Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T05:09:31.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Propeller aerodynamic optimisation to minimise energy consumption for electric fixed-wing aircraft

Part of: Sustainable Aerospace

Published online by Cambridge University Press:  16 June 2021

J.D. Hoyos Open the ORCID record for J.D. Hoyos [Opens in a new window] ,
J.P. Alvarado Open the ORCID record for J.P. Alvarado [Opens in a new window]  and
J.H. Jiménez Open the ORCID record for J.H. Jiménez [Opens in a new window]
J.D. Hoyos*
Affiliation:
Universidad Pontificia Bolivariana Aeronautical Engineering Faculty MedellnColombia
J.P. Alvarado
Affiliation:
Universidad Pontificia Bolivariana Aeronautical Engineering Faculty MedellnColombia
J.H. Jiménez
Affiliation:
Imperial College London Department of Aeronautics LondonUK
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An electric propulsion model for propeller-driven aircraft is developed with the aim of minimising the power consumption for a given airspeed and thrust. Blade Element Momentum Theory (BEMT) is employed for propeller performance predictions fed with aerodynamic aerofoil data obtained from a proposed combined Computational Fluid Dynamics (CFD)–Montgomerie method, which is also validated. The Two-Dimensional (2D) aerofoil data are corrected to consider compressibility, three-dimensional, viscous and Reynolds-number effects. The BEMT model showed adequate fitting with experimental data from the University of Illinois Urbana Champaign (UIUC) database. Additionally, Goldstein optimisation via vortex theory is employed to design pitch and chord distributions minimising the induced losses of the propeller. Particle swarm optimisation is employed to find the optimal value for a wide range of geometrical and operational parameters considering some constraints. The optimisation algorithm is validated through a study case where an existing optimisation problem is approached, leading to very similar results. Some trends and insights are obtained from the study case and discussed regarding the design of an optimal propulsion system. Finally, CFD simulations of the study case are carried out, showing a slight relative error of BEMT.

Keywords

PropellerOptimizationenduranceBEMCFDElectric aircraft
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1292 , October 2021 , pp. 1844 - 1870
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Adjunct professor at Universidad Pontificia Bolivariana.

