The CFD Computation and Validation of Effects of Adaptive Mesh Refinement in Sloshing Simulation in A Narrow Tank
Bibliographic record
Abstract
Strong and precise computational methods are necessary to discover and understand undesirable effects in tanks where liquids slosh, known as sloshing.Sloshing is important for many industrial applications, such as fuel tanks in ships, aircraft, and other transport vehicles.Numerical methods are commonly used in modelling sloshing behaviour, and adaptive mesh refinement (AMR) technology is an effective method used to increase numerical accuracy in sloshing simulations.The primary objective of this research is to conduct Computational Fluid Dynamics (CFD) calculations of sloshing phenomena to establish a methodology for observing the undesirable effects on the relevant system and assess the effectiveness of adaptive mesh refinement by comparing the results of surface impact pressures with experimental case results from literature.A 3D model of a rectangular tank partially filled with water is used to simulate the impact pressure caused by roll motion.The roll motion is based on experimental data and occurs at periods close to the tank's internal wave resonance period.The pressure results are observed from a single monitoring point.Numerical studies are performed using Star CCM+ software.The Volume of Fluid (VOF) based Eulerian method is utilised to model the free surface flow.The study demonstrates the ability of AMR to accurately model sloshing behaviour and compares fixed and AMR grids at different levels.Finally, the results of fixed and adaptive cartesian grids are compared and verified with corresponding experimental data.The results showed that the AMR grid provided higher numerical accuracy, lower computational cost and allowed for more accurate modelling of sloshing behaviour.It is emphasised that the importance of AMR for understanding sloshing behaviour and modelling it accurately.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".