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Record W4256316407 · doi:10.5383/ijtee.09.01.002

Heat Exchanger Modelling for Solar Organic Rankine Cycle

2014· article· en· W4256316407 on OpenAlexvenueno aff
Shadreck M. Situmbeko, Freddie L. Inambao

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
FundersInyuvesi Yakwazulu-Natali
KeywordsMechanicsOrganic Rankine cycleHeat transferThermodynamicsEvaporatorHeat exchangerShell and tube heat exchangerPlate fin heat exchangerMechanical engineeringConvective heat transferPressure dropPlate heat exchangerWorking fluidMaterials scienceChemistryEngineeringWaste heatPhysics

Abstract

fetched live from OpenAlex

The paper presents work done on the development of a heat exchanger model suitable for incorporation into a low temperature solar thermal power cycle. In particular it presents the mathematical model comprising heat transfer, mass transfer, and convective heat transfer coefficients, and velocity and pressure drop correlations for single and two phase flows. The preliminary evaporator model is based on a counter flow double pipe configuration; the flow boiling process incorporates both convective and nucleate boiling. The shell side heat transfer fluid consists of ethylene glycol at 50 % concentration; the tube side fluid flow is modelled on four candidate working fluids pre-selected from previous stages of the research study. The evaporator model is implemented on the engineering equation solver platform; following on the computer simulation results a further proposal is made for conversion of the model design into a feasible shell-and-tube heat exchanger. The outputs of the model study are in the form of the rate of heat exchange, size and type of the heat exchanger, whilst ensuring that the pressure drops and fluid velocities are within acceptable limits.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.377

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 head0.004
GPT teacher head0.170
Teacher spread0.166 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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Same venueInternational Journal of Thermal and Environmental EngineeringSame topicThermodynamic and Exergetic Analyses of Power and Cooling SystemsFrench-language works237,207