Analysis of thermoelastic characteristics of disk using linear properties of material
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".