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Record W4210619805 · doi:10.1139/tcsme-2021-0067

Aerodynamic performance analysis of a supercritical airfoil in the helicopter main rotor

2022· article· en· W4210619805 on OpenAlexvenueno aff
Inamul Hasan, R. Mukesh, Radha Krishnan P., R. Srinath

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAirfoilAerospace engineeringAerodynamicsTransonicHelicopter rotorMach numberRotor (electric)Angle of attackTrailing edgeEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Helicopters can be considered as any-terrain vehicles, as they can take off and land at any location. The aerodynamic characteristics of helicopters are more complicated than those of fixed-wing aircraft. The rotor is the source of lift and thrust for helicopters. The complex aerodynamic characteristics of helicopters are due to their rotational frame and variations in the velocity and pressure throughout the blades. Moreover, the airfoil undergoes phase changes because half of the phase exhibits a trailing edge toward the flow. In this study, four isolated helicopter rotor blades were analyzed using ANSYS Fluent in terms of flow in a static domain under non-rotating conditions. Supercritical airfoils used in high-speed aircraft have been found to be incredibly useful in the transonic region. They increased the critical and drag divergence Mach numbers. Incorporating supercritical airfoils in helicopter rotor blades ensures suitable flow characteristics and more than 50% efficiency compared to those of the HH02 blade in a stationary frame. Analyses were conducted for HH02 and NASA SC(2)-0714 airfoils with Mach numbers of 0.3, 0.4, and 0.5 without rotation. The post-processing results prove that the NASA SC(2)-0714 airfoil rotor achieves a better aerodynamic performance than the HH02 airfoil rotor.

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 score0.148
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.006
GPT teacher head0.184
Teacher spread0.179 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207