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Record W4206753268 · doi:10.4271/03-15-04-0031

Calculation of Air Velocity on the Helicopter Turboshaft Engines Inlet

2021· article· en· W4206753268 on OpenAlexaff
Farshid Bazmi, Afshin Rahimi

Bibliographic record

VenueSAE International Journal of Engines · 2021
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInletEnvironmental scienceAerospace engineeringMarine engineeringAeronauticsAutomotive engineeringMechanicsEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The turboshaft engine performance is closely related to the helicopter’s design, and because of its location beneath the helicopter’s main rotor, it has unique features that distinguish it from other families of gas turbine engines. The impact of the engine suction and main rotor’s blow in different flight regimes and climatic conditions lead to variations in speed, pressure, and temperature at the inlet of the turboshaft engines, which, in turn, will affect the design of the engine cycle. Therefore, in this article, the equations governing the airflow for turboshaft engines are enhanced to incorporate these effects. The equations in this article are derived using aerodynamics, flight dynamics, helicopter, and turboshaft design to lend the inlet velocity of the engine. In order to validate the analytical outcomes of these equations, a computational fluid dynamics (CFD) analysis is carried out to evaluate the turbulent flow at the T700-GE turboshaft inlet. The analytical and numerical results comparisons show a promising match that would allow future turboshaft engine designs to take advantage of the proposed solution for the turboshaft engine’s inlet velocity.

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)0.000
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.288
Teacher spread0.265 · 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
Published2021
Admission routes1
Has abstractyes

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Same venueSAE International Journal of EnginesSame topicRocket and propulsion systems researchFrench-language works237,207