Study of Liquid Motion and Pressure Forces Applied on the Walls of Partially Filled Moving Tank
Bibliographic record
Abstract
Various analytical, numerical and experimental studies have been developed to investigate the effect of liquid sloshing on the dynamic behavior of tank-trucks. However, this type of studies is still complex and expensive. Mechanical models are used to simulate complex phenomena. Using these models in the simulation of deformable bodies provides both geometrical and physical aspects. In this study, a new 3D mechanical model is applied to simulate liquid motion in partially filled tank. This model, which is developed in previous study, is able to simulate lateral, longitudinal and vertical displacements. It may also evaluate pressure forces applied on the tank walls. The main idea of this model is to represent the liquid as a mesh of spring-mass systems. The liquid was divided in multiple masses along each axis. The movement of each mass is simulated by displacement of its mass center; this constitutes the mesh nodes. Each adjacent two nodes are linked by flexible edges having a parallel spring and damper. The discretizing method of the liquid is applied; it is followed by computing of masses and initial coordinates of each node. We show, in detail, the method to obtain stiffness of the springs and damping coefficient of the dampers. Afterwards, equations of dynamic liquid motion are obtained. The system of equations is solved for some examples in order to compare results to the literature.
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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 | 0.000 |
| 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) | 0.001 | 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".