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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Prediction of Pressure-Discharge Curves of Trapezoidal Labyrinth Channels from Nonlinear Regression and Artificial Neural Networks

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Autor(es):
Lavanholi, Rogerio [1] ; de Camargo, Antonio Pires [2] ; Avila Bombardelli, Wagner Wilson [1] ; Frizzone, Jose A. [1] ; Ait-Mouheb, Nassim [3] ; da Silva, Eric Alberto [1] ; de Oliveira, Fabricio Correia [4]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Biosyst Engn, BR-13418900 Piracicaba, SP - Brazil
[2] Univ Estadual Campinas, FEAGRI UNICAMP, Agr Engn Coll, Av Candido Rondon 501, BR-13087875 Campinas, SP - Brazil
[3] Univ Montpellier, Dept Waters, Joint Res Unit Water Management Actors Terr, French Natl Inst Agr Food & Environm, F-34196 Montpellier - France
[4] Fed Univ Grande Dourados, Fac Agrarian Sci, BR-79825070 Dourados, MS - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING; v. 146, n. 8 AUG 1 2020.
Citações Web of Science: 0
Resumo

Emitters are important components of drip irrigation systems, and the use of labyrinths as a mechanism of energy dissipation stands out in the drippers' design. Relating the geometric characteristics of labyrinths with their operational and hydraulic characteristics is not trivial and generally requires the use of computational simulation tools. This study developed and evaluated models that can predict the discharge of labyrinth channels as a function of their geometry to make possible the rapid prediction of pressure-discharge curves due to modifications in the labyrinth geometry. An empirical mathematical model was developed based on nonlinear regression, and a computational model was trained based on artificial neural networks (ANNs). Twenty-four designs of prototypes were built in polymethyl methacrylate to operate at a discharge of approximately1.4 L h-1under 100 kPa. The pressure-discharge curve of each prototype was determined in the laboratory in the range 50-350 kPa. Based on the experimental data, the coefficients of an empirical nonlinear model were fitted, and 11 single-hidden-layer ANN architectures were compared. The best accuracy was provided by an ANN architecture with an input layer with six neurons, six neurons in the hidden layer, and an output layer with a single neuron. The maximum relative errors of the predicted discharges were 9.5% and 9.4% for the ANN and nonlinear models, respectively. Both models were accurate and enabled rapid prediction of the emitter's discharge. An open-source web application was developed to simulate the pressure-discharge curve of labyrinths within a range of geometric and operational characteristics. (c) 2020 American Society of Civil Engineers. (AU)

Processo FAPESP: 15/19630-0 - Processos de obstrução de emissores de irrigação causados por partículas sólidas
Beneficiário:Jose Antonio Frizzone
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 18/20099-5 - Processos de obstrução de gotejadores para irrigação por interações entre carbonato de cálcio e partículas sólidas
Beneficiário:Antonio Pires de Camargo
Linha de fomento: Auxílio à Pesquisa - Regular