A Dual-Mesh Approach to Enhance Accuracy of the Boundary Conditions for Unstructured Grid Modeling of Turbomachinery Flows
Bibliographic record
Abstract
Although unstructured grids have gained wide acceptance in many engineering applications, they still suffer from difficulties in achieving high accuracy at the inflow, outflow and mixing-plane interface boundaries of multi-stage turbomachinery configurations. To overcome these difficulties and hence to increase the accuracy of unstructured grid methods, a novel dual mesh approach is proposed. In contrast to conventional CFD techniques, the dual mesh approach works on two sets of meshes at the boundaries: one is the original mesh and the other is an auxiliary surface mesh created at run time. By properly coupling of such double meshes, the dual mesh approach can effectively increase the accuracy and conservation of the solutions at the inflow, outflow and mixing-plane interface boundaries, and it can also enjoy most of the sophisticated numerical algorithms originally developed for the structured-grid boundary conditions. With both compressor and turbine test cases, the dual mesh approach is demonstrated to be superior to the conventional method while its CPU-time penalty is marginal. Additionally the dual mesh approach may be also useful for any structured CFD solvers subject to some restrictions on the structured grid distributions at the inflow, outflow and mixing-plane interface boundaries, e.g. mesh uniformity in the circumferential direction.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".