The <i>h</i> -, <i>p</i> -, and <i>r</i> -refinements of Finite Element Analysis of Flexible Slider Crank Mechanism
Bibliographic record
Abstract
Three common refinement methods of achieving more accurate finite element solutions are to increase the number of elements, to employ higher-degree interpolation functions, and to implement an adaptive mesh by moving the nodes but maintaining the same number of elements as well as the degree of interpolation functions. This paper presents a finite element analysis by applying these refinements to a flexible slider crank mechanism. The formulation is based on the Euler—Lagrange equation, for which the Lagrangian includes the components related to the kinetic energy, the strain energy, and the work done by axial loads in a link that undergoes elastic transverse deflection. A beam element is modeled based on a translating and rotating motion. An error analysis is demonstrated by defining several error indicators, which are based on energy of an entire mechanism, transverse displacement, and bending strain of midpoints on crank and coupler. This paper also demonstrates the effect of the stiffness of the crank.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".