Baffle Design Analysis for a Road Tanker: Transient Fluid Slosh Approach
Bibliographic record
Abstract
Baffles are known to help reduce the amplitude of fluid slosh in partly filled tanks, particularly during braking and acceleration. The transient fluid slosh approach is proposed to evaluate the effectiveness of baffles designs. A computational fluid dynamic (CFD) fluid slosh model is developed using the VOF (volume of fluid) technique coupled with a Navier-Stokes solver. The validity of the model is demonstrated using the experimental data acquired with a scale model tank. The validated CFD model is subsequently formulated for a full scale tank and simulations are performed under excitations idealizing the straight-line braking maneuvers to investigate the anti-slosh role of four different transverse baffles concepts. The fluid slosh responses are analyzed in terms of the fundamental slosh frequency, and the resulting forces and moments under different fill volumes of liquid cargos of constant load. The simulation results show that the natural frequency and peak magnitudes of slosh forces and moments strongly depend on the baffle design and the fill volume. The transverse baffles yield significantly higher longitudinal mode slosh frequency, and lower longitudinal force and pitch moment. The shape and locations of baffles orifices play significant role in suppressing the transient fluid slosh. Highly effective anti-slosh effect is also observed for the partial baffle designs under higher fill levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".