Journal of Marine Science and Technology

, Volume 24, Issue 4, pp 1043–1056 | Cite as

An efficient calibration approach for cavitation model constants based on OpenFOAM platform

  • Houcun Zhou
  • Min XiangEmail author
  • Patrick N. Okolo
  • Zeping Wu
  • Gareth J. Bennett
  • Weihua Zhang
Original article


To precisely capture the cavitation process, it is essential to establish physical and mathematical models that describes the thermodynamic process between liquid and vapor. However, most mass transfer models represented by the cavitation model involves numbers of empirical constants which are case specific. The calibration for the cavitation model constants is therefore required for accurate numerical simulations. In this paper, a rapid and effective calibration approach for model coefficients using a surrogate-based approximate optimization (SAO) strategy is established based on OpenFOAM platform. Coefficients calibration has been carried out for the Kunz cavitation model using both two-dimensional and three-dimensional hydrofoil experimental data. The selection law for the Kunz model constants which defines a specific region that would guarantee low levels of errors is obtained, implying the mass transfer mechanism between gas and liquid. Furthermore, the optimized values for the model constants are gained. Validations have been carried out using the cases of the three-dimensional hemispherical head-form. Results show that the optimal constants perform much better in cavitation predictions. The proposed calibration method is validated for feasibility and efficiency for cavitation model constant calibration and can also be applied for the other models with various empirical constants.


Cavitation model Optimization Calibration Numerical simulation OpenFOAM 



The authors gratefully acknowledge support by the National Nature Science Foundation of China (NSFC, Grant nos. 51776221, 61872380) and foundation of State Key Laboratory of High Performance Computing of China (Grant no. 201803-01).


