No abstract

The application pattern for single, stationary spray sprinklers exposed to the wind was modelled to simulate water application in a centre pivot irrigated field. In the model, a dynamic square grid of cells for water application and a static square grid of cells for water collection were defined. The dynamic grid contained the information on the water application pattern of an isolated spray sprinkler, and this followed the motion of the centre pivot lateral. The static grid represented the entire the field, and received water from the dynamic grid at fixed time intervals. Model outputs included the applied water distribution pattern and measures of irrigation uniformity (radial, travelling path and global). A series of experiments using pivoted and single spray sprinklers were conducted simultaneously. The results from the model compared well with field observations. The resulting root mean square errors for the Heermann and Hein uniformity coefficient and the average applied water depth were 0.02 % and 0.08 mm, respectively. Model simulations were carried out to illustrate the effect of wind on irrigation uniformity.

No abstract

Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

Ballistic simulation has been successfully applied to impact sprinklers. However, ballistic simulation of center pivot sprinkler irrigation has been limited by the difficulty in estimating the initial drop velocity vector in fixed and rotating spray plate sprinklers. Initial velocity is severely affected by the impact of the jet on the sprinkler deflecting plate (or plates). In this work, experimental techniques based on drop photography have been employed to obtain the droplet velocity and angle in the vicinity of a fixed spray plate sprinkler, using three different nozzle diameters. Furthermore, simulation techniques based on the inverse solution of drop trajectory were combined to determine the initial velocity vector and energy loss at the spray. Our analysis suggests that the ballistic model can be used to simulate drop inverse trajectory in these sprinklers, although the ballistic model can benefit from 5-10% effective drag force screening. The ratio of initial drop velocity to jet velocity ranged between 0.67 and 0.82, while the kinetic energy losses in the spray sprinklers amounted to 33-55%.