correction factors.

Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target

“…Hence, if rotated in both REV1# and REV2#, respectively, in front and side protractors, the practical stick yaw angle and wind direction are given in Equations ( 38) and (39), respectively. In this way, when the camera is placed in front as in Figure 17 4 or at bottom as in Figure 17 5 , pure imaging cases, which only consider the yaw angle variation, can be simulated by changing REV1# as side view imaging cases, and REV2# as front view cases when the camera is placed in front. Accordingly, the pure bottom view case is simulated by changing REV1# when the camera is placed at bottom.…”

“…Computer vision is a multidisciplinary field of study that involves enabling computers to gain understanding of data (usually digital images and videos) and, in the process, acquire, process, and extract useful information for decision making [1]. Some of the popular areas of application of computer vision techniques include robotics [2], autonomous vehicles [3], and environmental monitoring [4]. The measurement of wind speed and direction is a crucial aspect of environmental monitoring, especially for areas such as fault diagnosis [5] and wind energy monitoring [6].…”

Understanding balloon-borne frost point hygrometer measurements after contamination by mixed-phase clouds

“…Water vapour mixing ratio (χ CFH ) in ppmv from the CFH was calculated from the frost (or dew) point temperature, the air pressure from the RS41 and the parameterization for saturation vapour pressure over ice (or liquid water) by Murphy and Koop (2005). The water vapour mixing ratio in ppmv derived from the RS41 (χ RS41 ) uses the relative humidity, air temperature, and air pressure from the RS41 and the parameterization for saturation vapour pressure over water by Hardy (1998) as used by Vaisala (2013.…”

Uncertainty in the Mobile Observation of Wind

“…The accuracy of wind estimates depends jointly on the physical setting on the vehicle and the characteristics of the anemometer in use. Hanlon et al conducted computational fluid dynamic simulations to estimate the flow distortion around a capped pickup truck and identified 3-10% wind enhancement, ultimately recommending anemometer placement ahead and above the vehicle [20]. Importantly, the potential error was found to be dependent on the angle of wind incident on the vehicle, with crosswinds generating the largest uncertainty [20].…”