Analysis of local head losses in microirrigation lateral connectors based on machine learning approaches

Abstract

The presence of emitters along the lateral, as well as of connectors along the manifold, causes additional local head losses other than friction losses. An accurate estimation of local losses is of crucial importance for a correct design of microirrigation systems. This paper presents a procedure to assess local head losses caused by 6 lateral start connectors of 32- and 40-mm nominal diameter each under actual hydraulic working conditions based on artificial neural networks (ANN) and gene expression programming (GEP) modelling approaches. Different input–output combinations and data partitions were assessed to analyse the hydraulic performance of the system and the optimum training strategy of the models, respectively. The range of the head losses in the manifold (hsM) is considerable lower than in the lateral (hsL). hsM increases with the protrusion ratio (s/S). hsL does not decrease for a decreasing s/S. There is a correlation between hsL and the Reynolds number in the lateral (ReL). However, this correlation might also be dependent on the flow conditions in the manifold before the derivation. The value of the head loss component due to the protrusion might be influenced by the flow derivation. DN32 connectors and hsM present more accurate estimates. Crucial input parameters are flow velocity and protrusion ratio. The inclusion of friction head loss as input also improves the estimating accuracy of the models. The range of the indicators is considerably worse for DN40 than for DN32. The models trained with all patterns lead to more accurate estimations in connectors 7 to 12 than the models trained exclusively with DN40 patterns. On the other hand, including DN40 patterns in the training process did not involve any improvement for estimating the head losses of DN32 connectors. ANN were more accurate than GEP in DN32. In DN40 ANN were less accurate than GEP for hsM, but they were more accurate than GEP for hsL, while both presented a similar performance for hscombined. Different equations were obtained using GEP to easily estimate the two components of the local loss. The equation that should be used in practice depends on the availability of inputs.