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Record W7162825068

Numerical Calculation of a Millmetre-sized Laval Nozzle and Optimization of the Length of Divergent Section Based on CFD

2014· article· zh· W7162825068 on OpenAlexaboutno aff
Yukui Cai, Liu Zhanqiang, 万熠

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languagezh
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleComputational fluid dynamicsRotational symmetryThrustSection (typography)TuyereDesign for manufacturabilityFlow (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Abstract:Micro laval nozzle has been widely used in such areas as micro propulsion system, supersonic air-jet pulverization,laser cutting,etc.The gas flow characteristics for micro nozzles with different section shapes and different divergent section lengths were analyzed with the aid of computational fluid dynamics(CFD) simulation technology, which helped to determine the rules of nozzle type selection and the optimum divergent section length. The simulation results indicated that the exit velocity of two-dimensional axisymmetric nozzle is larger than that of rectangular section one, which shows that the two-dimensional axisymmetric type is recommended when the nozzle is in millimeter level. The flow fields of the two-dimensional axisymmetric nozzles with different divergent section lengths were investigated through the comparison of their exit velocities, thrust forces and efficiencies, which presents that the optimal divergent length is 3 mm in this research. The proposed simulation method can be applied to the selection of other nozzle parameters and the optimization of length determination, which can help to reduce the difficulty of manufacturability of micro-nozzle with excellent performance.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.103
GPT teacher head0.456
Teacher spread0.353 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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