Vapor clouds density

Vapor Cloud Explosions. Lenoir and Davenport (Ref. 16) have summarized some major VCEs worldwide from 1921 to 1991. The materials involved in these incidents suggest that certain hydrocarbons—such as ethane, ethylene, propane, and butane—demonstrate greater potential for VCEs. Several factors may contribute to these statistics. These materials are prevalent in industry and are often handled in large quantities, increasing the potential for an incident. Certain inherent properties of the materials also contribute to their potential for explosion. These include flammability, reactivity, vapor pressure, and vapor density (with respect to air). 

The initial rate of spreading (often termed slumping) of a heavier-than-air vapor cloud can be significant, depending on the magnitude of the difference between the effective mean cloud/plume density and the air density. 

Because mixing in the vertical direction is suppressed by a stable density stratification, a slowly diluting vapor cloud that hugs the ground is generated. 

The laser irradiance (laser power/cm2) is an important parameter Different irradiance values lead to a vapor cloud of different density, and consequently the ion-molecule reactions can take place with highly different yields. 

For this reason, while responding to a spill or leak, we must consider environmental and topographical features of the surroundings, such as wind direction, the slope of the ground, and any natural or artificial barriers that may channel the liquid or vapors. It is critical in a non-fire incident, such as a spill or leak, to determine the type of petroleum liquid present and its source. Information about the material s vapor density enables us to make reasonable predictions as to the possible behavior of the emitting vapor. These factors may influence the route of approach, the positioning of firefighting apparatus and personnel, the need for and the route of evacuation, and the boundaries of the potential problem area. It is essential that no apparatus or other motor vehicles or personnel be located in the path that a vapor cloud will most likely follow. 

Meteorological conditions The conditions affect the consequence zones, but probabilities can also be developed based on the frequency of the wind direction, which may vary from night to day, and which could increase or decrease the potential for a vapor cloud to travel toward a high population density along the route. 

For the analysis of solid samples without prior digestion, the combination of cathodic sputtering with AAS has been proposed [21]. Jet-enhanced sputtering gives a high analyte number density and the atom vapor cloud is introduced into the flame or electrothermal atomiser. This approach is particularly feasible for the analysis of samples which are difficult to dissolve, such as refractory oxide-forming metals and alloys. 

Taken together, these effects tend to significantly increase the actual density of vapor clouds formed from flashing releases. The prediction of these effects is necessary to properly initialize the dispersion models. Otherwise, the cloud s hazard potential may be grossly misrepresented. 

The TNT model is well established for high explosives, but when applied to flammable vapor clouds it requires the c3q>losion yield, T), determined from past incidents. There are several physical differences between TNT detonations and VCE deflagrations that limit the theoretical validity. The TNO multi-energy method is directly correlated to incidents and has a defined efficiency term, but the user is required to specify a relative blast strength from 1 to 10. The Baker-Strehlow method uses flame speed data correlated with relative reactivity, obstacle density and geometry to replace the relative blast strength in the TNO method. Both methods produce relatively close results in examples worked. 

A Phillips high-density polyethylene (HDPE) plant suffered a maintenance error that led to total loss of reactor inventory, causing a vapor cloud explosion resulting in 23 fatalities and 130 injuries. Loss estimated at 1.4billion. See Figure 6.2. 

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