Optimization of Engine Mount Characteristics Using Experimental/Numerical Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper an experimental/numerical technique is developed for engine mount optimization. The method is general and can be applied to optimize active and passive vibration isolators or absorbers in any mechanical systems or civil structures. Engine mount optimization techniques mostly rely on an accurate mathematical model of the whole vehicle, which in most cases is not available or is too difficult to develop. As a result, the current approach for selecting engine mounts for a vehicle is based upon trial and error which is very time-consuming and expensive. The proposed technique counts upon experimental data for optimization and does not require any mathematical model of the vehicle or its components. The required experiments are similar to the current trial-and-error based experiments performed on a vehicle for mounts selection. The method is evaluated experimentally using a quarter car model and the results corroborate the proposed optimization method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it