Experimental study on portable multi-parameter intelligent wind measurement sensor.

To address the inability of traditional mechanical anemometers to satisfy the demand for high accuracy, real-time measurement, and dynamic monitoring of ventilation parameters in intelligent deep mining environments, this study focuses on the accuracy verification and analysis of a portable multi-parameter intelligent wind measurement sensor. Comparative analyses of direct-through and reflective ultrasonic measurement principles and their respective error characteristics were conducted, indicating the reflective structure’s superior accuracy. Experiments were carried out in a low-speed circular wind tunnel to investigate the impact of measurement angle deviations on instrument accuracy, resulting in the identification of an optimal measurement angle. Additionally, studies on average airflow velocity measurements were performed, leading to the generation of airflow velocity contour distributions. An average airflow velocity prediction model was developed and subsequently validated through field testing, demonstrating measurement errors within ± 0.1 m/s. This research provides both theoretical foundations and technical support for enhancing intelligent ventilation management in deep coal mining operations.