A Design Analysis Approach for Improving the Stability of Dynamic Systems with Application to the Design of Car-Trailer Systems
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Bibliographic record
Abstract
A design analysis approach is developed for improving the stability of dynamic systems subject to non-conservative forces. It combines genetic algorithms, sequential quadratic programming (SQP), and dynamic mode tracking (DMT). The proposed approach automatically optimizes the stability criterion and is applicable to rotor dynamics, wind turbine dynamics, aeronautics, and ground vehicle dynamics. The Routh-Hurwitz criterion has traditionally been used for determining the stability characteristics of these dynamic systems. In the conventional trial and error approaches, designers iteratively change the values of the design variables and reanalyze until an acceptable stability characteristic is achieved. This is both time-consuming and tedious. The proposed approach automates the design/analysis cycle by using the DMT technique to identify the modes; then, the SQP algorithm determines the stability criterion; and finally a genetic algorithm is applied to optimize design variables. The proposed integrated approach has been tested and evaluated numerically using a linearized car-trailer model with three degrees of freedom and the results demonstrate its feasibility and efficacy. The performed parametric sensitivity analysis revealed that the geometric parameters have a much greater influence on the lateral stability of the vehicle systems, compared with inertia parameters and torsional spring stiffness coefficients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it