Molecular-dynamics simulation of clustering processes in sea-ice floes

Herman, Agnieszka

doi:10.1103/physreve.84.056104

Cited by 40 publications

(63 citation statements)

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“…Previous experiments with an earlier version of the model demonstrated that polydispersity plays an important role in many aspects of the dynamics of sea ice composed of floes with power-law size distribution, including the formation of clusters in response to wind [19,20]. Not surprisingly, polydispersity also influences the behavior of the sheared systems studied here.…”

Section: The Role Of Polydispersitymentioning

confidence: 50%

“…This work is a continuation of previous numerical studies on sea ice composed of disk-shaped floes with power-law size distribution [19][20][21]. It examines the behavior of a 2D polydisperse granular material composed of frictional grains under pure-shear deformation (constant packing fraction, or, in the sea-ice nomenclature, ice concentration A).…”

Section: Introductionmentioning

confidence: 95%

“…The previous works [19][20][21] stressed the importance of the size-dependent response of individual grains (ice floes) to the forcing acting on them, relevant at low and medium packing fractions (A 1). However, the focus of this paper is on a slow deformation of a compact material in or close to the jammed state.…”

Section: Model Equationsmentioning

confidence: 99%

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Shear-Jamming in Two-Dimensional Granular Materials with Power-Law Grain-Size Distribution

Herman

2013

Entropy

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Although substantial progress has been made in recent years in research on sheared granular matter, relatively few studies concentrate on the behavior of materials with very strong polydispersity. In this paper, shear deformation of a two-dimensional granular material composed of frictional disk-shaped grains with power-law size distribution is analyzed numerically with a finite-difference model. The analysis of the results concentrates on those aspects of the behavior of the modeled system that are related to its polydispersity. It is demonstrated that many important global material properties are dependent on the behavior of the largest grains from the tail of the size distribution. In particular, they are responsible for global correlation of velocity anomalies emerging at the jamming transition. They also build a skeleton of the global contact and force networks in shear-jammed systems, leading to the very open, "sparse" structure of those networks, consisting of only ∼ 35% of all grains. The details of the model are formulated so that it represents fragmented sea ice moving on a two-dimensional sea surface; however, the results are relevant for other types of strongly polydisperse granular materials, as well.

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Section: The Role Of Polydispersitymentioning

confidence: 50%

Section: Introductionmentioning

confidence: 95%

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Shear-Jamming in Two-Dimensional Granular Materials with Power-Law Grain-Size Distribution

Herman

2013

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“…Some, like neXtSIM, treat the sea ice as a continuous medium, but with different rheologies (e.g. Coon et al (1974) and Hibler III (1979) modelled sea ice as an elasto-plastic material; Hunke and Dukowicz (1997) as an elasto-visco-plastic material; and Dansereau (2016) as an Maxwell elasto-brittle material), and are suitable for high ice concentration (> 80 %), while others that treat the ice as a discrete medium (Hopkins et al, 2004;Wilchinsky et al, 2010;Herman, 2011;Rabatel et al, 2015) are more suitable for low ice concentration (< 80 %) such as within the marginal ice zone.…”

Section: Introductionmentioning

confidence: 99%

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Rabatel¹,

Rampal²,

Carrassi³

et al. 2018

The Cryosphere

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Abstract. We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the freedrift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.

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“…Some, like neXtSIM, treat the sea ice as a continuous medium, yet with different rheologies (e.g. visco-plastic (Coon et al, 1974;Hibler III, 1979), elasto-visco-plastic, (Hunke and Dukowicz, 1997), or Maxwell-elasto-brittle, (Dansereau, 2016)), are suitable 10 for high ice concentration (> 80%) while others, that treat the ice as a discrete medium (Hopkins et al, 2004;Wilchinsky et al, 2010;Herman, 2011;Rabatel et al, 2015), are more suitable for low ice concentration (< 80%) such as within the marginal ice zone.…”

mentioning

confidence: 99%

Probabilistic forecast using a Lagrangian sea ice model: application for search and rescue operations

Rabatel¹,

Rampal²,

Carrassi³

et al. 2017

Preprint

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Abstract. We present a sensitivity analysis, and discuss the probabilistic forecast capabilities, of the novel sea ice model neXtSIM. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model, and uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean 5 position of the ensemble, over the mid-term forecast horizon of 10-days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a freedrift model, that is, when the ice rheology does not play any role on the modelled physics of the ice. The seasonal variability of the model sensitivity is presented, and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-10 drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter.For the winter period analysed in this study, we also show that, in contrast to of free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoys' trajectories, and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search-and-rescue 15 operations, although the sources of uncertainties assumed for the present experiment are not sufficient for a complete coverage of the observed IABP positions.

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Molecular-dynamics simulation of clustering processes in sea-ice floes

Cited by 40 publications

References 33 publications

Shear-Jamming in Two-Dimensional Granular Materials with Power-Law Grain-Size Distribution

Shear-Jamming in Two-Dimensional Granular Materials with Power-Law Grain-Size Distribution

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Probabilistic forecast using a Lagrangian sea ice model: application for search and rescue operations

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