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Optimization and Engineering

, Volume 16, Issue 4, pp 663–693 | Cite as

Constrained problem formulations for power optimization of aircraft electro-thermal anti-icing systems

  • Mahdi PourbagianEmail author
  • Bastien Talgorn
  • Wagdi G. Habashi
  • Michael Kokkolaras
  • Sébastien Le Digabel
Article

Abstract

Various constrained problem formulations for the optimization of an electro-thermal wing anti-icing system in both running-wet and evaporative regimes are presented. The numerical simulation of the system is performed by solving the conjugate heat transfer problem between the fluid and solid domains. The optimization goal is to reduce the energy use and power demand of the anti-icing system while ensuring a safe protection. The formulations are carefully proposed from the physical and mathematical viewpoints; their performance is assessed by means of several numerical test cases to discern the most promising for each regime. The design optimization is conducted using the mesh adaptive direct search algorithm using quadratic and statistical surrogate models in the search step. The influence of the models on the convergence speed and the quality of the obtained design solutions is investigated.

Keywords

Aircraft icing Electro-thermal anti-icing system Conjugate heat transfer Optimization Statistical surrogate 

Notes

Acknowledgments

This work was partially supported by a McGill Engineering Doctoral Award (MEDA), a GERAD postdoctoral fellowship and NSERC Discovery Grants 418250-2012 and 436193-2013. The authors would also like to thank the CLUMEQ consortium for its computer resources.

