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Design Innovation Paper

Integrated Design and Testing of an Anemometer for Autonomous Sail Drones

[+] Author and Article Information
Pugi Luca

Department of Industrial Engineering,
University of Florence,
Via di Santa Marta 3,
Firenze 50139, Italy
e-mail: luca.pugi@unifi.it

Allotta Benedetto

Department of Industrial Engineering,
University of Florence,
Via di Santa Marta 3,
Firenze 50139, Italy
e-mail: benedetto.allotta@unifi.it

Boni Enrico

Department of Information Engineering,
University of Florence,
Via di Santa Marta 3,
Firenze 50139, Italy
e-mail: enrico.boni@unifi.it

Guidi Francesco

Department of Information Engineering,
University of Florence,
Via di Santa Marta 3,
Firenze 50139, Italy
e-mail: Francesco.guidi@unifi.it

Montagni Marco

Department of Information Engineering,
University of Florence,
Via di Santa Marta 3,
Firenze 50139, Italy
e-mail: jonte987@gmail.com

Massai Tommaso

Department of Civil Engineering,
University of Florence,
Via di Santa Marta 3,
Firenze 50139, Italy
e-mail: tommaso.massai@unifi.it

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 18, 2017; final manuscript received August 29, 2017; published online December 19, 2017. Assoc. Editor: Evangelos Papadopoulos.

J. Dyn. Sys., Meas., Control 140(5), 055001 (Dec 19, 2017) (10 pages) Paper No: DS-17-1201; doi: 10.1115/1.4037840 History: Received April 18, 2017; Revised August 29, 2017

A correct estimation of both direction and intensity of wind velocity is fundamental for controlling an autonomous sail-boat. This kind of estimation has to be performed in a harsh environment considering the direct exposition of the sensor to salt, fog, and to any variable weather conditions. An important feature is represented by the sensor size, which has to be small compared to the drone size. Costs have to be optimized with respect to the overall small budget involved in the construction of the drone. Finally, extensive use on drones or in large sensor networks should be greatly advantaged by an easy substitutability in the case of accidental damage or system loss, an eventuality which is difficult to be completely avoided for large scale, prolonged monitoring activities. In this work authors propose a low cost ultrasonic planar anemometer with a very interesting price to performance ratio which is obtained by introducing a simple, original and innovative Arduino based architecture. Preliminary design and the results of calibration will be described, followed by testing activities performed on a low-speed large section wind tunnel, available at University of Florence supported by simple but effective computational fluid dynamic (CFD) simulations.

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Figures

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Fig. 1

Example of behavior of CL(α) and CD(α) functions

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Fig. 2

Definition of angles describing relative orientation of wind, hull, and rudder

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Fig. 3

Mechanical anemometers

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Fig. 4

Example of commercial anemometers from the site of an international supplier [18]

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Fig. 5

Generic principle of operation of an ultrasonic speed transducer

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Fig. 6

The UNIFI Sail Drone and the corresponding installation of the anemometer

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Fig. 7

Sensor frame built with bended, hard anodized aluminum pipes, evolution of the design from the first prototype to the current solution

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Fig. 8

Simplified scheme of the adopted measurement and signal conditioning system

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Fig. 9

Simplified scheme of the adopted measurement and signal conditioning system

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Fig. 10

The wind tunnel test of CRIACIV (Prato, Italy)

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Fig. 11

Prototype of tested anemometer in the CRIACIV wind-tunnel

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Fig. 12

Simplified scheme of applied filtering and calibrations applied to the sensor output

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Fig. 13

Output of the proposed sensor without any calibration concerning the angle of attack

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Fig. 14

Speed field of a sensor with an angle of attack of 0 deg (simplified CFD simulation with COMSOL Multiphysics™) and a mean flow speed of 40 km/h

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Fig. 15

Speed field of a sensor with an angle of attack of 45 deg (simplified CFD simulation with COMSOL Multiphysics™) and a mean flow speed of 40 km/h

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Fig. 16

Asymmetry of the behavior of the sensor according to the rotational direction of the sensor

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Fig. 17

Measurement error on the estimated flow direction at different testing speeds

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Fig. 18

Comparison between vf,vf*raw for different values of vf

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Fig. 19

Distribution of the estimation error during tests performed at different speeds

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