Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models

Flow resistance in subglacial conduits regulates the basal water pressure and sliding speeds of glaciers by controlling drainage efficiency and conduit enlargement and closure. Flow dynamics within subglacial conduits, however, remain poorly understood due to limited accessibility. Here we report th...

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Published in:Geophysical Research Letters
Main Authors: Chen, Yunxiang, Liu, Xiaofeng, Gulley, Jason D., Mankoff, Kenneth D.
Format: Text
Language:unknown
Published: Digital Commons @ University of South Florida 2018
Subjects:
Online Access:https://digitalcommons.usf.edu/kip_articles/5258
https://doi.org/10.1029/2018GL079590
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spelling ftunisfloridatam:oai:digitalcommons.usf.edu:kip_articles-6257 2023-05-15T16:21:56+02:00 Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models Chen, Yunxiang Liu, Xiaofeng Gulley, Jason D. Mankoff, Kenneth D. 2018-10-09T07:00:00Z https://digitalcommons.usf.edu/kip_articles/5258 https://doi.org/10.1029/2018GL079590 unknown Digital Commons @ University of South Florida https://digitalcommons.usf.edu/kip_articles/5258 https://doi.org/10.1029/2018GL079590 KIP Articles Subglacial Conduit Roughness Insight Computational Fluid Dynamics Models text 2018 ftunisfloridatam https://doi.org/10.1029/2018GL079590 2022-10-27T17:50:48Z Flow resistance in subglacial conduits regulates the basal water pressure and sliding speeds of glaciers by controlling drainage efficiency and conduit enlargement and closure. Flow dynamics within subglacial conduits, however, remain poorly understood due to limited accessibility. Here we report the results of the first computational fluid dynamics simulations of flow within a realistic subglacial conduit beneath Hansbreen, a polythermal glacier in Svalbard, Norway. The simulated friction factor is 2.34 ± 0.05, which is around 5 to 230 times greater than values (0.01–0.5) commonly used in glacier hydrological modeling studies. Head losses from sinuosity and cross‐sectional variations dominate flow resistance (∼ 94%), whereas surface roughness from rocks and ice features contributes only a small portion (∼6%). Most glacier hydrology models neglect head losses due to sinuosity and cross‐sectional variations and thus severely underestimate flow resistance, overestimating the conduit peak effective pressure by 2 times and underestimating the conduit enlargement area by 3.4 times, respectively. Text glacier glacier Svalbard Digital Commons University of South Florida (USF) Svalbard Norway Hansbreen ENVELOPE(15.650,15.650,77.075,77.075) Geophysical Research Letters 45 20
institution Open Polar
collection Digital Commons University of South Florida (USF)
op_collection_id ftunisfloridatam
language unknown
topic Subglacial Conduit Roughness
Insight
Computational Fluid Dynamics Models
spellingShingle Subglacial Conduit Roughness
Insight
Computational Fluid Dynamics Models
Chen, Yunxiang
Liu, Xiaofeng
Gulley, Jason D.
Mankoff, Kenneth D.
Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models
topic_facet Subglacial Conduit Roughness
Insight
Computational Fluid Dynamics Models
description Flow resistance in subglacial conduits regulates the basal water pressure and sliding speeds of glaciers by controlling drainage efficiency and conduit enlargement and closure. Flow dynamics within subglacial conduits, however, remain poorly understood due to limited accessibility. Here we report the results of the first computational fluid dynamics simulations of flow within a realistic subglacial conduit beneath Hansbreen, a polythermal glacier in Svalbard, Norway. The simulated friction factor is 2.34 ± 0.05, which is around 5 to 230 times greater than values (0.01–0.5) commonly used in glacier hydrological modeling studies. Head losses from sinuosity and cross‐sectional variations dominate flow resistance (∼ 94%), whereas surface roughness from rocks and ice features contributes only a small portion (∼6%). Most glacier hydrology models neglect head losses due to sinuosity and cross‐sectional variations and thus severely underestimate flow resistance, overestimating the conduit peak effective pressure by 2 times and underestimating the conduit enlargement area by 3.4 times, respectively.
format Text
author Chen, Yunxiang
Liu, Xiaofeng
Gulley, Jason D.
Mankoff, Kenneth D.
author_facet Chen, Yunxiang
Liu, Xiaofeng
Gulley, Jason D.
Mankoff, Kenneth D.
author_sort Chen, Yunxiang
title Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models
title_short Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models
title_full Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models
title_fullStr Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models
title_full_unstemmed Subglacial Conduit Roughness: Insights From Computational Fluid Dynamics Models
title_sort subglacial conduit roughness: insights from computational fluid dynamics models
publisher Digital Commons @ University of South Florida
publishDate 2018
url https://digitalcommons.usf.edu/kip_articles/5258
https://doi.org/10.1029/2018GL079590
long_lat ENVELOPE(15.650,15.650,77.075,77.075)
geographic Svalbard
Norway
Hansbreen
geographic_facet Svalbard
Norway
Hansbreen
genre glacier
glacier
Svalbard
genre_facet glacier
glacier
Svalbard
op_source KIP Articles
op_relation https://digitalcommons.usf.edu/kip_articles/5258
https://doi.org/10.1029/2018GL079590
op_doi https://doi.org/10.1029/2018GL079590
container_title Geophysical Research Letters
container_volume 45
container_issue 20
_version_ 1766009906482118656