37 research outputs found
Spatio-temporal analysis of wall-bounded turbulence: A multidisciplinary perspective via complex networks
L'abstract è presente nell'allegato / the abstract is in the attachmen
Complex Networks Unveiling Spatial Patterns in Turbulence
Numerical and experimental turbulence simulations are nowadays reaching the
size of the so-called big data, thus requiring refined investigative tools for
appropriate statistical analyses and data mining. We present a new approach
based on the complex network theory, offering a powerful framework to explore
complex systems with a huge number of interacting elements. Although interest
on complex networks has been increasing in the last years, few recent studies
have been applied to turbulence. We propose an investigation starting from a
two-point correlation for the kinetic energy of a forced isotropic field
numerically solved. Among all the metrics analyzed, the degree centrality is
the most significant, suggesting the formation of spatial patterns which
coherently move with similar vorticity over the large eddy turnover time scale.
Pattern size can be quantified through a newly-introduced parameter (i.e.,
average physical distance) and varies from small to intermediate scales. The
network analysis allows a systematic identification of different spatial
regions, providing new insights into the spatial characterization of turbulent
flows. Based on present findings, the application to highly inhomogeneous flows
seems promising and deserves additional future investigation.Comment: 12 pages, 7 figures, 3 table
A review on turbulent and vortical flow analyses via complex networks
Turbulent and vortical flows are ubiquitous and their characterization is
crucial for the understanding of several natural and industrial processes.
Among different techniques to study spatio-temporal flow fields, complex
networks represent a recent and promising tool to deal with the large amount of
data on turbulent flows and shed light on their physical mechanisms. The aim of
this review is to bring together the main findings achieved so far from the
application of network-based techniques to study turbulent and vortical flows.
A critical discussion on the potentialities and limitations of the network
approach is provided, thus giving an ordered portray of the current diversified
literature. The present review can boost future network-based research on
turbulent and vortical flows, promoting the establishment of complex networks
as a widespread tool for turbulence analysis
New insights into spatial characterization of turbulent flows: a complex network-based analysis
Despite much progress has been made, several mechanisms about turbulence dynamics are still
unclear. We propose an innovative approach based on complex networks theory, which combines
elements from graph theory and statistical physics, providing a powerful framework to investigate
complex systems.The network is built on a forced isotropic turbulent field, by evaluating the
temporal correlation of the kinetic energy for pairs of nodes within the Taylor microscale, λ. Among
all the parameters analyzed, the degree centrality, k, is one of the most meaningful, representing
how a node is linked to the others. We observe 3D patterns of high k values, which can be
interpreted as regions of spatial coherence. The turbulent network exhibits typical behaviors of
real and spatial networks (scale-free property). Similarly to other physical systems where complex
networks successfully apply, our approach can give new insights for the spatial characterization of
turbulence
Large-to-small scale frequency modulation analysis in wall-bounded turbulence via visibility networks
Scale interaction is studied in wall-bounded turbulence by focusing on the
frequency modulation (FM) mechanism of large scales on small scale velocity
fluctuations. Differently from amplitude modulation analysis, frequency
modulation has been less investigated also due to the difficulty to develop
robust tools for broadband signals. To face this issue, the natural visibility
graph approach is proposed in this work to map the full velocity signals into
complex networks. We show that the network degree centrality is able to capture
the signal structure at local scales directly from the full signal, thereby
quantifying FM. Velocity signals from numerically-simulated turbulent channel
flows and an experimental turbulent boundary layer are investigated at
different Reynolds numbers. A correction of Taylor's hypothesis for time-series
is proposed to overcome the overprediction of near-wall frequency modulation
obtained when local mean velocity is used as the convective velocity. Results
provide network-based evidences of the large-to-small FM features for all the
three velocity components in the near-wall region, with a reversal mechanism
emerging far from the wall. Additionally, scaling arguments in the view of the
quasi-steady quasi-homogeneous hypothesis are discussed, and a delay-time
between large and small scales very close to the near-wall cycle characteristic
time is detected. Results show that the visibility graph is a parameter-free
tool that turns out to be effective and robust to detect FM in different
configurations of wall-bounded turbulent flows. Based on present findings, the
visibility network-based approach can represent a reliable tool to
systematically investigate scale interaction mechanisms in wall-bounded
turbulence
Visibility network analysis of large-scale intermittency in convective surface layer turbulence
Large-scale intermittency is a widely observed phenomenon in convective
surface layer turbulence that induces non-Gaussian temperature statistics,
while such signature is not observed for velocity signals. Although approaches
based on probability density functions have been used so far, those are not
able to explain to what extent the signals' temporal structure impacts the
statistical characteristics of the velocity and temperature fluctuations. To
tackle this issue, a visibility network analysis is carried out on a
field-experimental dataset from a convective atmospheric surface layer flow.
Through surrogate data and network-based measures, we demonstrate that the
temperature intermittency is related to strong non-linear dependencies in the
temperature signals. Conversely, a competition between linear and non-linear
effects tends to inhibit the temperature-like intermittency behaviour in
streamwise and vertical velocities. Based on present findings, new research
avenues are likely to be opened up in studying large-scale intermittency in
convective turbulence.Comment: 4 figure
Spatial characterization of turbulent channel flow via complex networks
A network-based analysis of a turbulent channel flow numerically solved at
is proposed as an innovative perspective for the spatial
characterization of the flow field. Two spatial networks corresponding to the
streamwise and wall-normal velocity components are built, where nodes represent
portions of volume of the physical domain. For each network, links are active
if the correlation coefficient of the corresponding velocity component between
pairs of nodes is sufficiently high, thus unveiling the strongest kinematic
relations. Several network measures are studied in order to explore the
interrelations between nodes and their neighbors. Specifically, long-range
links are localized between near-wall regions and associated with the temporal
persistence of coherent patterns, namely high and low speed streaks.
Furthermore, long-range links play a crucial role as intermediary for the
kinematic information flow, as emerges from the analysis of indirect
connections between nodes. The proposed approach provides a framework to
investigate spatial structures of the turbulent dynamics, showing the full
potential of complex networks. Although the network analysis is based on the
two-point correlation, it is able to advance the level of information, by
exploiting the texture created by active links in all directions. Based on the
observed findings, the current approach can pave the way for an enhanced
spatial interpretation of the turbulence dynamics