Development of an Optimization Tool for Calibrating Crack Growth Material Models
Bibliographic record
Abstract
Abstract Crack growth models are typically calibrated using experimental data prior to conducting damage tolerance analyses of aircraft structures. This process can be trivial for crack growth models that only have a few fitting parameters. However, other models, such as the FASTRAN crack growth equation with the analytical crack-closure model, include several model parameters that make calibration challenging. To simplify the calibration process and improve the accuracy of crack growth simulations, an automated crack growth model optimization tool was developed. This tool also aims at improving the robustness of crack growth models by reducing their dependency on loading spectra and specimen geometries. To achieve this, numerical optimization is used to minimize the discrepancy between experimental data and analytical crack growth. To demonstrate this approach, material model parameters were calibrated for P-3/CP-140 aircraft applications using the FASTRAN crack growth equation with the analytical crack-closure model. The optimization tool was found to be very effective at fitting crack growth simulation results to experimental data by changing the values of selected parameters. It was, however, observed that the automated calibration process could find multiple sets of FASTRAN parameter values that provide equivalent correlations with experimental data. In an attempt to develop a more robust material model, multiple test configurations with different geometries and loading spectra were used to determine an optimal trade-off model that maximizes the correlation for multiple test configurations. The conducted tests demonstrated that this approach is viable and could be used to improve the robustness of crack growth simulations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".