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Record W3115868214 · doi:10.18280/ijdne.150612

Multi-Objective Optimization of Structural Parameters for Rotary Flow Jetting Tool Based on Orthogonal Experiment

2020· article· en· W3115868214 on OpenAlexvenueno aff
Leilei Liu, Jie Liu, Zuqing He, Huanle Liu, Chao Zhou, Xuegang Yan, Xiaojun Sun

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2020
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsNozzleFlow (mathematics)Spiral (railway)Jet (fluid)Mechanical engineeringEngineeringVolumetric flow rateMechanicsEngineering drawingAerospace engineering

Abstract

fetched live from OpenAlex

There is no precedent for the application of rotary flow jetting tool in the drainage gas recovery under gas wells. Based on the motion principles of jet flow and vortex flow, this paper designs a downhole rotary flow jetting tool, and verifies the feasibility of the tool through fluid simulation. Next, an indoor experiment device was established for drainage gas recovery with rotary flow jetting tool by the principle of similarity, and the structural parameters of the tool were subject to multi-objective optimization through orthogonal experiments. The optimized tool can achieve ideal rotary flow height and discharge volume. The results show that the proposed rotary flow jetting tool can effectively separate gas from liquid, and produce a rotary flow. The optimal structural parameters were determined as follows: the pitch diameter of spiral body is 45mm, the throat pipe length is 247mm, the spiral angle is 55°-60°, the spiral wing width is 4-6mm, and the nozzle diameter is 15-25mm. In addition, the number of side holes of the throat pipe has little effect on the jetting effect.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.243
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicCyclone Separators and Fluid DynamicsFrench-language works237,207