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Fractal analysis of agricultural nozzles spray

By Francisco Agüera, David Nuyttens, Fernando Carvajal and Julián Sánchez-Hermosilla

Abstract

Fractal scaling of the exponential type is used to establish the cumulative volume (V) distribution applied through agricultural spray nozzles in size x droplets, smaller than the characteristic size X. From exponent d, we deduced the fractal dimension (Df) which measures the degree of irregularity of the medium. This property is known as 'self-similarity'. Assuming that the droplet set from a spray nozzle is self-similar, the objectives of this study were to develop a methodology for calculating a Df factor associated with a given nozzle and to determine regression coefficients in order to predict droplet spectra factors from a nozzle, taking into account its own Df and pressure operating. Based on the iterated function system, we developed an algorithm to relate nozzle types to a particular value of Df. Four nozzles and five operating pressure droplet size characteristics were measured using a Phase Doppler Particle Analyser (PDPA). The data input consisted of droplet size spectra factors derived from these measurements. Estimated Df values showed dependence on nozzle type and independence of operating pressure. We developed an exponential model based on the Df to enable us to predict droplet size spectra factors. Significant coefficients of determination were found for the fitted model. This model could prove useful as a means of comparing the behavior of nozzles which only differ in not measurable geometric parameters and it can predict droplet spectra factors of a nozzle operating under different pressures from data measured only in extreme work pressures

Topics: pesticides application, modeling, LCC:Agriculture (General), LCC:S1-972, LCC:Agriculture, LCC:S, DOAJ:Agriculture (General), DOAJ:Agriculture and Food Sciences
Publisher: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Year: 2012
DOI identifier: 10.1590/S0103-90162012000100002
OAI identifier: oai:doaj.org/article:e1a12286784542c4be6648cbb6f8554c
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