Multibody Dynamics Modeling of a Continuous Rubber Track System: Part 2—Experimental Evaluation of Load Prediction
Bibliographic record
Abstract
Vehicles equipped with rubber track systems feature a high level of performance but are challenging to design due to the complex components involved and the large number of degrees of freedom, thus raising the need to develop validated numerical simulation tools. In this article, a multibody dynamics (MBD) model of a continuous rubber track system developed in Part 1 is compared with extensive experimental data to evaluate the model accuracy over a wide range of operating conditions (tractor speed and rear axle load). The experiment consists of crossing an instrumented bump-shaped obstacle with a tractor equipped with a pair of rubber track systems on the rear axle. Experimental responses are synchronized with simulation results using a cross-correlation approach. The vertical and longitudinal maximum forces predicted by the model, respectively, show average relative errors of 34% and 39% compared to experimental data (1–16 km/h). In both cases, the average relative error is lower for tractor speed from 1 to 7 km/h, namely 20% and 35%. The model and experimental amplitudes spectra of the force signals are compared using the coefficient of determination r 2. In the 1 to 7 km/h tractor speed range, the average vertical and longitudinal coefficients of determination are, respectively, 0.83 and 0.42. The coefficients, respectively, reduce to 0.27 and 0.14 for speeds over 7 km/h. In summary, the model can predict the maximum vertical and longitudinal forces in addition to the amplitude spectrum of those signals for operating conditions up to 7 km/h, regardless of the rear axle load, with accuracy acceptable for many applications, such as load case determination for preliminary structural design. Several factors affecting the accuracy of the model at higher tractor speed are identified for future work including suspension creeping, suspension compression characterization at high strain rates, and temperature dependence of material properties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".