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PhD Thesis

Spatio-temporal analysis of wall-bounded turbulence: A multidisciplinary perspective via complex networksDownload

Refereed journal papers:

  1. F. Kaiser, G. Iacobello, D.E. Rival, “Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation”,
    Proceedings of the Royal Society A (2024) 480(2291), 20230608. doi: 10.1098/rspa.2023.0608

  2. G. Iacobello, D.E. Rival, “Identifying dominant flow features from very-sparse Lagrangian data: a multiscale recurrence network-based approach”,
    Experiments in Fluids (2023) 64, 157. doi: 10.1007/s00348-023-03700-0

  3. G. Iacobello, S. Chowdhuri, L. Ridolfi, L. Rondoni, and S. Scarsoglio, “Coherent structures at the origin of time irreversibility in wall turbulence”,
    Nature Communications Physics (2023) 6, 91. doi: 10.1038/s42005-023-01215-y

  4. G. Iacobello, F. Kaiser, D.E. Rival, “Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data”,
    Physics of Fluids, 34, 025105 (2022). doi: 10.1063/5.0076731
    . 
  5. S. Chowdhuri, G. Iacobello, T. Banerjee, “Visibility network analysis of large-scale intermittency in convective surface layer turbulence”,
    Journal of Fluid Mechanics, (2021) 925, A38. doi: 10.1017/jfm.2021.720 – arXiv: 2102.07102
    . 
  6. G. Iacobello, L. Ridolfi, and S. Scarsoglio, “Large-to-small scale frequency modulation analysis in wall-bounded turbulence via visibility networks”,
    Journal of Fluid Mechanics (2021) 918, A13. doi: 10.1017/jfm.2021.279
    .
  7. G. Iacobello, L. Ridolfi, and S. Scarsoglio, “A review on turbulent and vortical flow analyses via complex networks”,
    Physica A: Statistical Mechanics and its Applications, (2020) 563, 125476. doi: 10.1016/j.physa.2020.125476
    .
  8. G. Iacobello, M. Marro, L. Ridolfi, P. Salizzoni and S. Scarsoglio, “Experimental investigation of vertical turbulent transport of a passive scalar in a boundary layer: Statistics and visibility graph analysis”,
    Physical Review Fluids, (2019) 4(10), 104501. doi: 10.1103/PhysRevFluids.4.104501
    .
  9. G. Iacobello, S. Scarsoglio, J. G. M. Kuerten, and L. Ridolfi, “Lagrangian network analysis of turbulent mixing”,
    Journal of Fluid Mechanics, (2019) 865, 546-562. doi: 10.1017/jfm.2019.79
    .
  10. G. Iacobello, S. Scarsoglio, J. G. M. Kuerten, and L. Ridolfi, “Spatial characterization of turbulent channel flow via complex networks”,
    Physical Review E, (2018) 98(1), 013107. doi: 10.1103/PhysRevE.98.013107
    .
  11. G. Iacobello, S. Scarsoglio, and L. Ridolfi, “Visibility graph analysis of wall turbulence time-series”,
    Physics Letters A, (2018) vol. 382(1), pp. 1–11. doi: 10.1016/j.physleta.2017.10.027
    .
  12. S. Scarsoglio, G. Iacobello, and L. Ridolfi, “Complex networks unveiling spatial patterns in turbulence”,
    International Journal of Bifurcation and Chaos, (2016) vol. 26, no. 13, p. 1650223. doi: 10.1142/S0218127416502230

Refereed chapters & Proceedings:

  1. F. Kaiser, G. Iacobello and D. E. Rival. ,”Aerodynamic state estimation from sparse sensor data by pairing Bayesian statistics with transition networks,” AIAA 2022-1669.
    AIAA SCITECH 2022 Forum, (2022). doi: 10.2514/6.2022-1669
  2. G. Iacobello, L.  Ridolfi, M. Marro, P. Salizzoni and S. Scarsoglio, “Complex network analysis of wind tunnel experiments on the passive scalar dispersion in a turbulent boundary layer”,
    Progress in Turbulence VIII, Springer Proceedings in Physics, (2019) vol. 226., Chapter 34. doi: 10.1007/978-3-030-22196-6  [PDF]

Publications are here reported for research purposes only, and copyright belongs to the respective parties.