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Record W7010517551

Impact of web permeability on high speed web transport

2019· other· en· W7010517551 on OpenAlexaff

Bibliographic record

VenueSHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) · 2019
Typeother
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsDragShear stressPorosityFlutterPorous mediumPermeability (electromagnetism)
DOInot available

Abstract

fetched live from OpenAlex

Measurements of drag on a moving web in a multi-span festoon show a stronger than expected dependency on the porosity of the web with a highly non-linear relationship. The experiments suggest a wall shear stress 3-4 times larger than non-porous webs or historical Couette flow data for solid walls. Previous DNS studies of boundary layers with passive porous surfaces predict a much smaller increase in wall shear stress for a porous wall of only 40%. Other DNS studies of porous walls with periodic transpiration do show a large increase in drag under certain periodic conditions of modest amplitude. Although those results are more aligned in magnitude with this study, the exact reason for the observed high drag for porous webs in this present study is not understood because there was no external disturbance applied to the web. Previously reported experimental data reported a strong linear correlation between wall shear stress and web permeability. The present paper refines those measurements in more detail and shows a highly nonlinear relationship between wall shear stress and permeability, but we offer no physical explanation for this relationship. It can be hypothesized that natural flutter of the web results in a similar mechanism shown in the periodic DNS study, but when the natural flutter was reduced by increasing web tension, there was only a small decrease of the drag.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0590.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.160
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University)Same topicFreshwater macroinvertebrate diversity and ecologyFrench-language works237,207