Developing Generic Load Cases by Defining Maximum Spindle Loads as a Function of Corner Weight & Tire Sidewall Height
Bibliographic record
Abstract
Generic spindle loads are used in the upfront analysis for vehicle durability development. They represent different load case into the vehicle suspension system, such as potholes, cornering, and braking. The advantage of using these generic load cases is that they can be used upfront in the durability development process before hardware is available. The generic spindle loads are cascaded through the suspension system to generate component loads which can then be used for stress analysis. The paper describes a study that was done to determine the validity of current generic spindle loads by analyzing spindle data from multiple vehicles in the same class. The paper will explain the initial data analysis that was done, which was normalizing the spindle loads by weight. In addition, the paper will then go into further detail on describing a relationship between spindle loads and tire sidewall height, which reduced the load scatter. To further develop a set of universal generic load cases that will fit a specific class of vehicles other tuning parameters were identified (which need additional analysis), that could reduce the load scatter even further. The most important finding was in the verification that this methodology appears to be feasible.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".