Advanced 3D Hamiltonian nodal position finite element method for nonlinear dynamic analysis of rotating solids
Bibliographic record
Abstract
This paper develops a novel 3D brick element by Nodal Position Finite Element Method (NPFEM) to effectively model rotating solids. It uses nodal positions instead of nodal displacements to formulate element’s strain and kinetic energies. This approach effectively avoids computational errors caused by spurious strains induced by large rigid-body rotations and can automatically account for stiffening effects arising from centrifugal forces. By directly solving for the positions of rotating elastic solids using Hamiltonian canonical equations, the new 3D NPFEM brick element allows elastic deformation to be efficiently and accurately extracted by subtracting the rigid-body motion from these positions. Additionally, the ability of the new 3D NPFEM brick element to model bending deformation is enhanced by directly introducing incompatible modes into the element shape functions. Numerical validation shows that the new 3D NPFEM brick element accurately models and analyzes the elastic deformation of rotating blades. It automatically captures nonlinear frequency responses of rotating solids without requiring special boundary and loading condition treatments commonly used in classic FEM . This advancement offers significant advantages by avoiding errors when modeling complex rotating solids or machines, thereby improving computational efficiency and accuracy.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".