Advertisement

Augmented Prediction of Turbulent Flows via Sequential Estimators

  • M. MeldiEmail author
  • A. Poux
Conference paper
Part of the ERCOFTAC Series book series (ERCO, volume 25)

Abstract

Among the numerous research aspects in the analysis of complex flow configurations of industrial interest, the accurate prediction of turbulent flows is one of the ultimate open challenges. Investigation via classical tools, such as experiments and numerical simulation, is difficult because of fundamental drawbacks which can not be completely excluded. Experiments provide a local description of flow dynamics via measurements sampled by sensors. A complete reconstruction of the flow behavior in the whole physical domain is problematic because of the non-linear, strongly inertial behavior of turbulence. While reduced-order models, such as POD (Lumley, Stochastic tools in turbulence. Academic Press, New York, 1970, [4]), have been extensively used for this purpose, they usually provide an incomplete reconstruction of turbulent flows for the aforementioned reasons.

References

  1. 1.
    Davidson, P.A.: Turbulence. An Introduction for Scientists and Engineers. Oxford University Press, Oxford (2004)zbMATHGoogle Scholar
  2. 2.
    Issa, R.I.: Solution of the implicitly discretized fluid flow equations by operator-splitting. J. Comput. Phys. 62, 40–65 (1986)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)CrossRefGoogle Scholar
  4. 4.
    Lumley, J.: Stochastic Tools in Turbulence. Academic Press, New York (1970)zbMATHGoogle Scholar
  5. 5.
    Meldi, M., Poux, A.: A reduced order Kalman Filter model for sequential data assimilation of turbulent flows. J. Comput. Phys. (2017).  https://doi.org/10.1016/j.jcp.2017.06.042MathSciNetCrossRefGoogle Scholar
  6. 6.
    Meldi, M., Salvetti, M.V., Sagaut, P.: Quantification of errors in large-eddy simulations of a spatially evolving mixing layer using polynomial chaos. Phys. Fluids 24, 035101 (2012)CrossRefGoogle Scholar
  7. 7.
    Pope, S.: Turbulent Flows. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  8. 8.
    Sagaut, P.: Large-Eddy Simulation for Incompressible Flows. An Introduction, 3rd edn. Springer, Berlin (2005)zbMATHGoogle Scholar
  9. 9.
    Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley, New York (2006)CrossRefGoogle Scholar
  10. 10.
    Suzuki, T.: Reduced-order Kalman-filtered hybrid simulation combining particle tracking velocimetry and direct numerical simulation. J. Fluid Mech. 709, 249–288 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institut Pprime, Department of Fluid Flow, Heat Transfer and CombustionCNRS - ENSMA - Universit’e de Poitiers, UPR 3346, SP2MI - TeleportFuturoscope Chasseneuil CedexFrance

Personalised recommendations