MétaCan
Menu
Back to cohort
Record W2803095287 · doi:10.1139/tcsme-2016-0065

FULL SCALE EXPERIMENT AND NUMERICAL ANALYSIS FOR THE PERFORMANCE OF HEAT EXCHANGER IN MOLTEN CARBONATE FUEL CELLS

2016· article· en· W2803095287 on OpenAlexvenueno aff
Seon-Hwa Kim, Byeong-Keun Choi, Young–Su AN

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerMoving bed heat exchangerPlate heat exchangerCombustionMaterials scienceNTU methodHeat transferShell and tube heat exchangerNuclear engineeringMechanicsThermodynamicsPlate fin heat exchangerMechanical engineeringEngineeringChemistryPhysics

Abstract

fetched live from OpenAlex

This study presents a numerical simulation of heat transfer and flow characteristics of the heat exchanger in molten carbonate fuel cell system. In this study, the actual size of the heat exchanger was simulated in order to avoid errors that can occur from the scale-down test, also the simulation gas (air) was verified with the heat duty of 800,000 kcal/hr. It is analyzed by using a commercial heat exchanger calculation code based upon the test condition. It is found that a reasonable agreement is obtained from comparison between the predicted results and the measured data. Furthermore, the verified similarity was presented in this analysis. In particular, the simulation gas used for the shell side service for the heat exchanger is obtained through the combustion calculation, i.e. by using a flow rate of the fuel gas. In addition, the performance of the heat exchanger is predicted under various conditions in the fuel cell operation conditions by the numerical model.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.010
GPT teacher head0.208
Teacher spread0.199 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicEngineering Applied ResearchFrench-language works237,207