CalibraÃÃo de Rugosidade de TubulaÃÃes de Redes de DistribuiÃÃo de Ãgua, Via MÃtodo Transiente Inverso com AplicaÃÃo de Algoritmo GenÃtico / Roughness pipe calibration in a water distribution network with Genetic Algorithm and Inverse Transient Analysis

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

03/09/2009

RESUMO

An important step in the modelling of hydraulic networks, calibration makes it possible to predict the behavior of physical parameters essential to decision-making. The present study used inverse transient analysis with a genetic algorithm to calibrate pipe roughness in a water distribution network with simulations based on variable parameters such as mutation, number of chromosomes, number of generations, number of nodes, transient time and type of demand variation (abrupt or smooth). Simulations were divided into cases, and each solution given by the computer model was evaluated by an objective function, based on the squared difference between estimated and actual results for the transient loads in the monitored node(s). An analysis of the solutions given by the model shows how the inverse method, the genetic algorithm and the choice of parameters influenced final results. A study of the cases revealed that for the network under study the efficiency of the method could not be improved by increasing isolated parameters of the genetic algorithm alone. In accordance with the inverse method employed, a set of roughness values was created by averaging the solutions found for the same parameters in ten successive runs of the genetic algorithm (seeds). The estimated hydraulic loads in the monitored node(s) were close to the actual loads, with calculated roughness values very similar to actual roughness values in some of the pipe sections. The efficiency of the method was evaluated by mean relative error (MRE) testing (comparing the mean relative errors of estimated and actual roughness values for each pipe). The best MRE result was 18.9%. The smallest relative error (1%) was observed in pipe 2, the biggest (59.1%) in pipe 10

ASSUNTO(S)

recursos hidricos calibraÃÃo de rugosidade redes de distribuiÃÃo de Ãgua algoritimo genÃtico calibration roughness transient genetic algorithms

ACESSO AO ARTIGO

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