References

Borer, N.K., Cox, D.E., and Wallace, R.D. Flight Performance Maneuver Planning for NASA’s X-57 “Maxwell” Flight Demonstrator - Part 1: Power-Off Glides, AIAA Aviation 2019 Forum, American Institute of Aeronautics and Astronautics, 2019, p 2855. https://doi.org/10.2514/6.2019-2855.Google Scholar
Rapinett, A. Zephyr: A High Altitude Long Endurance Unmanned Air Vehicle, Master’s thesis, University of Surrey, 2009.Google Scholar
Ma, R.H., Zhong, B. and Liu, P. Optimization design study of low-Reynolds-number high-lift aerofoils for the high-efficiency propeller of low-dynamic vehicles in stratosphere, Sci. China Technol. Sci., 2010, 53, 10, pp 2792–2807.CrossRefGoogle Scholar
Young, M., Keith, S. and Pancotti, A. An overview of advanced concepts for near space systems, 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference; Exhibit, American Institute of Aeronautics and Astronautics, 2012, p 4805. https://doi.org/10.2514/6.2009-4805.Google Scholar
Watts, H. The Design of Screw Propellers: With Special Reference to Their Adaptation for Aircraft, Longmans, Green, and Company, New York, 1920. https://books.google.com.co/books?id=KxhFAAAAMAAJ.Google Scholar
Drela, M. QPROP Formulation, Massachusetts Inst. of Technology Aeronautics and Astronautics, Cambridge, MA, 2006.Google Scholar
Heinzen, S.B., Charles, E. Hall, J. and Gopalarathnam, A. Passively varying pitch propeller for small UAS, 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2010.CrossRefGoogle Scholar
Patrao, A.C. Implementation of Blade Element Momentum/Vortex Methods for the Design of Aero Engine Propellers, research report 2017: 06, Mechanics and Maritime Sciences (M2) Chalmers University of Technology, Göteborg, Sweden, 2017.Google Scholar
Smedresman, A., Yeo, D. and Shyy, W. Design, fabrication, analysis, and dynamic testing of a micro air vehicle propeller, 29th AIAA Applied Aerodynamics Conference, 2011. https://doi.org/10.2514/6.2011-3817.CrossRefGoogle Scholar
Liu, S. and Janajreh, I. Development and application of an improved blade element momentum method model on horizontal axis wind turbines, Int. J. Energy Environ. Eng., 2012, 3, 1, pp 1–10. https://doi.org/10.1186/2251-6832-3-30.CrossRefGoogle Scholar
Park, D., Lee, Y., Cho, T. and Kim, C. Design and performance evaluation of propeller for solar-powered high-altitude long-endurance unmanned aerial vehicle, Int. J. Aerosp. Eng., 2018, 2018, pp 1–23. https://doi.org/10.1155/2018/5782017.Google Scholar
Alam, M.F., Thompson, D.S. and Keith Walters, D. Critical assessment of hybrid RANS-LES modeling for attached and separated flows, Critical Assessment of Hybrid RANS-LES Modeling for Attached and Separated Flows, BoD–Books on Demand, 2017, p 1.CrossRefGoogle Scholar
Montgomerie, B. Methods for Root Effects, Tip Effects and Extending the Angle of Attack to +/–180 with Application to Aerodynamics for Blades on Wind Turbines and Propellers, Tech. rep., Swedish Defence Research Agency Scientific Report, FOI-R–1305–SE, June 2004.Google Scholar
Goldstein, S. On the vortex theory of screw propellers, Proc. Roy. Soc. London. A, Containing Papers Math. Phys. Character, 1929, 123, 792, pp 440–465. http://www.jstor.org/stable/95206.Google Scholar
Betz, A. Schraubenpropeller mit geringstem Energieverlust. Mit einem Zusatz von l. Prandtl, Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse, 1919, 1919, pp 193–217. http://eudml.org/doc/59049 Google Scholar
Gur, O. and Rosen, A. Optimization of propeller based propulsion system, J. Aircr., 2009, 46, 1, pp 95–106. https://doi.org/10.2514/1.36055.CrossRefGoogle Scholar
Clancy, L. Aerodynamics, Pitman Aeronautical Engineering Series, Wiley, 1975. https://books.google.mv/books?id=zaNTAAAAMAAJ.Google Scholar
Chaput, A.J. Small UAV motor and propeller methods - A parametric system engineering model-based approach, 2018 AIAA Aerospace Sciences Meeting, American Institute of Aeronautics and Astronautics, 2018, p 0275. https://doi.org/10.2514/6.2018-0275.Google Scholar
MacNeill, R., Verstraete, D. and Gong, A. Optimisation of propellers for UAV powertrains, 53rd AIAA/SAE/ASEE Joint Propulsion Conference, American Institute of Aeronautics and Astronautics, 2017, p 5090. https://doi.org/10.2514/6.2017-5090.Google Scholar
