Vehicle-to-Vehicle NVH Performance Variance
Bibliographic record
Abstract
The effect of vehicle-to-vehicle variance on a comprehensive set of NVH performance measures is investigated. A complete experimental assessment of full vehicle NVH performance is often limited to a single vehicle due to test complexity, vehicle availability, and/or program timing constraints. However vehicle-to-vehicle performance variation is inherently present due to factors such as manufacturing variability, nonlinear response of component systems, and component mode coupling with vehicle structure. In past studies, NVH variability was investigated utilizing sample sizes up to one hundred vehicles but on a limited set of localized NVH input/response characteristics [1]. In this paper, the statistical sample size is reduced to five vehicles in order to study vehicle-to-vehicle variance on a more comprehensive set of NVH performance measures: structural modal and operational response. Modal response is evaluated at both the global vehicle and component levels. The variance in operational performance is evaluated both in vibration and acoustic response under road and powertrain inputs. The details of these test methodologies and results are presented. A discussion of recommended sample sizes for each test methodology is provided.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".