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Record W3039329436

Numerical Design of a Guide Vane for an Axial Fan

2020· article· en· W3039329436 on OpenAlexaff
Ali Saeidi, Pooya Taheri

Bibliographic record

VenueSummit (Simon Fraser University) · 2020
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGambitAirflowTurbulenceComputational fluid dynamicsFluentMechanical fanMechanical engineeringFinite volume methodEngineeringAxial compressorMechanicsSimulationAerospace engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

This study presents a custom guide vane design to substantially reduce the waste energy in axial fans. Ideally, there is no velocity component in the radial direction in axial fans, but in practice, the air leaving the axial fan has a large tangential component of velocity which produces a large amount of swirl kinetic energy. In order to solve this problem, a guide vane is designed to remove the rotational component of the air. The methodology described in this project is based on the fundamental governing continuity, momentum, and energy equations using the Finite Volume Method (FVM). In this project, the standard k-ω model is used for turbulent modeling. Two dimensional (2D) geometry of blades and airflow cross-section are designed using AutoCAD and CATIA while GAMBIT is employed to generate a suitable mesh for the three dimensional (3D) model. The mesh independence test is done to analyze the performance. The axial fan is simulated using FLUENT software to prove an increase in airflow rate after using the guide vane. Considering the final results, it can be observed that the airflow is increased up to 6.3%.

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: none
Teacher disagreement score0.925
Threshold uncertainty score0.463

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 head0.019
GPT teacher head0.208
Teacher spread0.188 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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