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Epanet Z 0 5 FULL Version Download

table5 displays the results found for the stress test runs where the pressure range is only 0.1 m (0.142 psi). over this narrow range, the demand versus pressure curve is almost vertical, approximating the case where nodes have no demand at pressure below some minimum value and full demand at a slightly higher pressure. the contents of the table’s columns are the same quantities as those displayed in table4. like the runs made for the larger pressure ranges, all the pda methods produce the same demand solution (when they converge).

in each run of epanet, the nr-based gga algorithm was started from a random initial pipe assignment. all pipe assignments were subsequently checked for their compliance with the water pressure and demand bounds. in cases where such constraints were violated, the pipe assignment was changed until a valid solution was found. for each pipe assignment, the program then optimizes the geometric network with the pipe constraints and maintains a non-dominated sorting genetic algorithm (nsga2) to identify the best solution. the nsga2 technique consists of iterative replacement of the worst performing individuals with new individuals in a generational manner (deb et al. 1996 ; deb et al. 1997 ). to improve the performance of the nsga2, a filter was applied to reduce the number of candidates that need to be evaluated by the algorithm. all gga runs were terminated after 500 generations of the algorithm. for each run, the nr-based gga algorithm was repeated four times to reduce statistical noise in the results. to determine the quality of the different pda solutions, performance indicators were used. these include:

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