Abstract
M.Ing.
Stochastic analysis of water distribution systems allow the performance of systems to be evaluated under more realistic conditions that involve both deterministic and probabilistic factors. A software package called Mocasim II has been developed to perform stochastic analysis on water supply systems. This allows the relationship between the reliability of the supply system and the capacity of its service reservoir(s) to be quantified using Monte Carlo analysis. In a Monte Carlo analysis the factors which influence the reliability of the system such as water demand, fires, and pipe failures are simulated stochastically over a long period of time. A reliability-capacity relationship is quantified by analysing the failure behaviour of different service reservoir sizes. A previous version of Mocasim used a simple mass balance model for calculating the flows in simple, linear distribution networks. Mocasim II extends the functionality of its predecessor by enabling the probabilistic modelling of more complex water distribution models. This was achieved by integrating the stochastic modelling technique into the Epanet hydraulic analysis software package. Mocasim II was designed using an object-oriented model which has various advantages such as ease of programme testing, upgrading and maintaining as well as minimum repetitive code and a logical structure. Additional capabilities of Mocasim II include the determination of probability distributions for network properties such as flow rate, pressure and water quality at any node in the network. This will assist in estimating the levels of service of a water supply system. This project focussed on developing sections of Mocasim II to be integrated with existing software such as Mocasim I, the Epanet hydraulic engine, OOTEN, and a random number generator. The software was tested thoroughly. This involved testing each class separately as well as applying it to a test case which is a simple network consisting of a source, reservoir and demand node. The theoretical background of the stochastic model has been investigated and various aspects discussed.
Various case studies in Windhoek-Namibia, Mabeskraal-South Africa and an Epanet design example demonstrate the capabilities of the software and benefits of a stochastic analysis. A workbook to be used in addition to the software's help facility has been developed.