8th International DAAAM Baltic Conference INDUSTRIAL ENGINEERING 19-21 April 2012, Tallinn, Estonia OPTIMAL GAP DISTANCE BETWEEN ROTORS OF MINI QUADROTOR HELICOPTER Aleksandrov, D.; Penkov, I. Abstract: This paper describes comparison between virtual simulation of rotors for flying platforms (mini UAV - Unmanned Aerial Vehicle) and real experiments. During virtual simulation there were conducted similar to real experiments with the use of scanned rotors (with 3D scanner) and same environment conditions. In quadrotor helicopter (quadrocopter) air flows that are going out from rotors and affecting each other were simulated with CFD (Computational Fluid Dynamics) software. Analysis of several helicopters that have different distances between rotors on different angular velocities were compared. Optimal gap distance between rotors is determined, when helicopter mass is lifting force (minimum impact of air flows to each other). Key words: UAV, helicopter, rotor, quadrocopter, lifting force, CFD. 1. INTRODUCTION At first the idea of unmanned aerial vehicles (UAV) started as hobby but in the last decades it found a huge potential both in military [1] and civil spheres. UAVs are capable of carrying out work conditions where the surrounding environment is dangerous or not available to human. There is a wide range of applications performed by UAVs, such as police [2], rescue [3] and firefighter needs [4], research, cinematography and other spheres. UAVs have exclusive capabilities like hovering, vertical takeoff and landing, limited launching spaces and good maneuvering. They have generated great interest in industrial and academic circles [5]. A thorough research was conducted in stability and controllability spheres. Those UAVs are not using innovative power sources and energy saving issue is very important. This paper illustrates comparison between virtual simulation of rotors for flying platforms (mini UAV) and real experiments. For real experiments it was built a testing device for measurment of real rotor angular velosity, lifting force and energy consumption of motor. During virtual simulation with CFD calculation there were conducted equivalent experiments where scanned with 3D scanner rotors and same environment conditions [6] were used. With CFD software the air flows that are going out from rotors and affecting each other in quadrotor helicopter [7] (quadrocopter) were simulated and lifting force was determined. Helicopters with different distances between rotors on different angular velocities were compared. Optimal gap distance between rotors is determined when helicopter mass is lifting force - air flows affect each other the minimally. 2. COMPARISON OF REAL AND VIRTUAL EXPERIMENTS 2.1 Real Experiments In real experiments a brushless motor Robbie 2827-34 with rotors 10 x 4.5 in (rotor length is 10 inches and the pitch is 4.5 inches per revolution) and 8 x 4.5 in
was used. For motor control it was used a brushless motor controller Mikrokopter BL-CTRL 1.2 and it was operated through RS232 port directly from PC (using UM232R USB Serial UART Development Module by FTDI). A previously calibrated strain gauge sensor PS-08844244 was used for force determination. Rotor rotation speed was measured with optical laser tachometer Omron CT6. Altogether were made measurements for 10 rotor angular velocities for each rotor size. 2.2 Virtual Experiments Virtual experiments for lifting force determination were conducted with CFD software SolidWorks Flow Simulation 2012 [8]. The real rotors were previously scanned with 3D scanner, then imported in SolidWorks as poind cloud data and created working models. After this rotor lifting force was determined on different rotor rotation speed [9] [10]. Both methods give approximately similar result (maximum error is about 3 %). This means that we can use CFD method for similar future analysis and calculation of impact of flows from different rotors on each other. 3. GAP DISTANCE OPTIMIZATION 3.1 Principle Gap distance optimization was made with the use of CFD software on simplified quadrotor helicopter model with scanned models of 10 in rotors. Separate calculations for different rotation speeds 1500, 3000, 4000 and 5000 rpm were done. For each angular velocity distance range between rotors changed from 5 to 140 mm (Fig. 2). 2.3 Results Figure 1 illustrates force (produced by 10 x 4.5 in rotor) dependency on rotor rotation speed. A measured force graph was created with the real experiments data and CFD force graph with PC calculation. Fig. 2. A simplified model of quadrotor helicopter for CFD analysis that shows the direction of rotor rotation. Fig. 1. Lifting force dependency on rotor rotation speed for 10 in rotor. 3.2 Results Figure 3 illustrates lifting force dependency on distance between rotors. A graph shows that force increases on distances from 5 to 35 mm and this growth is around 15 %. From distance of 70 mm lifting force is decreasing by around 2 % and then stabilizing. This level corresponds to same values we had in real experiments (Fig. 1).
