Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1793.vid In industrial applications, numerical computations are performed with the intention of estimating an integral quantity of interest. The error from a numerical scheme depends significantly on the mesh and node distributions. Optimization based reduced-space mesh adaptation is widely used to obtain an optimal node distribution, which minimizes the error in a final goal/functional. However, in reduced space methods, the flow solver’s residual needs to be completely converged at each iteration of optimization and the number of non-linear Newton iterations scales with the size of design variables. On the other hand, the full space approach converges the mesh and flow solution simultaneously and the number of Newton iterations required by the optimizer are independent of problem size. The current work introduces a goal oriented full-space optimization based r-adaptation to get a mesh which accurately computes a functional of interest. The traditional goal oriented error estimate is modified for use with full space and an adjoint-based objective function is proposed along with the means to calculate its first and second derivatives to retain quadratic convergence of Newton’s method. Convergence of the scheme is verified using 1D and 2D test cases with volume and boundary functionals and the approach is compared with the reduced-space method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".