Dynamics Fem Correlation Using Structural Optimization Tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dynamics finite element (FE) models are the principal tools for prediction of rotorcraft airframe oscillatory loads and vibrations. The accuracy and reliability of rotorcraft system-level dynamics FE models is established through correlation with ground vibration test (GVT) data. The traditional method of correlation involves manual iteration that relies heavily on the knowledge, intuition, and persistence of experienced engineers. A new, more efficient FEM correlation process has been developed to adapt to the increased pace of rotorcraft development programs. The process uses sensitivity-based optimization methods to systematically identify and manipulate the most relevant structural parameters to attain the best agreement with the GVT data. The sensitivity-based approach supplements engineering experience and physical insight with quantitative data that assists in the discovery of non-intuitive issues and provides focus on areas of the model that warrant attention. A framework of proprietary in-house codes and commercially-available LMS® software tools was assembled to support the new process, automating data flow, analysis, and model updates. The new tools and procedures make it possible to correlate modern, high-fidelity dynamics FE models at lower cost and with less dependence on the engineer's level of experience.
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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 | 0.000 | 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 it