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Performance and Design of Dehumidifier Wheels

2009· article· en· W2077613011 on OpenAlexaff
Wei Shang, Robert W. Besant

Bibliographic record

VenueHVAC&R Research · 2009
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLaminar flowWater vaporMechanicsHVACMass transferThermal conductionTransient (computer programming)Flow (mathematics)Regenerative heat exchangerCoupling (piping)Heat transferRange (aeronautics)ThermodynamicsEnvironmental scienceMaterials scienceAir conditioningMechanical engineeringEngineeringComputer scienceHeat exchangerMeteorologyPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

A new theoretical model is presented for the performance of dehumidifier wheels. The model is aimed at providing design guidance for manufacturers and selection and operation advice for HVAC system designers. To characterize the performance of dehumidifier wheels, water vapor effectiveness, ∊w, and water vapor mass ratio, Δw/Δw*, are defined. The theoretical model uses the transient response characteristics of the flow channel in the wheel matrix to predict the base case physical characteristics for fully developed laminar flow through the matrix where the heat transfer characteristics are first decoupled from the water vapor transfer. The simple algebraic equations, deduced for this base case of a dehumidifier wheel water vapor effectiveness, ∊w, and water vapor mass ratio, Δw/Δw*, show that the mass rate of the supply and regenerator flows should be equal and that both ∊w and Δw/Δw* go toward zero as the wheel speed decreases to low values. Corrections are made to these base case performance factors, which account for the coupling of the heat and water vapor transfer, as well as other smaller factors caused by entrance, heat conduction, carryover, and flow channel variation effects. It is concluded that more dehumidifier wheel performance data, along with a thorough analysis of uncertainties, will permit researchers to decrease the range of physical factor coefficients used in this model.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.099
GPT teacher head0.328
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

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 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

Citations7
Published2009
Admission routes1
Has abstractyes

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