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Record W3159086180 · doi:10.82308/30759

Structural design of the rotor and static structure of a microscale Rankine engine

2009· article· en· W3159086180 on OpenAlexfundno aff
Hassan Shahriar

Bibliographic record

VenueeScholarship@McGill (McGill) · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Thermodynamic Systems and Engines
Canadian institutionsnot available
FundersGeneral Motors of CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsDegree RankineMicroscale chemistryRotor (electric)Mechanical engineeringEngineeringComputer scienceEnvironmental scienceMathematicsProcess engineering

Abstract

fetched live from OpenAlex

L'objectif de cette thèse est de développer des outils qui permettront la conception de composants de moteur, afin de satisfaire les exigences de performance et de fiabilité de l'appareil dans des conditions extrêmes. Le méthode Ashby a été utilisée pour identifier un ensemble de matériaux - la zircone, la silice, de l'alliage de titane, de l'alliage nickel-cobalt, de silicium et de carbure de silicium – pour la structure statique isolante du rotor, ce qui révèle un compromis entre la performance, la fiabilité et la fabricabilité . Ultérieurement, les contraintes, et les déformations de la structure statique d'isolation ont été analysées par la méthode des éléments finis.Par la suite, une expérience idéalisée d'impact a été conçue et réalisée pour évaluer la fiabilité du rotor dans le cas d'un accident d'impact à grande vitesse avec la paroi latérale de la structure statique. La vitesse seuil d'ouverture des dommages dans des projectiles d'alumine (balles) un impact sur les objectifs de l'alumine (disques) a été retrouvé à 30 m/s, alors que le projectile en zircone sur la cible d'alumine a été jugé dans la région de 65 - 70 m/s. Ces résultats ouvrent la voie à la formulation de matériaux de conception pour le rotor et la paroi latérale statique du micro-moteur Rankine.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.919

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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