Numerical analysis of stiffened plates subjected to transverse uniform load through the constructal design method
Bibliographic record
Abstract
When it comes to engineering, high performance is always a desired goal. In this context, regarding stiffened plates, the search for better geometric configurations able to minimize the out-of-plane displacements become interesting. So, this study aimed to analyze several stiffened plates defined by the Constructal Design Method (CDM) and solved through the Finite Element Method (FEM) using the ANSYS® software. After that, these plates are compared among each other through the Exhaustive Search (ES) technique. To do so, a non-stiffened rectangular plate was adopted as reference. Then, a portion of its steel volume was converted into stiffeners through the ϕ parameter, which represents the ratio between the volume of the stiffeners and the total volume of the reference plate. Taking into consideration the value of ϕ = 0.3, 75 different stiffened plates arrangements were proposed: 25 with rectangular stiffeners oriented at 0°; 25 with rectangular stiffeners oriented at 45° and 25 with trapezoidal stiffeners oriented at 0°. Maintaining the total volume of material constant, it was investigated the geometry influence on the maximum deflection of these stiffened plates. The results have shown trapezoidal stiffeners oriented at 0° are more effective to reduce the maximum deflections than rectangular stiffeners also oriented at 0°. It was also observed that rectangular stiffeners oriented at 45° presented the smallest maximum deflections for the majority of the analyzed cases, when compared to the trapezoidal and rectangular stiffeners oriented at 0°.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".