Prediction of J-Integral dependence to residual stress and crack depth on NACA 0012-34 using FE and ANN
Bibliographic record
Abstract
This paper presents an approach of linking finite element method with artificial neural network to predict J-Integral parameter in desirable airfoil condition.Finite Element (FE) and Artificial Neural Network (ANN) have been employed for the purpose.In other words, a prediction of finite element results has been done using ANN.Ultimately results of two methods have been compared for different cases.Wing fracture is a well-known problem of the planes which depends on various parameters.The J-integral is a vital parameter in evaluations of structure fracture phenomena.On the other hand residual stresses play an influential role in fracture formation.In the current work, effect of residual stresses and crack depth on J-Integral has been investigated in a standard NACA0012-34 airfoil.As will be seen, residual stresses and crack depth influence J-Integral values.It also will be shown that predictions of ANN method are in a good agreement with those obtained by finite element method.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".