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Rock-Cutting Performance Experimental Research of Particle Water Jet Based on Orthogonal Experimental Method

2011· article· en· W1711590880 on OpenAlexvenueno aff
Guodong Ji, Haige Wang, Lulin Kong, Ming Ren

Bibliographic record

VenueAdvances in petroleum exploration and development · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsParticle (ecology)DrillingJet (fluid)Smoothed-particle hydrodynamicsRange (aeronautics)Materials scienceParticle sizeGeotechnical engineeringMechanicsPetroleum engineeringMechanical engineeringGeologyEngineeringComposite materialPhysicsChemical engineering

Abstract

fetched live from OpenAlex

Particle Impact Drilling is an efficient drilling technology for deep-well hard formation which cuts rock mainly by high-speed spherical particle impacting rock with the help of hydraulic action and mechanical action. In order to determine main influence factors and their order of priority, the orthogonal experiment was designed and the experimental data was analyzed with both the general method and the variance method. The analysis indicates that the order of priority of the main influence factors of particle water jet rock-cutting performance is pump pressure ps, impacting range S, particle mass concentration ω, confining pressure P and particle diameter dp. Moreover, ps is highly important and S and ω are important. In addition, high ps, moderate S, moderate ω, moderate dp and low P could effectively promote particle water jet to impact and cut rocks. Key words: Deep hard formation; Particle impact drilling; Particle water jet; Rock-cutting performance; Orthogonal experiment method; Analysis of variance

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.087
GPT teacher head0.352
Teacher spread0.266 · 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.

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
Published2011
Admission routes1
Has abstractyes

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