Shape optimization method for axisymmetric disks based on mesh deformation and smoothing approaches
Bibliographic record
Abstract
Axisymmetric disk structures with complex contour curves are widely used in aero-engines. The shape optimization is generally carried out to reduce the stress level of axisymmetric disks. In this article, a shape optimization method for axisymmetric disks based on radial basis function (RBF) mesh deformation and Laplace smoothing approaches is proposed. This method can obtain the optimized reduced control points selection of mesh deformation under the influence of design space based on greedy algorithm. RBF mesh deformation is used to change the axisymmetric contour shape. And after deformation, the local mesh quality is monitored and improved by Laplace smoothing. In this article, two illustrative examples used in aero-engines are carried out to validate the effectiveness of the proposed method, including an independent optimization of a turbine disk and a collaborative optimization of a turbine disk with a deflector for minimizing the maximum equivalent stress. To improve the computational efficiency, a two-dimensional (2D) axisymmetric FE model is established. Compared with initial results, optimized results in two examples obtained by the proposed optimization method reduce the maximum von Mises stress by 8.02% and 9.25%, respectively. It can be concluded that the proposed method has significant potential in the shape optimization design of axisymmetric disks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".