Overview of consequence modelling in the hazard assessment package Phast

This presentation provides an overview of the hazard assessment software package Phast for consequence modelling of accidental releases of toxic or flammable chemicals to the atmosphere. The consequence modelling involves the following three consecutive steps:

1. First discharge calculations are carried out to set release characteristics for the hazardous chemical (including depressurisation to ambient). Scenarios which may be modelled includes releases from vessels (leaks or catastrophic ruptures), short pipes or long pipes and releases of combustion products following a warehouse fire. Released considered include releases of sub-cooled liquid, superheated liquid or vapour releases. Furthermore are considered un-pressurised or pressurised releases, and continuous, time-varying or instantaneous releases.

2. Secondly dispersion calculations are carried out to determine the concentrations of the hazardous chemical when the cloud travels in the downwind direction. This includes effects of jet, heavy-gas and passive dispersion. In the case of a two-phase release rainout may occur, and pool formation/spreading and re-evaporation is modelled. Also effects of indoor dispersion (for indoor releases) and building wakes can be accounted for.

3. Subsequently toxic or flammable calculations are carried out. For flammables, ignition may lead to fireballs (instantaneous releases), jet fires (pressurised flammable releases), pool fires (after rainout) and vapour cloud fires or explosions. Radiation calculations are carried out for fires, while overpressure calculations are carried out for explosions. For each event, the probability of death is determined using toxic or flammable probit functions.

The current presentation presents a brief overview of the above consequence methodology. It also summarizes the “verification” that the code correctly solves the mathematical model (i.e. that the calculated variables are a correct solution of the equations), “validation” against experimental data to show how closely the mathematical model agrees with the experimental results, and a “sensitivity analysis” including a large number of input parameter variations to ensure overall robustness of the code, and to understand the effect of parameter variations on the model predictions.