This paper describes the results from a series of fire tests that were carried out to measure the effect of defects in thermal protection systems on fire engulfed propane pressure vessels.
In North America thermal protection is used to protect dangerous goods rail tank-cars from accidental fire impingement. They are designed so that a tank-car will not rupture for 100 min in a defined engulfing fire, or 30 min in a defined torching fire. One common system includes a 13 mm blanket of high-temperature ceramic fibre thermal insulation covered with a 3 mm steel jacket. Recent inspections have shown that some tanks have significant defects in these thermal protection systems. This work was done to establish what levels of defect are acceptable from a safety standpoint.
The tests were conducted using 1890 l (500 US gallon) ASME code propane pressure vessels (commonly called tanks in the propane industry). The defects tested covered 8% and 15% of the tank surface. The tanks were 25% engulfed in a fire that simulated a hydrocarbon pool fire with an effective blackbody temperature of 870 °C.
The fire testing showed that even relatively small defects can result in tank rupture if the defect area is engulfed in a severe fire, and the defect area is not wetted by liquid from the inside. A wall failure prediction technique based on uniaxial high-temperature stress rupture test data has been developed and agrees well with the observed failure times. 相似文献
Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s2, which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes. 相似文献