Grid-Generation Algorithms for Complex Glaze-Ice Shapes Reynolds-Averaged Navier–Stokes Simulations
Bibliographic record
Abstract
The paper presents the developments of novel mesh generation algorithms over complex glaze-ice shapes containing multicurvature ice-accretion geometries, such as single/double ice horns. The twofold approaches tackle surface geometry discretization as well as field mesh generation. First, an adaptive curvilinear curvature control algorithm is constructed, solving a one-dimensional elliptic partial differential equation with periodic source terms. This method controls the arc length grid spacing, so that high convex and concave curvature regions around ice horns are appropriately captured, and is shown to effectively treat the grid shock problem. Second, a novel blended method is developed by defining combinations of source terms with two-dimensional elliptic equations. The source terms include two common control functions, Sorenson and Spekreijse, and an additional third source term to improve orthogonality. This blended method is shown to be very effective for improving grid quality metrics for complex glaze-ice meshes with Reynolds-averaged Navier–Stokes resolution. The performance in terms of residual reduction per nonlinear iteration of several solution algorithms (point–Jacobi, Gauss–Seidel, alternating direction implicit, point, and line Successive Over-Relaxation) 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 this performance of the solution algorithm depends on the type of control function used. Finally, the algorithms are validated on standard complex experimental ice shapes, demonstrating the applicability of the methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".