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Record W2623561674 · doi:10.4050/f-0071-2015-10257

RotorShield Advanced Rotor Blade Erosion Protection, Application to V-22

2015· article· en· W2623561674 on OpenAlexaff
Jeffrey Nissen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsBlade (archaeology)Rotor (electric)ErosionComputer scienceAerospace engineeringEngineeringMechanical engineeringGeology

Abstract

fetched live from OpenAlex

Rotorcraft operating in desert and shore environments continue to experience severe rotor blade erosion. To mitigate damage from sand and rain, rotor blade leading edges have historically been designed with a metallic abrasion strip that serves as sacrificial material to absorb the damage. Erosion of the metal abrasion strip can become a major contributor aircraft downtime and maintenance activities. Sand erosion takes place during takeoff and landing, or during ground operations where dust, sand, and other debris are lifted by the rotor downwash. Rain erosion occurs during aircraft operation in heavy rainfall. To alleviate the maintenance costs associated with rotor erosion, a number of research efforts have investigated alternative rotor blade abrasion strip treatments to develop new structures or coatings that are more resistant to erosion damage. The ONR RotorShield erosion coating system is a technology applied to the V-22 to enable extended erosion protection and achieves the goals of reduced maintenance and repair costs associated with erosion damage. The erosion coating technology is compliant with V-22 rotor blade requirements such as: weight; fatigue; ice protection system; lightning strike and aerodynamics.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.591

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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