Reduced Convective Combustion Chamber Wall Heat Transfer Losses of Hydrogen-Fueled Engines by Vortex-Stratified Combustion - Part 2: Numerical Analyses
Bibliographic record
Abstract
In this second of two parts, the fundamentals of convective wall heat transfer losses are elucidated in the context of the desired objective toward its reduction in a direct-injected, hydrogen-fueled internal combustion engine. A comparative, transient 2D CFD analysis evaluated at 4500 RPM between a combustion chamber design representing current practice and the here-introduced “vortex-stratified combustion” process finds an approximately 50% reduction in the peak convective flux with the latter. The simulation results show that reduced heat flux of the vortex approach is driven by the combination of two effects: The first is finite-time diffusive mixing getting outpaced by the replenishment of pure air being introduced preferentially along the circumference of the combustion chamber due to the Coandă effect; this results in a distinct radial charge stratification during mixture preparation in the compression stroke, with a fuel-concentrated center and essentially pure air at the periphery. The second effect is the forced-segregation of different density reactants during the course of the combustion process caused by large body forces that result from the gravitational acceleration of the rapidly rotating charge, thereby constraining the combustible mixture and the flame to some distance from the walls. Evidence for this is observed by hot, low-density hydrogen being forced to remain near the center and cooler, heavier oxygen being inhibited from migrating from the outer periphery to react with the aforementioned hydrogen, and the distinct curvature of the radial gas temperature profile at a substantially greater distance from the wall than the thermal boundary layer thickness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".