Efficient Semi-Analytical Methodology for the Pre-Design Analysis of the Shock Response of Marine Structures
Bibliographic record
Abstract
We introduce a robust and computationally efficient methodology for numerical simulation of shock-structure interaction. The methodology is based on the use of some of the classical methods of mathematical physics, with the subsequent coupling between the fluid dynamics and structural parts using the finite-difference methodology. In order to demonstrate the versatility of the approach, we apply it to two rather different practically important problems of the interaction between shock waves and submerged cylindrical structures, aiming at providing insights that would be useful to engineers at the pre-design stage. We first consider a submerged cylindrical shell subjected to two consecutive shock waves, and analyze the effect of such loading in the context of both hydrodynamic fields and the structural stresses it induces. The most important result of this analysis is the observation, for certain values of the distance between the wavefronts, of a very significant increase of the maximum stress observed in the structure. Then, we consider a submerged cylindrical shell subjected to a single shock wave, but employ a more advanced shell theory than the one traditionally used, namely, the Reissner-Mindlin theory instead of the Kirchhoff-Love one. We demonstrate that such an advancement of the model not only leads to a very significant improvement of the accuracy of the respective simulations, but also allows for modeling relatively thick shells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".