Ride Dynamic Analysis of a Hybrid Discrete and Continuous Vehicle Model
Bibliographic record
Abstract
In this study the dynamic analysis of the vehicle system modeled as a hybrid discrete and continuous system has been investigated. The discrete system modeling has formed the traditional ride dynamic analysis model for vehicle systems. In such a model the chassis is assumed as a discrete block attached to different degrees of freedom that can account for the unsprung masses, engine and driver among many others. However, this model cannot accommodate all the aspects of a chassis which is strictly a continuous system. In the present study the chassis is assumed as a flexible beam on which a pair of single degree of freedom systems is added to account for the engine and driver. Moreover, the beam is mounted on a couple of spring-damper elements that simulate the suspension of the vehicle which results in a combination of continuous and discrete systems. In order to solve this problem, the assumed mode method has been employed using the mode shapes of a free-free beam. The method is applied on a city bus and the natural frequencies of the undamped system were calculated. Further, the frequency responses for ground and engine excitations were obtained. The method is validated by comparing the results with those obtained by finite element method where the beam is meshed as a continuous system while all other DOF were assumed as lumped masses connected to the chassis with spring damper elements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".