As the global population moves into cities, gas distribution networks are becoming more crowded. There may be cases when some of the pipelines reach critical saturation of clients and a single event such as an immediate pressure decrease wave can start a chain reaction. Pressure can fall below critical even for a short time, and that can cause problems in the whole grid.
In order to mitigate a risk of such a situation, users can model these events and prepare the grid for emergencies, for example by a small house gas storage. Pipeline simulators traditionally do not model such high speed dynamic cases. We have some real measured flow, pressure and temperature data with a sampling frequency of 20 Hz. We compared the real measured data with our modeled pressure undershoot wave.
We would like to show that the simulated data match the measured data to a high degree. This match especially depends on space and time discretization, when we tried time steps down to 0.1 millisecond. We have encountered some problems, such as very high demands on the calculation time and results database size and some oscillations due to numerical problems of very small numbers. We would also like to show a way how to minimise these demands.
The article presented will also contain tips and tricks and recommended simplifications, in order to be able to simulate very dynamic events and help gas distribution companies model transients in their network with linepack that is next to none.
In conclusion, we present a way how to adjust a pipeline simulator in order to be able to calculate events on a millisecond scale, so that there is an exact simulation of pressure decrease in case of an overcrowded distribution network. We will show that a more universal software can perform the desired calculation to a satisfying degree of precision, so that a specialized CFD package may not be needed.
Gas distribution networks are becoming complicated as the population in global cities is on the rise and many countries are implementing gasification for most of their city dwellers. This increase in complexity raises some specific challenges for simulation of gas distribution networks.
