A Three-Node Triangular Constant Strain Element for Evaluation of Stress Concentration Factor of a Rectangular Thin Plate Under Tension Load
Bibliographic record
Abstract
In many engineering fields such as aerospace, marine, automotive and mechanical, the design of planar structures consisting of rectangular plates commonly requires the use of holes as a technological solution in fastening assemblies. The major goal of this research is to examine the impact of stress concentration factors (SCF) in the specific case of planar structures, highlight their relevance as a potential source of structural failure, and compare the various techniques employed to gauge their magnitude. Particular emphasis will be placed on rectangular plates with holes since these plates' shape discontinuities alter the stress field, leading to a local increase in the stress field that, if not accurately predicted and analyzed at the design stage, could endanger the entire structure. For the studied case (rectangular plate with central hole subjected to uniform tensile field), a new finite element formulation based on 3-noded triangular element has been suggested to estimate the field variables (stress). MATLAB programming has been developed by considering this element. Analysis has been carried out for the same structure (rectangular plate) with analysis software Abaqus. Finding was compared to several analytical solutions published in the literature, based on Heywood, Howland, and Flynn formulations. Obtained results were in good agreement.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".