Percolation and cluster distribution. II. layers, variable-range interactions, and exciton cluster model

Hoshen, Joseph; Kopelman, Raoul; Monberg, Eric M.

doi:10.1007/bf01011724

Cited by 110 publications

(22 citation statements)

References 44 publications

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“…50.0; not shown) permits estimation of the minimum ionic surficial mass below which no electric signals were detected: 50.0 lg cm -2 . It is worth noting that the ionic surficial loading for the 10 filters that did not show any signal, is not far off the theoretical value (24.2 lg cm ) estimated by Weschler (1991) for pure 520 nm NH 4 HSO 4 particles on the basis of the percolation theory (Hoschen and Kopelman 1976;Hoschen et al 1978). However, as Tencer (2008) reported, the value at which the aerosol starts to conduct the electrical signal and at which a bridging process commences, may vary according to the geographical region and the related aerosol properties (chemistry and size).…”

Section: Conductivity Measurements In the Aec: Minimum Aerosol Loadingmentioning

confidence: 99%

Determination of aerosol deliquescence and crystallization relative humidity for energy saving in free-cooled data centers

Ferrero

D'Angelo

Rovelli

et al. 2014

Int. J. Environ. Sci. Technol.

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This study examines an innovative application of the aerosol deliquescence and crystallization determination, for corrosion prevention and energy-saving strategies in free-cooled data centers. Aerosol deliquescence and crystallization were investigated by combining standardized aerosol sampling techniques (i.e. EN-14907) with the assessment of the electrical effects of aerosol, while varying relative humidity within a specially designed aerosol exposure chamber. Aerosol samples collected in the Po Valley (Northern Italy) were analysed; a clearly defined hysteresis cycle (deliquescence and crystallization at 60.5 ± 0.8 and 47.9 ± 0.7 % of RH, respectively) was found. Results were applied to a data center designed for the Italian National Oil and Gas Company, making it possible to identify a critical area for direct free cooling at this data center. As a result, aerosol hydration was avoided (thus preventing aerosol from damaging electrical components) and a large amount of energy saved (using free cooling instead of air-conditioning); the potential energy saving achieved in this way was 79 % (compared to the energy consumption of a traditional air-conditioning system): 215 GWh of energy was saved, and 78 fewer kt of equivalent CO 2 was emitted per year. Moreover, in order to evaluate whether a real-time estimation of the aerosol hydration state within a data center could be performed, measured deliquescence and crystallization were compared through simulations performed using three different models: two thermodynamic models for deliquescence and a parametric model for crystallization. The results obtained tend to converge in terms of deliquescence, whereas in the case of crystallization, they failed to effectively simulate experimental aerosol behaviour.

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Section: Conductivity Measurements In the Aec: Minimum Aerosol Loadingmentioning

confidence: 99%

Determination of aerosol deliquescence and crystallization relative humidity for energy saving in free-cooled data centers

Ferrero

D'Angelo

Rovelli

et al. 2014

Int. J. Environ. Sci. Technol.

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“…It can be shown that the value of p c is proportionally inverse to the size of the "basic element" of the texture (Hoshen et al [9], and also Sec. 2.3 in Pikaz and Averbuch [1] in the context of digital images).…”

Section: On the Relation Between The Co-occurrences Matrices And Connmentioning

confidence: 96%

On the Relation between Second-Order Statistics, Connectivity Analysis, and Percolation Models in Digital Textures

Pikaz¹,

Averbuch²

1998

Graphical Models and Image Processing

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“…We then go through the velocity matrix and create the velocity cluster map by setting every element of V (x, y) above C · V to 1 and the rest to 0. We then go trough this cluster map and identify the cluster size distribution using a Hoshen-Kopelman algorithm [Hoshen et al (1978)]. By doing this, we can identify the cluster size distribution p(s, C), where s is the size of any given cluster.…”

Section: Velocity Burst Definitionmentioning

confidence: 99%

Mechanical origin of b-value changes during stimulation of deep geothermal reservoirs

2015

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Background:The distribution of induced-earthquake magnitudes in deep geothermal reservoirs is a classical tool for monitoring reservoirs. It typically shows some important fluctuations through time and space. Despite being a very crude information (i.e., a scalar quantity) of very complex mechanical stress evolution, understanding these variations could still give us insights into the mechanics of the reservoir. Here, we analyze the output of a simple quasi-static physical model of a single fault and propose a new way to describe bursts that could be compared to seismic events. Methods: Our model is an elastic fiber bundle model describing multiple ruptures along a single major fault of the reservoir. It consists of a set of fibers with various strengths, arranged in a planar L × L two-dimensional array, linking together two elastic half-spaces. It mimics a fault zone with various asperities. During load, the fibers break quasi-statically according to a stress threshold distribution. Contrary to classical fiber bundle model, here, when a fiber breaks, it redistributes the load on the surviving fibers through long range elastic interactions. Interestingly, the elasticity of the half-spaces which changes the range of the stress distribution, characterizes two distinct regimes. In a stiff regime, we find an effective ductile deformation regime at large scale since the damage distribution is very difuse. Conversely in softer systems, the distance between two consecutive breaking fibers gets smaller and failure of the interface is localized, exhibiting an effective brittle regime. Results: We analyze two types of burst distributions: a classical one built from the statistics of the broken bonds during each failure step and a new one defines from a waiting time matrix of the fracture front propagation. The first one reproduces several known results for this type of model. The new one evidences the existence of effective creeping advances of the front with statistics that follow a Gutenberg-Richter distribution, in particular, in the ductile regime (stiff systems). Conclusions:We proposed a new definition of bursts in a fault model, based on the local fracture front velocity. We find that b v -values for the distribution of the velocity clusters are very consistent with Gutenberg-Richter distribution of induced seismicity. We link the b v -value fluctuations in the reservoirs to the influence of the velocity threshold level that could be related to recording limitation. b b -values obtained from the broken bond statistics are hardly comparable to seismic events because of the lack of space contiguousness of broken fibers during bursts. In the light of our results, we discuss the implications of b-value changes in geothermal reservoir in terms of fault asperities and normal stress evolution.

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Percolation and cluster distribution. II. layers, variable-range interactions, and exciton cluster model

Cited by 110 publications

References 44 publications

Determination of aerosol deliquescence and crystallization relative humidity for energy saving in free-cooled data centers

Determination of aerosol deliquescence and crystallization relative humidity for energy saving in free-cooled data centers

On the Relation between Second-Order Statistics, Connectivity Analysis, and Percolation Models in Digital Textures

Mechanical origin of b-value changes during stimulation of deep geothermal reservoirs

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