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On designing low pressure loss working spaces for a planar Stirling micromachine

2015· article· en· W2292481387 on OpenAlexaff
M.-A. Hachey, Étienne Léveillé, Luc G. Fréchette, Fabien Formosa

Bibliographic record

VenueJournal of Physics Conference Series · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Thermodynamic Systems and Engines
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsStirling engineStirling cyclePiston (optics)Regenerative heat exchangerWorking fluidMechanical engineeringPlanarFlow (mathematics)Displacement (psychology)EngineeringComputer scienceMechanicsPhysicsOpticsComputer graphics (images)Heat exchanger

Abstract

fetched live from OpenAlex

In this paper, research was undertaken with the objective to design low pressure loss working spaces for a Stirling cycle micro heat engine operating from low temperature waste heat. This planar free-piston heat engine is anticipated to operate at the kHz level with mm 3 displacement. Given the resonant nature of the free-piston configuration, the complexity of its working gas' flow geometry and its projected high operating frequency, flow analysis is relatively complex. Design considerations were thus based on fast prototyping and experimentation. Results show that geometrical features, such as a sharp 90° corner between the regenerator and working spaces, are strong contributors to pressure losses. This research culminated into a promising revised working space configuration for engine start-up, as it considerably reduced total pressure losses, more than 80% at Re = 700, from the original design.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.233
Teacher spread0.207 · 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 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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