Abstract

In microirrigation systems, start connectors are used at lateral-manifold or microtube-lateral junctions, and minor losses occur when water flows through the start connectors. The connector’s geometry may cause contractions and/or expansions in the flow section, inducing local head losses that should be considered when designing irrigation subunits. The objective of this research was to analyze the experimental datasets available from previous studies in order to develop and validate mathematical models to estimate local head losses through start connectors using dimensional analysis and artificial neural networks. A dataset with 55,331 records of local head loss as a function of flow rate through several geometries of start connectors was evaluated. Four models based on dimensional analysis and artificial neural networks were developed and validated. The local head losses through start connectors depended on the fluid properties, operating hydraulic conditions, and geometric characteristics of the start connectors. For the full and simplified models based on dimensional analysis, 95% of discharge predictions had relative errors less than 13.0% and 14.3%, respectively. For the full and simplified models based on neural networks, 95% of discharge predictions had relative errors less than 12.2% and 12.7%, respectively. A web application was developed to facilitate the discharge predictions. Depending on the connector’s geometry, the minor losses through start connectors represent a significant percentage of the total head loss along the lateral.

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Data Availability Statement

Some or all data, models, or code used during the study were provided by a third party. The models based on dimensional analysis and artificial neural networks were generated from the test database provided by the Irrigation Testing Laboratory (LEMI/ESALQ/USP). The three databases served previously for publication of individual articles by the researchers Bombardelli et al. (2019), Vilaça et al. (2017), and Zitterell et al. (2013), which belong to the same laboratory. Direct requests for these materials may be made to the provider as indicated in the acknowledgments.
All data mentioned in the manuscript are available from the corresponding author by request. So, the corresponding author also agrees to dispose of the material, if requested.

Acknowledgments

The authors are grateful to the following Brazilian institutions for their financial support: the Federal Department of Science and Technology (MCT), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001, the São Paulo State Scientific Foundation (FAPESP), and the National Institute of Science and Technology in Irrigation Engineering (INCTEI). Also, the authors are grateful to the Irrigation Testing Laboratory (LEMI) for providing their database of start connectors.

