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Record W4281803339 · doi:10.18280/acsm.460203

Investigation of Dynamic Factors in Different Sections of HVC by Static and Free Vibration Modal Analysis

2022· article· en· W4281803339 on OpenAlexvenueno aff
Abhishek Agarwal, L. Mthembu

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsChassisVibrationStructural engineeringModal analysisTruckModalFinite element methodNoise, vibration, and harshnessEngineeringDeformation (meteorology)Cross section (physics)Natural frequencyAcousticsAutomotive engineeringMaterials sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

One of the essential purposes of the automotive chassis is to maintain the vehicle's shape and bear the various loads applied to the vehicle. When a heavy-duty vehicle, e.g., a truck, travels on the road, the chassis is subject to vibrations produced by surface roughness and excitation due to the vibration of body parts. The present study aims to design, model, and perform a static structural & vibrational modal analysis on a commercial vehicle (Truck) chassis considering both conventional Structural steel and P100/6061 Al Metal Metrix Composite (MMC) using ANSYS CFX to determine the strength of chassis with transverse sections. Free vibrational modal analysis determines the mass participation factor and resonance frequency for square section and C section, respectively, for both materials using the finite element technique. It was observed that the use of the C section causes an increase in deformation for all the natural frequencies, which is not preferred. The high mass participation factor along the x-direction signifies that any external excitation along this direction would likely cause resonance and amplitude build-up. The C section profile of the chassis shows 1.69% higher deformation than the square section in both cases; hence it is not desirable for the design.

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 score0.328
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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