Small-volume omnidirectional intelligent airflow sensor with differential pressure measurement

Currently, instruments capable of simultaneously measuring airflow velocity and direction are costly and bulky, which presents a challenge in high spatial and temporal resolution in airflow measurement. To address these issues, this paper explores a method that uses artificial neural networks to calculate airflow velocity and direction, capable of measuring the velocity and direction of airflow within the range of 2–12 m/s with high spatiotemporal resolution. Based on the results of comparing the pressure difference response to wind speed in flow simulation, the airflow sensor is designed in a hexagonal prism shape, using low-cost, tiny MEMS pressure sensor chips, with an overall size of only 13 mm × 13 mm × 9 mm, and a power consumption of less than 1.2 mW. Performance tests in the wind tunnel showed that the average error in airflow velocity measurement was 0.20 m/s, and the average error in airflow direction measurement was 0.74°.