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Record W4402439093 · doi:10.11159/htff24.135

Pressure Drop and Flow Loss Approximation by Comparison Between Mathematical Model and Numerical Model of a Simplified Fluid Mount

2024· article· en· W4402439093 on OpenAlexvenueno aff
Alfiya Ashraf, Nader Vahdati, Yap Yit Fatt

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPressure dropMechanicsFlow (mathematics)MountData flow modelNumerical modelsDrop (telecommunication)Fluid dynamicsComputer scienceComputer simulationPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Vibration isolators like hydraulic engine mounts are employed in the automotive industry to mitigate the effect of vibration and noise produced in the vehicle by the engine.Several analyses on the transient response of the hydraulic engine mounts have been carried out in literature to improve the performance of the device.In this paper, efforts have been taken to study the oscillatory fluid flow behaviour of the working fluid inside these mounts.A simplified fluid mount model is considered, and its respective numerical and mathematical models are developed to study the effect of input parameters such as frequency and displacement on pressure loss across the inertia track.Comparison of the inertia track pressure loss obtained from both the models provides validation on the modelling approach.This approach can be further utilized to develop a correlation between, the input displacement, the inertia track geometry, the fluid density and viscosity and the pressure loss across the inertia track in hydraulic engine mounts and thereby enhancing the damping efficiency of these 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 score0.471
Threshold uncertainty score0.773

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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