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Record W2068539382 · doi:10.1115/ihtc14-23403

Heat-Transfer at Supercritical Pressures

2010· article· en· W2068539382 on OpenAlexaff
Igor Pioro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercritical fluidCoolantNuclear engineeringHeat transferNuclear powerEnvironmental scienceThermal power stationProcess engineeringPetroleum engineeringMechanical engineeringWaste managementEngineeringMechanicsThermodynamicsNuclear physicsPhysics

Abstract

fetched live from OpenAlex

The first works devoted to the problem of heat transfer at supercritical pressures started as early as the 1930s. E. Schmidt and his associates investigated free-convection heat transfer to fluids at the near-critical point with the objective of developing a new effective cooling system for turbine blades in jet engines. In the 1950s, the idea of using supercritical “steam”-water appeared to be rather attractive for steam generators / turbines to increase thermal efficiency of fossil-fired power plants. Intensive work on this subject was mainly performed in the former USSR and in the USA in the 1950s–1980s. Therefore, the most investigated flow geometry at supercritical pressures is circular tubes with water as the coolant. Currently, using supercritical “steam” in fossil-fired power plants is the largest industrial application of fluids at supercritical pressures. At the end of the 1950s and the beginning of the 1960s, some studies were conducted to investigate the possibility of using supercritical water as a coolant in nuclear reactors. Several concepts of nuclear reactors were developed. However, this idea was abandoned for almost 30 years, and then regained momentum in the 1990s as a means to improve the performance of water-cooled nuclear reactors. Main objectives of using supercritical water in nuclear reactors are increasing the efficiency of modern nuclear power plants, which is currently 30–35%, to circa 43–50%, and decreasing operational and capital costs by eliminating steam generators, steam separators, steam dryers, etc. Therefore, objectives of the current paper are to assess the work that was performed and to understand specifics of heat transfer at supercritical pressures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.994

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)0.0070.000

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 head0.209
Teacher spread0.203 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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