SootImage: An image recreation, post-processing validation procedure for sooting axisymmetric flames
Bibliographic record
Abstract
Validation is a vital part of any computational fluid dynamics study. Validation is done by comparing the computed results to the experimental measurements performed in a known configuration. In sooting flames, such a comparison is non-trivial since the measurements have a wide range of uncertainty due to difficulties in directly measuring soot volume fractions. This work introduces a different way to verify the computations using a software package called SootImage. In this proposed methodology, comparisons are not made directly to the computed properties of interest (soot volume fraction and temperature). Instead, a post-processing procedure is performed to obtain an image of the flame based on the computed properties. This reconstructed image is compared to an actual image of the flame being studied. The algorithm of the image reconstruction utilized within SootImage is presented in detail. Finally, the usage of SootImage is demonstrated on a co-flowing, laminar ethylene/air diffusion flame . Program summary Program Title: SootImage CPC Library link to program files: https://doi.org/10.17632/nc5myw64km.1 Developer's repository link: https://github.com/VictorChernov/SootImage Licensing provisions: CC BY NC 3.0 Programming language: MATLAB Nature of problem: The software allows comparing images of a real flame with the recreated image of a simulated flame. This requires two major steps. One is the recreation of the image of an axisymmetrical flame from the computed soot volume fraction and temperature fields for a known camera and optical configuration. The second is cropping and position images to obtain a meaningful comparison. Solution method: The solution recreates the image by integrating the soot thermal radiation over a line-of-sight. Soot is assumed to emit as gray body. The integrated radiation is passed through the color filters of the camera and is translated to pixel values. After the process is done for the whole flame, the width, height and origin of both flames are found, and images that can be easily compared are created.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".