Pressure Wave in Liquid Generated by Pneumatic Pistons and Its Interaction with a Free Surface
Bibliographic record
Abstract
Numerical analysis of a pressure wave generated in a liquid [Formula: see text] upon impact of the pneumatic pistons and its interaction with a free surface has been performed for the geometry and parameters of the plasma compression system prototype constructed by General Fusion Inc. Stress wave developing in the hammer–anvil piston assembly is first simulated using high-fidelity structural mechanics research code, then propagated through the liquid [Formula: see text] with several solvers within OpenFOAM[Formula: see text] software and also with nonlinear acoustics in-house code based on the Westervelt equation. In the current system, a pressure wave transmitted into the liquid [Formula: see text] is characterized by a complex temporal double peak structure and strong spatial amplitude variation. An imprint of discrete pulses remains detectable during the entire propagation of the combined wave. An excellent agreement between the results produced with different numerical codes is obtained. Nonlinear effects associated with equation of state are found to be significant at impact velocities of [Formula: see text], while at lower velocities of [Formula: see text] the difference between the results obtained with linear and nonlinear equations of state is negligible. Liquid–gas interface dynamics during the compression process of a spherical gas cavity is captured very well by the compressibleInterFoam within OpenFOAM.
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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 | 0.000 |
| 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 | 0.001 |
| 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".