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  A perturbation-theoretic approach to Lagrangian flow networks

Fujiwara, N., Kirchen, K., Donges, J. F., Donner, R. V. (2017): A perturbation-theoretic approach to Lagrangian flow networks. - Chaos, 27, 3, 035813.
https://doi.org/10.1063/1.4978549

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Fujiwara, N.1, Author
Kirchen, K.1, Author
Donges, Jonathan Friedemann2, Author              
Donner, Reik V.2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols. Perturbation-theoretic methods have found wide applications in many areas of physics, ranging from classical celestial mechanics to quantum physics. At various occasions, they have proven useful for addressing scientific problems where an explicit analytical treatment is not possible, but the system under study can be considered as a minor modification of another problem where such an analytical solution can be obtained. This work combines basic concepts of perturbation theory with Lagrangian flow networks, a novel tool that allows characterizing structural properties of flows by means of complex network theory. Specifically, by means of an eigenvector decomposition of the associated transition matrix (i.e., a spatial discretization of the Perron-Frobenius operator of the flow), it is studied how minor modifications affect the steady-state distribution of passively advected particles in a given flow pattern. One main potential field of application of the developed framework is atmospheric and ocean physics, where the proposed approach may help in developing more efficient strategies for coping with contaminations of fluid or gaseous media by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols (e.g., to determine the most efficient positions for removing the contaminating substances or anticipate their temporary trapping and natural precipitation)

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 Dates: 2017
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.4978549
PIKDOMAIN: Earth System Analysis - Research Domain I
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7576
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Atmosphere
Research topic keyword: Oceans
Model / method: Nonlinear Data Analysis
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Whole Earth System Analysis
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 27 (3) Sequence Number: 035813 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808