Increased accuracy of SHPB test apparatus to better evaluate naval steels
Bibliographic record
Abstract
The use of high strength steel alloys for shipbuilding applications has increased in recent years in an effort to decrease costsassociated with the manufacture (i.e. material and welding costs) and operation (i.e. fuel economy) of naval vessels. The use ofthinner hull plate has implications for many design criteria, including high strain rate (impact and shock loading) performance.Increasingly, numerical modeling is being used to simulate high strain rate loading events on naval vessels, such as collisions and weapons attacks, with a goal of assessing operational limits. Accurate and reliable high strain rate material data must be used to ensure the accuracy of the numerical models. Confidence in measured data can only be achieved if the potential sources of errorin the measurement system have been eliminated, minimized or characterized. The mechanical behavior of three naval alloys, MIL S-16216K (HY-80), ASTM A517 grade F and CSA G40-21 350WT cat 5 were quantified under high strain rate (103s-1) compression using a Split Hopkinson Pressure Bar (SHPB) apparatus. A systematic error analysis was conducted on the SHPB apparatus to identify potential sources of error in the test set-up, data acquisition and data processing. The identified sources of error were then eliminated, minimized or compensated for, in order to improve the accuracy of the testing apparatus. SHPB compression data are compared to quasi-static tensile behavior. Metallography was conducted before and after high strain rate testing in order to investigate the deformation mechanisms that occurred in the alloys during the high strain rate loading events.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".