The results of all presented ΔQ methods (A, B, C and D) are compared to the results obtained using the EPANET2 hydraulic solver (R). The first tests of the ΔQ method implementation, including all variations (A, B, C and D) were run on the NYT network. These different approaches share the same modified FBN (Figure 5(b)). There are two loops in the network: the larger loop 1 contains a reservoir along with the nodes numbered from 2 to 15, while loop 2 has nodes 11, 9, 16 and 20. The location of the split is arbitrary and inconsequential. Loop 1 was split in the proximity of node 6 where a new node 6′ is introduced as a start node for the downstream pipe. Loop 2 was split close to node 20, with the new node 20′ being generated. Flow corrections for loops 1 and 2, ΔQ1 and ΔQ2, respectively, are introduced as demands in the nodes 6 and 20, and in the nodes 6′ and 20′ as negative demands or inflows. A comparison of obtained results, for 1,000 generations and population of 100, is given in Table 2.
GA optimization algorithm . | Fitness function f [106 $] . | CPU time t [s] . | Speedup factor [-] . |
---|---|---|---|
EPANET2 DLL (R) | 38.6 | 390 | / |
Upgraded ΔQ (A) | 38.6 | 18.5 | 21.1 |
Fixed ΔQ (B) | 40.2 | 5 | 78.0 |
Variable ΔQ (C) | 39.8 | 5.5 | 70.9 |
Fixed iteration ΔQ (D) | 39.0 | 18.4 | 21.2 |
GA optimization algorithm . | Fitness function f [106 $] . | CPU time t [s] . | Speedup factor [-] . |
---|---|---|---|
EPANET2 DLL (R) | 38.6 | 390 | / |
Upgraded ΔQ (A) | 38.6 | 18.5 | 21.1 |
Fixed ΔQ (B) | 40.2 | 5 | 78.0 |
Variable ΔQ (C) | 39.8 | 5.5 | 70.9 |
Fixed iteration ΔQ (D) | 39.0 | 18.4 | 21.2 |