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

Tara Hanlon,David Risk

doi:10.5194/amt-13-191-2020

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.

Highlights

Many scientific applications require local measurements of wind speed and direction in the lower atmosphere
The Computational fluid dynamic (CFD) and field experiments showed that the measured wind at the anemometer location varied under wind yaw angle
The bias found in the CFD varied with the rotation of the truck, and the field results concluded that the measured wind differed in head, cross, and tailwind conditions

Summary

Introduction

Many scientific applications require local measurements of wind speed and direction in the lower atmosphere. Vehicle-based wind measurements are used to study severe weather-related meteorology (Belušicet al., 2014; Straka et al, 1996; Taylor et al, 2011) and lake meteorology (Brook et al, 2013; Curry et al, 2017), and they are integrated into methane measurement studies to detect, quantify, and map emission plumes from oil and gas developments (Atherton et al, 2017; Rella et al, 2015; Zazzeri et al, 2015). Existing mobile measurement platforms include car, sport utility vehicle (SUV), and truck-mounted anemometers, the quality and accuracy of the anemometer measurements are not well understood. Instruments measuring wind speed and direction must be placed in a location that is not directly impacted by the flow and pressure perturbation produced by the moving vehicle (Straka et al, 1996). Mobile platforms often mount sensors in locations

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