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Record W2782864927 · doi:10.1115/imece2017-71764

Thermodynamic Analysis of Ocean Thermal Energy Conversion System With Different Working Fluids

2017· article· en· W2782864927 on OpenAlexaff
Dashu Li, Li Zhang, Xili Duan, Xiaosuai Tian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsOcean thermal energy conversionRefrigerantWorking fluidEvaporatorSeawaterEnvironmental scienceThermalThermal efficiencyThermal energyEnergy transformationThermodynamicsChemistryHeat exchangerGeologyOceanographyPhysics

Abstract

fetched live from OpenAlex

A thermodynamic model is developed for ocean thermal energy conversion (OTEC) systems. Considering the narrow temperature range in the evaporator, different refrigerants including R717, R134a and R600 were analyzed and compared under sub-critical state with practical ocean thermal conditions. The results show that larger ocean temperature differences will lead to higher evaporation pressures, and less pumping power requirements for all pumps, i.e., warm sea water pump, cold sea water pump and pumps for the working fluid. The thermal efficiency of different systems and the net power output were found to be closely related to ocean temperature difference, with a positive linear relationship. It was also found that R717 provides the highest thermal efficiency with the least pump power requirement. This working fluid could potentially be used for OTEC system development. This study provides useful insights to the design and equipment selection of OTEC 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.190
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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