Global Robust Optimization of Computationally Expensive Systems: A Lavel Rotor Suspended by Fluid Film Bearings
Why this work is in the frame
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Bibliographic record
Abstract
Kriging based methods enable the deterministic and robust optimization of computationally expensive systems. With a limited amount of function evaluations the optima are found in an iterative process with expected improvement as infill sampling criteria. Outputs of computer models can be stochastic and/or the models do not always succeed to perform the analysis. For the latter case, a problem is said to be affect by a hidden constraint. Regression Kriging can be included in the methods to deal with the stochastic model outputs. A new method is introduced to handle the hidden constraint in both deterministic and robust optimization. The methods are used to optimize a validated model of a Laval rotor suspended by plain journal bearings. To capture the non-linear behaviour of the self-excited vibrations, a computationally expensive time-transient run-up analysis needs to be performed. The output of this model is stochastic and the model fails to perform a run-up for some combinations of model inputs. The most influential control variables and uncertainties are indicated with an efficient global sensitivity study and used to optimize the system. With the extension of the deterministic and robust optimization, the optima are successfully obtained and can be compared.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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