Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
Bibliographic record
Abstract
Seismic resilience of critical infrastructure, such as liquefied natural gas (LNG) storage tanks, is essential to the safety and economic well-being of the general public. This paper studies the effect of different ground motions on large LNG storage tanks under four different site conditions. Key parameters of structural design and dynamic analysis, including von Mises stress of outer and inner tanks, tip displacement, and base shear, are analyzed to directly evaluate the safety performance of the large LNG tanks. Because the size of an LNG tank is too large to perform any experiments on a physical prototype, Smoothed Particle Hydrodynamics-Finite Element Method (SPH-FEM) simulation is used as a feasible and efficient method to predict its seismic response. First, the accuracy of the SPH-FEM method is verified by comparing sloshing frequencies obtained from theoretical formulation to experimental results and SPH-FEM models. Then, the seismic response of a real-life 160,000 m3 LNG prestressed storage tank is evaluated with different liquid depths under four site classes. Simulation results show that the tip displacements of the LNG tank at liquid levels of 25% and 50% under site class IV are nearly identical to that of 75% and 100% under site class II. In addition, the maximum von Mises stress of the inner tanks exceeds 500 MPa in all four site classes and jeopardizes the structural integrity of large LNG tanks. As a result, optimization of structural design and the establishment of an early warning system are imperative to the safety of LNG tanks at high liquid 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 | 0.000 |
| 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".