In the problem of the protection by the consequences of an explosion is actual for many indus- trial application involving storage of gas like methane or hydrogen, refuelling stations and so on. A simple and economic way to reduce the peak pressure associated to a deflagration is to supply to the confined environ- ment an opportune surface substantially less resistant then the protected structure, typically in stoichiometric conditions, the peak pressure reduction is around the 8 bars for a generic hydrocarbon combustion in an adia- batic system lacking of whichever mitigation system. In general the problem is the forecast of the peak pres- sure value (PM) of the explosion. This problem is faced using CFD codes modelling the structure in which the explosion is located and setting the main parameters like concentration of the gas in the mixture, the volume available, the size of vent area and obstacles (if included) and so on. In this work the idea is to start from em- pirical data to train a Neural Network (NN) in order to find the correlation among the parameters regulating the phenomenon. Associated to this prediction a fuzzy model will provide to quantify the uncertainty of the predicted value.
Application of a Neuro-Fuzzy System to calculate the Uncertainty of the peak pressure value during the deflagrations of air – hydrogen mixtures
CARCASSI, MARCO NICOLA MARIO
2007-01-01
Abstract
In the problem of the protection by the consequences of an explosion is actual for many indus- trial application involving storage of gas like methane or hydrogen, refuelling stations and so on. A simple and economic way to reduce the peak pressure associated to a deflagration is to supply to the confined environ- ment an opportune surface substantially less resistant then the protected structure, typically in stoichiometric conditions, the peak pressure reduction is around the 8 bars for a generic hydrocarbon combustion in an adia- batic system lacking of whichever mitigation system. In general the problem is the forecast of the peak pres- sure value (PM) of the explosion. This problem is faced using CFD codes modelling the structure in which the explosion is located and setting the main parameters like concentration of the gas in the mixture, the volume available, the size of vent area and obstacles (if included) and so on. In this work the idea is to start from em- pirical data to train a Neural Network (NN) in order to find the correlation among the parameters regulating the phenomenon. Associated to this prediction a fuzzy model will provide to quantify the uncertainty of the predicted value.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
