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Enregistrement W4293702564

Focus: Model of Tumbling Sand Castles

2012· preprint· en· W4293702564 sur OpenAlexaboutno aff
Vincent Topin, Yann Monerie, Frédéric Péralès, Farhang Radjaï

Notice bibliographique

RevueHAL (Le Centre pour la Communication Scientifique Directe) · 2012
Typepreprint
Langueen
DomainePsychology
ThématiqueGambling Behavior and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFocus (optics)GeologyPhysicsOptics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Underwater avalanches can generate deadly tsunamis and damage oil platforms. But understanding the threat has been difficult because of the complexities of water-grain mixtures. A new two-dimensional model simulates the collapse of a tower of grains immersed in a fluid by computing the forces on each grain from the fluid and from other grains. The results—presented in Physical Review Letters—show that the presence of the fluid has two conflicting effects: it restrains the collapse but then lubricates the grains as they tumble outwards. The model may one day help in predicting risk in underwater environments as well as optimizing wet processing techniques used in the food and steel industries.In 1929, a seafloor avalanche triggered by an earthquake off the coast of Newfoundland swept debris out over 400 miles, snapping 12 transatlantic telegraph cables along its path. Such a long “runout” distance is common in submarine landslides. But the explanation for the long runout is unclear because researchers have focused more on “dry” avalanches and landslides. Wet grains are harder to model, since the fluid can restrain grain motion through cohesion and drag, while also helping the grains slide past each other. Previous studies have often simplified the wet-grain problem by adding a small number of grains (or some granular effects) to a fluid flow, or by adding some fluid effects to a granular flow. However, many grain-fluid mixtures in underwater environments and industrial situations have nearly equal parts solid and liquid. Farhang Radjaï of the University of Montpellier 2 in France and his colleagues have developed a computational model of granular flows in a fluid environment. As in similar work with dry grains, they concentrate on a specific situation in which a tower or column of grains falls under the influence of gravity [1]. The team divided this collapse into discrete time steps at which they calculated the forces on every grain and the overall motion of the fluid. The method builds on earlier simulations of dry grains [2] but now includes the fluid forces that act on the grains, such as pressure and cohesion. The simulation simultaneously keeps track of the fluid motion by solving the hydrodynamic (Navier-Stokes) equations, with the grains providing a time-varying “container.”Coupling the grain and fluid motion together is computationally intense, so the team has reduced the problem to two dimensions. Radjaï says this is justified because 2D models of dry grains have shown a close correspondence with 3D experiments. The grains in this case are modeled as millimeter-sized disks that are initially stacked against a wall in a rectangular column. This setup is unstable, so the grains immediately fall into a heap that spills out away from the wall (see movies).The team observed the collapse for different aspect ratios (height vs width) of the column while also exploring three options for the surrounding medium: no fluid, water, and a viscous fluid. The runout distances were similar for the no-fluid and water cases, which was surprising, since the no-fluid collapse took less than half the time as both fluid cases. The team explained this by calculating the average kinetic energy in the grains. When the fluid is present, the grains lose some of their gravitational potential energy to fluid motion. However, the fluid later helps to keep the outward horizontal flow moving by reducing friction forces on the grains. “The fluid gives back most of the energy it took,” Radjaï says.The team plans to extend their simulations into three dimensions and to study wave formation in the fluid flow. The work could help researchers assess the risk that underwater avalanches (and the tsunamis they generate) pose to offshore installations and coastal communities. Down the road, models like this one may benefit industrial methods for mixing liquid and granular ingredients in food processing and steel making.The analysis is “quite nice and interesting for understanding the transfer of energy in the different regimes and the role played by the ambient fluid,” says Philippe Gondret from the University of Paris-Sud in Orsay, France. The work extends earlier efforts with dry particles by making it possible to simulate “a broad collection of solid particles immersed in a fluid,” Gondret says.–Michael Schirber

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,068
Score d'incertitude au seuil0,135

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0020,001
Intégrité de la recherche0,0030,001
Charge utile insuffisante (le modèle a refusé de juger)0,0110,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,093
Tête enseignante GPT0,335
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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