Near rotating rotor endings appear small turbulent areas where air flow is twisting upwards. In the space between rotors there is a place, where air flows are runing into each other and gathered flow goes upwards. This stream particulary compensates lifting force. Figure 5 illustrates quadrotor helicopter air flow velocities and their directions when distance between rotors is 140 mm. At this distance the influence of air flows from rotors is insufficient and each rotor works as if it stayed separately. Fig. 3. Lifting force dependency on distance between rotors for different angular velocities. Figure 4 shows air flow velocities and their directions of quadrotor helicopter when distance between rotors is 10 mm (front and top views). Fig. 5. Air velocity distribution near rotors, distance between rotors is 140 mm, velocity range 0 13 m/s. Fig. 4. Air velocity distribution near rotors, distance between rotors is 10 mm, velocity range 0 13 m/s. 3.3 Optimization For optimization with use of MATLAB software the artificial neural network was built. Figure 6 shows surface of compliance of neural network, which allows to find produced lifting force at concrete rotor rotation speed and distance between rotors. With neural network it is possible to find optimal distance (where produced force is maximum) on certain rotor angular velocity.
platforms and real experiments and shows that results are trustworthy. With the CFD software there were simulated air flows that are going from rotors and how they affect each other on quadrotor helicopter and their influence on produced lifting force. Optimal gap distance between rotors can be determined with artificial neural network, when helicopter mass is lifting force. 5. FUTURE RESEARCH Fig. 6. A surface of compliance of neural network. For example, distance between rotors of quadrotor helicopter with mass 1150 g (rotor rotation speed in hovering 4000 rpm) must be optimized. Mass of 1 mm central cross section 0.25 grams will be also taken into account. Optimal distance is 32.65 mm. On figure 7 a lifting force parameter shows what force is produced by rotor on certain distance between rotors. A reduced force graph shows lifting force minus weight (in Newton) of lengthened central cross. The difference in motor energy consumption between optimal distance and 140 mm gap is about 20 %. Fig. 7. Optimal distance between rotors for working angular velocity 4000 RPM. 4. CONCLUSION This paper describes comparison between virtual simulation of rotors for flying Since CFD calculation does not allow to measure power consumption, next step will be to conduct real experiments of influence of gap distance between rotors on lifting force. With CFD it is impossible to measure how motor power changes with variation of distance between rotors, because crossing air flows can create additional loads on motor. These parameters will be controlled next. 6. REFERENCES [1] Girard, A. R., Howell, A. S., Hedrick, J.K. Border patrol and surveillance missions using multiple unmanned air vehicles, In Proceedings of the 43rd IEEE decision and control (Tilbury, D.), Atlantis, Bahamas, December 14 17, 2004, 620 625. [2] Coifman, B., McCord, M., Mishalani, R. G., Iswalt, M., Ji, Y. Roadway traffic monitoring from an unmanned aerial vehicle, IEE Proc. Intell. Transp. Syst., 2006, 153 (1), 11-20. [3] Ryan, A., Hedrick, J. K. A modeswitching path planner for UAV-assisted search and rescue. In Proceedings of the 44th IEEE conference on decision and control (Dawn, M.), Seville, Spain, December 12-15, 2005, 1471-1476. [4] Casbeer, D. W., Kingston, D. B., Beard, R. W., McLain, T. W. Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int. J. Syst. Sci., 2006, 37 (6), 351 360.
[5] Peng, K., Cai, G., Chen, B. M., Dong, M., Luma, K. Y., Lee, T. H. Design and implementation of an autonomous flight control law for a UAV helicopter. Automatica, 2009, 45, 2333-2338. [6] Aleksandrov, D., Penkov, I., In 11th Int. Symp. Topical Problems in the Field of Electrical and Power Engineering (Zakis, J.), Pärnu, Estonia, January 16-21, 2012, 259 262. [7] Pounds, P., Mahony, R., Corke, P., Modelling and control of a large quadrotor robot. Control Engineering Practice, 2010, 18 (7), 691-699. [8] Hines, J. SolidWorks Flo Simulation, Dassault Systems, Concord, 2011. [9] Yongjie, S., Qijun, Z., Feng, F., Guohua, X., A New Single-blade Based Hybrid CFD Method for Hovering and Forward-flight Rotor Computation. Chi. Jour. of Aeronautics, 2011, 24, 127-135. [10] Pape, A., L., Beaumier, P., Numerical optimization of helicopter rotor aerodynamic performance in hover. Aerospace Sci. and Techn., 2005, 9, 191 201. 7. ABOUT AUTHORS Corresponding author: Ph. D. Candidate Dmitri Aleksandrov, Doctoral student. Peterburi tee 46 314, 11415, Tallinn, Estonia, dmitri.aleksandrov@gmail.com, (+372) 58 194 349. Co-author: Igor Penkov, Docent. Ehitajate tee 5 V417, 19086, Tallinn, Estonia, igor.penkov@ttu.ee, (+372) 620 3306.