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Record W2893409195 · doi:10.1115/omae2018-77321

Experimental Investigation of Flow Field Past a Spherical Particle Settling in Viscoelastic Fluids Using Particle Image Velocimetry Technique

2018· article· en· W2893409195 on OpenAlexafffund
Sumanth Kumar Arnipally, Majid Bizhani, Ergün Kuru

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSettlingViscoelasticityParticle image velocimetryMechanicsElasticity (physics)Materials scienceViscosityRheologyPhysicsComposite materialThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

Experimental investigation of flow field past a spherical particle settling in viscoelastic fluids using particle image shadowgraphy techniques studies have shown that the settling velocity of particles in viscoelastic fluids decreased significantly with the increasing elasticity of the fluids. However, our understanding of how and why the change in fluid elasticity influences the particle settling velocity are not yet fully developed. An experimental study, therefore, has been conducted to understand the reasons behind why the settling velocity of the particles decrease with the increasing fluid elasticity. The main objectives were: (i) to investigate the fluid flow field behind the settling particle by using particle image velocity (PIV) technique; (ii) to understand the changes caused by the elasticity of the fluid on the flow field past the settling particle; (iii) more specifically, to determine how the fluid velocity profile and the resultant drag forces acting on the settling particle change with the increasing fluid elasticity. Two different viscoelastic fluids were formulated by mixing 3 grades of HPAM polymer (MWs: 500,000; 8,000,000; 20,000,000; concentrations: 0.09% and 0.1%wt). The fluids were designed to have almost identical shear viscosity but significantly different elastic properties. The shear viscosity and elasticity of the fluids were determined by performing shear viscosity and frequency sweep oscillatory measurements, respectively. The settling velocities of the spherical particles in viscoelastic polymer fluids were measured by using particle image shadowgraph technique. The fluid flow field behind the settling particle was determined by using the PIV technique. Results of the PIV measurements demonstrated that negative wakes were present in viscoelastic fluids. The stagnation point (i.e. the point where the velocity becomes zero and above that the fluid starts moving in the direction opposite to the particle movement) was closer to the particle settling in the higher elasticity fluid than that in the lower elasticity fluid. The velocity of the fluid in the recirculation region was higher for the flow of the fluid with higher elasticity. The presence of negative wakes having fast moving fluid in the reverse direction near the settling particle possibly creates an additional drag force (acting on the particle in the direction opposite the particle movement), which would eventually slow down the settling particle. Knowledge of the settling behavior of particles is indispensable to design and optimize numerous industrial operations such as cuttings transport in oil and gas well drilling and proppant transport in hydraulic fracturing. In this study, by conducting experiments under controlled conditions, we were able to show how the change in fluid elasticity influenced the particle settling velocity. The results from this fundamental study can be used for development of optimum drilling and fracturing fluid formulations for effective transport of cuttings and proppants.

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 score0.504
Threshold uncertainty score0.397

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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