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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 OpenAlex
Farshid Bazmi, Afshin Rahimi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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

<div>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.</div>

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.264

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 head0.022
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