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Record W2145669857 · doi:10.1109/vppc.2010.5729039

System design and optimization of the world's fastest hydrogen fuel cell vehicle

2010· article· en· W2145669857 on OpenAlexaff
Edward Hillstrom, Kevin Ponziani, Benjamin Sinshiemer, Carington Bork, Marcello Canova, Yann Guezennec, Giorgio Rizzoni, Mike Procter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBallard Power Systems (Canada)
Fundersnot available
KeywordsPropulsionAutomotive engineeringTransient (computer programming)AnodeCathodeNuclear engineeringFuel efficiencyElectrical engineeringEngineeringComputer scienceAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

The Buckeye Bullet 2 is the world's fastest hydrogen fuel cell electric vehicle, with a certified FIA record of 487.433 km/hr (302.877 mi/hr). This paper provides the basic details of the overall vehicle and focuses on the design, testing, and optimization of the propulsion system. A unique fuel cell system was designed, tested, and integrated to produce over 500 kW of power, more than twice of its original rating. A unique pressure control is required to run the cathode system at maximum pressure during the race, and to manage the transient pressure pulses that occur when the race vehicle manual transmission is shifted. This causes rapid changes in the consumption of reactants, leading to severe pressure spikes that were limited with a custom tuned pressure relief system for the anode and cathode.

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)0.000
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.159
Teacher spread0.155 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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