Deformation and Stress Analysis of the Fullerene by Super Element
Bibliographic record
Abstract
Accurate prediction of static and dynamic response of nano structures is one of the important interests of scientists in the last decade. Nano bearing as an important part of nano machines has been extensively implemented in recognizing and disassembling cancerous cells and building molecular support structures for strengthening bones. For this reason, Molecular Dynamic Method and some experimental methods are implemented in this area. As nano ball bearing is one of the most important components of nano machines, a large number of studies are concentrated to analyze it statically and dynamically. In this paper, a Fullerene is simulated by a spherical super element whose stress, deformation and natural frequency are calculated. The Fullerene is considered to be the C60 which is properly similar with a 66 surface-node spherical super element. In this study the mechanical properties of the fullerene and boundary conditions of the nano ball bearing under loading are introduced and stress and natural frequency of a fullerene under concentrated load is presented with two different strategies, super element and conventional elements. Compatible findings of these two methods validate and confirm the results. Findings indicate that applying 1 super element for the simulation of the fullerene leads to same results as implementing 154764 conventional elements.
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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) | 0.002 | 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".