Optimized gauging for tire–rim loading identification
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
The determination of the tire–rim interface loadings is a difficult but key task for the aircraft wheel designer to predict the wheel service life. In conjunction with an optimal parameterization of these loadings previously defined by the authors, the optimal sensor placement problem is considered to identify the loading parameters at best. An optimization procedure of the wheel instrumentation, which consists of several strain gauges , is thus proposed to minimize the uncertainties of the sought parameters during the identification process. Two criteria are reviewed, namely, the determinant and the lowest eigenvalue of the Fisher information matrix , and different optimization procedures are assessed. The effectiveness of the method is proven considering the identification of an inflation case. The optimized instrumentations lead to drastically reduced uncertainties of the loading parameters and thus ensure reliable inverse identifications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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