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Record W2206910269 · doi:10.4271/2010-01-0854

Investigating the Optimum Practical Hydrogen Working Pressure for Gaseous Hydrogen Fueled Vehicles

2010· article· en· W2206910269 on OpenAlexaff
Ryan Harty, Steven Mathison, David Cun, Mark G. McDougall, Angela Nanalal, Livio Gambone, Bradley John Smith, Nikunj Gupta

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsHydrogenHydrogen vehicleComputer scienceEnvironmental scienceHydrogen fuelAutomotive engineeringNuclear engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Today's fuel cell powered vehicles typically utilize compressed hydrogen storage systems with a nominal working pressure of either 35MPa or 70Mpa. This coexistence of working pressures has, in a large part, developed in isolation, in that automakers have primarily considered vehicle side issues when choosing the storage system pressure. This study looks at hydrogen fueling from a holistic perspective by considering both vehicle side and station side issues with the goal to determine an optimum hydrogen working pressure. The approach utilized is to first conduct a data driven study of vehicle fueling at different working pressures and ambient temperatures to determine the vehicle and thermodynamic considerations of hydrogen fueling. This data is then contrasted with the hydrogen station hardware required to perform fueling at these temperatures and pressures. An optimum practical pressure is investigated, and recommendations are made for first establishing and converging towards this working pressure as an industry.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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