References

Abadi, M., et al. 2016. “TensorFlow: A system for large-scale machine learning.” In Proc. 2th USENIX Symp. on Operating Systems Design and Implementation, 265–283. Berkeley, CA: USENIX Association.
Al-Amoud, A. I. 1995. “Significance of energy losses due to emitter connections in trickle irrigation lines.” J. Agric. Eng. Res. 60 (1): 1–5. https://doi.org/10.1006/jaer.1995.1090.
Bagarello, V., V. Ferro, G. Provenzano, and D. Pumo. 1997. “Evaluating pressure losses in drip-irrigation lines.” J. Irrig. Drain. Eng. 123 (1): 1–7. https://doi.org/10.1061/(ASCE)0733-9437(1997)123:1(1).
Bombardelli, W. W. A., A. P. Camargo, J. A. Frizzone, R. Lavanholi, and H. S. Rocha. 2019. “Minor losses caused by fittings used in micro-irrigation systems.” Rev. Bras. Eng. Agríc. Ambient. 23 (7): 492–498. https://doi.org/10.1590/1807-1929/agriambi.v23n7p492-498.
Bralts, V. F., and C. D. Kesner. 1983. “Drip irrigation field uniformity estimation.” Trans. ASAE 26 (5): 1369–1374. https://doi.org/10.13031/2013.34134.
Buckingham, E. 1914. “On physically similar systems: Illustrations of the use of dimensional equations.” Phys. Rev. 4: 345–376. https://doi.org/10.1103/PhysRev.4.345.
Celik, H. K., D. Karayel, M. E. Lupeanu, A. E. W. Rennie, and I. Akinci. 2015. “Determination of head losses in drip irrigation laterals with cylindrical in-line type emitters through CFD analysis.” Bulg. J. Agric. Sci. 21 (3): 703–710.
Chatterjee, S., and J. Simonoff. 2013. Handbook of regression analysis. Hoboken, NJ: Wiley.
Chollet, F. 2015. “keras, GitHub.” Accessed January 20, 2020. https://github.com/fchollet/keras.
Chollet, F. 2017. Deep learning with python. Shelter Island, NY: Manning.
Demir, V., H. Yurdem, and A. Degirmencioglu. 2007. “Development of prediction models for friction losses in drip irrigation laterals equipped with integrated in-line and on-line emitters using dimensional analysis.” Biosyst. Eng. 96 (4): 617–631. https://doi.org/10.1016/j.biosystemseng.2007.01.002.
Duran-Ros, M., G. Arbat, J. Barragán, F. Ramírez de Cartagena, and J. Puig-Bargués. 2010. “Assessment of head loss equations developed with dimensional analysis for micro irrigation filters using effluents.” Biosyst. Eng. 106 (4): 521–526. https://doi.org/10.1016/j.biosystemseng.2010.06.001.
Dursun, M., and S. Özden. 2014. “An efficient improved photovoltaic irrigation system with artificial neural network based modeling of soil moisture distribution: A case study in Turkey.” Comput. Electron. Agric. 102 (Mar): 120–126. https://doi.org/10.1016/j.compag.2014.01.008.
Elnesr, M. N., and A. A. Alazba. 2017. “Simulation of water distribution under surface dripper using artificial neural networks.” Comput. Electron. Agric. 143 (Dec): 90–99. https://doi.org/10.1016/j.compag.2017.10.003.
Fox, R. W., A. T. McDonald, and P. J. Pritchard. 2011. Introduction to fluid mechanics. 8th ed. Hoboken, NJ: Wiley.
Frizzone, J. A., P. S. L. Freitas, R. Rezende, and M. A. Faria. 2012. Microirrigação: Gotejamento e microaspersão. [In Portuguese.] Maringá, Brazil: Eduem.
Gomes, A. W. A., J. A. Frizzone, O. Rettore Neto, and J. H. Miranda. 2010. “Local head losses for integrated drippers in polyethylene pipes.” Engenharia Agrícola 30 (3): 435–446. https://doi.org/10.1590/S0100-69162010000300008.
Haykin, S. 1999. Neural networks: A comprehensive foundation. Supper Saddle River, NJ: Prentice-Hall.
Helal, E., M. Sobeih, and M. E. El-din. 2018. “Effect of floating bridges on open channels’ flow and bed morphology.” J. Irrig. Drain. Eng. 144 (9): 04018026. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001331.
Hornik, K., M. Stinchcombe, and H. White. 1989. “Multilayered feedforward networks are universal approximators.” Neural Networks 2 (5): 359–366. https://doi.org/10.1016/0893-6080(89)90020-8.
ISO. 2008. Pressure losses in irrigation valves: Test method. ISO 9644. Geneva: ISO.
Juana, L., L. Rodríguez-Sinobas, and A. Losada. 2002. “Determining minor head losses in drip irrigation laterals. I: Methodology.” J. Irrig. Drain. Eng. 128 (6): 376–384. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:6(376).
Kingma, D. P., and J. L. Ba. 2015. “ADAM: A method for stochastic optimization.” In Proc., 3rd Int. Conf. for Learning Representations, 1–15. Ithaca, NY: Cornell Univ.
Kumar, M., N. S. Raghuwanshi, and R. Singh. 2011. “Artificial neural networks approach in evapotranspiration modeling: A review.” Irrig. Sci. 29 (1): 11–25. https://doi.org/10.1007/s00271-010-0230-8.
Lamm, F. R., J. E. Ayars, and F. S. Nakayama. 2006. Microirrigation for crop production: Design, operation and management. Amsterdam, Netherlands: Elsevier.
Lemons, D. S. 2017. A student’s guide to dimensional analysis. New York: Cambridge University Press.
Maier, H. R., and G. C. Dandy. 2000. “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and application.” Environ. Modell. Software 15 (1): 101–124. https://doi.org/10.1016/S1364-8152(99)00007-9.
Martí, P., G. Provenzano, A. Royuela, and G. Palau-Salvador. 2010. “Integrated emitter local loss prediction using artificial neural networks.” J. Irrig. Drain. Eng. 136 (1): 11–22. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000125.
Mattar, M. A., and A. I. Alamoud. 2015. “Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters.” Comput. Electron. Agric. 114 (Jun): 189–201. https://doi.org/10.1016/j.compag.2015.04.007.
Melo, L. J. V., E. L. Silva, and M. A. Faria. 2000. “Head loss in connectors used in trickle irrigation systems.” Cienc. Agrotecnol. 24: 205–214.
Minns, A. W., and M. J. Hall. 2009. “Artificial neural networks as rainfall-runoff models.” Hydrol. Sci. J. 41 (3): 399–417. https://doi.org/10.1080/02626669609491511.
