MétaCan
Menu
Back to cohort

Application of Supercritical Pressures in Power Engineering

2017· book-chapter· en· W2602970891 on OpenAlexaff
Igor Pioro, Mohammed Mahdi

Bibliographic record

VenueAdvances in chemical and materials engineering book series · 2017
Typebook-chapter
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBrayton cycleSupercritical fluidRankine cycleCoolantNuclear engineeringWorking fluidHeat transferDegree RankineHeliumMaterials scienceThermodynamicsCabin pressurizationTurbineThermal power stationEnvironmental scienceChemistryPower (physics)EngineeringComposite material

Abstract

fetched live from OpenAlex

SuperCritical Fluids (SCFs) have unique thermophyscial properties and heat-transfer characteristics, which make them very attractive for use in power industry. In this chapter, specifics of thermophysical properties and heat transfer of SCFs such as water, carbon dioxide and helium are considered and discussed. Also, particularities of heat transfer at SuperCritical Pressures (SCPs) are presented, and the most accurate heat-transfer correlations are listed. SuperCritical Water (SCW) is widely used as the working fluid in the SCP Rankine “steam”-turbine cycle in fossil-fuel thermal power plants. This increase in thermal efficiency is possible by application of high-temperature reactors and power cycles. Currently, six concepts of Generation-IV reactors are being developed, with coolant outlet temperatures of 500°C~1000°C. SCFs will be used as coolants (helium in GFRs and VHTRs; and SCW in SCWRs) and/or working fluids in power cycles (helium; mixture of nitrogen (80%) and helium [20%]; nitrogen, and carbon dioxide in Brayton gas-turbine cycles; and SCW “steam” in Rankine cycle).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.201
Teacher spread0.197 · 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.

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in chemical and materials engineering book seriesSame topicHeat transfer and supercritical fluidsFrench-language works237,207