Advertisement

Meccanica

, Volume 50, Issue 8, pp 2179–2199 | Cite as

Dynamic behavior of pumps: an efficient approach for fast robust design optimization

  • Gabriele Tosi
  • Emiliano MucchiEmail author
  • Roberto d’Ippolito
  • Giorgio Dalpiaz
Article

Abstract

Classical design optimization procedures rely on the application of optimization algorithms upon system simulations. These algorithms need a large number of samples to converge on the optimal point. Hence design optimization needs large simulation time since each simulation has to be performed many times. In this paper an original and useful methodology to study and optimize mechanical systems is presented. The proposed methodology combines different techniques such as design of experiments, response surface models and evolutionary algorithms leading to larger time reduction with respect to the classical design optimization approach. On the one hand the proposed methodology gives the optimal combination of variables and on the other hand provides important results to understand the influence of each input to the system outputs. Moreover, a robust design process has been carried out in order to consider the manufacturing tolerances of the real mechanical system and assess their effect on the system performance. The results offer important information and design insights that would be very difficult to obtain without such a procedure. In order to demonstrate the methodology effectiveness, two case studies have been accounted: the optimization of the vibration level of a vane pump and a gear pump. To simulate the dynamical behaviour of the two pumps, mathematical models have been used. These models have been developed and validated by the authors in previous works. The mathematical models include the main important phenomena involved in the pumps operation and they have been validated on the basis of experimental data. The main operational and geometrical input variables have been taken into account in the optimization procedure of such pumps.

Keywords

Optimization Response surface modelling DOE Pump dynamics 

Notes

Acknowledgments

This work has been developed within the Advanced Mechanics Laboratory (MechLav) of Ferrara Technopole, realized through the contribution of Regione Emilia-Romagna—Assessorato Attività Produttive, Sviluppo Economico, Piano telematico–POR-FESR 2007-2013, Activity I.1.1.

