Intelligent Identification System with Applications to Transient Aeroelastic Data
Bibliographic record
Abstract
In recent years, intelligent systems have become popular tools in dealing with practical problems in science and engineering. In aeroelasticity applications, a key step in developing a robust intelligent system is to construct an appropriate mathematical model which reproduces the important features of the aeroelastic system. Structural nonlinearity in aeroelasticity can be classied as dieren tiable (such as polynomial spring) or piecewise dieren tiable (such as freeplay). The corresponding mathematical models are completely dieren t, the former is a truly nonlinear system, and the later can be described by three linear systems according to the locations of the switching points. Hence, identifying the specic type of structural nonlinearity is an important component for an intelligent system. Given a transient data arising from an aeroelastic system with structural nonlinearity, this paper rst discusses the following questions: 1) how to detect the existence of the nonlinearity, 2) how to determine the specic type of structural nonlinearity, and 3) how to estimate the switching points for a freeplay model. In the second part of the present paper, we present a Kalman-based approach for the system identication. For a freeplay aeroelastic system, the Expectation Maximization algorithm and the linear Kalman lter are used to estimate the system parameters. For an aeroelastic model with polynomial nonlinearity, the extended Kalman lter or the unscented lter must be employed. Finally, we demonstrate that the developed intelligent system can be used to accurately predict the asymptotic state of a nonlinear aeroelastic system. Results obtained using wind-tunnel experimental transient data are reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".