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Record W4409770872 · doi:10.1063/5.0266200

Tuning bubble trajectories in a yield stress fluid

2025· article· en· W4409770872 on OpenAlexafffund
M. Goral, I.A. Frigaard

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsBubbleYield (engineering)MechanicsStress (linguistics)Classical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

Understanding and managing gas release in yield stress fluids is crucial for both industrial operations and environmental sustainability. Of particular concern are emissions of carbon dioxide and methane from tailings ponds, where uncontrolled releases contribute to greenhouse gas emissions and may also present safety risks. Within these ponds, bubbles may be trapped or ascend, according to their size and the rheology of the pond fluids. As bubbles ascend, the deformed region around the bubble forms a “damaged” zone within which the fluid rheology apparently does not fully recover after passage of the bubble. Equally, future bubbles are attracted toward the pathways of previous bubbles. Potentially, this combination can lead to formation of a root-like network of bubble pathways over time, thereby impacting the overall bubble dynamics. In this paper, we outline laboratory experiments involving rising bubbles in Carbopol, a yield stress fluid. We focus on bubble behavior proximal to a deliberately damaged zone. Specifically, we examine individual bubble trajectories at various distances from a manually sheared vertical layer and for different Carbopol concentrations. We show how bubbles are drawn toward the regions that have been sheared and quantify the critical distance needed in order for bubble rise to be independent of the damaged zone. We quantify bubble trajectory dynamics and show that similar effects can be achieved by a simple toy model that characterizes the “damage” via a deficit in the yield stress.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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