Prediction of pressure-discharge curves of trapezoidal labyrinth channels from nonlinear regression and artificial neural networks
| Autor: | Nassim Ait-Mouheb, Fabrício Correia de Oliveira, Rogério Lavanholi, José Antônio Frizzone, Wagner W. A. Bombardelli, Eric Alberto da Silva, Antonio Pires de Camargo |
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| Přispěvatelé: | Escola Superior de Agricultura 'Luiz de Queiroz' (ESALQ), Universidade de São Paulo (USP), University of Campinas [Campinas] (UNICAMP), Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Federal University of Grande Dourados (UFGD), Universidade de São Paulo = University of São Paulo (USP), Universidade Estadual de Campinas = University of Campinas (UNICAMP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0207 environmental engineering
02 engineering and technology Drip irrigation 01 natural sciences 010309 optics [SPI]Engineering Sciences [physics] 0103 physical sciences 020701 environmental engineering Irrigation Water Science and Technology Civil and Structural Engineering Mathematics Chara Artificial neural network biology Dissipation biology.organism_classification Hydraulic Agricultural and Biological Sciences (miscellaneous) Mechanism (engineering) Non linear regression Neural NetWork Biological system Nonlinear regression Simulation |
| Zdroj: | Journal of Irrigation and Drainage Engineering Journal of Irrigation and Drainage Engineering, American Society of Civil Engineers, 2020, 146 (8), ⟨10.1061/(ASCE)IR.1943-4774.0001485⟩ Journal of Irrigation and Drainage Engineering, 2020, 146 (8), ⟨10.1061/(ASCE)IR.1943-4774.0001485⟩ |
| ISSN: | 0733-9437 |
| DOI: | 10.1061/(ASCE)IR.1943-4774.0001485⟩ |
| Popis: | 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 approximately 1.4 L h −1 under 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. |
| Databáze: | OpenAIRE |
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