Determining the Intermediate and High Strain Rate Properties of Bar Bending Braking System Material in a Forward Velocity Sled
Bibliographic record
Abstract
Abstract The Bar Bending Braking System (BBBS) in a forward velocity sled utilizes the plastic deformation of the metallic bars to dissipate the Kinetic energy of impact and generate the required deceleration pulse. The National Aerospace Laboratories forward velocity sled’s BBBS material, Mild steel St37-2, is characterized in compression at room temperature, using the quasi-static tests and the Split Hopkinson Pressure Bar (SHPB) at strain rates ranging from 10− 3/s − 2500/s. Very high strain rate dependence of yield and flow stress was observed from these tests as there was nearly a 100% increase in the yield stress between the highest and lowest strain rates. Thinner samples (L/D < 0.5) were used for the SHPB tests apart from the usual ratio (L/D = 0.5) samples and it was observed that the stress strain curves of these thinner samples were consistent with those of the samples with L/D = 0.5 showing that there was no significant effect of the three-dimensional state of stress on the curves. The Compression stress-strain data for the steel, which has over 12 stress strain curves at different strain rates, is analysed to determine the Viscoplastic Johnson-Cook (JC) model and the Cowper-Symonds (CS) Model. The JC Model was developed over a limited strain rate range (0.001-300/s), as linear dependence of the log(strainrate) to the yield or flow stress was only possible in this range. The CS model was developed for the entire range and effectively models the stress strain curves throughout the range of strain rate. The values of the JC and CS constants are compared with existing literature for St 37 steel and are found similar for strain rate sensitivity though there is considerable difference in the static plasticity constants.
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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 | 0.001 | 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) | 0.002 | 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".