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Enregistrement W6978333690 · doi:10.7939/r3-3j43-t421

Field and Laboratory Investigation of Frazil Floc and Surface Ice Properties

2024· dissertation· en· W6978333690 sur OpenAlex

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aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
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Notice bibliographique

RevueUniversity of Alberta Library · 2024
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueEurasian Exchange Networks
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPancake iceSnowIce divideLead (geology)MeltwaterHydrology (agriculture)Arctic ice packIce formation

Résumé

récupéré en direct d'OpenAlex

Frazil ice particles and flocs can adhere to underwater structures causing blockage to water intakes and large accumulation of frazil ice in the channel may cause flooding and property damage. After frazil floc rises to the water surface the resulting surface ice profoundly impacts river hydraulics and bank stability. Significant progress has been made in investigating the properties and evolution of frazil ice particles. However, the physical process in which frazil particles flocculate into flocs and rise to the surface forming ice pans remains largely unknown due to limited data available on the properties of frazil floc and surface ice. The motivation of this study was to determine the properties of frazil floc and surface ice as well as their evolution under changing environmental conditions to better model and predict their physical behavior throughout the river freeze-up. The use of oblique images of river surfaces captured at long focus distances for long-term monitoring of surface ice conditions and ice pan properties was explored. Image data from a public camera mounted on a building rooftop captured during five freeze-up seasons was used. A deep learning based hybrid image processing algorithm was developed and evaluated to compute surface ice concentrations as well as ice pan sizes and shapes. The ice pans detected were generally elliptical shaped and their diameters ranged from 0.55 to 15.03 m. A lognormal distribution was a good fit for the ice pan size distributions for all years. Time series analysis showed that the appearance of ice pans coincided with supercooling and daily mean ice pan diameter varied from 1 to 3 m. These results demonstrate the viability of this method, which may open opportunities to identify and use public camera images for surface ice quantification. Properties of frazil flocs were measured for the first time in field by deploying a submersible camera system a total of eleven times during supercooling in the North Saskatchewan, Peace, and Kananaskis Rivers. A lognormal distribution was found to be a good fit for the floc size distribution. The mean floc size ranged from 1.19 to 5.64 mm and decreased linearly as the local Reynolds number increased. The average floc number concentration ranged from 1.80 × 10-4 to 1.15 × 10-1 cm-3. The average floc volumetric concentration ranged from 2.05 × 10-7 to 4.56 × 10-3 and was found to correlate strongly with the fractional height above the bed through a power law relationship. No significant correlations were found between the air-water heat flux and floc properties. Floc number concentration and mean size increased significantly just before peak supercooling and reached a maximum near the end of principal supercooling. To explore how the supercooling curve and frazil ice particle and floc properties vary under different air-water heat flux scenarios, a series of laboratory experiments were conducted in which frazil particles and flocs were generated and imaged when the cold room air temperature was increased or decreased threefold at different times during supercooling events. It was found that increasing the heat flux raised the mean particle number concentration by 25 – 33 % but did not significantly affect the mean floc number concentration. Decreasing the heat flux only produced significant effects when the change occurred before peak supercooling, reducing mean particle and floc number concentration by 10 and 22 %, respectively. Time series analysis showed that varying heat flux during different supercooling phases led to significantly different responses in the supercooling curve and particle and floc evolution. Additional laboratory frazil tank experiments were performed to investigate the correlation between the time series of frazil particle and floc properties under different air temperatures and turbulent dissipation rates. A strong linear relationship between particle and floc number concentrations was found with the floc-to-particle number concentration ratio ranging from 0.29 - 0.35. The ratio was reduced by 12 – 17 % when the turbulent dissipation rate was lower. A moderate to strong nonlinear correlation was found between mean particle and floc sizes described by an exponential relationship when particle mean sizes increased or decreased significantly. When particle mean size reaches an approximate equilibrium, a weak to moderate linear correlation was found between mean particle and floc size and the negative slope suggests they are inversely correlated.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,216
Score d'incertitude au seuil0,858

Scores Codex et Gemma par catégorie

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

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 Opus0,009
Tête enseignante GPT0,200
Écart entre enseignants0,191 · 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