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Multivariate multifractal detrended fluctuation analysis of 3D wind field signals

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  • Zhang, Xiaonei
  • Zeng, Ming
  • Meng, Qinghao

Abstract

Characterizing the dynamic behavior underlying wind field from experimental multivariate signals is a challenging problem of continuous interest. In this work, we propose the multivariate multifractal detrended fluctuation analysis (MV-MFDFA) method to directly study the fractal dynamics of multichannel data in a complex system. By conducting several simulations on synthetic multivariate series, the validity of the proposed MV-MFDFA is illustrated. Then we utilize MV-MFDFA to analyze the 3D wind field signals collected at two different airflow environments, i.e., indoor and outdoor environments. Results show that the indoor and outdoor three wind vectors show multifractal properties, and the multifractal degrees of outdoor three wind vectors are stronger than those of corresponding indoor three wind vectors. By analyzing the indoor and outdoor multivariate wind speed, we find that the indoor and outdoor multivariate wind speed are antipersistent long-range correlation, and the indoor multivariate wind speed exhibits weaker multifractal properties than that of outdoor multivariate wind speed. Moreover, the multifractality of indoor multivariate wind speed depends mainly on the large fluctuations, while the multifractality of outdoor multivariate wind speed depends mainly on the small fluctuations. These findings indicate that the MV-MFDFA allows better understanding the dynamical mechanisms governing 3D wind variability.

Suggested Citation

  • Zhang, Xiaonei & Zeng, Ming & Meng, Qinghao, 2018. "Multivariate multifractal detrended fluctuation analysis of 3D wind field signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 513-523.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:513-523
    DOI: 10.1016/j.physa.2017.08.073
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    1. Balkissoon, Sarah & Fox, Neil & Lupo, Anthony, 2020. "Fractal characteristics of tall tower wind speeds in Missouri," Renewable Energy, Elsevier, vol. 154(C), pages 1346-1356.
    2. Wang, Fang & Han, Guosheng, 2023. "Coupling correlation adaptive detrended analysis for multiple nonstationary series," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
    4. Meo, Marcos M. & Iaconis, Francisco R. & Del Punta, Jessica A. & Delrieux, Claudio A. & Gasaneo, Gustavo, 2024. "Multifractal information on reading eye tracking data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    5. Li, Shuping & Li, Jianfeng & Lu, Xinsheng & Sun, Yihong, 2022. "Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    6. Milena Kojić & Petar Mitić & Marko Dimovski & Jelena Minović, 2021. "Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants," Mathematics, MDPI, vol. 9(7), pages 1-17, March.

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