A New Approach on Mounting Systems Optimization
Bibliographic record
Abstract
Vehicles are assemblies of subsystems or modules, which are developed in parallel at multiple locations and often for more than one vehicle. CAE software provides the integration of modules in a complete vehicle in parallel; however the whole system requires final adjustments and tunings. These adjustments, especially in suspensions and mounting systems are very time consuming and are generally based upon trial and error techniques. To reduce the number of trials, usually noise path analysis (NPA) is used. In this technique, the noise and vibration paths for each mount to the objective point are measured. Using the measured data, the dominant path is detected. A highly experienced NVH engineer now can use the information to tune the mount to satisfy the target response. This technique is appropriate if the subsystems are weakly coupled. This situation is not usually the case in engine mounting systems where any modification in one of the mounts may change the dominant path. An important step to reduce refinement time is to develop a method to obtain the overall model of the whole vehicle. In this paper, we introduce a new approach in vehicle’s NVH development. In this approach, the model of the vehicle for mounting system optimization is obtained based on the FRF synthesis. A hybrid analytical/experimental model of the vehicle is developed to predict the NVH response of the vehicle for any given mounting system. This model along with an optimization technique is used to arrive at the optimum mounting system for any objective function. The optimization method is linked with the noise path analysis (NPA), which is used to specify the dominant directions that the noise/vibration is transferred to the response point. These directions are used in the optimization procedure to find the optimum mounting system with minimum calculation time. Experimental results on a full size car are presented to evaluate new approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".