Lawrence, D., and Mohseni, K. Efficiency analysis for long duration electric MAVs, Infotech@Aerospace, American Institute of Aeronautics and Astronautics, 2012, p 7090. https://doi.org/10.2514/6.2005-7090.Google Scholar
Mccrink, M. and Gregory, J. Blade element momentum modeling of low-Reynolds electric propulsion systems, J. Aircr., 2016, 54, pp 1–14. https://doi.org/10.2514/1.C033622.Google Scholar
Hansen, M. Aerodynamics of wind turbines, Earthscan, 2013. https://books.google.com.co/books?id=GVDHDPix6YC Google Scholar
Glauert, H. The Elements of Aerofoil and Airscrew Theory, Cambridge Science Classics , Cambridge University Press, 1983. https://doi.org/10.1017/CBO9780511574481.Google Scholar
HernÁndez, J. and Crespo, A. Aerodynamic calculation of the performance of Horizontal axis wind turbines and comparison with experimental results, Wind Eng., 1987, 11, 4, pp 177–187. http://www.jstor.org/stable/43749310.Google Scholar
Sriti, M. and Khchine, Y.E. Improved blade element momentum theory (BEM) for predicting the aerodynamic performances of horizontal Axis wind turbine blade (HAWT), Technische Mechanik. Sci. J. Fundamentals Appl. Eng. Mech., 2018, 38, 2, pp 191–202. https://doi.org/10.24352/UB.OVGU-2018-028.Google Scholar
Rwigema, M.K. Propeller blade element momentum theory with vortex wake deflection, 27th International Congress of the Aeronautical Sciences, 2010, pp 1–9.Google Scholar
Hansen, M.O., SØrensen, J.N., Voutsinas, S. and Madsen, H.A. State of the art in wind turbine aerodynamics and aeroelasticity, Prog. Aerosp. Sci., 2006, 42, 4, pp 285–330. https://doi.org/10.1016/j.paerosci.2006.10.002.CrossRefGoogle Scholar
Bak, C., Johansen, J. and Andersen, P. Three-dimensional corrections of airfoil characteristics based on pressure distributions, 2006 European Wind Energy Conference and Exhibition, European Wind Energy Association (EWEA), 2006, pp 1–10.Google Scholar
Tsien, H.-S. Two-dimensional subsonic flow of compressible fluids, J. Aeronaut. Sci., 1939, 6, 10, pp 399–407. https://doi.org/10.2514/8.916.CrossRefGoogle Scholar
von Karman, T. Compressibility effects in aerodynamics, J. Aeronaut. Sci., 1941, 8, 9, pp 337–356. https://doi.org/10.2514/8.10737.CrossRefGoogle Scholar
Gu, H., Cai, X., Zhou, J., Li, Z., Shen, S. and Zhang, F. A coordinate descent method for multidisciplinary design optimization of electric-powered winged UAVs, 2018 International Conference on Unmanned Aircraft Systems (ICUAS), 2018, pp 1189–1198.CrossRefGoogle Scholar
Wall, D.L. Optimum Propeller Design for Electric UAVs, Master’s thesis, Auburn University, 2012.Google Scholar
Coder, J.G. and Maughmer, M.D. Comparisons of theoretical methods for predicting airfoil aerodynamic characteristics, J. Aircr., 2014, 51, 1, pp 183–191.CrossRefGoogle Scholar
Viterna, L.A. and Janetzke, D.C. Theoretical and Experimental Power from Large Horizontal-Axis Wind Turbines, Tech. rep., NASA Lewis Research Center Technical Report, NASA TM-82944, 09 1982. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19820025954.pdf Google Scholar
Lindenburg, C. Investigation into Rotor Blade Aerodynamics, Tech. rep., Energy Research Centre of the Netherlands (ECN) Wind Energy publication, ECN-C–03025, 2003.Google Scholar
Spera, D.A. Models of lift and drag coefficients of stalled and unstalled airfoils in wind turbines and wind tunnels, Tech. rep., NASA Technical Report NASA/CR-2008-215434, July 2012. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20090001311.pdf Google Scholar
Barrett, R. and Ning, A. Comparison of airfoil precomputational analysis methods for optimization of wind turbine blades, IEEE Trans. Sustain. Energy, 2016, 7, 3, pp 1081–1088. https://doi.org/10.1109/TSTE.2016.2522381.CrossRefGoogle Scholar
Munduate, X. and Ferrer, E. CFD predictions of transition and distributed roughness over a wind turbine airfoil, 47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition, American Institute of Aeronautics and Astronautics, 2009, p 269. https://doi.org/10.2514/6.2009-269.Google Scholar
Morgado, J., Vizinho, R., Silvestre, M. and PÁscoa, J. XFOIL vs CFD performance predictions for high lift low Reynolds number airfoils, Aerosp. Sci. Technol., 2016, 52, pp 207–214. https://doi.org/10.1016/j.ast.2016.02.031.CrossRefGoogle Scholar
