Application of System Identification for Efficient Suspension Tuning in High-Performance Vehicles: Quarter-Car Study
Bibliographic record
Abstract
<div class="htmlview paragraph">One popular complement to track testing that successful race teams use to better understand their vehicle's behavior is dynamic shaker rig testing. Compared to track testing, rig testing is more repeatable, costs less, and can be conducted around the clock. While rig testing certainly is an attractive option, an extensive number of tests may be required to find the best setup. To make better use of rig test time, more efficient testing methods are needed. One method to expedite rig testing is to use rig test data to generate a model of the experiment and then applying the model to identify potential gains for further rig study.</div> <div class="htmlview paragraph">This study develops the method at the quarter-car scale, using data from a quarter-car rig with a Penske 7300 shock absorber. The method is first validated using data generated from a known quarter-car model to assure the method can reproduce the original model behavior. Next, the method is applied to experimental data collected from an existing quarter-car rig. The results show that this method can be used to accurately predict sensor measurements during quarter-car rig tests for various actuator inputs and shock selections. Future work will apply the lessons learned from this study to develop a full car model using 8-post rig data.</div>
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".