Features of the Hilbert-Huang transformation and examples of its application for analysis of nonstationary processes in flows

D. S. Mironov, V. A. Lebiga, V. N. Zinovyev, A. Yu Pak, Jiun-Jih Miau, Kung-Ming Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the main tasks of experimental gas dynamics is to study the mechanisms of formation and evolution of fluctuations of flow parameters that arise when flowing around the studied bodies. The processing of the experimental data in this case is reduced to the extraction of the component from total signal due to the influence of the process under study. As a rule, pulsation processes are of non-stationary nature, and for the most effective analysis of such phenomena it is necessary to use special methods of time-frequency analysis. One such method proposed by N. Huang et al. is Hilbert-Huang transformation. This transformation recently finds more and more applications for the analysis of various processes. Hilbert-Huang transform consists of two main stages: an empirical mode decomposition and Hilbert transform. As a result of the decomposition, the signal is represented as a finite sum of so-called intrinsic mode functions. These modes are essentially a basis and unique for each signal. To each of the intrinsic modes, the Hilbert transform can be applied, with the result of which any can compute amplitudes and frequencies of these modes for each instant of time. Possibilities and features of empirical mode decomposition procedure and of the overall method are demonstrated. As an examples results of decomposition of the experimental signal of a thermoanemometer are considered. These signals are obtained in various flows characterized by a high level and a significant instability of flow fluctuations.

Original languageEnglish
Title of host publication19th International Conference on the Methods of Aerophysical Research, ICMAR 2018
EditorsVasily Fomin
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417472
DOIs
Publication statusPublished - 2018 Nov 2
Event19th International Conference on the Methods of Aerophysical Research, ICMAR 2018 - Akademgorodok, Novosibirsk, Russian Federation
Duration: 2018 Aug 132018 Aug 19

Publication series

NameAIP Conference Proceedings
Volume2027
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other19th International Conference on the Methods of Aerophysical Research, ICMAR 2018
CountryRussian Federation
CityAkademgorodok, Novosibirsk
Period18-08-1318-08-19

Fingerprint

Hilbert transformation
decomposition
gas dynamics

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Plant Science
  • Physics and Astronomy(all)
  • Nature and Landscape Conservation

Cite this

Mironov, D. S., Lebiga, V. A., Zinovyev, V. N., Pak, A. Y., Miau, J-J., & Chung, K-M. (2018). Features of the Hilbert-Huang transformation and examples of its application for analysis of nonstationary processes in flows. In V. Fomin (Ed.), 19th International Conference on the Methods of Aerophysical Research, ICMAR 2018 [030104] (AIP Conference Proceedings; Vol. 2027). American Institute of Physics Inc.. https://doi.org/10.1063/1.5065198
Mironov, D. S. ; Lebiga, V. A. ; Zinovyev, V. N. ; Pak, A. Yu ; Miau, Jiun-Jih ; Chung, Kung-Ming. / Features of the Hilbert-Huang transformation and examples of its application for analysis of nonstationary processes in flows. 19th International Conference on the Methods of Aerophysical Research, ICMAR 2018. editor / Vasily Fomin. American Institute of Physics Inc., 2018. (AIP Conference Proceedings).
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abstract = "One of the main tasks of experimental gas dynamics is to study the mechanisms of formation and evolution of fluctuations of flow parameters that arise when flowing around the studied bodies. The processing of the experimental data in this case is reduced to the extraction of the component from total signal due to the influence of the process under study. As a rule, pulsation processes are of non-stationary nature, and for the most effective analysis of such phenomena it is necessary to use special methods of time-frequency analysis. One such method proposed by N. Huang et al. is Hilbert-Huang transformation. This transformation recently finds more and more applications for the analysis of various processes. Hilbert-Huang transform consists of two main stages: an empirical mode decomposition and Hilbert transform. As a result of the decomposition, the signal is represented as a finite sum of so-called intrinsic mode functions. These modes are essentially a basis and unique for each signal. To each of the intrinsic modes, the Hilbert transform can be applied, with the result of which any can compute amplitudes and frequencies of these modes for each instant of time. Possibilities and features of empirical mode decomposition procedure and of the overall method are demonstrated. As an examples results of decomposition of the experimental signal of a thermoanemometer are considered. These signals are obtained in various flows characterized by a high level and a significant instability of flow fluctuations.",
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Mironov, DS, Lebiga, VA, Zinovyev, VN, Pak, AY, Miau, J-J & Chung, K-M 2018, Features of the Hilbert-Huang transformation and examples of its application for analysis of nonstationary processes in flows. in V Fomin (ed.), 19th International Conference on the Methods of Aerophysical Research, ICMAR 2018., 030104, AIP Conference Proceedings, vol. 2027, American Institute of Physics Inc., 19th International Conference on the Methods of Aerophysical Research, ICMAR 2018, Akademgorodok, Novosibirsk, Russian Federation, 18-08-13. https://doi.org/10.1063/1.5065198

Features of the Hilbert-Huang transformation and examples of its application for analysis of nonstationary processes in flows. / Mironov, D. S.; Lebiga, V. A.; Zinovyev, V. N.; Pak, A. Yu; Miau, Jiun-Jih; Chung, Kung-Ming.

19th International Conference on the Methods of Aerophysical Research, ICMAR 2018. ed. / Vasily Fomin. American Institute of Physics Inc., 2018. 030104 (AIP Conference Proceedings; Vol. 2027).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - One of the main tasks of experimental gas dynamics is to study the mechanisms of formation and evolution of fluctuations of flow parameters that arise when flowing around the studied bodies. The processing of the experimental data in this case is reduced to the extraction of the component from total signal due to the influence of the process under study. As a rule, pulsation processes are of non-stationary nature, and for the most effective analysis of such phenomena it is necessary to use special methods of time-frequency analysis. One such method proposed by N. Huang et al. is Hilbert-Huang transformation. This transformation recently finds more and more applications for the analysis of various processes. Hilbert-Huang transform consists of two main stages: an empirical mode decomposition and Hilbert transform. As a result of the decomposition, the signal is represented as a finite sum of so-called intrinsic mode functions. These modes are essentially a basis and unique for each signal. To each of the intrinsic modes, the Hilbert transform can be applied, with the result of which any can compute amplitudes and frequencies of these modes for each instant of time. Possibilities and features of empirical mode decomposition procedure and of the overall method are demonstrated. As an examples results of decomposition of the experimental signal of a thermoanemometer are considered. These signals are obtained in various flows characterized by a high level and a significant instability of flow fluctuations.

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Mironov DS, Lebiga VA, Zinovyev VN, Pak AY, Miau J-J, Chung K-M. Features of the Hilbert-Huang transformation and examples of its application for analysis of nonstationary processes in flows. In Fomin V, editor, 19th International Conference on the Methods of Aerophysical Research, ICMAR 2018. American Institute of Physics Inc. 2018. 030104. (AIP Conference Proceedings). https://doi.org/10.1063/1.5065198