Development of a new THINC/WLIC method based on a separate evaluation of the geometrical fidelity and the interface sharpness
Bibliographic record
Abstract
This paper puts forward a novel approach for the evaluation of the geometrical fidelity and the interface sharpness of the VOF advection schemes separately and quantitatively. This new evaluation has elucidated the trade-off relationship of the geometrical fidelity and the interface sharpness between the existing schemes of the original THINC and the THINC/WLIC. By investigating and resolving this trade-off relationship, we have developed a novel THINC-based scheme that exhibits high performance with regard to both geometrical fidelity and interface sharpness, despite employing an algorithm as concise as those of the original THINC and the THINC/WLIC. The novel scheme, designated as "THINC/Advanced WLIC (THINC/AWLIC)," has been developed by redefining the weight function of the preceding THINC/WLIC so that the contribution of the first-order upwind flux can be variably blended with the usage of the control parameter. The results of the multiple benchmark tests in two and three dimensions demonstrate that both the geometrical fidelity and the interface sharpness are significantly enhanced if the control parameter is appropriately determined. Furthermore, the effectiveness of THINC/AWLIC has been confirmed in numerical tests with intricate velocity fields and practical multiphase flow simulations. These results support the applicability of THINC/AWLIC not only in canonical benchmarks but also in realistic fluid simulations.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".