Careful Parameter Study to Enhance the Effect of Injecting Heavy Fuel Oil Into a Crossflow Using Numerical Approaches
Bibliographic record
Abstract
The flow and spray parameters can have noticeable roles in heavy fuel oil (HFO) spray finesse. As known, the interaction between droplets and cross flow should be considered carefully in many different industrial applications such as the process burners and gas turbine combustors. So, it would be so important to investigate the effect of injecting HFO into a crossflow more subtly. In this work, the effects of various flow and spray parameters on the droplet breakup and dispersion parameters are investigated numerically using the finite-volume-element method. The numerical method consists of a number of different models to predict the droplets breakup and their dispersion into a cross flow including the spray-turbulence interaction one. An Eulerian–Lagrangian approach, which suitably models the interaction between the droplets and turbulence, and also models the droplets secondary breakup is used to investigate the interactions between the flow and the droplet behaviors. After validating the computational method via comparing them with the data provided by the past researches, four test cases with varying swirl number, air axial velocity, droplet size, and fuel injection velocity are examined to find out the effects of preceding parameters on some spray characteristics including the droplets path, sauter mean diameter (SMD), and dispersed phase mass concentration. The results show that the droplets inertia and the flow velocity magnitude have significant effects on spray characteristics. As the droplets become more massive, the deflection of spray in flow direction becomes less. Also, increasing of flow velocity causes more deflection for sprays with the same droplet sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".