Adaptive curvature control grid generation algorithms for complex glaze ice shapes RANS simulations
Bibliographic record
Abstract
The paper presents the development of a novel adaptive curvature control algorithm for the generation of meshes around complex glaze ice shapes. First, the theory of elliptic grid generation is reviewed, laying out the physical/parametric/computational space approaches. Second, the choice of control functions ensuring spacing, curvature and orthogonality requirements are described for 2D mesh generation, including a novel blended approach which mixes the use of Sorenson and Spekreijse functions and parabolic methods. Third, a novel 1D automated curvature control algorithm is used to control the arclength grid spacing, so that high curvature regions around ice horns are appropriately captured while avoiding the grid shock problem. Finally, the performance of several solution algorithms (Point-Jacobi, Gauss-Seidel, ADI, Point and Line SOR) are discussed within the context of a full MultiGrid operator. Details are given on the various formulations used in the linearization process. It is shown that the performance of the solution algorithm depends on the type of control function used. Validation of the final algorithms is done within the framework of CANICE2D-NS, by running the embedded multi-block Reynolds-Averaged Navier-Stokes flow solver to obtain the pressure distribution, lift and drag coefficients for standard icing test cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".