# Matching the Liquid Atomization Model to Experimental Data Obtained from Selected Nozzles
## 1 Introduction  
Liquid spraying is a process implemented in a number of sectors of the economy, e.g., environmental engineering, chemical engineering, power engineering, food technology, forestry, and agriculture. Quality and efficiency are the basic issues related to the liquid atomization process [1; 2]. The quality of liquid atomization is characterized by the degree and homogeneity of atomization, which is influenced by controlled and uncontrolled factors [3; 4]. Among the controlled factors are the geometry and dimensions of the atomizer, as well as the type of liquid, while the uncontrolled factors include disturbances, vibrations, and liquid turbulence. In the case of spraying processes, effectiveness is determined by the level and uniformity of liquid distribution on the sprayed surface. The uniformity of liquid distribution over the entire width of the spray is affected by, among other factors, liquid pressure and type of nozzle, the angle of the liquid stream and its symmetry, and the height of the boom over the protected plants [5; 6; 7]. Many authors also pay attention to the deflection of the field boom during spraying and its impact on the evenness of spraying [8; 9]. For field crops, flat fan nozzles are used, and the stream is in the form of a fan. On the other hand, in orchard crops, swirl nozzles are used, and the stream of liquid is cone-shaped. The listed types of nozzles belong to the group of slotted nozzles, which are subject to wear during operation. The wear of the nozzles leads to a change in the flow rate. The stream of sprayed liquid can also change shape due to mechanical damage to the sprayer or as a result of timescale deposits inside it. In case of field sprayers, the uniformity of the distribution of the liquid fall sprayed is checked on the grooved table [10; 11; 12]. The uniformity of the liquid distribution can be unequivocally determined on the basis of the coefficient of variation, which is calculated as the ratio of the standard deviation to the average value. The lower the coefficient, the more uniform the spraying is; however, when the coefficient is above 10%, it is unacceptable for use with plant protection products [13; 14]. Mathematical modeling is often used in agriculture, among other methods, to present complex and non-linear relationships, predict, classify, evaluate, and optimize processes, and increase environmental performance. In a study of uniformity of spray liquid distribution, mathematic modelling was used, among other methods, by [PERSON] and [PERSON] [15]. The scientists carried out tests on the uniformity of liquid distribution on an automatic grooved table, over which the liquid from a stationary field sprayer was sprayed, using various technical and technological factors. The mathematical model was developed on the basis of non-linear regression. [PERSON] et al. [16] conducted research in a wind tunnel, and their aim was to develop predictive models for air-induction nozzles. [PERSON] and [PERSON] [17; 18] presented optimization methods that can be applied during the production of nozzles. [PERSON] et al. [19] proposed a mathematical model to predict the movement of droplets on a virtual leaf surface. [PERSON] and [PERSON] [20] presented the results of optimizing the spraying process, the aim of which was to maximize the average coverage degree while minimizing the coverage unevenness coefficient. [PERSON] et al. [21] presented a model based on computational liquid dynamics (CFD) for evaluating spray liquid distribution in a greenhouse crop. Based on the analysis of the test results, they showed that both the distance from the protected plants and the flow rate had an impact on the distribution of droplet sizes. Models based on computational liquid dynamics are also often used to predict and assess spray drift [22; 23]. [PERSON] et al. [24] conducted research based on the Maximum Entropy Principle (MEP), the Linearized Instability Sheet Atomization (LISA) model, and Computational Liquid Dynamics (CLD) simulations.