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Record W2627007441 · doi:10.3390/app7060606

Effects of Nozzle Configuration on Rock Erosion Under a Supercritical Carbon Dioxide Jet at Various Pressures and Temperatures

2017· article· en· W2627007441 on OpenAlexaboutno aff
Man Huang, Yong Kang, Xiaochuan Wang, Yi Hu, Deng Li, Can Cai, Feng Chen

Bibliographic record

VenueApplied Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersNatural Science Foundation of Hubei ProvinceNational Natural Science Foundation of China
KeywordsNozzleSupercritical fluidJet (fluid)Volume (thermodynamics)InletErosionSupercritical carbon dioxideMaterials sciencePressure dropMechanicsWater jetDrop (telecommunication)Carbon dioxideChemistryComposite materialThermodynamicsMechanical engineeringGeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

The supercritical carbon dioxide (SC-CO2) jet offers many advantages over water jets in the field of oil and gas exploration and development. To take better advantage of the SC-CO2 jet, effects of nozzle configuration on rock erosion characteristics were experimentally investigated with respect to the erosion volume. A convergent nozzle and two Laval nozzles, as well as artificial cores were employed in the experiments. It was found that the Laval nozzle can enhance rock erosion ability, which largely depends on the pressure and temperature conditions. The enhancement increases with rising inlet pressure. Compared with the convergent nozzle, the Laval-1 nozzle maximally enhances the erosion volume by 10%, 21.2% and 30.3% at inlet pressures of 30, 40 and 50 MPa, respectively; while the Laval-2 nozzle maximally increases the erosion volume by 32.5%, 49.2% and 60%. Moreover, the enhancement decreases with increasing ambient pressure under constant inlet pressure or constant pressure drop. The growth of fluid temperature above the critical value can increase the enhancement. In addition, the jet from the Laval-2 nozzle with a smooth inner profile always has a greater erosion ability than that from the Laval-1 nozzle.

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 score0.378
Threshold uncertainty score0.498

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 studies0.0010.001
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 head0.012
GPT teacher head0.260
Teacher spread0.249 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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