Optimized syngas mixer design for dual‐fuel diesel engines: A <scp>CFD</scp> ‐driven approach to enhance efficiency
Bibliographic record
Abstract
Abstract The efficiency of dual‐fuel diesel engines running on syngas is critically dependent on the quality of the air–fuel mixture. This study presents a computational fluid dynamics (CFD) investigation to optimize a syngas mixer design, aiming to enhance mixture homogeneity. Five distinct mixer geometries, including a baseline T‐joint, an extended pipe, a tapered pipe‐tip, and designs with side holes, were systematically evaluated. Using the RNG k ‐ ε turbulence model in ANSYS Fluent, simulations were conducted to analyze pressure distribution, velocity profiles, turbulent kinetic energy (TKE), and the syngas mass fraction uniformity index (UI) at an engine speed of 2000 rpm. The results demonstrate that geometry profoundly influences mixing performance. Model 3, featuring a tapered pipe‐tip, emerged as the optimal configuration, achieving a near‐perfect UI of 0.9997. This superior homogeneity was driven by its ability to generate the highest TKE (21.25 m 2 /s 2 ), which promoted vigorous mixing. However, this design also incurred the largest pressure drop (330 Pa). Model 4 offered a balanced alternative with a high UI (0.9989) and lower pressure loss. All evaluated designs surpassed the performance of conventional compressed natural gas mixers, highlighting the efficacy of geometry optimization for syngas applications. This research provides a validated design pathway for developing highly efficient and sustainable dual‐fuel engine systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".