Morigi, S., L. Reichel, and F. Sgallari. 2017. “Fractional Tikhonov regularization with a nonlinear penalty term.” J. Comput. Appl. Math. 324 (Nov): 142–154. https://doi.org/10.1016/j.cam.2017.04.017.
Munson, B. R., D. F. Yong, T. H. Okiishi, and W. W. Huebsch. 2009. Fundamentals of fluid mechanics. 7th ed. Hoboken, NJ: Wiley.
Nguyen, P. M., A. Haghverdi, J. Pue, Y. Botula, K. V. Le, W. Waegeman, and W. M. Cornelis. 2017. “Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils.” Biosyst. Eng. 153 (Jan): 12–27. https://doi.org/10.1016/j.biosystemseng.2016.10.013.
Perboni, A., J. A. Frizzone, A. P. Camargo, and M. F. Pinto. 2015. “Modelling head loss along emitting pipes using dimensional analysis.” Engenharia Agrícola 35 (3): 442–457. https://doi.org/10.1590/1809-4430-Eng.Agric.v35n3p442-457/2015.
Porto, R. M. 2006. Hidráulica básica. [In Portuguese.] São Carlos, Brazil: São Carlos School of Engineering, Univ. of São Paulo.
Prechelt, L. 1998. “Automatic early stopping using cross validation: Quantifying criteria.” Neural Networks 11 (4): 761–767. https://doi.org/10.1016/S0893-6080(98)00010-0.
Provenzano, G., V. Alagna, D. Autovino, J. M. Juarez, and G. Rallo. 2016. “Analysis of geometrical relationships and friction losses in small-diameter lay-flat polyethylene pipes.” J. Irrig. Drain. Eng. 142 (2): 04015041. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000958.
Provenzano, G., P. Di Dio, and G. P. Salvador. 2007. “New computational fluid dynamic procedure to estimate friction and local losses in coextruded drip laterals.” J. Irrig. Drain. Eng. 133 (6): 520–527. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:6(520).
Provenzano, G., and D. Pumo. 2004. “Experimental analysis of local pressure losses for microirrigation laterals.” J. Irrig. Drain. Eng. 130 (4): 318–324. https://doi.org/10.1061/(ASCE)0733-9437(2004)130:4(318).
Provenzano, G., D. Pumo, and P. Di Dio. 2005. “Simplified procedure to evaluate head losses in drip irrigation laterals.” J. Irrig. Drain. Eng. 131 (6): 525–532. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:6(525).
Puig-Bargués, J., M. Duran-Ros, G. Arbat, J. Barragán, and F. Ramirez de Cartagena. 2012. “Prediction by neural networks of filtered volume and outlet parameters in micro-irrigation sanf filters using effluents.” Biosyst. Eng. 111 (1): 126–132. https://doi.org/10.1016/j.biosystemseng.2011.11.005.
Rocha, H. S., P. A. A. Marques, A. P. Camargo, J. A. Frizzone, and E. Saretta. 2017. “Internal surface roughness of plastic pipes for irrigation.” Rev. Bras. Eng. Agríc. Ambient. 21 (3): 143–149. https://doi.org/10.1590/1807-1929/agriambi.v21n3p143-149.
Shanker, M., M. Y. Hu, and M. S. Hung. 1996. “Effect of data standardization on neural network training.” Omega 24 (4): 385–397. https://doi.org/10.1016/0305-0483(96)00010-2.
Sobenko, L. R., A. P. Camargo, T. A. Botrel, J. D. M. Santos, J. A. Frizzone, M. F. Oliveira, and J. V. L. Silva. 2018. “An iris mechanism for variable rate sprinkler irrigation.” Biosyst. Eng. 175 (Nov): 115–123. https://doi.org/10.1016/j.biosystemseng.2018.09.009.
Sobenko, L. R., J. A. Frizzone, A. P. Camargo, E. Saretta, and H. S. Rocha. 2019. “Characterization of venturi injector using dimensional analysis.” Rev. Bras. Eng. Agríc. Ambient. 23 (7): 484–491. https://doi.org/10.1590/1807-1929/agriambi.v23n7p484-491.
Swamee, P. K. 1993. “Design of a submarine oil pipeline (good head loss equations)” J. Transp. Eng. 119 (1): 159–170. https://doi.org/10.1061/(ASCE)0733-947X(1993)119:1(159).
Vekariya, P. B., R. Subbaiah, and H. H. Mashru. 2011. “Hydraulics of microtube emitters: A dimensional analysis approach.” Irrig. Sci. 29(4): 341–350. https://doi.org/10.1007/s00271-010-0240-6.
Vilaça, F. N., A. P. Camargo, J. A. Frizzone, L. Mateos, and R. Koech. 2017. “Minor losses in start connectors of microirrigation laterals.” Irrig. Sci. 35 (3): 227–240. https://doi.org/10.1007/s00271-017-0534-z.
Wu, W., W. E. I. Chen, H. Liu, S. Yin, and Y. Niu. 2014. “A new model for head loss assessment of screen filters developed with dimensional analysis in drip irrigation systems.” Irrig. Drain. 63 (4): 523–531. https://doi.org/10.1002/ird.1846.
Yildirim, G. 2007. “An assessment of hydraulic design of trickle laterals considering effect of minor losses.” Irrig. Drain. 56 (4): 399–421. https://doi.org/10.1002/ird.303.
Yurdem, H., V. Demir, and A. Mancuhan. 2015. “Development of a simplified model for predicting the optimum lengths of drip irrigation laterals with coextruded cylindrical in-line emitters.” Biosyst. Eng. 137 (Sep): 22–35. https://doi.org/10.1016/j.biosystemseng.2015.06.010.
Zanetti, S. S., E. F. Sousa, and V. P. S. Oliveira. 2007. “Estimating evapotranspiration using artificial neural network and minimum climatological data.” J. Irrig. Drain. Eng. 133 (2): 83–89. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83).
Zayed, M., A. El Molla, and M. Sallah. 2018. “An experimental investigation of head loss through a triangular ‘V- shaped’ screen.” J. Adv. Res. 10 (Mar): 69–76. https://doi.org/10.1016/j.jare.2017.12.005.
Zitterell, D. B., J. A. Frizzone, and O. Rettore Neto. 2013. “Dimensional analysis approach to estimate local head losses in microirrigation connectors.” Irrig. Sci. 32(3): 169–179. https://doi.org/10.1007/s00271-013-0424-y.
Zitterell, D. B., J. A. Frizzone, O. Rettore Neto, and M. N. Ullmann. 2009. “Head loss in microtubes and connectors used in microsprinkler systems.” Engenharia Agrícola 29 (4): 591–604. https://doi.org/10.1590/S0100-69162009000400009.
Zong, Q., T. Zheng, H. Liu, and C. Li. 2015. “Development of head loss equations for self-cleaningscreen filters in drip irrigation systems using dimensional analysis.” Biosyst. Eng. 133 (May): 116–127. https://doi.org/10.1016/j.biosystemseng.2015.03.001.

