Two-Phase Flow Regimes Characterization via Pressure Heat Map Recognition
Bibliographic record
Abstract
Precise recognition of flow regimes in industrial two-phase flow system is reliant on visual aids. However, opacity of pipe medium poses a significant challenge and presents a pressing need for reliable identification of flow regimes. This study aims to characterize stratified, slug, elongated bubble and dispersed bubble flow by evaluating the performance of four prominent deep learning architectures. Numerical investigation was conducted for pressure signal data collection for individual flow regimes by varying inlet gas and liquid superficial velocities. Frequency with time domain heatmaps were generated from pressure signals and were pre-processed. Data sets were trained, validated and tested using EfficientNetB80, MobileNetV2, Xception, and DenseNet deep learning architectures. Gradient-weighted Class Activation Mapping (Grad-CAM) were created to visualize the targeted features in each architecture. Corresponding performances were evaluated to identify the best deep learning architecture for flow regime characterization from pressure signals heatmaps. MobileNetV2 with accuracy of 92.5% outperformed EfficientNetB80 and DenseNet each with accuracy of 88.46% and Xception having accuracy of 84.62%. Higher rate of confusion in distinguishing between elongated bubble and dispersed bubble flow lead to lower accuracies in the applied lagging models. MobileNetV2 proves to be an effective CNN architecture for identifying two-phase flow regimes from pressure signal heatmaps in industrial systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".