Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Noise-vibration-harshness (NVH) is now playing an important role in electric vehicle development process. Experience shows that the NVH criteria must be considered at the very early stages of the concept design phase. Finite Elements (FE) models are widely used to simulate the vehicle design. To achieve a correct accuracy of a FE model, the results of an experimental modal analysis (EMA) are commonly applied to a FE model via correlation and updating processes. Thus, different kinds of optimization might be used throughout the concept design duration. This paper describes, first, the use of a parametric optimization to tune a FE model in high frequencies relying on the results of the EMA test. Then the frequency response analysis is conducted to detect the critical frequencies for the NVH performance. Based on the results of this analysis, a topographic optimization is performed. The aim of this optimization is to improve the NVH behavior by mitigating the resonance peaks in specified frequency range and to produce a shape which can be stamped with required constraints. Both parametric and topography optimizations are carried out with a help of a dedicated FE software. The study shows two main aspects of optimization: the speed-up of the updating time of a FE model and the reducing of an e-motor noise and vibration by implementing new optimized shapes.</div></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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".