A Novel Technique for Frequency and Time Optimization of Automotive Engine Mount Parameters
Bibliographic record
Abstract
In this paper, we examine a linear one degree of freedom engine mount to obtain optimum mount parameters in a passive configuration. An engine mount is a device that may be used to isolate vehicle body from the engine vibrations - forced excitation, while minimizing the effects of road-induced disturbances on the engine - base excitation. The linearity of the system allows us to analyze the frequency and time response characteristics in both excitation cases analytically. Optimal damping and stiffness values for the isolator are obtained by minimizing certain cost functions in the frequency and time domains, respectively. In the frequency domain the cost function is based on the root mean square (RMS) of the absolute acceleration and relative displacement in the frequency domain, and in the time domain it is based on the transmitted acceleration and displacement. The time and frequency responses of the isolator are optimized by varying the stiffness and damping ratios for both base and forced excitation cases. These optimal values are obtained, and the results are verified numerically. In this case, although the mathematical model is linear, it is interesting to note that the time and frequency optimal values are not the same. As a result, this exercise shows that no passive-mount is adequate to perfectly deal with all application specs and isolation criteria. In this paper, a novel approach is suggested to select the mount parameters for various passive or active configurations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".