Bibliographic record
Abstract
A great number of investigations on fatigue analysis and liquid sloshing has been carried out in different fields of application such as space vehicles, large ground tanks, cargo ships, and highway truck tankers. A variety of approaches to solve the sloshing problem due to different requirements, tank characteristics, and peculiarities of input disturbance gives different, sometimes contradictory, results which are not directly applied to the car tank system. One of the most important components in the fuel tank system is strap, which has vital role in road safety. Fuel tank strap can be separated due to fatigue during vehicle operation, causing the tank to be unsupported and increasing the risk of fuel leak. Its failure can cause very dangerous accidents. The effect of sloshing load, one of the main applied loads on the tank structure, on life of the strap has not been studied carefully before. This thesis presents the numerical analysis of liquid behaviour in sloshing situation and the fatigue analysis of the fuel tank straps due to slosh loads exerted on the tank walls in an accelerating car. In this study, computational fluid dynamics (CFD) and finite element method (FEM) are employed to simulate transient sloshing of fuel inside the tank and find the location of the critical point of crack initiation and predict the life of the fuel tank strap. Using the volume of fluid (VOF) method, a new developed method in CFD, the range of loads on the walls of the fuel tank is determined for the operational conditions that the vehicle experience. An equivalent mechanical model is developed to verify the CFD sloshing simulation result through the normal impact load applied by the effective mass of a pendulum system. The finite element (FE) is further used to create the virtual model of the strap and perform a static analysis. The stresses and strains from the static analysis are then used as input to locate the critical point and predict the life expectancy of the strap under slosh loading.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".