Experimental and Simulation Analysis of Spray and Combustion Characteristics in a Swirl-Chamber Diesel Engine
Bibliographic record
Abstract
A swirl-chamber diesel engine has an indirect injection system in which fuel is injected into a pre-chamber called the swirl-chamber that is separated from the main chamber. Indirect fuel injection systems can be directly mechanically controlled by the camshaft, which is cheaper than electronic control. For these reasons, they are used in diverse industrial applications and automobiles. However, optimization of the swirl-chamber shape and performance tests have been mainly experimental, and there has been insufficient verification of the accuracy of simulations. Thus, we have attempted to verify simulations using a rapid compression and expansion machine that can reproduce the combustion in one engine cycle, with a chamber like a swirl chamber in the cylinder head to visualize the behavior of evaporative sprays and the combustion process. In this study, the authors focused on the wall impingement of the fuel spray and took photos of its liquid phase and ignition. Furthermore, the authors constructed simulation models using commercial 3D-CFD software based on the experimental results and established an analytical method. As a result, flow, spray, and ignition behavior in the swirl chamber were clarified through visualization experiments. In addition, numerical simulation models were constructed to reproduce the in-cylinder pressure and rate of heat release during combustion obtained in experiments using a rapid compression and expansion machine.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".