Using graph theory and symbolic computing to generate efficient models for multi-body vehicle dynamics
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".