MODAL ANALYSIS-BASED CLASSIFICATION OF LIQUID JETS IN CROSSFLOW
Bibliographic record
Abstract
Breakup of liquid jets in crossflow contain unique embedded patterns based on the type of the pertained flow regime. Recognition of these patterns and correlating them to the underlying flow schemes is a possible but challenging task due to their complex nature. In this research, we have utilized unsupervised reduced-order models to create a set of modes that could be employed to analyze the attributes of different snapshots. They may be imported to feature-based supervised classifier to diagnose multiple flow regimes. These models include proper orthogonal decomposition, principal component analysis, and dynamic mode decomposition (DMD). Snapshots are being extracted by high-speed imaging of the flow field of 14 different cases at various categories. These images are then stacked into a high-dimensional matrix as the training set for the support vector machine and random forest (RF) classifiers to learn. Then, the generated classifiers in the previous step are used to predict which category belongs to every dataset of the six newly imported cases. Afterward, the accuracy level of different permutations of reduced-order models and machine learning algorithms is calculated. Results indicate that using dynamic modes of DMD in partnership with the RF algorithm outperforms every other model with the highest accuracy rate of 95%. Finally, a decision-maker application that classifies the datasets based on the first three models with the highest accuracy levels is introduced to provide a user-friendly environment for data classification at all other potential conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".