Influence of Ultrasonic Wind Sensor Position on Measurement Accuracy under Full-Scale Conditions

An efficient wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm

Wind power is widely used in industry, meteorology, shipping and so on. Accurate measurement of wind parameters is the key to improve the efficiency of wind power application. But at present, wind parameters are largely measured by different devices based on time difference method, which is easily influnced by enviromental noise. Beam-forming algorithm can improve the ability to resist environmental noise and the accuracy of hardware itself. Therefore, the beam-forming algorithm can be used to measure wind parameters in the high noise environment. However, the efficiency of the algorithm depends on how to search for spectral peak. In this paper, a three-dimensional wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm is proposed to improve the waveform searching efficiency of beamforming algorithm. It first searches for rough target wind parameters globally, and then searches for precise target wind parameters locally. Through simulation verification, the proposed algorithm can measure the wind parameters after 0.087s under the condition of system error of 50dB and environmental noise of 20dB, the accuracy of wind speed is 0.5%, the accuracy of wind direction is 1%, and the accuracy of pitch angle is 0.5%. Compared with the wind measurement by traversal method, the proposed algorithm can improve the wind measurement efficiency by about 20 times, and has similar or even better measurement results.. And by comparing with other algorithms, the advantages of this algorithm are verified.

A Direct-Writing Approach for Fabrication of CNT/Paper-Based Piezoresistive Pressure Sensors for Airflow Sensing

Airflow sensor is a crucial component for monitoring environmental airflow conditions in many engineering fields, especially in the field of aerospace engineering. However, conventional airflow sensors have been suffering from issues such as complexity and bulk in structures, high cost in fabrication and maintenance, and low stability and durability. In this work, we developed a facile direct-writing method for fabricating a low-cost piezoresistive element aiming at high-performance airflow sensing, in which a commercial pen was utilized to drop solutions of single-walled carbon nanotubes onto tissue paper to form a piezoresistive sensing element. The encapsulated piezoresistive element was tested for electromechanical properties under two loading modes: one loading mode is the so-called pressure mode in which the piezoresistive element is pressed by a normal pressure, and another mode is the so-called bending mode in which the piezoresistive element is bended as a cantilever beam. Unlike many other developed airflow sensors among which the sensing elements are normally employed as cantilever beams for facing winds, we designed a fin structure to be incorporated with the piezoresistive element for airflow sensing; the main function of the fin is to face winds instead of the piezoresistive element, and subsequently transfer and enlarge the airflow pressure to the piezoresistive element for the normal pressure loading mode. With this design, the piezoresistive element can also be protected by avoiding experiencing large strains and direct contact with external airflows so that the stability and durability of the sensor can be maintained. Moreover, we experimentally found that the performance parameters of the airflow sensor could be effectively tuned by varying the size of the fin structure. When the fin sizes of the airflow sensors were 20 mm, 30 mm, and 40 mm, the detection limits and sensitivities of the fabricated airflow sensors were measured as 8.2 m/s, 6.2 m/s, 3.2 m/s, 0.0121 (m/s)-2, 0.01657 (m/s)-2, and 0.02264 (m/s)-2, respectively. Therefore, the design of the fin structure could pave an easy way for adjusting the sensor performance without changing the sensor itself toward different application scenarios.