A Spherical Directional Anemometer Sensor System

Leoni, Alfiero; Barile, Gianluca; Muttillo, Mirco; Pantoli, Leonardo; Stornelli, Vincenzo; Ferri, Giuseppe; Paolucci, Romina; Vita, Luca

doi:10.3390/proceedings1040388

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Although it was thought that miniaturization was difficult due to the complicated housing shape, a compact 2D airflow sensor was developed by placing a thermal sensor element inside [ 19 , 20 , 21 , 22 ]. Similarly, it is expected that an airflow sensor that fulfills the required specifications for a drone is realized by minute built-in mechanical DP sensors inside the housing [ 17 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Compact Sphere-Shaped Airflow Vector Sensor Based on MEMS Differential Pressure Sensors

Haneda

Matsudaira

Noda

et al. 2022

Sensors

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This paper presents an airflow vector sensor for drones. Drones are expected to play a role in various industrial fields. However, the further improvement of flight stability is a significant issue. In particular, compact drones are more affected by wind during flight. Thus, it is desirable to detect air current directly by an airflow sensor and feedback to the control. In the case of a drone in flight, the sensor should detect wind velocity and direction, particularly in the horizontal direction, for a sudden crosswind. In addition, the sensor must also be small, light, and highly sensitive. Here, we propose a compact spherical airflow sensor for drones. Three highly sensitive microelectromechanical system (MEMS) differential pressure (DP) sensor chips were built in the spherical housing as the sensor elements. The 2D wind direction and velocity can be measured from these sensor elements. The fabricated airflow sensor was attached to a small toy drone. It was demonstrated that the sensor provided an output corresponding to the wind velocity and direction when horizontal wind was applied via a fan while the drone was flying. The experimental results demonstrate that the proposed sensor will be helpful for directly measuring the air current for a drone in flight.

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Section: Introductionmentioning

confidence: 99%

Compact Sphere-Shaped Airflow Vector Sensor Based on MEMS Differential Pressure Sensors

Haneda

Matsudaira

Noda

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Other types of anemometers include ultrasonic anemometers that detect changes in the phase of a sound, and laser anemometers that use coherently scattered light from air. The hot-wire anemometers detect wind speed based on the small temperature difference between the wires placed between the wind and the downwind side (shadow) of the wind [42][43][44]. One advantage of non-mechanical anemometers is that they are less sensitive to icing.…”

Section: Wind Forecasting Backgroundmentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

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“…The pitot tube type uses the DP between the static and dynamic inlets and can measure the wind speed, regardless of the external environment [18]. It is also easy to downsize and measure wind direction by increasing the number of inlets [19], [20]. Therefore, the pitot tube type is considered the most suitable compact instrument for measuring wind speed and direction.…”

Section: Introductionmentioning

confidence: 99%

“…The measurement method converts the DPs among multiple inlets on the surface of the sensor structure to wind speed and direction [20]- [25]. Although miniaturizing the housing is considered difficult because of its complex shape, a compact sensor has been realized by placing a thermo-sensitive element inside the housing [1], [2], [4], [19], [26], [27]. Since then, various DP airflow sensors have been developed for various applications.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Airflow Vector Sensor Using Multiple MEMS Differential Pressure Sensors

2023

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As airflow information is significant in various fields, several airflow measurement devices have been developed. Differential pressure (DP)-type sensors, including pitot tubes, are extensively used because they can directly measure dynamic pressure due to airflow. However, this method has a disadvantage in that it is difficult to measure some wind directions because of the non-monotonic pressure distribution around the device. This study proposes a compact sphere airflow vector sensor using a neural network model that compensates for nonmonotonicity. It consists of three built-in microelectromechanical system (MEMS)based DP sensor elements that simultaneously measure the DPs around the spherical sensor housing. The measured DPs are converted into 2D wind speed and direction using a neural network. The network model was trained using the simulated output of each sensor, and it was confirmed that the proposed design guideline contributed to achieving high accuracy. An airflow sensor was fabricated on the basis of the designed model. A network model was trained using the sensor responses in a wind tunnel experiment. The wind speed and direction accuracies obtained by the neural network model for 2-10 m/s and 0°-359° were 0.24 m/s and 3.62°, respectively. Finally, we demonstrated a drone flight test by attaching the calibrated sensor to a toy drone. This study is expected to contribute to realizing highly accurate low airflow measurement devices.INDEX TERMS Airflow sensor, neural network, differential pressure sensor, machine learning, drone. FIGURE 1. Conceptual diagram of the proposed airflow vector sensor.

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A Spherical Directional Anemometer Sensor System

Cited by 5 publications

References 15 publications

Compact Sphere-Shaped Airflow Vector Sensor Based on MEMS Differential Pressure Sensors

Compact Sphere-Shaped Airflow Vector Sensor Based on MEMS Differential Pressure Sensors

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

Neural Network-Based Airflow Vector Sensor Using Multiple MEMS Differential Pressure Sensors

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