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ICONE23-2024 DEVELOPMENT OF "INTERMEDIATE HEAT EXCHANGE" SIMULATOR OF THE DMS FOR CO-GENERATION

2015· article· en· W2706517550 on OpenAlexaboutno aff
Tomohiko Ikegawa, Kazuaki Kito, Koji Nishida

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2015
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsModular designHeat exchangerNuclear engineeringProcess engineeringFlexibility (engineering)Nuclear powerEngineeringMechanical engineeringEnvironmental scienceComputer scienceOperating system

Abstract

fetched live from OpenAlex

Hitachi-GE Nuclear Energy Ltd. (Hitachi-GE) has developed a conceptual design for the Double MS: modular simplified & medium small reactor (DMS) under the sponsorship of The Japan Atomic Power Company. The DMS is one of the small-to-medium sized reactors (SMRs) of boiling water reactor (BWR) type, which has been predicted to overcome almost all economy of scale concerns when compared to proven conventional advanced BWR (ABWR) technologies. The DMS design was dedicated to generate electric power, and in order to enhance flexibility for usage of the DMS, the University of Saskatchewan, Hitachi-GE and Hitachi Ltd. (Hitachi) have collaborated on a joint research and development (R&D) initiative to study the utilization of heat and steam from the balance of plant (BOP) for thermal utilization (TU) applications such as district heating, process heating, etc. Based on the DMS of the 300MWe class, Hitachi is developing heat balance evaluation tools for the BOP system and the intermediate heat exchanger (IHX) system which is implemented for heat transfer from the BOP to the TU application in order to detect radioactive materials from the BOP and to prevent leakage to the TU applications. In this paper, a part of the IHX system heat balance simulator for the co-generation DMS is described.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.444

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 science0.0010.000
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 head0.054
GPT teacher head0.239
Teacher spread0.185 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2015
Admission routes1
Has abstractyes

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