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Record W2121660915 · doi:10.1115/1.2909610

Exergy Analysis of Condensation of a Binary Mixture With One Noncondensable Component in a Shell and Tube Condenser

2008· article· en· W2121660915 on OpenAlexafffund
Yousef Haseli, İbrahim Dinçer, G.F. Naterer

Bibliographic record

VenueJournal of Heat Transfer · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsOntario Tech University
FundersAtomic Energy of Canada Limited
KeywordsExergyExergy efficiencyCondenser (optics)Mass flow rateThermodynamicsCondensationDimensionless quantityMechanicsHeat exchangerMaterials scienceEnvironmental scienceVolumetric flow rateMass flowShell and tube heat exchangerPhysics

Abstract

fetched live from OpenAlex

The exergy (second-law) efficiency is formulated for a condensation process in a shell and one-path tube exchanger for a fixed control volume. The exergy efficiency ηex is expressed as a function of the inlet and outlet temperatures and mass flow rates of the streams. This analysis is utilized to assess the trend of local exergy efficiency along the condensation path and evaluate its value for the entire condenser, i.e., overall exergy efficiency. The numerical results for an industrial condenser, with a steam-air mixture and cooling water as working fluids, indicate that ηex is significantly affected by the inlet cooling water and environment temperatures. Further investigation shows that other performance parameters, such as the upstream mixture temperature, air mass flow rate, and ratio of cooling water mass flow rate to upstream steam mass flow rate, do not have considerable effects on ηex. The investigations involve a dimensionless ratio of the temperature difference of the cooling water and environment to the temperature difference of condensation and the environment. Numerical results for various operational conditions enable us to accurately correlate both the local and overall exergy efficiency as linear functions of dimensionless temperature.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.196
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations6
Published2008
Admission routes2
Has abstractyes

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