Complex network analysis of wind tunnel experiments on the passive scalar dispersion in a turbulent boundary layer

In this work, data of passive scalar plumes in a turbulent boundary layer are investigated. The experiments are performed in a&nbsp;wind tunnel where a passive scalar is injected through an L-shaped tube. Two source configurations&nbsp;are analysed for two different tube diameters. The passive scalar concentration is then measured at different distances from the source and wall-normal locations. By exploiting the recent advances of complex networks theory, the concentration time-series are mapped into networks, through the visibility algorithm. The resulting networks inherit the temporal features of the mapped time-series, revealing non-trivial information about the underlying transport process. This work represents an example of the great potentialities of the complex network approach for the analysis of turbulent transport and mixing.</p

Canonical horizontal visibility graphs are uniquely determined by their degree sequence

Horizontal visibility graphs (HVGs) are graphs constructed in correspondence with number sequences that have been introduced and explored recently in the context of graph-theoretical time series analysis. In most of the cases simple measures based on the degree sequence (or functionals of these such as entropies over degree and joint degree distributions) appear to be highly informative features for automatic classification and provide nontrivial information on the associated dynam- ical process, working even better than more sophisticated topological metrics. It is thus an open question why these seemingly simple measures capture so much information. Here we prove that, under suitable conditions, there exist a bijection between the adjacency matrix of an HVG and its degree sequence, and we give an explicit construction of such bijection. As a consequence, under these conditions HVGs are unigraphs and the degree sequence fully encapsulates all the information of these graphs, thereby giving a plausible reason for its apparently unreasonable effectiveness

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics

LL’s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1

Visibility in the topology of complex networks

Taking its inspiration from the visibility algorithm, which was proposed by Lacasa et al. (2008) to convert a time-series into a complex network, this paper develops and proposes a novel expansion of this algorithm that allows generating a visibility graph from a complex network instead of a time-series that is currently applicable. The purpose of this approach is to apply the idea of visibility from the field of time-series to complex networks in order to interpret the network topology as a landscape. Visibility in complex networks is a multivariate property producing an associated visibility graph that maps the ability of a node “to see” other nodes in the network that lie beyond the range of its neighborhood, in terms of a control-attribute. Within this context, this paper examines the visibility topology produced by connectivity (degree) in comparison with the original (source) network, in order to detect what patterns or forces describe the mechanism under which a network is converted to a visibility graph. The overall analysis shows that visibility is a property that increases the connectivity in networks, it may contribute to pattern recognition (among which the detection of the scale-free topology) and it is worth to be applied to complex networks in order to reveal the potential of signal processing beyond the range of its neighborhood. Generally, this paper promotes interdisciplinary research in complex networks providing new insights to network science. © 2018 Elsevier B.V

VisExpA: Visibility expansion algorithm in the topology of complex networks

In this study, we provide the VisExpA (Visibility Expansion Algorithm), a computational code that implements a recently published method, which allows generating a visibility graph from a complex network instead of a time-series that is currently applicable. The proposed algorithm is applied to a complex network and it uses a node-wise control-attribute (network-nodes topological measure) to define the node-heights to which the original (time-series) visibility algorithm is applied. The VisExpA applies the idea of visibility graph from the field of time-series to complex networks and it allows interpreting the network topology as a landscape, making it a valuable tool of analysis in many disciplines. © 2019 The Author

Backward Degree a new index for online and offline change point detection based on complex network analysis

How to identify an upcoming transition in a time series continues to be an important open research issue. In various fields of physical sciences, engineering, finance and neuroscience abrupt changes can occur unexpectedly and are difficult to manage during the temporal evolution of the dynamic system. In this work, we developed a new unsupervised method called “Backward Degree” based on a new topological graph index that we introduce, which can be used to detect not only offline point of change, but also can effectively be used as an early warning system for online detection of upcoming abrupt changes. Specifically, based on the well-established algorithm “Visibility graph”, which was introduced by Lacasa et al. (2008) we convert a time series into a complex network and then we apply our proposed approach. The results, on a number of synthetic and financial datasets demonstrate that the proposed methodology correctly identifies change points during the evolution of time series validating the advantages of the proposed methodology for effective detection an upcoming abrupt transitions. © 2022 Elsevier B.V

Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis

Renewable energy sources, where wind energy is an important part, are increasingly participating in developing economies and environmental benefits. Wind power is strongly dependent on wind velocity and thus identifying patterns in wind speed data is an important issue for forecasting the generated power from a wind turbine and it has significant importance for the renewable energy market operations. In this work we approach the problem of identification of the underlying dynamic characteristics and patterns of wind behavior using two approaches of non-linear time series analysis tools: Recurrence Plots (RPs) and Complex Network analysis. The proposed methodology is applied on wind time series collected by cup anemometers located on a wind turbine installed in Greece. We show that the proposed approach provides useful information which can characterize distinct two time intervals of the data, one ranging from 2 to 4.5 days and another from 5 to 8.5 days. Also analysis can identify and detect dynamical transitions in the system's behavior and also reveals information about the changes in state inside the whole time series. The results will be useful in wind markets, for the prediction of the produced wind energy and also will be helpful for wind farm site selection. © 201

Detection of low-dimensional chaos in wind time series

In the present work we investigated the existence of low-dimensional deterministic chaos in wind time series. The time series were obtained from the New Anchialos (Greece) Air Base Measurement station. In a first place we used the raw data without any noise filtering. Characteristic times were extracted using power spectrum and average mutual information function. The estimation of invariant measures, such as the correlation dimension and Lyapunov exponents indicate the possible existence of a low-dimensional attractor. After noise removal with the use of the local projective method the analysis indicates in a more clear way the existence of a low-dimensional attractor. In addition, the null hypothesis was tested for the dynamical characteristics of the wind time series by using the surrogate data test and the corresponding results provide significant evidence for the existence of low-dimensional chaotic dynamics underlying the wind time series. (C) 2008 Elsevier Ltd. All rights reserved

Analysis of magnetohydrodynamic channel flow through complex network analysis

Velocity time series of hydrodynamic and magnetohydrodynamic (MHD) turbulent flow are analyzed by means of complex network analysis in order to understand the mechanism of fluid patterns modification due to the external magnetic field. Direct numerical simulations of two cases are used, one for the plane hydrodynamic turbulent channel flow at the low Reynolds number of 180, based on the friction velocity, and the corresponding MHD flow with an external streamwise magnetic field with a magnetic interaction number of 0.1. By applying the visibility graph algorithm, we first transformed the time series into networks and then we evaluated the network topological properties. Results show that the proposed network analysis is not only able to identify and detect dynamical transitions in the system's behavior that identifies three distinct fluid areas in accordance with turbulent flow theory but also can quantify the effect of the magnetic field on the time series transitions. Moreover, we find that the topological measures of networks without a magnetic field and as compared to the one with a magnetic field are statistically different within a 95% confidence interval. These results provide a way to discriminate and characterize the influence of the magnetic field on the turbulent flows. © 2021 Author(s)

Detection of jet axis in a horizontal turbulent jet via nonlinear analysis of minimum/maximum temperature time series

We have analyzed experimental temperature time series from a horizontal turbulent heated jet, in order to identify the jet axis location using non linear measures. The analysis was applied on both, the original time series as well as on the extreme value (minimum and maximum values) time series. In our analysis we employed mainly nonlinear measures such as mutual information and cumulative mutual information. The results show that the analysis of the extreme values time series using cumulative mutual information permits to distinguish the jet axis time series from the rest of the jet, as well as discriminate regions of the jet located close to jet axis or close to the boundaries. Furthermore, it is of interest that the application of simple statistical measures and clustering techniques shows that the use of extremes time series let us distinguish with greater confidence the jet axis than the use of the original one. © 2019 CHAOS 2011 - 4th Chaotic Modeling and Simulation International Conference, Proceedings. All rights reserved