Machine Learning and Automatic Mesh Optimization: Watershed Technologies for Heat Transfer and Fluid Flow Optimal Simulations
Bibliographic record
Abstract
Many areas of CFD and CHT require, or ought to be using, large samplings to perform parametric explorations and, ultimately, optimization of flow-based components or processes. This is demanding in 3D and even more so for multidisciplinary problems combining CFD, CHT, and, often, CSD. Nowhere is this problem more apparent than in the certification of aircraft, rotorcraft, and jet engines for flying into known icing. The required analyses involve the simultaneous simulation of high-Mach external aerodynamics over the aircraft, small and large droplets and ice crystals impingement, low-Mach internal aerodynamics inside ice protection systems or in engines, conjugate heat transfer across multiple fluid-structure interfaces, liquid-to-ice-to-liquid-crystals phase changes, changing geometries due to ice accretion or ablation on external and internal components, fluid-structure interaction induced deformations, and ice cracking and tracking.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".