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Record W4403986585 · doi:10.54097/bs666q70

The Applications and Prospects of Hydrogen Internal Combustion Engine

2024· article· en· W4403986585 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsCombustionInternal combustion engineHydrogenEnvironmental scienceNuclear engineeringAutomotive engineeringPhysicsEngineeringChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Hydrogen internal combustion engines (HICs) are an emerging technology with significant potential for clean energy propulsion. However, they currently face several technical challenges, such as abnormal combustion phenomena, low energy density, and complexities in control under transient operating conditions. The primary technological focus areas include the integration of hydrogen combustion systems, ensuring safety, and enhancing reliability. HICs are vital for promoting environmental protection and sustainable development due to their potential for reducing greenhouse gas emissions. This paper provides an in-depth analysis of the current state of HIC technology, addressing key challenges and recent research efforts aimed at overcoming these issues. The discussion includes advancements in combustion technology, safety measures, and reliability improvements. With continued innovation, HICs are expected to become a viable alternative in various applications, supporting the global transition to cleaner energy solutions. This overview underscores the importance of further research and development to realize the full potential of hydrogen internal combustion engines.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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