TY  - JOUR
A1  - Kollosche, Matthias
A1  - Kofod, Guggi
A1  - Suo, Zhigang
A1  - Zhu, Jian
T1  - Temporal evolution and instability in a viscoelastic dielectric elastomer
JF  - Journal of the mechanics and physics of solids
N2  - Dielectric elastomer transducers are being developed for applications in stretchable electronics, tunable optics, biomedical devices, and soft machines. These transducers exhibit highly nonlinear electromechanical behavior: a dielectric membrane under voltage can form wrinkles, undergo snap-through instability, and suffer electrical breakdown. We investigate temporal evolution and instability by conducting a large set of experiments under various prestretches and loading rates, and by developing a model that allows viscoelastic instability. We use the model to classify types of instability, and map the experimental observations according to prestretches and loading rates. The model describes the entire set of experimental observations. A new type of instability is discovered, which we call wrinkle-to-wrinkle transition. A flat membrane at a critical voltage forms wrinkles and then, at a second critical voltage, snaps into another state of winkles of a shorter wavelength. This study demonstrates that viscoelasticity is essential to the understanding of temporal evolution and instability of dielectric elastomers. (C) 2014 Elsevier Ltd. All rights reserved.
KW  - Dielectric elastomer
KW  - Viscoelasticity
KW  - Snap-through instability
KW  - Phase transition
KW  - Wrinkling
Y1  - 2015
U6  - https://doi.org/10.1016/j.jmps.2014.11.013
SN  - 0022-5096
SN  - 1873-4782
VL  - 76
SP  - 47
EP  - 64
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Hofer-Temmel, Christoph
A1  - Houdebert, Pierre
T1  - Disagreement percolation for Gibbs ball models
JF  - Stochastic processes and their application
N2  - We generalise disagreement percolation to Gibbs point processes of balls with varying radii. This allows to establish the uniqueness of the Gibbs measure and exponential decay of pair correlations in the low activity regime by comparison with a sub-critical Boolean model. Applications to the Continuum Random Cluster model and the Quermass-interaction model are presented. At the core of our proof lies an explicit dependent thinning from a Poisson point process to a dominated Gibbs point process. (C) 2018 Elsevier B.V. All rights reserved.
KW  - Continuum random cluster model
KW  - Disagreement percolation
KW  - Dependent thinning
KW  - Boolean model
KW  - Stochastic domination
KW  - Phase transition
KW  - Unique Gibbs state
KW  - Exponential decay of pair correlation
Y1  - 2019
U6  - https://doi.org/10.1016/j.spa.2018.11.003
SN  - 0304-4149
SN  - 1879-209X
VL  - 129
IS  - 10
SP  - 3922
EP  - 3940
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Emary, Clive
A1  - Malchow, Anne-Kathleen
T1  - Stability-instability transition in tripartite merged ecological networks
JF  - Journal of mathematical biology
N2  - Although ecological networks are typically constructed based on a single type of interaction, e.g. trophic interactions in a food web, a more complete picture of ecosystem composition and functioning arises from merging networks of multiple interaction types. In this work, we consider tripartite networks constructed by merging two bipartite networks, one mutualistic and one antagonistic. Taking the interactions within each sub-network to be distributed randomly, we consider the stability of the dynamics of the network based on the spectrum of its community matrix. In the asymptotic limit of a large number of species, we show that the spectrum undergoes an eigenvalue phase transition, which leads to an abrupt destabilisation of the network as the ratio of mutualists to antagonists is increased. We also derive results that show how this transition is manifest in networks of finite size, as well as when disorder is introduced in the segregation of the two interaction types. Our random-matrix results will serve as a baseline for understanding the behaviour of merged networks with more realistic structures and/or more detailed dynamics.
KW  - Random matrices
KW  - Phase transition
KW  - Random eigenvalues
KW  - Population dynamics
KW  - Community matrix
KW  - Ecological network
Y1  - 2022
U6  - https://doi.org/10.1007/s00285-022-01783-7
SN  - 0303-6812
SN  - 1432-1416
VL  - 85
IS  - 3
PB  - Springer
CY  - Heidelberg
ER  -