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Record W4401416825 · doi:10.3390/aerospace11080644

Number of Blades’ Influence on the Performance of Rotor with Equal Solidity in Open and Shrouded Configurations: Experimental Analysis

2024· article· en· W4401416825 on OpenAlexaff
Abdallah Dayhoum, Alejandro Ramirez‐Serrano, Robert J. Martinuzzi

Bibliographic record

VenueAerospace · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSolidityRotor (electric)Structural engineeringEngineeringMathematicsComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

This study explores the implications of the number of blades on the performance of both open and shrouded rotors. By conducting a thorough experimental analysis at a fixed solidity ratio, this research seeks to enhance our understanding of rotor dynamics and efficiency. Two-, three-, four-, and five-bladed rotors were designed and manufactured to have the same solidity ratio. This leads to smaller chord distribution values for higher blade numbers. The experimental analysis aims to quantify the effects of the number of blades and provides a comparative analysis of performance differences between the two rotor configurations (shrouded and open). For the open rotor, results indicate that increasing the number of blades has a minimal impact on overall performance. This is due to the decrease in the tip loss factor being counterbalanced by a decline in efficiency caused by the two-dimensional airfoil performance, which results from a smaller chord and a lower Reynolds number. In contrast, the shrouded rotor exhibits a noticeable performance decay with an increased blade count. Since tip loss is inherently absent in shrouded designs, the decline is primarily attributed to the two-dimensional airfoil performance. This decay occurs while maintaining a constant solidity ratio, highlighting the significant effect of blade count on shrouded rotor efficiency, thereby contributing to the optimization of rotor design in various engineering applications.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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