Development of an unmanned aerial vehicle UAV for air quality measurements in urban areas Patrick Haas 1, Christophe Balistreri 2, Piero Pontelandolfo 2, Gilles Triscone 3 University of Applied Sciences Western Switzerland, hepia, Geneva, Switzerland Hasret Pekoz 4 Université Pierre et Marie Curie, Paris, France and Antonio Pignatiello 4 Bologna University, Bologna, Italy Based on an UAV platform from fly-n-sense, a system for air quality measurement is developed. The system is able to measure PM10, O3 and NO2 (see fig. 1 and 2). The wind speed is also determined by the use of the stabilization system and based on the pitching angle obtained under wind conditions. Detailed maps of pollution concentration can be done. The phenomenon related to particle movements or pollutant diffusion can be outlined with a positioning tolerance smaller than one meter. The main objective of the UAV measurement campaign is to determine boundary conditions for CFD calculations at the borders of a city. The aerodynamic development of the UAV includes CFD simulations, wind tunnel experiments, in flight measurements and finally, the sensors selection, integration and validation. A j K Nomenclature = amplitude of oscillation = waypoint index = trailing-edge (TE) nondimensional angular deflection rate B I. Introduction ased on an UAV platform, a system for air quality measurement is developed. The system is able to measure PM10, O 3 and NO x (see fig. 1 and 2). The wind speed is also determined by the use of the stabilization system and based on the pitching angle obtained under wind conditions. Detailed maps of pollution concentration can be measured. The phenomenon related to particle movements or pollutant diffusion can be outlined with a positioning tolerance smaller than one meter. The main objective of the UAV measurement campaign is to analyze in situ the pollutant distribution, to validate CFD simulation results, and to determine boundary conditions for CFD calculations at the borders of a city or a calculation domain. The development of the UAV includes the following chapters: The aerodynamics using CFD simulations, wind tunnel experiments and finally in flight measurements 1 Professor, hepia-cmefe, patrick.haas@hesge.ch, AIAA Senior Member 2 Research Assistant, hepia-cmefe, AIAA Member 3 Research Coordinator, hepia 4 Master Student 1
The sensors selection, integration and validation The UAV performances evaluation in real situations Fig. 1: The hexacopter UAV equipped with the NO 2 payload Fig. 2: The NO 2 payload with 16 bits digitalizer and 2.4 GHz data link to ground station II. UAV aerodynamics Wind tunnel study The aerodynamics of the UAV was first studied in wind tunnel. The system was installed on a six-component balance at the top of a 2m profile (Fig. 3 and 4). The study parameters are the wind speed, the propeller speed and the pitch angle. The forces and moments are recorded to describe completely the UAV aerodynamics. These data describe the complete behavior of the UAV in flight. The dynamics of such aircraft is complex (Fig. 5). Fs and Ms are the resultant force and moment generated by the propellers. These last can have different rotational speeds if a motion of the UAV on an axis shall be done. The relative motion of the fluid around the structure gives an aerodynamic force FA applied to the aerodynamic center CA. FA can be decomposed in a drag FD and a lift FL. In the case the UAV is stationary and the wind is constant in direction and amplitude, the situation is simple and a direct relation between the pitch angle and the wind speed exists. From the measurements in wind tunnel we extract the situations when the sustentation force Fsz is equal to the UAV weight G, and the horizontal force measured by the balance is zero. In this situation, we obtain a direct estimation of the wind speed and direction as a function of the inclination angle. This angle is a combination of the pitch and roll angles. The results of this analysis are given hereafter (fig. 6). 2
Fig. 3: Wind tunnel test set- up Fig. 4: Pitch adjustable support and six- component balance Fig. 5: Summary of forces and moments acting on the UAV. 3
