Optimisation of engine mounting systems using experimental FRF vehicle model
Bibliographic record
Abstract
Engine and body mount systems play a crucial role in vehicles' comfort. Engine mounts protect the engine from excessive movement and forces due to low frequency road and high frequency engine excitations. On the other hand, body mounts protect the cabin from vibration forces exerted by the body. Normally, a complete set of mounts is conceived at early stages of design, subsequently the set is tuned in the refinement stage to improve the vehicle's noise, vibration, and harshness (NVH) response. Currently, noise path analysis (NPA) is used for mount tuning. This method is helpful, but it is based mostly on trial and error, and it does not lead to an optimum mounting set. In this work, a new technique is implemented to simplify vehicles' mount optimisation. This technique employs substructuring synthesis and standard NVH testing to obtain frequency response function (FRF) model of a vehicle. The model is linked to several optimisation routines to predict the optimum set of mounts for a desired objective function. For verification and evaluation, the method is applied to tuning the mounting set of a pick-up truck. Experimental measurements showed good correlation with optimised response.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".