Sensitivity analysis on the model input parameters of flapping wing kinematics for optimum level flight via particle swarming optimization

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    A systematic reviewing process to assess the sensitivity of the input parameters of flapping wing kinematics for optimum level flight is presented. This is done prior to the development of a stroke optimization model to predict the aerodynamic performance of an insect (hawk moth) during flight. A systematic iterative process-population-based stochastic algorithm, known as particle swarming optimization, is used. In the search for an optimal realistic wing kinematic motion, several constraints for stable flight are defined following the observational wing kinematics data from experiments on real insects. This is to avoid any physically-unrealistic solutions of the wing motion. Two stages of sensitivity analyses are conducted via partial sensitivity analysis, or one-at–a-time. First, sensitivity screening analyses are performed to gauge the dependability of the solution output, i.e. total force and total power, on each model input parameter; a total of 9 model input parameters. Then, the second stage of sensitivity analysis to measure on how the most sensitive model input parameters will affect the optimized kinematics are conducted. The results have shown that the wing length and the wing translational lift coefficient are the most sensitive aerodynamic model input parameters among other inputs.

     


  • Keywords


    flapping wing; kinematic; PSO; optimal flapping motion; level flight.

  • References


      [1] Stanford B, Beran P & Patil M (2013), Optimal flapping-wing vehicle dynamics via Floquet multiplier sensitivities. Journal of Guidance, Control, and Dynamics 36, 454–466.

      [2] Ghommem M et al. (2012), Global optimization of actively morphing flapping wings. Journal of Fluids and Structures 33, 210–228.

      [3] Ghommem M, Collier N, Niemi AH & Calo VM (2014) On the shape optimization of flapping wings and their performance analysis. Aerospace Science and Technology 32, 274–292.

      [4] Isogai K & Harino Y (2007), Optimum aeorelastic design of a flapping wing. AIAA Journal 44, 2040–2048.

      [5] Stanford B, Beran P, Snyder R & Patil M (2013), Stability and power optimality in time-periodic flapping wing structures. Journal of Fluids and Structures 38, 238–254.

      [6] Taha HE, Hajj MR & Nayfeh AH (2013), Wing kinematics optimization for hovering micro air vehicles using calculus of variation. Journal of Aircraft 50, 610–614.

      [7] Berman GJ & Wang ZJ (2007), Energy-minimizing kinematics in hovering insect flight. Journal of Fluid Mechanics 582, 153–168.

      [8] Khan ZA & Agrawal SK (2011), Optimal hovering kinematics of flapping wings for micro air vehicles. AIAA Journal 49, 257–268.

      [9] Tuncer IH & Kaya M (2005), Optimization of flapping airfoils for maximum thrust and propulsive efficiency. AIAA journal 43, 2329–2336.

      [10] Zheng L, Hedrick T & Mittal R (2013), A multi-fidelity modelling approach for evaluation and optimization of wing stroke aerodynamics in flapping flight. Journal of Fluid Mechanics 721, 118–154.

      [11] Soueid H, Guglielmini L, Airiau C & Bottaro A (2009), Optimization of the motion of a flapping airfoil using sensitivity functions. Computers & Fluids 38, 861–874.

      [12] Kaya M, Tuncer IH, Jones KD & Platzer MF (2009), Optimization of flapping motion parameters for two airfoils in a biplane configuration. Journal of Aircraft 46, 583–592.

      [13] Kennedy J (2010), Particle swarm optimization. Encyclopedia of Machine Learning, 760–766.

      [14] Faisal AHM & Filippone A (2016), Aerodynamic model for insect flapping wings with induced flow effect. Journal of Aircraft 53(3), 701-712.

      [15] Faisal AHM & Filippone A (2016), Aerodynamic model for tandem flapping wings. AIAA Journal 54(12), 3849-3858.

      [16] Chen WN et al. (2010), A novel set-based particle swarm optimization method for discrete optimization problems. IEEE Transactions on Evolutionary Computation 14, 278–300.

      [17] Pontani M & Conway BA (2010), Particle swarm optimization applied to space trajectories. Journal of Guidance, Control, and Dynamics 33.

      [18] Saltelli A, Chan K & Scott EM (2000), Sensitivity analysis. Wiley.

      [19] Willmott AP & Ellington CP (1997), The mechanics of flight in the hawk moth Manduca sexta. I. Kinematics of hovering and forward flight. The Journal of Experimental Biology 200, 2705–2722.

      [20] Nabawy MR & Crowther WJ (2015), Aero-optimum hovering kinematics. Bioinspiration & Biomimetics Journal 10, 1–10.

      [21] Sun M & Du G (2003), Lift and power requirements of hovering insect flight. Acta Mechanica Sinica 19, 458–469.

      [22] Liu Y & Sun M (2008), Wing kinematics measurement and aerodynamics of hovering droneflies. Journal of Experimental Biology 211, 2014–2025.

      [23] Altshuler DL, Dickson WB, Vance JT, Roberts SP & Dickinson MH (2005), Short-amplitude high-frequency wing strokes determine the aerodynamics of honeybee flight. Proceedings of the National Academy of Sciences of the United States of America 102, 18213–18218.

      [24] Stanford B, Kurdi M, Beran P & McClung A (2012), Shape, structure, and kinematic parameterization of a power-optimal hovering wing. Journal of Aircraft 49, 1687–1699.

      [25] Willmott AP & Ellington CP (1997), The mechanics of flight in the hawk moth Manduca sexta. II. Aerodynamic consequences of kinematic and morphological variation. Journal of Experimental Biology 200, 2723–2745.

      [26] Wakeling JM & Ellington CP (1997), Dragonfly Flight III: Lift and power requirements. The Journal of Experimental Biology 200, 583–600.

      [27] Marden JH (1989), Bodybuilding dragonflies: costs and benefits of maximizing flight muscle. Physiological Zoology 62, 505–521.

      [28] Willmott AP (2005), Mechanics of hawk moth flight. Cambridge University.

      [29] Clerc M & Kennedy J (2002), The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evolutionary Computation 6, 58–73.


 

View

Download

Article ID: 21329
 
DOI: 10.14419/ijet.v7i4.13.21329




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.