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Record W2901152326 · doi:10.1115/smasis2018-8033

Linear and Nonlinear Viscoelastic Behavior of MR Fluids: Effect of Temperature

2018· article· en· W2901152326 on OpenAlexaff
Masoud Hemmatian, Ramin Sedaghati, Subhash Rakheja

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsViscoelasticityRheometerAmplitudeRheologyMaterials scienceNonlinear systemMechanicsShear (geology)Magnetic fieldNewtonian fluidShear modulusThermodynamicsComposite materialPhysicsOptics

Abstract

fetched live from OpenAlex

This study aims to investigate the influence of temperature on the linear and nonlinear rheological behavior of a MR fluid, MRF 132DG, using a rotational rheometer. The experiments were designed to obtain properties of the fluid under oscillatory shear strain in the amplitude and frequency sweep modes, while maintaining different constant temperatures (−5, 0, 20 and 50 °C). The data were used to evaluate the storage and loss moduli under different levels of magnetic flux density considering the linear as well as nonlinear viscoelastic regions. The critical strain amplitudes were further obtained. Results showed enhanced linear viscoelastic region with increasing magnetic field density. Moreover, the effects of temperature and magnetic field on the frequency dependency of the fluid properties are illustrated for small and large amplitudes of shear strains.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.331

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 head0.004
GPT teacher head0.237
Teacher spread0.233 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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