References

  1. 1.
    Mucchi E, Tosi G, D’Ippolito R, Dalpiaz G (2010) A robust design optimization methodology for external gear pumps. In: Proceedings of the ASME 2010 10th Biennal conference on engineering systems design and analysis ESDA2010, Istanbul, TurkeyGoogle Scholar
  2. 2.
    Mucchi E, Dalpiaz G (2013) Analysis of the evolution of pressure forces in variable displacement vane pumps using different approaches. In: Proceedings of the ASME 2013 international design engineering technical conferences & computers and information in engineering conference IDETC/CIE 2013 August 4–7, 2013, Portland, OR, USAGoogle Scholar
  3. 3.
    Mucchi E, Dalpiaz G, Fernàndez del Rincòn A (2010) Elasto-dynamic analysis of a gear pump. Part I: pressure distribution and gear eccentricity. Mech Syst Signal Process 24:2160–2179ADSCrossRefGoogle Scholar
  4. 4.
    Mucchi E, Dalpiaz G, Rivola A (2010) Elasto-dynamic analysis of a gear pump. Part II: meshing phenomena and simulation results. Mech Syst Signal Process 24:2180–2197ADSCrossRefGoogle Scholar
  5. 5.
    Papalambros P, Gunawan S, Chan K-Y, Brudnak M, Van den Bergh G (2007) Software integration for simulation-based analysis and robust design automation of HMMWV rollover bahavior, SAE InternationalGoogle Scholar
  6. 6.
    Zang C, Friswell MI, Mottershead JE (2005) A review of robust optimal design and its application in dynamics. Comput Struct 83:315–326CrossRefGoogle Scholar
  7. 7.
    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315CrossRefGoogle Scholar
  8. 8.
    Snyman JA, Hay AM (2002) The dynamic-Q optimization method: an alternative to SPQ? Comput Math Appl 44:1589–1598MathSciNetCrossRefGoogle Scholar
  9. 9.
    Steenackers G, Presezniak F, Guillaume P (2009) Development of an adaptive response surface method for optimization of computation-intensive models. Comput Ind Eng 57:847–855CrossRefGoogle Scholar
  10. 10.
    Breitkopf P, Naceur H, Rassineux A, Villon P (2005) Moving least squares response surface approximation: formulation and metal forming applications. Comput Struct 83:1411–1428CrossRefGoogle Scholar
  11. 11.
    Chen W, Zhou X, Wang H, Wang W (2010) Multi-objective optimal approach for injection molding based on surrogate model and particle swarm optimization algorithm. J Shanghai Jiaotong Univ (Sci) 15(1):88–93MathSciNetCrossRefGoogle Scholar
  12. 12.
    Henao CA, Maravelias CT (2011) Surrogate-based superstructure optimization framework. AIChE J 57(5):1216–1232Google Scholar
  13. 13.
    Rikards R, Abramovich H, Auzins J, Korjakins A, Ozolinsh O, Kalnins K, Green T (2004) Surrogate models for optimum design of stiffened composite shells. Compos Struct 63:243–251CrossRefGoogle Scholar
  14. 14.
    Queipo NV, Arevalo CJ, Pintos S (2005) The integration of design of experiments, surrogate modeling and optimization for thermoscience research. Eng Comput 20:309–315CrossRefGoogle Scholar
  15. 15.
    Perez VM, Renaud JE, Watson LT (2002) Adaptive experimental design for construction of response surface approximation. AIAA J 40(12):2495–2503ADSCrossRefGoogle Scholar
  16. 16.
    Lorenzen TJ, Anderson VL (1993) Design of experiments, a no-name approach. Marcel Dekker, New YorkGoogle Scholar
  17. 17.
    Krishnamurthy T (2003) Response surface approximation with augmented and compactly supported radial basis functions. In: Proceedings of 44th AIAA/ASME/ASCE/AHS/ASC, Norfolk, VirginiaGoogle Scholar
  18. 18.
    Storn R, Price K (1996) Minimizing the real function of ICEC’96 contest by differential evolutions. In: Proceedings of the international conference on evolutionary computation, Nagoya, JapanGoogle Scholar
  19. 19.
    Papalambros PY, Wilde DJ (2010) Principles of optimal design. Cambridge University Press, CambridgeGoogle Scholar
  20. 20.
    Noesis Solutions (2008) OPTIMUS theoretical background. Leuven, BelgiumGoogle Scholar
  21. 21.
    Barbarelli S, Bova S, Piccione R (2009) Zero-dimensional model and pressure data analysis of a variable-displacement lubricating vane pump. SAE International (2009-01-1859)Google Scholar
  22. 22.
    Cantore G, Paltrinieri F, Tosetti F, Milani M (2008) Lumped parameters numerical simulation of a variable displacement vane pump for high speed ice lubrification. SAE International (2008-01-2445)Google Scholar
  23. 23.
    Mucchi E, Dalpiaz G, Rivola A (2011) Dynamic behaviour of gear pumps: effect of variations in operational and design parameters. Meccanica 46(6):1191–1212CrossRefGoogle Scholar
  24. 24.
    Sanchez SM, A robust design tutorial. In: Tew JD, Manivannan S, Sadowski DA, Seila AF (eds) Proceedings of the 1994 winter simulation conferenceGoogle Scholar
  25. 25.
    Taguchi G (1995) Quality engineering (Taguchi Methods) for the development of electronic circuit technology. IEEE Trans Reliab 44(2):225–229CrossRefGoogle Scholar
  26. 26.
    Kalos MH, Whitlock PA (1986) Monte Carlo methods: vol 1st, basics. Wiley, New YorkCrossRefGoogle Scholar
  27. 27.
    Schinozuka M (1972) Monte Carlo solution of structural dynamics. Comput Struct 2:855–874CrossRefGoogle Scholar
  28. 28.
    D’Ippolito R, Donders S, Van der Auweraer H (2008) Virtual prototypes for uncertainty and variability-based product engineering. In: Talabă D, Amditis A (eds) Product engineering: tools and methods based on virtual reality. Springer, Dordrecht, pp 427–448CrossRefGoogle Scholar
  29. 29.
    Mucchi E, D’Elia G, Dalpiaz G (2012) Simulation of the running in process in external gear pumps and experimental verification. Meccanica 47(3):621–637CrossRefGoogle Scholar
  30. 30.
    Mucchi E, Rivola A, Dalpiaz G (2014) Modelling dynamic behaviour and noise generation in gear pumps: procedure and validation. Appl Acoust 77:99–111CrossRefGoogle Scholar
  31. 31.
    Zardin B, Paltrinieri F, Brghi M, Milani M (2004) About the prediction of pressure variation in the inter-teeth volumes of external gear pumps. In: Proceedings of the 3rd FPNI-PhD symposium on fluid power, Terrassa, Spain, June 30–July 2Google Scholar
  32. 32.
    Borghi M, Bonacini C (1991) Calcolo delle pressioni sui fianchi degli ingranaggi di machine oleodinamiche ad ingranaggi esterni, Oleodinamica-Pneumatica, 118–124Google Scholar
  33. 33.
    Childs D, Moes H, Van Leeuwen H (1997) Journal bearing impedance descriptions for rotordynamic application. J Lubr Technol 99:198–214CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Gabriele Tosi
    • 1
  • Emiliano Mucchi
    • 1
    Email author
  • Roberto d’Ippolito
    • 2
  • Giorgio Dalpiaz
    • 1
  1. 1.Engineering Department in FerraraUniversità degli Studi di FerraraFerraraItaly
  2. 2.NOESIS SolutionsLouvainBelgium

Personalised recommendations