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Record W2922305535 · doi:10.1016/j.egypro.2019.01.612

Numerical solution for thermodynamic model of charge-discharge cycle in compressed hydrogen tank

2019· article· en· W2922305535 on OpenAlexaff
Feng Li, Tianqi Yang, Jinsheng Xiao, Pierre Bénard, Richard Chahine

Bibliographic record

VenueEnergy Procedia · 2019
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNational Science Foundation
KeywordsCompressed hydrogenInflowMechanicsHydrogenLiquid hydrogenCharge conservationThermodynamicsHydrogen storageEuler's formulaMaterials scienceChemistryCharge (physics)PhysicsMathematics

Abstract

fetched live from OpenAlex

The safety and convenience of hydrogen storage are significant for fuel cell vehicles. Based on mass conservation equation and energy conservation equation, two thermodynamic models (single zone model and dual zone model) have been established to study the hydrogen gas temperature and tank wall temperature for compressed hydrogen storage tank. With two models, analytical solution and Euler solution for single zone (gas zone) charge-discharge cycle have been compared, Matlab/Simulink solution and Euler solution for dual zone (gas zone, wall zone) charge-discharge cycle have been compared. Three charge-discharge cycle cases (Case 1, constant inflow temperature; Case 2, variable inflow temperature; Case 3, constant inflow temperature variable outflow temperature) and two compressed hydrogen tanks (Type III 25L, Type IV 99L) charge-discharge cycle are studied by Euler method. Results show Euler method can well predict hydrogen temperature and tank wall temperature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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