Koch, L.D. Design and Performance Calculations of a Propeller for Very High Altitude Flight, Tech. rep., NASA/TM–1998-206637, February 1998. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19980017535.pdf Google Scholar
Bianchini, A., Balduzzi, F., Rainbird, J.M., Peiro, J., Graham, J.M.R., Ferrara, G. and Ferrari, L., An experimental and numerical assessment of airfoil polars for use in Darrieus wind turbines–Part II: Post-stall data extrapolation methods, J. Eng. Gas Turb. Power, 2015, 138, 3, 032603. https://doi.org/10.1115/1.4031270.CrossRefGoogle Scholar
AraÚjo, A., Silva, A., Santos, C., Corte Real, E., Menezes, E., Rocha, G., Rodrigues, V., Borges, W., Costa, J.A. and Bezerra, C. An assessment of different turbulence models on a CFD simulation of air flow past a S814 airfoil, 24th ABCM International Congress of Mechanical Engineering, ABCM, 2017, p 9. https://doi.org/10.26678/ABCM.COBEM2017.COB17-0306.Google Scholar
Menter, F.R. Two-equation eddy-viscosity turbulence models for engineering applications, AIAA J., 1994, 32, 8, pp 1598–1605. https://doi.org/10.2514/3.12149.CrossRefGoogle Scholar
Wang, S., Ingham, D.B., Ma, L., Pourkashanian, M. and Tao, Z. Turbulence modeling of deep dynamic stall at relatively low Reynolds number, J. Fluids Struct., 2012, 33, pp 191–209. https://doi.org/10.1016/j.jfluidstructs.2012.04.011.CrossRefGoogle Scholar
Menter, F.R., Langtry, R. and Völker, S. Transition modelling for general purpose CFD codes, Flow Turbulence Comb., 2006, 77, 1, pp 277–303. https://doi.org/10.1007/s10494-006-9047-1.CrossRefGoogle Scholar
GÜnel, O., KoÇ, E. and Yavuz, T. CFD vs. XFOIL of airfoil analysis at low Reynolds numbers, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), 2016, pp 628–632. https://doi.org/10.1109/ICRERA.2016.7884411.CrossRefGoogle Scholar
White, F. Fluid Mechanics, 5th ed., McGraw-Hill Series in Mechanical Engineering, McGraw Hill, New York, 2003.Google Scholar
Salim, S. and Cheah, S. Wall y+ strategy for dealing with wall-bounded turbulent flows, Proceedings of the International MultiConference on Engineerings and Computer Science, Hong Kong, 2009, pp 2165–2170.Google Scholar
APC, APC Propeller Performance Data, 2020, https://www.apcprop.com/technical-information/ performance-data/. Accessed 15, February, 2020.Google Scholar
Dougherty, F. TAIR: A Transonic Airfoil Analysis Computer Code, NASA Technical Memorandum, Ames Research Center, 1981. https://books.google.com.co/books?id= 2eJCAQAAIAAJ Google Scholar
Brandt, J., Deters, R., Ananda, G. and Selig, M., UIUC Propeller Database, 2015, http://m-selig.ae.illinois.edu/props/propDB.html. Accessed 15 February, 2020.Google Scholar
Tjhai, C. Developing Stochastic Model of Thrust and Flight Dynamics for Small UAVs, Master’s thesis, University of Minnesota, 2013.Google Scholar
Dai, X., Quan, Q., Ren, J. and Cai, K., An analytical design-optimization method for electric propulsion systems of multicopter UAVs with desired hovering endurance, IEEE/ASME Trans. Mechatron., 2019, 24, 1, pp 228–239.CrossRefGoogle Scholar
Anderson, J. Fundamentals of Aerodynamics, 5th ed., McGraw-Hill Education, New York, 2011. https://books.google.com.co/books?id=xwY8PgAACAAJ Google Scholar
Larrabee, E.E. Practical design of minimum induced loss propellers, SAE Trans., 1979, 88, pp 2053–2062. http://www.jstor.org/stable/44699041 Google Scholar
Reuben, U.J., Hiraki, K. and Shohei, M. Airfoil considerations in the design of high performance, low Reynolds number propellers, Int. J. Res. - GRANTHAALAYAH, 2018, 6, 9, pp 373–384.CrossRefGoogle Scholar
“Airfoil Tools”, 2020, http://airfoiltools.com/, Accessed 15 February 2020.Google Scholar
Gur, O. and Rosen, A. Optimizing electric propulsion systems for unmanned aerial vehicles, J. Aircr., 2009, 46, 4, pp 1340–1353. https://doi.org/10.2514/1.41027.CrossRefGoogle Scholar
Gur, O. and Rosen, A. Multidisciplinary design optimization of a quiet propeller, 14th AIAA/CEAS Aeroacoustics Conference (29th AIAA Aeroacoustics Conference), American Institute of Aeronautics and Astronautics, 2012, p 3073. https://doi.org/10.2514/6.2008-3073.Google Scholar