References

  1. Al-Khalil K et al (1997) Validation of thermal ice protection computer codes: part 3-the validation of ANTICE. In: 35th Aerospace sciences meeting and exhibit, RenoGoogle Scholar
  2. Audet C, Dennis JE (2006) Mesh adaptive direct search algorithms for constrained optimization. SIAM J Optim 17(1):188–217zbMATHMathSciNetCrossRefGoogle Scholar
  3. Audet C, Dennis JE (2009) A progressive barrier for derivative-free nonlinear programming. SIAM J Optim 20(1):445–472zbMATHMathSciNetCrossRefGoogle Scholar
  4. Audet C et al (2008) Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search. J Glob Optim 41(2):299–318zbMATHMathSciNetCrossRefGoogle Scholar
  5. Baker AA et al (2004) Composite materials for aircraft structures. AIAA, Reston VirginiaGoogle Scholar
  6. Beaugendre H et al (2006) Development of a second generation in-flight icing simulation code. J Fluids Eng 128(2):378–387CrossRefGoogle Scholar
  7. Buschhorn ST et al (2013) Electrothermal icing protection of aerosurfaces using conductive polymer nanocomposites. In: 54th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, BostonGoogle Scholar
  8. Capey EC (1965) Alleviation of thermal stresses in aircraft structures. Aeronatical Research Council, LondonGoogle Scholar
  9. Croce G et al (2002) CHT3D: FENSAP-ICE conjugate heat transfer computations with droplet impingement and runback effects. In: 40th Aerospace sciences meeting and exhibit, RenoGoogle Scholar
  10. da Silva GAL et al (2005) Simulation of an airfoil electro-thermal anti-ice system operating in running wet regime. In: 43rd AIAA aerospace sciences meeting and exhibit, RenoGoogle Scholar
  11. Dussin D et al (2009) Hybrid grid generation for two-dimensional high-Reynolds flows. Comput Fluids 38(10):1863–1875zbMATHCrossRefGoogle Scholar
  12. Fletcher R, Leyffer S (2002) Nonlinear programming without a penalty function. Math Program 91(2):239–269zbMATHMathSciNetCrossRefGoogle Scholar
  13. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Menlo ParkzbMATHGoogle Scholar
  14. Gramacy RB, Taddy MA (2010) dynaTree: an R package implementing dynamic trees for learning and design. Software available at http://CRAN.R-project.org/package=dynaTree
  15. Hackerman N (1952) Effect of temperature on corrosion of metals by water. Ind Eng Chem 44(8):1752–1755CrossRefGoogle Scholar
  16. Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21(4):345–383zbMATHCrossRefGoogle Scholar
  17. Jones DR et al (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455–492zbMATHCrossRefGoogle Scholar
  18. Le Digabel S (2011) Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans Math Softw 37(4):44:41–44:15CrossRefGoogle Scholar
  19. Mayer C et al (2007) Wind tunnel study of electro-thermal de-icing of wind turbine blades. Int J Offshore Polar Eng 17(3):182Google Scholar
  20. McKay MD et al (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42(1):55–61CrossRefGoogle Scholar
  21. Messinger BL (1953) Equilibrium temperature of an unheated icing surface as a function of air speed. J Aeronaut Sci 20(1):29–41CrossRefGoogle Scholar
  22. Miller D et al (1997) Validation of thermal ice protection computer codes: part 1-program overview. In: 35th AIAA aerospace sciences meeting and exhibit, RenoGoogle Scholar
  23. Mivehchi H, Varvani-Farahani A (2010) The effect of temperature on fatigue strength and cumulative fatigue damage of FRP composites. Procedia Eng 2(1):2011–2020CrossRefGoogle Scholar
  24. Pourbagian M, Habashi WG (2012) Parametric analysis of energy requirements of in-flight ice protection systems. In: 20th Annual conference of the CFD society of Canada, CanmoreGoogle Scholar
  25. Pourbagian M, Habashi WG (2013a) CFD-based optimization of electro-thermal wing ice protection systems in de-icing mode. In: 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, GrapevinveGoogle Scholar
  26. Pourbagian M, Habashi WG (2013b) Surrogate-based optimization of electrothermal wing anti-icing systems. J Aircr 50(5):1555–1563CrossRefGoogle Scholar
  27. Pourbagian M, Habashi WG (2013c) On optimal design of electrothermal in-flight ice protection systems. In: 5th AIAA atmospheric and space environments conference, San DiegoGoogle Scholar
  28. Reid T et al (2011) FENSAP-ICE: 3D simulation, and validation, of d-icing with inter-cycle ice accretion. In: SAE 2011 international conference on aircraft and engine icing and ground deicing, ChicagoGoogle Scholar
  29. Reid T et al (2013a) FENSAP-ICE simulation of icing on wind turbine blades, part 1: performance degradation. In: 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, GrapevineGoogle Scholar
  30. Reid T et al (2013b) FENSAP-ICE simulation of icing on wind turbine blades, part 2: ice protection system design. In: 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, GrapevineGoogle Scholar
  31. Schonlau M et al (1997) Global versus local search in constrained optimization of computer models. In: 1997 Joint AMS-IMS-SIAM summer conference, WashingtonGoogle Scholar
  32. Sinnett M (2007) 787 No-bleed systems: saving fuel and enhancing operational efficiencies. Boeing Commer AERO Mag 4(1):6–11Google Scholar
  33. Starke EAJ (1996) Accelerated aging of materials and structures, the effects of long-term elevated-temperature exposure. National Academy Press, Washington, DCGoogle Scholar
  34. Strehlow RH, Moser R (2009) Capitalizing on the increased flexibility that comes from high power density electrothermal deicing. Flight Dyn 2000:01–03Google Scholar
  35. Taddy MA et al (2011) Dynamic trees for learning and design. J Am Stat Assoc 106(493):109–123MathSciNetCrossRefGoogle Scholar
  36. Talgorn B et al (2015) Statistical surrogate formulations for simulation-based design optimization. ASME J Mech Des 137(2):021405-021401–021405-021418CrossRefGoogle Scholar
  37. Thomas SK et al (1996) Aircraft anti-icing and de-icing techniques and modeling. J Aircr 33(5):841–854CrossRefGoogle Scholar
  38. Vaz AIF, Vicente LN (2007) A particle swarm pattern search method for bound constrained global optimization. J Glob Optim 39(2):197–219zbMATHMathSciNetCrossRefGoogle Scholar
  39. Yaslik AD (1991) Three-dimensional numerical simulation of transient heat transfer occurring in electrothermal deicing systems. University of Toledo, ToledoGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Mahdi Pourbagian
    • 1
    Email author
  • Bastien Talgorn
    • 2
    • 3
  • Wagdi G. Habashi
    • 1
  • Michael Kokkolaras
    • 2
    • 3
  • Sébastien Le Digabel
    • 3
    • 4
  1. 1.CFD Laboratory, Department of Mechanical EngineeringMcGill UniversityMontrealCanada
  2. 2.Department of Mechanical EngineeringMcGill UniversityMontrealCanada
  3. 3.GERAD (Group for Research in Decision Analysis) A Multi-university Research CenterMontrealCanada
  4. 4.Département de mathématiques et génie industrielÉcole Polytechnique de MontréalMontrealCanada

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