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Record W2482148890 · doi:10.1115/1.4033770

Design and Performance Evaluation of a Heat Exchanger Network for a Cogeneration DMS to Various Thermal Utilization Applications

2016· article· en· W2482148890 on OpenAlexafffund
Gaoming Ge, Carey J. Simonson

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Saskatchewan
FundersInnovation Saskatchewan
KeywordsCogenerationHeat exchangerProcess engineeringEnvironmental scienceHeat recovery ventilationNuclear engineeringComputer scienceMechanical engineeringEngineeringElectricity generationPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

Hitachi-GE developed a 300-MWe-class modular simplified and medium small reactor (DMS) between 2000 and 2004. It was designed to have merits over traditional nuclear power plants in areas of lower initial capital investment, flexibility, enhanced safety, and security. The balance of plant (BOP) system of the DMS was originally designed for supplying just electricity. In this study, the cogeneration DMS that supplies both electricity and heat is under investigation. The heat exchanger (HX) network, mainly consisting of the BOP heat exchanger, water pump, and the heat exchangers that deliver heat to the thermal utilization (TU) applications, must operate in an efficient way to keep the overall system costs low. In this paper, the configuration of a heat exchanger network that serves for various TU applications is investigated first. A numerical model for the heat exchanger network is built, and sensitivity studies are performed to estimate the energy efficiency and exergy efficiency of the whole heat exchanger network under different design and operating conditions (e.g., different water temperatures and flow rates). Important design and operating parameters, which significantly impact the performance of the network, are evaluated and presented.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.034
GPT teacher head0.258
Teacher spread0.225 · 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 designSimulation or modeling
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
Published2016
Admission routes2
Has abstractyes

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