Prediction of Two-Phase Flow Patterns Using Machine Learning Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Predicting the two-phase flow pattern prediction is important to many industries such as power generation and oil and gas; for example, knowing the type of flow pattern is crucial for an accurate calculation of the pressure on the system. The transient nature of two-phase flows makes analyzing and predicting the flow pattern for a normal straight pipe a very complex procedure. The situation becomes more complex when a piping component is disturbing the fully developed flow in a straight pipe. In this work, the flow pattern downstream of an orifice was experimentally investigated for an intermittent flow pattern at orifice-to pipe area ratios of 0.14, 0.25 and 0.56. The flow pattern downstream of the orifice was identified using a probability density function (PDF) of the time signal void fraction as well as identified using a high-speed imaging system. All tests were presented by the calculated superficial velocity of the mixture based on the area of the orifice being used and the volumetric quality. The predicted flow pattern was identified using a Machine Learning Algorithm known as the Classification Learner environment in MATLAB. This method was able to predict the flow pattern downstream of the orifice with a total error of 9%.
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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 it