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Record W2900660545 · doi:10.25071/10315/35290

Analysis Of Thermal Losses In Air-Water Interfacial Solar Heating Systems

2018· article· en· W2900660545 on OpenAlexafffund
Shuzhe Wang, Paul G. O’Brien

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermalMaterials scienceEnvironmental scienceWater heatingSolar water heatingNuclear engineeringEngineering physicsSolar energyMeteorologyPhysicsElectrical engineeringEngineeringWaste management

Abstract

fetched live from OpenAlex

This paper reports a numerical analysis of solardriven seawater desalination systems with two configurations: a floating photo-thermal film at the air-water interface with and without an insulation layer located beneath its surface.Heat transfer processes from the film including conduction, convection and radiation have been evaluated at different film temperatures to determine the relative contributions of different heat loss mechanisms from the film.Convective heat transferred to the bulk seawater is found to be the dominant heat loss path, and can be reduced from 6.95 ×10 7 J/m 2 •h to 8.77 × 10 5 J/m 2 •h, a difference of almost two orders of magnitude, by inserting an insulation layer beneath the photo-thermal film.Thus, the addition of an insulating layer is suggested as an important design component to effectively minimize heat losses in air-water interfacial solar heating systems.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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