Design Optimization of Rail Vehicles with Passive and Active Suspensions: A Combined Approach Using Genetic Algorithms and Multibody Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARYA genetic algorithm (GA) is combined with a multibody dynamics software (A'GEM) in an effective approach to the design of rail vehicles with passive and active suspensions. The conflicting requirements of lateral stability, curving performance, and vertical ride quality are assessed using realistic multibody models from A'GEM, and the GA is used to solve the multi-criteria optimization problem with a relatively large number of design variables. Despite discontinuous lateral stability and ride quality objective functions with many local optima, the GA is able to find global solutions. In the process, the relative importance of different design variables are identified and the tradeoffs between different optimization criteria are clearly revealed.
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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 | 0.000 |
| 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