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Combined Heat and Power Design Considerations for the APR1400

2017· article· en· W2610779917 on OpenAlexaboutno aff
Michał Wierzchowski, Robert M. Field

Bibliographic record

VenueJournal of Energy and Power Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
FundersKorea Electric Power CorporationInternational Atomic Energy Agency
KeywordsComputer sciencePower (physics)Nuclear engineeringReliability engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

To date, nuclear cogeneration applications have been limited, primarily to district heating in Eastern Europe and heavy water production in Canada.With the current global price for oil and energy, this technology is not economically viable for most countries.However, oil and fossil fuel prices are known to be highly volatile, and the Paris Agreement calls for a reduction in fossil fuel use.Under these circumstances, heat supplied by nuclear power may abruptly return to favor.To prepare for such a scenario, this study will investigate design considerations for a prototypical modern nuclear power plant, the Korean APR1400 (advanced power reactor 1400) (e.g., Shin Kori Units 3, 4, Shin Hanul 1, 2, Barakah Units 1, 2, 3, 4).Nuclear cogeneration can impact balance of plant system and component design for the condensate, feedwater, extraction steam, and heater drain systems.The APR1400 turbine cycle will be reviewed for a parametric range of pressures and flow rates of the steam exported for cogeneration to identify major design challenges.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.026
GPT teacher head0.228
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreMethods

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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