Prediction of Two-Phase Flow Patterns Using Machine Learning Algorithms
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%.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".