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Record W4386072881 · doi:10.11159/htff23.176

Performance Investigation of Axial-flow Fan with Exhaust-side Eccentric Blockage Disk

2023· article· en· W4386072881 on OpenAlexvenueno aff
Yuki Matsuo, Chihiro Sugiyama, Ibuki Matsumura, Koichi Nishibe, Kotaro SATO

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEccentricAxial compressorFlow (mathematics)MechanicsComputer scienceMaterials scienceAutomotive engineeringPhysicsStructural engineeringAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Axial-flow fans, which have a simple structure, are suitable for the cooling of heating elements, such as the electronic components inside personal computers (PCs), owing to their high flow rate and low differential pressure performance. However, under normal operating environments, the performance and efficiency of fans are degraded because their intake or exhaust side is surrounded by numerous components (obstacles) inside a PC. Several studies have been conducted to elucidate the influence of different obstacle geometries and their positions on fan performance to mitigate adverse effects. Kang et al. In addition, experimental and Computational Fluid Dynamics (CFD) results revealed that when the distance was extremely narrow, a flow instability with a pair of high-pressure and low-pressure regions that propagate in the circumferential direction was generated in the distance. An experimental study of the relationship between the eccentricity of the installation position of the intake-side obstacle and the fan performance has also been conducted On the other hand, when obstacles are installed on the exhaust side, the fan performance at a specific distance between the obstacle and the fan is higher compared to the case without the obstacle [3]. However, the available results are limited and a more systematic investigation is needed to elucidate the mechanism of the improvement in fan 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)0.000
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 score0.172
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.170
Teacher spread0.165 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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