  1. 1.
    Franc JP, Michel JM (2005) Fundamentals of cavitation. Fluid mechanics and its applications. Springer, Netherlands. CrossRefzbMATHGoogle Scholar
  2. 2.
    Ahn BK, Lee CS, Kim HT (2010) Experimental and numerical studies on super-cavitating flow of axisymmetric cavitators. Int J Nav Archit Ocean Eng 2(1):39–44. CrossRefGoogle Scholar
  3. 3.
    Merkle CL, Feng J, Buelow PEO (1998) Computational modeling of the dynamics of sheet cavitation. In: Proceedings of the 3rd international symposium on cavitation, pp 1–9Google Scholar
  4. 4.
    Kunz RF, Boger DA, Chyczewski TS, Stinebring DR, Gibeling HJ, Govindan TR (1999) Multi-phase CFD analysis of natural and ventilated cavitation about submerged bodies. FEDSM ‘99 1999 ASME/JSME joint fluids engineering conference, pp 1–9Google Scholar
  5. 5.
    Schnerr GH, Sauer J (2001) Physical and numerical modeling of unsteady cavitation dynamics. In: Proceedings of the 4th international conference on multiphase flow, New Orleans, LA, USAGoogle Scholar
  6. 6.
    Singhal AK, Athavale MM, Li H, Jiang Y (2002) Mathematical basis and validation of the full cavitation model. J Fluids Eng 124(3):617–624CrossRefGoogle Scholar
  7. 7.
    Zwart PJ, Gerber AG, Belamri T (2004) A two-phase flow model for predicting cavitation dynamics. In: ICMF 2004 international conference on multiphase flowGoogle Scholar
  8. 8.
    Huang B, Wang GY (2011) A modified density based cavitation model for time dependent turbulent cavitating flow computations. Chin Sci Bull 56(19):1985–1992CrossRefGoogle Scholar
  9. 9.
    Konstantinov SY, Tselischev DV, Tselischev VA (2015) Numerical cavitation model for simulation of mass flow stabilization effect in ANSYS CFX. Mod Appl Sci 9(4):21–31Google Scholar
  10. 10.
    Bensow RE, Bark G (2010) Simulating cavitating flows with les in openfoam. In: 5th European conference on computational fluid dynamics, ECCOMAS CFD (June), p 18Google Scholar
  11. 11.
    Meng G, Tan L, Cao S, Wang Y, Yun X, Wanshi Q (2014) Numerical prediction of performance drop due to cavitation in a centrifugal pump. In: 2014 ISFMFE—6th international symposium on fluid machinery and fluid engineering, pp 1–5.
  12. 12.
    Roohi E, Zahiri AP, Pasandideh-Fard M (2012) Numerical simulation of cavitation around a two-dimensional hydrofoil using VOF method and les turbulence model. In: Proceedings of the eighth international symposium on cavitation (CAV 2012), pp 661–666Google Scholar
  13. 13.
    Roohi E, Zahiri AP, Passandideh-Fard M (2013) Numerical simulation of cavitation around a two-dimensional hydrofoil using VOF method and les turbulence model. Appl Math Model 37(9):6469–6488MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang Y, Luo XW, Ji B, Liu SH, Wu YL, Xu HY (2010) A thermodynamic cavitation model for cavitating flow simulation in a wide range of water temperatures. Chin Phys Lett 27(1):016401CrossRefGoogle Scholar
  15. 15.
    Vaidyanathan R, Senocak I, Wu J, Shyy W (2003) Sensitivity evaluation of a transport-based turbulent. J Fluids Eng Trans ASME 125:447–458CrossRefGoogle Scholar
  16. 16.
    Goel T, Thakur S, Haftka RT, Shyy W, Zhao J (2008) Surrogate model-based strategy for cryogenic cavitation model validation and sensitivity evaluation. Int J Numer Methods Fluids 58(9):969–1007CrossRefGoogle Scholar
  17. 17.
    Passandideh-Fard M, Roohi E (2008) Transient simulations of cavitating flows using a modified volume-of-fluid (VOF) technique. Int J Comput Fluid Dyn 22(1–2):97–114CrossRefGoogle Scholar
  18. 18.
    Tani N, ichi Tsuda S, Yamanishi N, Yoshida Y (2009) Development and validation of new cryogenic cavitation model for rocket turbopump inducer. In: Proceedings of the 7th international symposium on cavitation (CAV2009), Ann Arbor, Michigan, USA, pp 1–10Google Scholar
  19. 19.
    Morgut M, Nobile E, Biluš I (2011) Comparison of mass transfer models for the numerical prediction of sheet cavitation around a hydrofoil. Int J Multiph Flow 37(6):620–626CrossRefGoogle Scholar
  20. 20.
    Liu HL, Wang J, Wang Y, Zhang H, Huang H (2014) Influence of the empirical coefficients of cavitation model on predicting cavitating flow in the centrifugal pump. Int J Nav Archit Ocean Eng 6(1):119–131CrossRefGoogle Scholar
  21. 21.
    Tseng CC, Wang LJ (2014) Investigations of empirical coefficients of cavitation and turbulence model through steady and unsteady turbulent cavitating flows. Comput Fluids 103:262–274MathSciNetCrossRefGoogle Scholar
  22. 22.
    Pendar MR, Roohi E (2016) Investigation of cavitation around 3D hemispherical head-form body and conical cavitators using different turbulence and cavitation models. Ocean Eng 112:287–306CrossRefGoogle Scholar
  23. 23.
    Roohi E, Pendar MR, Rahimi A (2016) Simulation of three-dimensional cavitation behind a disk using various turbulence and mass transfer models. Appl Math Model 40(1):542–564MathSciNetCrossRefGoogle Scholar
  24. 24.
    Asnaghi A, Feymark A, Bensow RE (2017) Improvement of cavitation mass transfer modeling based on local flow properties. Int J Multiph Flow 93:142–157. MathSciNetCrossRefGoogle Scholar
  25. 25.
    Kinzel M, Lindau J, Kunz R (2017) A unified homogenous multiphase CFD model for cavitation. In: American society of mechanical engineers, fluids engineering division (publication) FEDSM, vol 1B-2017Google Scholar
  26. 26.
    Hu F, Wu Z, Wang D, Zhang W (2017) Sequential approximate optimization method. Guofang Keji Daxue Xuebao J Natl Univ Def Technol 39(1):92–101Google Scholar
  27. 27.
    Bonte MHA, van den Boogaard AH, Huétink J (2008) An optimisation strategy for industrial metal forming processes. Struct Multidiscip Optim 35(6):571–586CrossRefGoogle Scholar
  28. 28.
    Bonte MHA, Fourment L, Do TT, Boogaard AHVD, Huétink J (2010) Optimization of forging processes using finite element simulations: a comparison of sequential approximate optimization and other algorithms. Struct Multidiscip Optim 42(5):797–810CrossRefGoogle Scholar
  29. 29.
    Wang D, Hu F, Ma Z, Wu Z, Zhang W (2014) A CAD/CAE integrated framework for structural design optimization using sequential approximation optimization. Adv Eng Softw 76:56–68CrossRefGoogle Scholar
  30. 30.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, Perth, Australia, IEEE Service Center, Piscataway, NJ, vol 4, pp 1942–1948Google Scholar
  31. 31.
    Wu Z, Wang D, Hu F, Zhang W (2016) Surrogate based grain design optimization for solid rocket motor. J Solid Rocket Technol 39(03):321–326Google Scholar
  32. 32.
    Ebrahim K, Erfan K, Khodayar J, Seyyed MJ (2017) The investigation of natural super-cavitation flow behind three-dimensional cavitators: full cavitation model. Appl Math Model 45:165–178MathSciNetCrossRefGoogle Scholar
  33. 33.
    Dimotakis PE, Gaebler HF, Hamaguchi HT (1987) Two dimensional NACA 66(MOD) hydrofoil high speed water tunnel tests. California Institute of Technology, Pasadena, CA (Unpublished).
  34. 34.
    Rouse H, Stephenson MJ (1948) Cavitation and pressure distribution: head forms at zero angle of yaw. State University of Iowa, Iowa CityGoogle Scholar

Copyright information

© JASNAOE 2018

Authors and Affiliations

  • Houcun Zhou
    • 1
  • Min Xiang
    • 1
    Email author
  • Patrick N. Okolo
    • 2
  • Zeping Wu
    • 1
  • Gareth J. Bennett
    • 2
  • Weihua Zhang
    • 1
  1. 1.National University of Defense TechnologyChangshaChina
  2. 2.University of Dublin, Trinity CollegeDublinIreland

Personalised recommendations