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Record W2014922994 · doi:10.1115/1.1804539

Effects of Pore Size Variations on Regenerative Wheel Performance

2005· article· en· W2014922994 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2005
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPressure dropMechanicsGaussianFlow (mathematics)Materials scienceHydraulic diameterDrop (telecommunication)ChemistryMechanical engineeringEngineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

Manufacturing tolerances usually cause the air flow channel pore sizes to have a random variation in the matrices of regenerative wheels. The effects of random pore size distribution on pressure drop across a regenerative energy wheel transferring heat and moisture and effectiveness are investigated using analytical methods. Compared to an identical wheel with no pore size variation, simple algebraic expressions for pressure drop ratio, Δp/Δp0, and effectiveness ratio, ε/ε0, are developed for a Gaussian distribution of flow channel hydraulic diameters. Graphical results are presented showing that large random variations in flow channel pore size decrease the pressure drop across a wheel and the effectiveness (sensible, latent, and total) significantly for a regenerative wheel. Optical and micrometer measurements of four typical regenerative wheels showed a random variation in flow channel hydraulic diameters. These data imply significant decreases in Δp/Δp0 and ε/ε0 for each wheel.

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.

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 score0.120
Threshold uncertainty score0.397

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 head0.005
GPT teacher head0.216
Teacher spread0.210 · 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