Bibliographic record
Abstract
An engine is described that operates exclusively on stoichiometric H2-O2 mixtures with significant amounts of excess hydrogen circulated to effect controlled combustion of excessively rich mixtures within the engine cylinder combined with exhaust water condensation and removal. A two-zone quasi-dimensional model for predicting the performance and the likelihood of the onset of knock developed earlier for CH4-H2-air operation has been extended suitably to predict the approximate performance of this H2-O2 engine arrangement. A detailed chemical kinetic scheme for the oxidation reactions of H2-O2 mixtures is adopted in the knock prediction of this model. A prior knowledge of the variation of the combustion period needed for this predictive model was estimated through processing corresponding data for H2-air mixtures. The limited experimental results reported by Furuhama, et al., were used to validate the corresponding predicted values. Some aspects of the performance of such a H2-O2 engine were investigated and shown to be capable of knock free operation with very high work production efficiency. Moreover, the setup is equally attractive for simultaneous production of power and heat in a co-generation version where the H2O heat evolved through cooling and condensation is usefully utilized. The feasibility of adopting other diluents such as argon in this engine was also examined and shown to be less attractive than with recirculating hydrogen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".