Since two different methods are used for power transmission to deep well pumps, there are two types of deep well pumps: vertical shaft deep well pumps and submersible deep well pumps. A typical submersible pump placed within the well, basic height terms, and well characteristics is presented in Figure 1. Critical SubmergenceThe vertical distance between the pump water inlet and the dynamic water surface is defined as "submergence" (S). If the submergence is less than critical submergence (Sc), then a vortex is generated. As a result, the pump loses suction, and the efficiency decreases (

Although flow in biological materials sometimes behaves like a continuous one, it cannot be simulated with continuity-based modeling when it comes to discontinuous flow behavior. The Discrete Element Method (DEM) in combination with Computational Fluid Dynamics (CFD) is a computational method for modeling particles in fluid flow by tracking their motion. DEM is widely used in the field of engineering, and its use in the agricultural field is increasing. This study analyzes the CFD-DEM relationship of biological material in aerodynamic systems and reviews current applications. In the article, the definition of aerodynamic systems as a basic principle, particle-fluid and particle-particle interaction forces in the system, modeling of particle motions, CFD-DEM coupling method, and analysis applications of agricultural aerodynamic systems are examined. In this study, simulation experiments were carried out at 100 g/s and 200 g/s straw feeding values at each value of 18-15-12-10-8-6-4 m/s air and straw inlet velocities. The flow near the cyclone walls caused the straw particles to be directed towards the lower exit end of the cyclone. At feed densities of 100 g/s and 200 g/s, the least particle output was obtained at a rate of 18 m/s. The highest cyclone output efficiency was obtained at feed densities of 100 g/s and 200 g/s at a velocity of 12 m/s. The compatibility of the trial simulation results with the literature showed that the CFD-DEM application is an important approach to study the behavior of particulate matter in fluids.

Nowadays submersible deep well pumps are the most used irrigation systems in agriculture field. Efficient operation and economical life of pumps is an important issue. One of the most important parameters affecting pump efficiency and life is cavitation The cavitation is one of the problems frequently faced in the pump systems that widely used in the agriculture field. The cavitation could cause more undesired effects such as loss of hydraulic performance, erosion, vibration and noise. This paper presents a novel model for the detection of vortex cavitation in the deep well pump used in the agriculture system using adaptive neural fuzzy networks. The data submergence, flow rate, power consumption, pressure values, and noise values used for training the ANFIS (Adaptive-Network Based Fuzzy Inference Systems) network are acquired from an experimental pump. In this study, we use to the sixty-seven data for training process, while the fifteen data have used for testing of our model. The average percentage error (APE) has obtained as 0.08 % and as 0.34 % respectively for 67 training data and for 15 test data. The performance of the implemented model shows the advantages of ANFIS. The result of this study shows that ANFIS can be successfully used to detect vortex cavitation. This paper has two novel contributions which are the usage of noise value on cavitation detection and find out cavitation by using adaptive neural fuzzy networks. During the cavitation, the pump parameters must change by controller for prevent unwanted pump errors. The strategy proposed could be preliminary study of automatic pump control. Also proposed novel control strategy can be used for cavitation control in agriculture irrigation pumps, because of easy set up and no need extra cost. The ANFIS based model has real-time applicable thanks to rapid and easy control. It is possible to set safe boundaries in submergence in this model. Thus, users by adjusting controllable parameters can prevent cavitation and increase pump efficiency.

The vortex means the mass of air or water that spins around very fast that often faced in the agriculture irrigation systems used the pump. The undesired effects like loss of hydraulic performance, erosion, vibration and noise may occur because of the vortex in pump systems. It is important to detect and prevent vortex for the economic life and efficiency of the agriculture pump. The image processing and neuro-fuzzy based novel model is proposed for the detection of a vortex in the deep well pump used in the agriculture system with this paper. The used images and datasubmergence, flow rate, the diameter of the pipe, power consumption, pressure values and noise values-is acquired from an experimental pump. The proposed approach consists of three steps; Neuro-Fuzzy Learning, Image Processing and Neuro-Fuzzy Testing. In the first step, the eightytwo data have employed for the training process of the Neuro-Fuzzy. Then, the images derived from a camera placed near the experimental pump are used to detect vortex in the image processing step. Finally, the relevant data to vortex cases have employed for the testing process of the Neuro-Fuzzy. The result of this study demonstrates that image processing and neuro-fuzzy based design can be successfully used to detect vortex formation. This paper has provided novel contributions in the vortex detection issue such as find out vortex cases by using image processing and Neuro-Fuzzy. The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.

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