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New Strategies for the Tailoring of High-Performance Multiphase Polymer-Based Materials
Published in Gabriel O. Shonaike, George P. Simon, Polymer Blends and Alloys, 2019
Most bulk properties of a blend are usually determined by those of a major continuous phase. There are however two types of interesting situations where desirable properties are generated by a different morphology: the cocontinuous interweaving of two phases and the continuous spider-web-like organization of a very minor phase. Moreover, both types of morphologies can promote true synergism, i.e., generating particular properties much better than those expected from the additivity laws, and sometimes even better than those of any of the components. A theoretical approach to such a behavior has been reviewed (24) based on general scaling relationships from percolation theory. Here we present four typical cases showing the breadth and the potential of this area in basic experimental research on morphology and in its promising applications.
Prediction of percolation threshold
Published in Xi Frank Xu, Multiscale Theory of Composites and Random Media, 2018
Percolation refers to formation of a long-range global connectivity in a random system when probability of local connectivity reaches beyond a certain value (i.e. percolation threshold). The concept of bond percolation was first introduced in Broadbent and Hammersley (1957), and since then discrete percolation theory has been developed based on lattice models with a wide range of application in statistical physics, material science, geology, biology, complex networks, and epidemiology, etc. In the early 1960s, the discrete percolation based on lattice points was generalized to continuum percolation in modeling of wireless networks (Gilbert, 1961). Compared to its discrete counterpart, the continuum percolation theory presents certain unique advantages in exploring the relationship between geometry of microstructure and percolation properties of a random medium.
Electrical Properties
Published in Ko Higashitani, Hisao Makino, Shuji Matsusaka, Powder Technology Handbook, 2019
Ken-ichiro Tanoue, Hiroaki Masuda, Yosufumi Otsubo
The main task of percolation theory is to predict the transport properties of the percolating network above the critical probability.16 Scaling argument shows a power law dependence of physical quantity Q on the difference of particle concentration from the critical value Cc17Q=k(C−Cc)n
Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis
Published in International Journal of Production Research, 2022
Kayvan Miri Lavassani, Zachary M. Boyd, Bahar Movahedi, Jason Vasquez
The concept of disruption propagation in the supply chain has been well recognised and researched in the area of operations research over the past decades (c.f. Bhamra, Dani, and Burnard 2011; Ivanov, Sokolov, and Pavlov 2013; Ghadge et al. 2022; Sindhwani, Jayaram, and Saddikuti 2022). The concepts associated with ripple effects, bullwhip effects, and cascading failure, as well as implications of percolation theory in the supply chain, are well studied and understood in the previous research on supply chain disruption. The analysis tools utilised in this study are based on random failures and intelligent (targeted) attacks within percolation theory, as well as the cascading failure of networked structures.
Manufacturing diffusion trends from the perspective of trade network: the belt vs. the road
Published in Maritime Policy & Management, 2022
Shunan Yu, Dongyue Niu, Feixiong Liao, Yonglei Jiang
Percolation theory is used to study the clustering phenomenon in random environments, as it is capable of exploring the emergence of clusters and the dynamic changes in cluster sizes in topological networks in the fields of physics, chemistry, and material science. Therefore, we aim to analyze the diffusion trends of manufacturing firms using this theory to provide a new perspective for manufacturing industry diffusion.
A hybrid approach for transmission grid resilience assessment using reliability metrics and power system local network topology
Published in Sustainable and Resilient Infrastructure, 2021
Binghui Li, Dorcas Ofori-Boateng, Yulia R. Gel, Jie Zhang
Resilience is defined as the capacity to anticipate, prepare for, respond to, and recover from significant disruptions (Sharma et al., 2018; Wilbanks & Kates, 2010). Due to its complex and interdisciplinary nature, previous studies have been conducted over a wide range of domains (Čaušević et al., 2019; Gasser et al., 2019; Wang et al., 2016). Graph theory and measures of complex network analysis have been applied to a variety of studies on the resilience of power systems (Ezzeldin & El-Dakhakhni, 2019). The electric power systems are often represented as networks/graphs, and the resilience and/or robustness of the power grid is then evaluated by assessing the dynamics of the topological properties of the graph, such as the node degree distribution, clustering coefficient, average path length, giant component size, etc. Holmgren (2006) examines the error and attack tolerance of the Nordic and the western United States transmission grids by scrutinizing the topological characteristics of the networks. Chassin and Posse (2005) develop a simple model to estimate the topology of the North American Eastern and Western power grids, which are combined with the scale-free network connectivity probability distribution to estimate the loss-of-load probability (LOLP). Chassin and Posse (2005) also compare the values of LOLP with other LOLP estimates previously obtained using standard power engineering methods. Ezzeldin and El-Dakhakhni (2019) scrutinize the robustness of the Ontario power network by evaluating several key network metrics. Guidotti et al. (2016) develop a unified methodology to model the network dependencies and interdependencies and incorporates the methodology in a six-step probabilistic procedure to assess the resilience of critical infrastructure. In several studies (Cuadra et al., 2015; Pagani & Aiello, 2013; Rueda et al., 2017), a network with higher network robustness under extreme events is linked to higher clustering coefficient, lower average path length and/or higher mean degree. Furthermore, Wang et al. (2019) assess the structural robustness of the transmission network for central China by using percolation theory and the giant component size under random and deliberate node-based events. Percolation theory is based on quantifying the proportion of the network that is still connected after extreme events.