MétaCan
Menu
Back to cohort

Effectiveness of Desiccant Coated Regenerative Wheels from Transient Response Characteristics and Flow Channel Properties—Part I: Development of Effectiveness Equations from Transient Response Characteristics

2009· article· en· W1985157536 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.

Bibliographic record

VenueHVAC&R Research · 2009
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDesiccantTransient (computer programming)AirflowMechanicsDimensionless quantityTransient responseFlow (mathematics)Materials scienceSteady state (chemistry)DiffusionHumidityConstant (computer programming)Environmental scienceThermodynamicsControl theory (sociology)Mechanical engineeringEngineeringComputer scienceChemistryComposite materialPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

During steady operation, regenerative wheels are subjected to cyclic step changes in inlet properties. The equation for the transient humidity step response of a desiccant coated rotary air-to-air wheel is developed from physical principles using a model similar to that used for the sensible energy or temperature response. This fully developed flow model is used to derive a characteristic moisture transfer time constant in terms of four dimensionless flow-channel desiccant-coating properties and the time duration of airflow through the wheel. Since the moisture diffusion time delays within the desiccant coating are significant for typical energy wheels and often desiccant dryer wheels, an empirical term is introduced with this time constant to account for the effects of the duration of exposure in the two types of wheels.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.068
GPT teacher head0.297
Teacher spread0.229 · 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