Active decoupler hydraulic engine mount design with application to variable displacement engine
Bibliographic record
Abstract
In automotive industry, engine vibration isolation has been a challenging task, and given the emergence of new vehicles with more stringent performance characteristics, engine vibration isolation has become an even more demanding issue. Most engine mounts are passive — that is, their parameter values and characteristics are fixed — and as a result, they may not properly attenuate the complicated vibration transmitted from the engine. In this paper, the development of a new active mount is described. This paper describes modeling, development, and experimental analysis of an active engine mount, which is specifically designed to address the Variable Displacement Engine (VDE) isolation problem. An electromechanical actuator is fabricated and retrofitted inside the inertia track plate of a hydraulic engine mount. The plunger of the electromechanical actuator moves upon receiving the signal from the controller, and it changes the dynamic performance of the mount accordingly based on frequency, amplitude, and phase of the activation signal. Experimental results are presented for different control signals. Simulated and experimental results are compared to validate the mathematical model. The experimental results demonstrate the performance of the designed active engine mount to deal with complicated vibration patterns, specifically those created by VDEs.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".