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Experimental methods to determine the performance of desiccant coated fixed-bed regenerators (FBRs)

2021· article· en· W3199400680 on OpenAlexafffund
Easwaran N. Krishnan, Hadi Ramin, A. Gurubalan, Carey J. Simonson

Bibliographic record

VenueInternational Journal of Heat and Mass Transfer · 2021
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of SaskatchewanCanadian Light Source
KeywordsDesiccantHumidityMaterials scienceNuclear engineeringMoistureSteady state (chemistry)Relative humidityEnvironmental scienceProcess engineeringHeat exchangerThermodynamicsComposite materialChemistryPhysics

Abstract

fetched live from OpenAlex

Desiccant coated fixed-bed regenerators (FBRs) transfer heat and moisture between two airstreams having different temperature and humidity. The experiments on FBRs are challenging because of their transient nature of operation. Unlike other energy exchangers, FBRs do not attain a steady-state condition; instead, they reach a quasi-steady state where the humidity and temperature of the outlet air continuously vary with time. This paper shows that measuring the temperature and humidity of a time-varying airstream with temperature and humidity sensors may lead to errors in the effectiveness in the order of 15–20%. Meanwhile, the method proposed in this paper reduces errors to ±5–8%, which is comparable to the accepted uncertainties in steady-state test standards. The major contributions of this paper are: (i) development and verification of new experimental methods to determine the effectiveness of desiccant coated FBRs and (ii) verification of the bag sampling method (BSM) for humidity measurements proposed by test standards. The results are verified using a validated numerical model and a correlation from the literature.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.209

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 head0.018
GPT teacher head0.285
Teacher spread0.266 · 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.

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

Citations9
Published2021
Admission routes2
Has abstractyes

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