Novel Physical Modelling under Multiple Dimensionless Numbers Similitudes for Precise Representation of Molten Metal Flow
Bibliographic record
Abstract
Physical model experiments, together with numerical model calculations, are essential for scientific investigations such as molten metal flow in casting processes. Considering the physical modelling of flow phenomena, a common method is used to construct a physical model with a reduced scale ratio and then, experiment is carried out under one or two dimensionless number(s) similitude(s). It is an ideal condition of the experiment to establish the simultaneous similitude of multiple dimensionless numbers (SMDN) concerned with the objective flow phenomena but was considered difficult or impossible to realize in practice. This chapter presents a breakthrough in this matter. A simple relationship between the physical properties of fluids and the scale ratio of the physical model is clearly expressed for the simultaneous similitude of the Froude, Reynolds, Weber, Galilei, capillary, Eötvös and Morton numbers. For establishing the physical modelling to represent molten Fe flow phenomena under the SMDN condition, the physical properties of some molten metals can be demonstrated to meet the required relationships. Furthermore, this novel concept is also applicable for other combinations of molten metals. Precise, safe, and easy physical model experiments will be conducted under the SMDN condition that exactly mimics industrial casting operations in higher-temperature 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".