Simulations for the explosion in a water-filled tube including cavitation using the SPH method
Bibliographic record
Abstract
The damage to a structure due to an underwater explosion, which must necessarily include the phenomenon of cavitation, is a difficult problem to simulate. In this paper, the smoothed particle hydrodynamics (SPH) method is used to address the simulation of a fully explicit three-dimensional (3D) underwater explosion in a rigid or deformable cylinder, incorporating properly the physical phenomenon of cavitation. To this purpose, a general 3D SPH code has been implemented using the Open-MP programming interface. The various components of the code have been validated using four test cases, namely the Sjögreen test case for validation of the Riemann solver, the 1D cavitating flow in an open tube test case for validation of the cavitation model, the 1D pentaerythritol tetranitrate detonation test case for validation of the Jones–Wilkins–Lee model and a 3D high-velocity impact test case for validation of the elastic–perfectly plastic constitutive model. Following this validation, a 3D explosion within a water-filled rigid cylinder and a water-filled deformable aluminum tube are simulated with the general SPH code. The results of these simulations are compared against some available experimental data and some numerical simulations obtained using an alternative approach. These comparisons are generally in good agreement with both the experimental and numerical data, demonstrating that the SPH method can be used to simulate general 3D underwater explosion problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".