MétaCan
Menu
Back to cohort
Record W4405920642 · doi:10.3390/app15010151

Structural Designing of Supersonic Swirling Devices Based on Computational Fluid Dynamics Theory

2024· article· en· W4405920642 on OpenAlexaboutno aff
Qian Huang, Huirong Huang, Xueyuan Long, Yuan Tian, Meng Jiang

Bibliographic record

VenueApplied Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersCentre Scientifique et Technique du BâtimentChongqing Municipal Education CommissionNatural Science Foundation of ChongqingNational Natural Science Foundation of China
KeywordsSupersonic speedComputational fluid dynamicsComputer scienceAerospace engineeringMechanicsPhysicsEngineering

Abstract

fetched live from OpenAlex

The supersonic swirling device is a new apparatus that can be used for natural-gas liquefaction. The structure of the supersonic swirling device has an important impact on the liquefaction efficiency. Therefore, this study presents a structural design method for supersonic cyclones based on CFD theory. Using the production parameters of a liquefied natural gas (LNG) peak-shaving station as the study case, a detailed design and design comparison of each part of the supersonic swirling separator are carried out. An optimum LNG supersonic swirling separator design was obtained. To ensure that the designed supersonic swirling separator achieved better liquefaction effectiveness, it was ascertained that no large shockwaves were generated in the de Laval nozzle, the pressure loss on the swirler was small, and the swirler was able to produce a large centripetal acceleration. The opening angle of the diffuser and the length of the straight tube were designed considering the location at which normal shockwaves were generated. The location at which shockwaves are generated and the friction effect are important parameters that determine the gap size. With this design guidance, the optimal structural dimensions of the supersonic swirling device for a given processing capacity were determined as follows: a swirler with six vanes and an 8 mm wide channel; a 10D-long straight tube, an opening angle of 20° between the straight tube and the divergent section, and a gap size of 2 mm. Compared with “Twister II”, the new device has better liquefaction efficiency.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.472

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 studies0.0000.000
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueApplied SciencesSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207