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Record W2976242749 · doi:10.26637/mjm0704/0005

Analysis of thermoelastic characteristics of disk using linear properties of material

2019· article· en· W2976242749 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMalaya Journal of Matematik · 2019
Typearticle
Languageen
FieldEngineering
TopicElasticity and Wave Propagation
Canadian institutionsCanadian Institutes of Health Research
Fundersnot available
KeywordsThermoelastic dampingMaterials scienceMechanicsComposite materialPhysicsThermalThermodynamics

Abstract

fetched live from OpenAlex

In this paper, finite element method (FEM) is applied on vibrating disk to study thermoelastic characteristics (stress, strain and displacement). Thermoelastic characteristics of disk are examined under two distinct cases of temperature distribution (uniform and steady-state). The material properties young's modulus, coefficient of thermal expansion and density are considered as constant as well as linear function of radius of the disk The materials Aluminimu $(\mathrm{Al})$ and Alumina $\left(\mathrm{Al}_2 \mathrm{O}_3\right)$ are considered for construction of functionally graded material (FGM) disk. Further, Poisson's ratio taken as constant because an effect of Poisson's ration on thermoelastic characteristics is negligible. To find solution of governing equation standard discretization approach of finite element method is used. The Graphical results show's significance variation of the Radial stress, Circumferential stress, Radial strain, Circumferential strain and Displacement with respect to normalized radial distance and Kibel Number. The analysis of the results shows that thermoelastic characteristics are not independent of temperature distribution as well as material properties.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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