Robust NVH Engineering Using Experimental Methods - Source Characterization Techniques for Component Transfer Path Analysis and Virtual Acoustic Prototyping
Bibliographic record
Abstract
A major challenge in automotive NVH engineering is to approach complex structure-borne sound and vibration problems with sufficient accuracy but reasonable experimental effort. Typical issues encountered are poor correlation between objective component performance criteria tested for during bench validation and corresponding subjective targets evaluated during system validation in the actual vehicle. Additional challenges arise from the need to impose assumptions on sophisticated physical vibration problems to reduce the complexity to a level feasible for conventional experimental test methods. This paper addresses all mentioned issues by elaborating on a system NVH engineering approach employing Virtual Acoustic Prototyping (VAP) (related to what is now often called component Transfer Path Analysis) to synthesize time domain sound and vibration responses of vibrating machinery operated in a virtual vehicle environment. One crucial step of VAP is to characterize the strength of vibrating machinery by independent quantities at the significant coupling degrees of freedom (DoF). This study puts special focus on the measurement of free velocity, suitable for machinery operated when resiliently mounted as per ISO 9611, and the in-situ measurement of blocked forces, applicable for sources connected to any type of receiving structure during operation, as per ISO/DIS 20270. In order to reduce complexity of the underlying measurements this paper investigates the possibility of using collocated sensor arrays and methods to validate assumptions imposed to abstract away from rotational coupling DoF. An electric power steering (EPS) system inducing vibrations into a sub-frame-type structure is considered as a representative automotive source-receiver installation to investigate the feasibility of free velocity and in-situ blocked force approach with respect to independent source characterization for component Transfer Path Analysis (TPA) and VAP. The obtained Virtual Acoustic Prototype is expanded using an algorithm to synthesize realistic time domain data, enabling NVH engineers to conduct reliable objective and subjective design evaluations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".