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Record W3159294656 · doi:10.18280/mmep.080216

Finite Element Simulation for Electric-Car Chassis Development from Scratch

2021· article· en· W3159294656 on OpenAlexvenueno aff
Alief Wikarta, Rizaldy Hakim Ash Shiddieqy, Khosmin, Indra Sidharta

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsChassisvon Mises yield criterionScratchStiffnessStructural engineeringFinite element methodRigidity (electromagnetism)Stress (linguistics)Materials scienceAutomotive engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Indonesian engineers need to design and build their electric car chassis from scratch. These cars need to possess high torsional stiffness and maximum Von Mises Stress value. Therefore, this determines the value of Torsional Stiffness and Von Mises Stress from an electric car scratch chassis using Finite element simulation. A twist load was placed in front, with back support used to determine the torsional stiffness. The stress was analyzed using three types of loading, namely; vertical, lateral, and braking. Meanwhile, the JIS G 3141 galvanized plate with a thickness of 1 mm and 1.2 mm from Indonesia's marketplace was the chassis material used. The simulation results showed that the maximum Von Mises Stress value with a thickness of 1.2 mm produces safe strength, with a torsional stiffness of 11735 Nm/deg. In conclusion, the rigidity and strength of the chassis with a plate thickness of 1.2 mm is in a proper category. Furthermore, this chassis can be used for the development from scratch of an electric car.

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: Methods · Consensus signal: none
Teacher disagreement score0.760
Threshold uncertainty score0.827

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 head0.044
GPT teacher head0.248
Teacher spread0.204 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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