Reduced Convective Combustion Chamber Wall Heat Transfer Losses of Hydrogen-Fueled Engines by Vortex-Stratified Combustion - Part 1: Background and Optical Engine Observations
Bibliographic record
Abstract
A vortex-stratified combustion process for hydrogen-fueled reciprocating internal combustion engines is introduced to increase the thermal efficiency by reducing the convective heat transfer losses to the surrounding walls during combustion. The process imposes a highly ordered rotational field upon the charge in a separate, transverse, cylindrically shaped combustion chamber by means of channels that connect with the main chamber enclosed by the engine cylinder and piston. Gaseous hydrogen is injected directly during the compression stroke, while air enters into the combustion chamber tangentially and preferentially along the circumference due to the Coandă effect. The two streams entrain one another and develop into a vigorous vortex by virtue of the chamber and channel geometries. As mixing proceeds, the fuel is confined radially from the combination of finite-time diffusion being outpaced by the replenishment of pure air at the periphery, and the centripetal field formed by the rotating flow acting on the different density gas mixture. Combustion takes place with a flame propagation that initially follows the rotation of the bulk flow, and then curls radially inward toward the center. This work investigates the process in a fired, optically accessible 2-stroke hydrogen-fueled direct-injected engine tested at up to 5000 RPM. This paper, the first of two parts, explains the theoretical background; presents the schlieren observations; results of zero-dimensional cylinder pressure indication; and apparent heat release measurements comparing two combustion chamber designs - one that actualizes a homogeneous mixture without specific charge motion directionality, and another with the here-introduced vortex-stratified approach.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".