A semi-analytical and numerical approach for solving 2-D and 6-D nonlinear and complex functionally graded tubular systems
Bibliographic record
Abstract
Abstract This study delves into nonlinear vibratory responses of functionally graded (FG) tubes subjected to transverse loads, considering material properties that vary with temperature. A refined beam model established for the tubes satisfies the stress boundary conditions on inner and outer surfaces of the tubes. The nonlinear vibration equations for these functionally graded tubes are meticulously derived with employment of the Zhang–Fu high-order shear deformation beam model, the von Kármán equation, and Hamilton’s principle. The proposed approach is applied to address externally excited nonlinear FG tube systems, encompassing both the 2 degrees of freedom (DOF) single-mode systems and 6 DOF multi-mode systems. Utilizing Galerkin’s method, the resulting discretized nonlinear governing equations allow for the analyses of single and multi-mode tubular system behavior. In solving for the tubular system, an approach implementing the P-T method is managed to be implemented, which yields a continuous semi-analytical solution throughout the entire time domain considered. The approach also demonstrates the advances on the development of a genuinely new computational method with broad impact. In comparison to the widely used Runge-Kutta (R-K) method, the proposed approach demonstrates superior efficiency, accuracy, and reliability, especially for highly nonlinear and complex systems like the FG tubular systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".