Using graph theory and symbolic computing to generate efficient models for multi-body vehicle dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Linear graph theory, invented in 1736 by Leonhard Euler, has been combined with principles of physics to develop algorithms for formulating the dynamic equations for multi-body multi-domain systems. This graph-theoretic formulation allows electrical, mechanical, and hydraulic systems to be modelled within a common framework. The formulation has been implemented in a symbolic computer program, DynaFlexPro, that automatically generates compact and efficient sets of system equations that lead to reduced simulation times compared with most commercial multi-body dynamics software. In this article, models of pneumatic tyres are incorporated into the symbolic computer implementation, which is used to create real-time simulations of vehicle dynamics. The tyre component forms a list of symbolic expressions for important tyre variables, such as inclination and slip angle, that are used to calculate tyre forces and moments during simulation. If the transient behaviour of the tyre is important, the user can request that additional relaxation length equations be included in the model. The tyre component allows the user to choose from several tyre model functions that describe the generation of forces and moments at the tyre contact patch and can also accommodate user-developed tyre model functions. A brief introduction to the linear graph formulation procedure used by DynaFlexPro is given, as well as an explanation of how the tyre component works within the linear graph framework. As an example, optimized simulation code is generated for a three-dimensional vehicle model, and results are validated using an equivalent model in the MSC.ADAMS® commercial software package.
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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) | 0.001 | 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