Vehicle ride analysis using interactive graphics
Why this work is in the frame
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Bibliographic record
Abstract
General–purpose programs are gaining popularity in several application such as machine dynamics, robotics and vehicle dynamics. These programs include mechanism programs such as IMP, DADS, ADAMS and DRAM, and simulation languages such as ACSL and CSMP. Two important drawbacks of many of these are that the problem formulation can be very tedious and that the user is required to know specialised theory and syntax. We present two interactive graphics–based general–purpose programs for the modelling, analysis and design of lumped parameter mechanical and hydromechanical systems. The programs CAMSYD and CANVAS receive model representation in terms of schematic diagrams, derive system equations symbolically and give graphical output of system response. The majority of vehicle models used in ride comfort studies can be represented as lumped–parameter mechanical systems of rigid bodies interconnected by springs, dampers and revolute joints. The bodies do not form closed kinematic chains and they undergo only small angular motions. The suspensions are represented by two–port force–generators. These forces can be passive or active, linear or non–linear. For this class of multibody systems, we present a self–formulating program called CAMSYD. The second program, CANVAS, is used to model and analyse hydromechanical systems at the component level. The suspension units which are modelled as black–box force–generators in CAMSYD can now be conceived of as comprising hydraulic actuators, accumulators, feedback controllers, etc. CANVAS uses a bond graph approach to physical system modelling so that components from different energy domains can coexist. However, the user is not required to know about bond graphs since the model is built on the computer screen using icons representing various physical components. The use of the two programs is demonstrated by applying them to some typical vehicle dynamics problems.
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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