A numerical model for calculating the vaporization rate of a fuel droplet exposed to a convective turbulent airflow
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop a three‐dimensional (3D) numerical model capable of predicting the vaporization rate of a liquid fuel droplet exposed to a convective turbulent airflow at ambient room temperature and atmospheric pressure conditions. Design/methodology/approach The 3D Reynolds‐Averaged Navier‐Stokes equations, together with the mass, species, and energy conservation equations were solved in Cartesian coordinates. Closure for the turbulence stress terms for turbulent flow was accomplished by testing two different turbulence closure models; the low‐Reynolds number (LRN) k‐ε and shear‐stress transport (SST). Numerical solution of the resulted set of equations was achieved by using blocked‐off technique with finite volume method. Findings The present predictions showed good agreement with published turbulent experimental data when using the SST turbulence closure model. However, the LRN k‐ε model produced poor predictions. In addition, the simple numerical approach employed in the present code demonstrated its worth. Research limitations/implications The present study is limited to ambient room temperature and atmospheric pressure conditions. However, in most practical spray flow applications droplets evaporate under ambient high‐pressure and a hot turbulent environment. Therefore, an extension of this study to evaluate the effects of pressure and temperature will make it more practical. Originality/value It is believed that the numerical code developed is of great importance to scientists and engineers working in the field of spray combustion. This paper also demonstrated for the first time that the simple blocked‐off technique can be successfully used for treating a droplet in the flow calculation domain.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".