Turbulent Flow Physics and Control: The Role of Big Data Analyses Tools

  • Andrew S. Magstadt
  • Pinqing Kan
  • Zachary P. Berger
  • Christopher J. Ruscher
  • Matthew G. Berry
  • Melissa A. Green
  • Jacques Lewalle
  • Mark N. Glauser


We are studying several problems involving turbulence and big data that range from more efficient and lower noise in next generation jet propulsion systems to bio-inspired concepts for energy production. Specific examples include flows over airfoils (flapping and stationary) and other complex bodies such as turrets and high-speed jet flows. These research activities involve the collection of massive amounts of data from multi-scale computer simulations and/or large-scale experiments. Such experiments/simulations routinely produce terabytes of multi-modal data (velocity, pressure, acoustics, etc.) in fractions of a second. Time-resolved particle image velocimetry data, for example, has requirements of 10 kHz or higher sampling rates in time along with spatial resolution requirements over a broad range of spatial scales observed in high Reynolds and Mach number turbulent flows. Common questions that arise include: How do we compare and contrast data that have different levels of granularity, density (or sparseness), and distribution (e.g. uniform, checkered, lattice, random, etc.)? Can we combine such fields that span in space and time to develop a holistic systems-level understanding? This is important in linking numerical simulations that apply lenses with varying magnifications to the same system, as well as integrating qualitative and quantitative experimental observations with computer simulations. We will discuss our general efforts to apply big data analyses/modeling tools (the “right filters”) to identify patterns and predictive models rather than just a posteriori trends, statistics, and distributions. Our advanced tools include proper orthogonal decomposition, stochastic estimation, optimal inferred decomposition, wavelet analysis, and Lagrangian coherent structure methods which we are using for understanding, modeling, and controlling such flows. In this paper we focus on high Reynolds and Mach number jets, both axisymmetric and more complex. We have also utilized compressive sensing to examine high-dimensional airfoil data but will not discuss those results here and instead refer the reader to other papers in this volume which focus on this approach in some detail.


Particle Imagine Velocimetry Shear Layer Proper Orthogonal Decomposition Oblique Shock Lagrangian Coherent Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to acknowledge two funding sources that made this work possible: (1) an SBIR Phase I and II project with Spectral Energies, LLC, and the AFRL turbine engine division under the direction of program manager Dr. Barry V. Kiel, and (2) an AFOSR grant, number FA9550-15-1-0435, under the guidance of program manager Dr. R. Ponnappan.


