Dynamic optimization with a non-smooth LPV system in aero-engine transition state acceleration process
Bibliographic record
Abstract
In this paper, we study the dynamic optimization of the acceleration process in the engine transition state. It is difficult for general linear and nonlinear models to portray the complex mechanical characteristics of an aero-engine. We consider a non-smooth linear parameter variation (LPV) system with nonlinear terms as a mathematical model for this acceleration process. First, a control parameterization method is used to transform the problem into a parameter selection problem. Then, a smoothing technique is used to deal with the non-smooth state constraints. Finally, in order to obtain the global optimal solution of this dynamic optimization problem, an optimization algorithm based on a combination of a gradient descent method and a modified particle swarm optimization is designed to solve the equivalent nonlinear programming problem. The effectiveness and superiority of the proposed algorithm is computationally verified by using the LPV model identified from the actual data.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".