Spindle Vertical Acceleration Control in Fixed-Reacted Road Test Simulations
Bibliographic record
Abstract
In inertially-reacted road test simulations, spindle accelerations that are acquired in the field can be replicated precisely and their replication improves the accuracy of the simulations versus simulations in which spindle accelerations are not replicated. In fixed-reacted road test simulations, on the other hand, spindle accelerations are often not replicated due to the belief that doing so will introduce unrealistic loads into the test specimen. Although this is true with regards to the lower frequency range of the control band, it is shown in this paper that replicating spindle accelerations in the upper frequency range of the control band can improve the accuracy of fixed-reacted road test simulations. In order to analyze the effects of spindle vertical acceleration control on suspension loads, models were created for a half vehicle on the road and on a fixed-reacted road test simulator. Differences in spindle loads and in sprung and unsprung mass motions between the two models are discussed. Theoretical and experimental analyses presented in this paper point to the detrimental effects of replicating spindle accelerations at lower frequencies and the beneficial effects of replicating spindle accelerations at higher frequencies.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".