  1. 1.
    N. Aubry, P. Holmes, J.L. Lumley, E. Stone, The dynamics of coherent structures in the wall region of a turbulent boundary layer. J. Fluid Mech. 192 (1), 115–173 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Z. Bai, T. Wimalajeewa, Z. Berger, G. Wang, M. Glauser, P.K. Varshney, Low-dimensional approach for reconstruction of airfoil data via compressive sensing. AIAA J. 53 (4), 920–933 (2014)CrossRefGoogle Scholar
  3. 3.
    Z.P. Berger, The effects of active flow control on high-speed jet flow physics and noise. Ph.D. thesis, Syracuse University (2014)Google Scholar
  4. 4.
    Z. Berger, M. Berry, P. Shea, M. Glauser, N. Jiang, B. Noack, S. Gogineni, E. Kaiser, A. Spohn, Analysis of high speed jet flow physics with time-resolved piv, in 52nd AIAA Aerospace Sciences Meeting (1226) (2014)Google Scholar
  5. 5.
    Z. Berger, P. Shea, M. Berry, B. Noack, S. Gogineni, M. Glauser, Active flow control for high speed jets with large window piv. J. Flow Turbul. Control 94 (1), 97–123 (2015)CrossRefGoogle Scholar
  6. 6.
    M.G. Berry, A.S. Magstadt, M.N. Glauser, C.J. Ruscher, S.P. Gogineni, B.V. Kiel, An acoustic investigation of a supersonic, multi-stream jet with aft deck: characterization and acoustically-optimal operating conditions, in 54 th AIAA ASM (AIAA, San Diego, 2016), vol. 2321022Google Scholar
  7. 7.
    A.V. Cavalieri, G. Daviller, P. Comte, P. Jordan, G. Tadmor, Y. Gervais, Using large eddy simulation to explore sound-source mechanisms in jets. J. Sound Vib. 330, 4098–4113 (2011)CrossRefGoogle Scholar
  8. 8.
    G. Daviller, Étude numérique des effets de température dans les jets simples et coaxiaux. Ph.D. thesis, Institut P’, CNRS - Université de Poitiers (2010)Google Scholar
  9. 9.
    W.K. George, Lectures in turbulence for the 21st century. University of Oslo. Accessed 15 Jan 2016
  10. 10.
    M.N. Glauser, Coherent structures in the axisymmetric turbulent jet mixing layer. Ph.D. dissertation, University of Buffalo (1987)CrossRefGoogle Scholar
  11. 11.
    M. Glauser, W. George, Orthogonal decomposition of the axisymmetric jet mixing layer including azimuthal dependence, in Advances in Turbulence, ed. by G. Comte-Bellot, J. Mathieu (Springer, Heidelberg, 1987), pp. 357–366CrossRefGoogle Scholar
  12. 12.
    M. Glauser, X. Zheng, C.R. Doering, The dynamics of organized structures in the axisymmetric jet mixing layer, in Turbulence and Coherent Structures (Springer, Dordrecht, 1991), pp. 253–265zbMATHGoogle Scholar
  13. 13.
    M.E. Goldstein, Aeroacoustics of turbulent shear flows. Annu. Rev. Fluid Mech. 16 (1), 263–285 (1984)CrossRefzbMATHGoogle Scholar
  14. 14.
    M.A Green, C.W. Rowley, G. Haller, Detection of Lagrangian coherent structures in three-dimensional turbulence. J. Fluid Mech. 572, 111–120 (2007)Google Scholar
  15. 15.
    M.A. Green, C.W. Rowley, A.J. Smits, Using hyperbolic Lagrangian coherent structures to investigate vortices in bioinspired fluid flows. Chaos 20, 017510 (2010)Google Scholar
  16. 16.
    M.A. Green, C.W. Rowley, A.J. Smits, The unsteady three-dimensional wake produced by a trapezoidal pitching panel. J. Fluid Mech. 685, 117–145 (2011)CrossRefzbMATHGoogle Scholar
  17. 17.
    A.M. Hall, An experimental investigation of low-dimensional techniques for large scale noise source characterization in a heated jet. Ph.D. dissertation, Syracuse University (2008)Google Scholar
  18. 18.
    J.W. Hall, A.M. Hall, J.T. Pinier, N.G. Mark, Cross-spectral analysis of the pressure in a mach 0.85 turbulent jet. AIAA J. 47, 54–59 (2009)Google Scholar
  19. 19.
    Y. Huang, M.A. Green, Detection and tracking of vortex phenomena using Lagrangian coherent structures. Exp. Fluids 56 (7), 1–12 (2015). doi: 10.1007/s00348-015-2001-z.
  20. 20.
    P. Kan, J. Lewalle, G. Daviller, Comparison of near-field events and their far-field acoustic signatures in experimental and numerical high speed jets, in International Symposium on Turbulence and Shear Flow Phenomena (TSFP-8) (2013)Google Scholar
  21. 21.
    P. Kan, J. Lewalle, Z.P. Berger, M. Glauser, The properties and localizations of acoustic sources of high speed jet, in 53 rd AIAA ASM, Kissimmee (2015). AIAA-2015-0737Google Scholar
  22. 22.
    P. Kan, C.J. Ruscher, J. Lewalle, M.N. Glauser, S. Gogineni, B.V. Kiel, Extracting near-field structures related to noise production in high speed jets, in 54 th AIAA ASM, San Diego (2016). AIAA 2016-0004Google Scholar
  23. 23.
    J. Lepicovsky, K. AHUJA, W. Brown, R. Burrin, Coherent large-scale structures in high Reynolds number supersonic jets. AIAA J. 25 (11), 1419–1425 (1987)CrossRefGoogle Scholar
  24. 24.
    J. Lewalle, K.R. Low, M.N. Glauser, Properties of individual jet noise sources identified from far-field pressure data. Int. J. Aeroacoust. 5–6 (11), 651–674 (2012)CrossRefGoogle Scholar
  25. 25.
    J. Lewalle, P. Kan, S. Gogineni, Mach-number dependence of acoustic source properties in high speed jets – Part I: ensemble statistics of acoustically active regions, in 52nd AIAA Aerospace Sciences Meeting (1228) (2014)Google Scholar
