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Record W2586016887 · doi:10.1115/1.4035856

Pressurized Heavy Water Reactor Technology: Its Relevance Today

2017· article· en· W2586016887 on OpenAlexaboutno aff
Arun K. Nayak, S. Banerjee

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2017
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMOX fuelPressurized water reactorNuclear powerNatural uraniumEnvironmental scienceNuclear engineeringElectricityNuclear technologyProduction (economics)Fuel cycleHeavy waterUraniumWaste managementProcess engineeringEngineeringMaterials scienceNuclear physics

Abstract

fetched live from OpenAlex

The pressurized heavy water reactor (PHWR) technology was conceived in Canada and has moved to several nations for commercial production of electricity. Currently, 49 power reactors operate with PHWR technology producing nearly 25 GWe. The technology is flexible for adopting different fuel cycle options which include natural uranium, different mixed oxide (MOX) fuel, and thorium. The technology has made substantial improvement in materials, construction, and safety since its inception. PHWRs have demonstrated excellent performance historically. Their safety statistics are excellent. Indian PHWRs also have shown economic competitiveness even in small sizes, thus providing an ideal design for new entrants. While the technology features of PHWRs are available even in textbooks, the objective of this paper is to highlight the historical development and salient features, and innovations for further improvement in operation, safety and economics. Thus, this paper shall serve as a curtain raiser for the special issue “Pressurized Heavy Water Reactors (PHWRs) Safety: Post Fukushima.”

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.006
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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
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

Citations4
Published2017
Admission routes1
Has abstractyes

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