Modeling and validation of dispersion following an instantaneous vapor or two-phase release from a pressurized vessel

Abstract

This article presents a new model for the initial dispersion phase of energetic instantaneous expansion following a catastrophic vessel rupture. This model has been implemented as a new sub-model in the Phast dispersion model and is based on more sound physical principles than the old Phast submodel. It allows for both vapor and two-phase releases. In the case of two-phase releases, droplets are assumed to expand radially, potentially leading to time-varying droplet rainout and the formation of a spreading evaporating liquid pool. For ground-level vapor or two-phase releases the correctness of the numerical predictions is confirmed against an analytical solution. The model has been validated against previously published experimental data for pressurized releases with and without rainout. This includes ground-level releases for nitrogen vapor and flashing liquid propylene, and elevated flashing liquid releases for Freon 11, Freon 12, propane and butane. Overall the old model tends to under-predict the cloud radius and cloud speed versus time, while the new model more closely agrees with small-scale experimental data. Therefore the new model produces smaller concentrations and doses, and is less conservative. For two-phase releases the new model predicts an increased amount of rainout, which is more in line with the experimental data.

Categorization of iso-pentane flashing spray based on morphology, thermodynamical and mechanical effects

“…Hervieu and Veneau (1996) showed that the drop size decreased and velocity increased at higher spray pressure. The similar trends were observed by Witlox et al, (2017aWitlox et al, ( , 2010Witlox et al, ( , 2017b, who measured the drop size in the far-field region, at spray distance more than 40 cm for spray dispersion under different pressures. Taking R134a as a release medium, Yildiz et al (2004) investigated the characteristics of two-phase flashing jet under a harsh environment, and found that the spray pressure had a strong influence on the onset breakup location and particle mean velocity, but less on the particle diameter.…”

A new correlation for hazardous area classification based on experiments and CFD predictions

“…In this scenario, the leakage usually occurs from large holes, which are of the order of magnitude of the equipment or accessory. For this emergency scenarios, Witlox et al described and implemented in a computer code, a model for the initial dispersion phase of energetic instantaneous expansion following a catastrophic vessel rupture, leading to an instantaneous loss of containment. The model was based on physical principles and can deal with a wide range of scenarios including both pressurized jet releases and unpressurized releases.…”

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

“…The consequence of jet fire and pool fire may be predicted via empirical, computational fluid dynamics (CFD), or integrated models. − Empirical models allow rapid predictions, but their accuracy is limited due to neglecting the impact of surface roughness, which impacts the bulk liquid evaporation rate. ,, CFD models, such as FDS (Fire Dynamics Simulator) and FLACS (Flame ACceleration Simulator) models, are capable of capturing the influence of surface roughness, but at significant computational cost and time-consuming. ,− Integrated models, such as the one proposed by Dadashzadeh (with a citation of over 40 according to the Google Scholar record) , and the integrated methodology developed by Baalisampang et al, are utilizing a combination of both CFD and empirical equations to give a more superior consequence model with the consideration of interaction of events within an evolving accident scenario, ,, and those integrated models are even more complicated and not easy to follow. The integrated models used by commercial software PHAST provide a tradeoff between the accuracy and computational cost while still capturing the influence of surface roughness on dispersion, pool spreading, and bulk liquid evaporation, as validated by experiment results over a broad range of fire scenarios. ,− Meanwhile, considering the complexity of combustion kinetics and dynamics at the molecular level, there is still a lack of first-principles model on prediction of the fire consequences. ,, Alternatively, machine learning techniques can be used to correlate the consequence for specific fire scenarios with chemical and physical properties of organic compounds to provide a straightforward, fast, yet accurate correlative tools. − …”