Energy harvesting from vibration of a hydraulic engine mount using a turbine
Bibliographic record
Abstract
This work presents the design and analysis of a novel technique to capture the dissipated vibration energy of a hydraulic engine mount (HEM) using a hydraulic turbine. Hydraulic engine mount systems are designed to isolate engine and chassis vibrations in vehicles. Energy dissipation in hydraulic mounts happens because of the fluid resistance in two passages known as decoupler and inertia track. This type of engine mount is known for its economic noise, vibration, and harshness (NVH) suppression performance among various categories of engine mounts (passive, active and semi-active). A low cost technique with minimum design modifications to currently available hydraulic mounts can recycle; instead of dissipation, the excessive vibration energy of the engine compartment. In the first stage of this work, a novel design is introduced which replaces the decoupler with a hydraulic turbine to harvest and restore the vibration energy. The turbine design and selection has been done according to the fluid characteristics inside a typical engine mount. In particular, the resonant frequency was on the main interests since it corresponds to the highest fluid flow in hydraulic mounts. The mount vibration isolation performance and energy generation efficiency is studied in both frequency and time domains and the simulation results demonstrate that a considerable amount of energy can be harvested. Additionally, the effect of inertia track elimination on the turbine output power and mount vibration isolation performance is also investigated. The second stage of the work describes the potential of the embedded turbine to be used in a control structure to adjust the vibration isolation and damping properties of the HEM. In this configuration, the turbine is utilized as the system actuator to insert some flow energy and adjust the resonance frequency and amplitude of the mount's dynamic response. Simulation results are used to verify both the energy harvesting and vibration response performance of the proposed system.
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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 | 0.000 | 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".