Combining virtual simulation and physical vehicle test data to optimize durability testing
Bibliographic record
Abstract
ABSTRACT This paper describes an ongoing project to model a vehicle on a computer with a multibody dynamics simulation software package and to merge that work with physical proving ground and laboratory tests in order to shorten vehicle development time. The intention is to mirror as closely as possible the behaviour of a physical vehicle in order to assist in determining its durability characteristics under varying road conditions. This modelling work is important because, if done with sufficient fidelity, it can be used in order to assess vehicle responses by using different suspension components or payloads. Also, potential issues associated with vehicle structure, suspension components or payload positioning can be observed on a computer prior to performing physical tests. The process has the potential to reduce vehicle development cost and time. The virtual dynamic vehicle model has been created by using Automatic dynamic analysis of mechanical systems (ADAMS) software package. The calculated outputs from the model are being compared to force and displacement data collected from actual vehicle on‐road testing or a servo‐hydraulic road test simulator (RTS). The virtual model can be adjusted until the calculated responses are in close agreement with those of the physical vehicle, thus linking the virtual and real‐world results.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".