Solving 2-D Slamming Problems by an Improved Higher-Order Moving Particle Semi-Implicit Method
Bibliographic record
Abstract
Abstract A higher-order moving particle semi-implicit (MPS) method was further developed to solve 2-D water entry problems. To overcome the inconsistency in the original MPS methods, a pressure gradient correction was implemented to guarantee the first-order consistency of the gradient. The corrective matrix was modified by using the derivative of the kernel function. A particle shifting technique was also applied to improve the numerical stability. Validation studies were carried out for water entry of a rigid wedge with the tilting angles of 0, 10, and 20, and two rigid ship sections. Convergence studies were conducted on domain size, particle spacing, and time step. A particle convergence index method was proposed to evaluate numerical uncertainties in the improved MPS method. Uncertainties in numerical solutions due to spatial discretization were calculated. The predicted impact pressures and forces by the present method are in good agreement with experimental data. Introduction Slamming can cause local structural deformation and damages. It is important to solve highly nonlinear water-entry problems involving breaking free surfaces. Many studies on the impact of pressures/ loads during water entry or slamming have been performed using experiments, analytical solutions, and numerical simulations. Extensive drop tests have been conducted. For example, Bisplinghoff and Doherty (1952) carried out a series of experiments for free-fall wedges. Because ship motions in waves can lead to asymmetric water entry, wedges with different tilt angles were tested by Barjasteh et al. (2016). A more detailed review of studies based on theoretical, potential-flow, and computational fluid dynamics (CFD) methods is presented in the work of Peng and Qiu (2018).
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".