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Record W2997971143 · doi:10.18280/mmep.060419

Limit Elastic Analysis of E-FGM Rotating Disk with Temperature Dependent Mechanical Properties

2019· article· en· W2997971143 on OpenAlexvenueno aff
Royal Madan, Kashi Nath Saha, Shubhankar Bhowmick

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsLimit (mathematics)Materials scienceComposite materialPhysicsMechanicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

In present work, exponential law based grading of composition is considered for high speed rotating disks and Young's modulus is calculated for two cases namely; temperature dependent and temperature independent applications.In temperature dependent applications, Young's modulus is assumed to vary with temperature as well throughout the spatial coordinates of the disk.The effective Young's modulus of FGM is then calculated using modified rule of mixture.Limit elastic analysis is performed by using variational principle.Results show the effect of temperature over stresses and obtained limit elastic speed.It is observed that limit speed first increases with an increase in aspect ratio and starts decreasing after reaching a critical value.Limit speed also increases with increase in grading index.Effective yield stress of FGM is computed and plotted along with other stresses to identify location of yielding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.011
GPT teacher head0.168
Teacher spread0.157 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2019
Admission routes1
Has abstractyes

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