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Record W2038959498 · doi:10.1115/imece2010-40220

Vibration Energy Harvesting of a Hydraulic Engine Mount Using a Turbine

2010· article· en· W2038959498 on OpenAlexaff
Omid Mohareri, Siamak Arzanpour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNoise, vibration, and harshnessVibrationVibration isolationTurbineAutomotive engineeringInertiaHarshnessEngineeringEnergy harvestingHydraulic machineryEnergy (signal processing)Computer scienceAcousticsMechanical engineering

Abstract

fetched live from OpenAlex

The hydraulic engine mount (HEM) has been designed to provide a vibration isolation characteristic to control road and engine induced vibrations in vehicles by using two fluid passages known as decoupler and inertia track. These types of engine mounts are known for their best noise, vibration, and harshness (NVH) suppression performance among other different types of engine mounts. However, a low cost technique to recycle the dissipated energy of the system in the process of vibration suppression is significantly advantageous. A novel design structure in which the decoupler is replaced with a water turbine to capture and restore the vibration energy of the system is presented in this paper. The turbine design and selection has been done based on the upper and lower chamber pressures and the fluid flow rates in the system’s resonant frequency. The mount vibration isolation and energy generation performance is studied in both frequency and time domains. The simulation results demonstrate that a considerable amount of energy can be harvested from the engine vibration sources. This recent study demonstrates a novel energy harvesting technique in vehicles that require minimum design modifications of conventional hydraulic mounts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.201
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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