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Record W4300686866 · doi:10.1139/tcsme-2022-0060

Performance analysis of vibration characteristics on VCR engine using hybrid honeycomb structure

2022· article· en· W4300686866 on OpenAlexvenueno aff
C. Senthilkumar, D. Vasudevan

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationNatural rubberStructural engineeringHoneycombVibration isolationMaterials scienceHoneycomb structureAutomotive engineeringComposite materialEngineeringAcoustics

Abstract

fetched live from OpenAlex

There are a number of techniques available to reduce the engine vibration, and vibration isolation is one among those techniques. Such vibrations can be isolated using hybrid engine mounts that absorb the forces caused by vibration. Consequently, the present work proposes a hybrid aluminium mount filled with silica gel to isolate the engine vibration. Experiments were carried out on the variable compression ratio engine mounted on the hybrid honeycomb structure, and the free, forced vibrations, and frequency domains were analysed. The test results portrayed that the performance of the engine with a hybrid mount is found to be better than the conventional rubber mount. The hybrid sandwich panel with a honeycomb structure crowded with silica gel as fibrous material was utilized to isolate the engine vibrations. Compared with conventional mount (1.502 m/s 2 ), high amount of vibration was reduced by honeycomb structured mount (0.814 m/s 2 ) using different load conditions and fuel input pressure of 150 bar. Vibration in conventional mount for blower closing condition was 1.546 m/s 2 and that in honeycomb structured mount was 1.4 m/s 2 .

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.185
Teacher spread0.177 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicVehicle Noise and Vibration ControlFrench-language works237,207