Decomposition of High-Dimensional Shape Optimization Problems Through Quantifying Design Variable Correlation
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel strategy for the shape optimization procedures using a recently developed metamodel-based decomposition algorithm for High-dimensional, Expensive and Black-box (HEB) design problems. A metamodel named High Dimensional Model Representation (HDMR) is used for decomposition of design variables in a complex aerodynamic profile optimization process as a HEB design problem. The approach uncovers and quantifies design variable correlations. Weak correlations are neglected and strong ones are kept for grouping. In this way, the vast search space is decomposed to small ones, and the large-scale CFD simulation based optimization is replaced by smaller-scale sub-problems. Though a typical gas turbine compressor airfoil shape has been selected as the case study in this paper, the methodology is introduced as a general procedure for shape optimization problems. The obtained results from the decomposition also show good agreement with the aerodynamics of such turbomachinery airfoils and found promising.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it