Research developments in numerical methods of fluid-structure interactions in naval architecture and ocean engineering
Bibliographic record
Abstract
It is a challenge to solve complex fluid-structure interaction (FSI) problems through theoretical derivations, whereas numerical simulation provides an effective solution and is widely applied in naval architecture and marine engineering. Based on grid treatment, FSI methods are classified into the body-fitted grid method, non-body-fitted grid method, overset grid method and particle-based method. The research development of these four types of methods is then reviewed. Both the body-fitted grid method and overset grid method can accurately capture the interface and are suitable for high Reynolds number flow problems, and the former is generally employed when structural deformation is considered, while the latter often works well when considering rigid body motion with complex geometric shapes. The non-body-fitted grid method can avoid the mesh update operation to make calculations simpler, and is widely used in the simulation of flow control, development of underwater flexible bionic vehicles and interference of multi-body motion. The particle-based method plays an increasingly important role in simulating strong nonlinear fluid-structure interaction problems involving severe free surface deformation, slamming, explosion, etc. The properties of different FSI problems determine the applicability of different methods. How to select a suitable numerical method and combine the advantages of various methods to develop novel numerical methods that can handle more challenging problems are important development directions for FSI algorithms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".