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Record W1147261740 · doi:10.1177/1350650115599013

Non-Newtonian behavior in canola-oil-based bio-hydraulic oil

2015· article· en· W1147261740 on OpenAlexaff
Hamid A Elemsimit, Dana Grecov

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology · 2015
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRheometerRheologyCanolaViscoelasticityShear thinningMaterials scienceHydraulic fluidShear rateVegetable oilShear (geology)ViscosityPetroleum engineeringPetroleumEnvironmental scienceComposite materialPulp and paper industryChemistryMechanical engineeringGeologyEngineeringFood scienceOrganic chemistryHydraulic machinery

Abstract

fetched live from OpenAlex

The use of vegetable oils can offer important environmental advantages with respect to biodegradability and renewability, along with good performance in a range of different applications. Unlike petroleum-based lubricants, which have been studied and developed over a century, knowledge related to vegetable-oil-based lubricants is limited. In this work, the rheological properties of industrial canola-oil-based bio-lubricants were investigated using a rotary rheometer. The bio-hydraulic oil exhibited constant viscosity at both moderate and high shear rates, as well as shear thinning at low shear rates and temperatures less than 30 ℃. Frequency sweep tests revealed significant viscoelasticity in the bio-hydraulic oil, which developed over time. Time dependence and structure recovery effects were also investigated. These experiments reveal some characteristic liquid crystal fingerprints. To the best of our knowledge, this study is the most extended rheological characterization of low-viscosity vegetable-oil-based lubricants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.015
GPT teacher head0.219
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering TribologySame topicLubricants and Their AdditivesFrench-language works237,207