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Go to Journal of Irrigation and Drainage Engineering
Journal of Irrigation and Drainage Engineering
Volume 146Issue 5May 2020

History

Received: Jul 15, 2019
Accepted: Dec 12, 2019
Published online: Mar 4, 2020
Published in print: May 1, 2020
Discussion open until: Aug 4, 2020

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Professor, Tuiuti Univ. of Paraná, Av. Sydnei Antonio Rangel Santos, 238, Curitiba, PR 82010-330, Brazil (corresponding author). ORCID: https://orcid.org/0000-0002-4958-9149. Email: [email protected]
Ph.D. Student, Dept. of Biosystems Engineering, College of Agriculture “Luiz de Queiroz,” Univ. of São Paulo, Piracicaba, SP 13418-900, Brazil. ORCID: https://orcid.org/0000-0002-5939-5524. Email: [email protected]
Professor, Agricultural Engineering College, Univ. of Campinas, Campinas, SP 13083-970, Brazil. ORCID: https://orcid.org/0000-0001-5164-2634. Email: [email protected]
Professor, Dept. of Biosystems Engineering, College of Agriculture “Luiz de Queiroz,” Univ. of São Paulo, Piracicaba, SP 13418-900, Brazil. ORCID: https://orcid.org/0000-0002-4251-1496. Email: [email protected]
Sergio Nascimento Duarte [email protected]
Professor, Dept. of Biosystems Engineering, College of Agriculture “Luiz de Queiroz,” Univ. of São Paulo, Piracicaba, SP 13418-900, Brazil. Email: [email protected]

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