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Record W3208943602 · doi:10.32604/cmc.2022.021406

Analysis of Flow Structure in Microturbine Operating at Low Reynolds Number

2021· article· en· W3208943602 on OpenAlexaff

Bibliographic record

VenueComputers, materials & continua/Computers, materials & continua (Print) · 2021
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsReynolds numberTip clearanceMechanicsAerodynamicsLaminar flowVortexCasingChord (peer-to-peer)Degree RankineFlow (mathematics)Rotor (electric)StatorPhysicsMechanical engineeringEngineeringComputer scienceTurbulenceThermodynamics

Abstract

fetched live from OpenAlex

In this paper, three-dimensional flows in laminar subsonic cascades at relatively low Reynolds numbers (Re < 2500) are presented, based on numerical calculations. The stator and rotor blade designs are those for a MEMS-based Rankine microturbine power-plant-on-a-chip with 109-micron chord blades. Blade passage calculations in 3D were done for different Reynolds numbers, tip clearances (from 0 to 20%) and incidences (0° to 15°) to determine the impact of aerodynamic conditions on the flow patterns. These conditions are applied to a blade passage for a stationary outer casing. The 3D blade passage without tip clearance indicates the presence of two large symmetric vortices due to the interaction between hub/casing boundary layers and the blade. Opening the tip clearance introduces the tip vortex, which tends to become dominant above a tip clearance of 10%. In addition to providing a description and understanding of the 3D flow in a MEMS microturbine, these results suggest the importance of considering 3D flows in the design of microturbomachinery, even though the geometry is dominantly 2D.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.039
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.200
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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueComputers, materials & continua/Computers, materials & continua (Print)Same topicTurbomachinery Performance and OptimizationFrench-language works237,207