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Record W4382203857 · doi:10.4050/f-0079-2023-18046

Aeroelastic Design of a High Speed Highly Efficient Rotor

2023· article· en· W4382203857 on OpenAlexaff
Chris Sutton, Claude Matalanis, Ramin Modarres, Byung-Young Min, В.Й. Климченко, Brian Wake

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsAeroelasticityConceptual designSizingDesign for manufacturabilityRotor (electric)Computer scienceBlade (archaeology)EngineeringAerodynamicsMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The aeroelastic design of a high speed highly efficient rotor, from conceptual layout to detailed design, is presented. The overall strategy is described along with details and a demonstration case on the conceptual design optimization strategy. In Phase I, rapid design iterations were performed using lower order quick turnaround tools to establish basic design aspects such as rotor head sizing and stiffnesses. In Phase II, higher order tools are implemented along with a more realistic structural representation of the head and blades. The outcome is a robust preliminary design to be analyzed with CFD-CSD and tuned further in detailed design without requiring major rework. Finally, dynamic tailoring performed on the blade during final design is presented. This was done to maintain or improve frequency placements and vibration levels as relatively small, surgical changes were made to the blade detailed design (ply thicknesses, orientations, blade weights, etc.) to satisfy strength, life, and manufacturability requirements. This work highlights the importance of upfront aeromechanics optimization to establish a robust early conceptual design, and high-fidelity analysis through final design to maintain desired characteristics.

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 score0.225
Threshold uncertainty score0.318

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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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
Published2023
Admission routes1
Has abstractyes

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