Fig. 6: Relative UAV - wind speed as a function of the inclination angle in stationary flight (wind tunnel results) III. UAV aerodynamics CFD study A complete study of the UAV aerodynamics was also done using CFD technics. The results obtained are useful to describe the flow around the vehicle. It gives also a global understanding of the flow patterns generated by the six propellers. The air volume mixed by the UAV propellers is evaluated. This represents the spatial discretization of the measurements done by the gas sensors. For this CFD analysis, a 3D scan of the propellers have been done and is introduced in the mesher (figs. 7 and 8). A patch independent scheme is used with tetrahedral elements and prism at the walls. Interface technics are used to give a rotational speed to the propellers without the need to remesh the global volume (fig. 9). The solver set for this analysis is time dependent and uses a SIMPLE scheme. The Spalart-Allmaras turbulence model has been chosen and the mesh adapted locally to obtain y+ close to 5 (fig. 10). The model has approx. 15 millions of cells and need about 9.0 seconds of real time for the flow to establish. This takes about one day calculation with our cluster. Figs. 11 to 13 show the results of the flow around the UAV in terms of pathlines, pressures on the UAV body and velocities in the domain. A detailed analysis of these results highlights the aerodynamic performances of the UAV and finally its flight possibilities in term of payload, accelerations, speeds, ecc. From these we can also see the volume of air mixed by the drone is approximately a cylinder of 2 meters radius and 10 m length. 2 m above the UAV, the air starts to move significantly, and the jet is clearly visible until 8 m downward. These dimensions represent the spatial discretization of the measurements system. 4
Fig. 7: 3D scan of the propellers Fig. 8: A file of points is obtained and used as a geometry boundary in the mesher 5
Fig. 9: Interfaces technics used to give a rotational speed to the propellers Fig. 10: Mesh y+ at the UAV surfaces Fig. 11: Pathlines and speed at the surfaces Fig. 12 Contours of velocity magnitude 6
Fig. 13: Velocity vectors and static pressure values on the UAV body IV. Gas sensors selection The gas sensor selection has been for the PM10 suspended particulated matter (SPM), O3 and NO2. We first start with the evaluation of semiconductor (metallic oxide) sensors of low cost and easy to find on the market. These products have been compared to state of the art sensors used traditionally by the services responsible of the air quality in major cities. These product are generally based on the following technologies: PM10 Laser diffraction (light scattering) Gravimetry O3 UV absorption (AUV) Differential Optical Absorption Spectroscopy (DOAS) NO2 Differential Optical Absorption Spectroscopy (DOAS) Luminol Chemiluminescence (CL) The NO2 payload is represented in fig. 2. It includes a 16 bit digitalizer linked to ground station by a 2.4 GHz radio signal. The O3 and PM10 payloads are represented respectively in figs. 14 and 15. The weights of the payloads are approx. 1 1.2 Kg. 7
V. First flights A measurement campaign is under way. Flights have been done and data collected. The flight performances and the wind speed measurement capabilities established during the study are evaluated. 8
Fig. 14: O3 AUV payload Fig. 15: PM10 LOAC payload installed on the UAV VI. Conclusion A measurement campaign is under way. Flights have been done and data collected. The flight performances and the wind speed measurement capabilities established during the study are evaluated. Acknowledgments SPair, HES-SO The preferred spelling of the word acknowledgment in American English is without the e after the g. Avoid expressions such as One of us (S.B.A.) would like to thank Instead, write F. A. Author thanks Sponsor and financial support acknowledgments are also to be listed in the acknowledgments section References 1 Benzemrane, K., Santosuosso, G. L. and Damm, G., Unmanned aerial vehicle speed estimation via non-linear adaptive observers", Proceeding of the American Control Conference IEEE (2007), pp. 985-990. 2 Neumann, P. P., Asadi, S., Lilienthal, A. J., Bartholmai, S. and Schiller, J. H., Autonomus gas-sensitive microdrone, IEEE Robotics & Automation Magazine, Vol. 19, No. 1, pp. 50-61. 3 Triscone, G., "The Clean City Project", Internal document, Project description, University of Applied Sciences Western Switzerland, Call 2011. 4 WHO Regional Office for Europe, "Health aspects of air pollution, results from the WHO project. 5 Hanrahan, P. L., "The plume volume molar ratio method for determining NO2/NOx ratios in modeling Part I : Methodology", Journal of the air & wastage managemen association, Vol. 49, pp. 1324-1331. 6 "Qualité de l'air à Genève", Rapport annuel du service de la protection de l'air SPAir, Etat de Genève, 2011. 7 Pignatiello, A., "Development of a micro-drone based system for environmental measurements", Internal report, Master thesis, hepia- University Bologna, 2012. Balistreri, C., " 9