  26. 26.
    J. Lewalle, P. Kan, S.P. Gogineni, Mach-number dependence of acoustic source properties in high speed jets–Part I: ensemble statistics of active regions, in 52nd AIAA ASM, National Harbor (2014). AIAA-2014-1228Google Scholar
  27. 27.
    M.J. Lighthill, On sound generated aerodynamically. I. General theory. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 211 (1107), 564–587 (1952)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    M.J. Lighthill, On sound generated aerodynamically. II. Turbulence as a source of sound. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 222 (1148), 1–32 (1954)MathSciNetzbMATHGoogle Scholar
  29. 29.
    K. Low, Z. Berger, S. Kostka, B. El Hadidi, S. Gogineni, M. Glauser, A low-dimensional approach to closed-loop control of a mach 0.6 jet. Exp. Fluids 54, 1–17 (2013)Google Scholar
  30. 30.
    J.L. Lumley, The structure of inhomogeneous turbulent flows, in Atmospheric Turbulence and Radio Wave Propagation, ed. by A.M. Yaglom, V.I. Tatarsky (Nauka, Moscow, 1967), pp. 166–178Google Scholar
  31. 31.
    A.S. Magstadt, M. Berry, Z. Berger, P. Shea, C.J. Ruscher, S.P. Gogineni, M. Glauser, An investigation of sonic & supersonic axisymmetric jets: correlations between flow physics and far-field noise. J. Flow Turbul. Control (in review, 2016)Google Scholar
  32. 32.
    A.S. Magstadt, M.G. Berry, Z.P. Berger, P.R. Shea, M.N. Glauser, C.J. Ruscher, S. Gogineni, An investigation of sonic & supersonic axisymmetric jets: correlations between flow physics and far-field noise, in Turbulent Shear Flow Phenomenon 9 (2015)Google Scholar
  33. 33.
    L. Perret, E. Collin, J. Delville, Polynomial identification of POD based low-order dynamical system. J. Turbul. 7, N17 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    J.T. Pinier, Low-dimensional techniques for active control of high-speed jet aeroacoustics. Ph.D. dissertation, Syracuse University (2007)Google Scholar
  35. 35.
    J.T. Pinier, M.N. Glauser, Dual-time piv investigation of the sound producing region of the controlled and uncontrolled high-speed jet, in Advances in Turbulence XI: Proceeding of the 11th EUROMECH European Turbulence Conference, vol. 117 (2007), pp. 392–394Google Scholar
  36. 36.
    A. Powell, On the mechanism of choked jet noise. Proc. Phys. Soc. Sect. B 66 (12), 1039 (1953)Google Scholar
  37. 37.
    H.S. Ribner, The generation of sound by turbulent jets, in Advances in Applied Mechanics, vol. 03 (Academic, New York, 1964), pp. 103–182Google Scholar
  38. 38.
    C. Ruscher, S. Gogineni, K. Viswanath, B. Kiel, M. Berry, A. Magstadt, M. Glauser, Aeroacoustic validation of a simulation for a complex nozzle, in 51st AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Orlando (2015)Google Scholar
  39. 39.
    C.J. Ruscher, A.S. Magstadt, M.G. Berry, M.N. Glauser, P.R. Shea, K. Viswanath, S.P. Gogineni, B.V. Kiel, A.J. Giese, Analysis of a supersonic 3-stream jet flow, Part I: nozzle design, experiments, and simulations. AIAA J. (in review, 2016)Google Scholar
  40. 40.
    L. Sirovich, Turbulence and the dynamics of coherent structures, part I: Coherent Structures. Q. Appl. Math. 45, 561–571 (1987)MathSciNetzbMATHGoogle Scholar
  41. 41.
    C.M. Stack, D.V. Gaitonde, L. Agostini, M.G. Berry, A.S. Magstadt, M.N. Glauser, Numerical investigation of a supersonic multistream jet with an aft-deck, in AIAA SciTech (2016). AIAA 2016-2058Google Scholar
  42. 42.
    C.K. Tam, On the noise of a nearly ideally expanded supersonic jet. J. Fluid Mech. 51, 69–95 (1972)CrossRefzbMATHGoogle Scholar
  43. 43.
    C.K. Tam, Supersonic jet noise. Annu. Rev. Fluid Mech. 27, 17–43 (1995)Google Scholar
  44. 44.
    C.K.W. Tam, Mach wave radiation from high-speed jets. AIAA J. 47 (10), 2440–2448 (2009)Google Scholar
  45. 45.
    C.E. Tinney, M.N. Glauser, L.S. Ukeiley, Low-dimensional characteristics of a transonic jet. Part 1. Proper orthogonal decomposition. J. Fluid Mech. 612, 107–141 (2008)Google Scholar
  46. 46.
    C.E. Tinney, L.S. Ukeiley, M.N. Glauser, Low-dimensional characteristics of a transonic jet. Part 2. Estimate and far-field prediction. J. Fluid Mech. 615, 53–92 (2008)Google Scholar
  47. 47.
    L. Ukeiley, L. Cordier, R. Manceau, J. Delville, M. Glauser, J.P. Bonnet, Examination of large-scale structures in a turbulent plane mixing layer. Part 2. Dynamical systems model. J. Fluid Mech. 441 (1), 67–108 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Andrew S. Magstadt
    • 1
  • Pinqing Kan
    • 1
  • Zachary P. Berger
    • 1
    • 2
  • Christopher J. Ruscher
    • 3
  • Matthew G. Berry
    • 1
  • Melissa A. Green
    • 1
  • Jacques Lewalle
    • 1
  • Mark N. Glauser
    • 1
  1. 1.Department of Mechanical and Aerospace EngineeringSyracuse UniversitySyracuseUSA
  2. 2.Penn State UniversityState CollegeUSA
  3. 3.Computational DivisionSpectral EnergiesDaytonUSA

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