Polymeric Hopkinson Bar-Confinement Chamber Apparatus to Evaluate Fluid Cavitation
Bibliographic record
Abstract
Mild traumatic brain injury associated with blast exposure is an important issue, and cavitation of the cerebrospinal fluid (CSF) has been suggested as a potential injury mechanism; however, physical measurements are required to evaluate cavitation thresholds. Modifications to a Split Hopkinson Pressure Bar (SHPB) apparatus were investigated with the aim to generate localized fluid cavitation and measure the cavitation threshold of fluids. The proposed design incorporated a novel closed cavitation chamber to generate localized cavitation resulting from a reflected compression pulse, which was generated by a spherical steel striker and Polymethyl methacrylate (PMMA) incident bar. A numerical model of the incident bar was developed and validated with 24 independent tests (cross-correlation: 0.970–0.997), and this was extended to a numerical model of the apparatus including the chamber, validated with 27 independent tests (cross-correlation: 0.921) to predict the tensile fluid pressure in the chamber. Tests on distilled water were performed with comparable numerical results for the chamber strain (R 2 : 0.875) and chamber end-wall velocity (R 2 : 0.992). The pressure in the chamber was determined from the model to avoid introducing a nucleation site via a pressure gauge, and was verified with a first-order approximation showing good agreement (R 2 : 0.892). The 50% probability of cavitation for distilled water was −3.32 MPa ±3%, comparable to values in the literature. This novel apparatus, including a closed confinement chamber integrated with a polymeric SHPB apparatus was able to create localized fluid cavitation with loading comparable to blast exposure. Future studies will include the measurement of CSF cavitation pressure.
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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 | 0.001 | 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 | 0.001 | 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".