A New Equivalent Static Load (ESL) Creation Procedure for Complete Vehicle
Bibliographic record
Abstract
When dealing with the structural behavior of a car body, analyzing the dynamic distortion in all body closure openings in a complete vehicle, provides a better understanding of the body characteristics compared to traditional static load cases such as static torsional body stiffness. This is particularly relevant for non-traditional vehicle layouts and electric vehicle architectures where mass distribution and in particular battery mass and stiffness play a completely different effect with respect to the internal combustion engine vehicles. A methodology typically adopted to measure the body response, e.g. when driving a vehicle on a rough pavé road, is the so-called Multi Stethoscope (MSS). The MSS is measuring the distortion in each body closure opening in two diagonals. During the virtual development, the distortion is described by the relative displacement in diagonal direction in time domain using a modal transient analysis. The results are shown as Opening Distortion Fingerprint ODF and used as assessment criteria within Solidity and Perceived Quality. By applying the Principal Component Analysis (PCA) on the time history of the distortion, a Dominant Distortion Pattern (DDP) can be identified. The DDP means that, for a given pavé time history, more than 50 % of the body deformation states are similar to each other. This paper presents a deeper analysis about the forces which are associated with this Dominant Distortion Pattern (DDP). The new aspect of this analysis is that all forces (54 in total) between the wheel suspension and the trimmed body are considered. Based on the results of this force analysis, a new procedure for creating an Equivalent Static Load (ESL) was developed. Finally, by automating this creation procedure it is shown how the new ESL can be integrated in the virtual vehicle development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.202 | 0.081 |
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".