Long-range correlation analysis among non-stationary passive scalar series in the turbulent boundary layer

Chen Dongwei, Hu Fei, Xu Jingjing and Liu Lei

Physica A: Statistical Mechanics and its Applications, 2019, vol. 517, issue C, 290-296

Abstract: In this paper, we investigated the cross correlations among non-stationary passive scalars series. Based on the detrended cross correlation analysis (DCCA), we found that there existed long-range cross correlations among these series. The DCCA exponents for each pair of passive scalars are all greater than 0.5. This implies that the cross correlation is persistent. Moreover, the degree of cross correlation between H2O and PM2.5 is greater compared to CO2 and PM2.5 as well as CO2 and H2O. The same analysis is also performed on randomly shuffled series. The calculated DCCA exponents fluctuate slightly over 0.5, which verifies that there indeed exist cross correlations among original series.

Keywords: Detrended cross-correlation analysis; Long-range correlation; Passive scalars; Turbulent boundary layer (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437118312263
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:517:y:2019:i:c:p:290-296

DOI: 10.1016/j.physa.2018.09.094

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().