Regression models for assessing the efficiency of vibratory separation of parsnip seeds taking into account air dynamics based on numerical simulation and field experiment

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.275592

Keywords:

aerodynamic screen, vibratory movement, light-weight seed, linear regression, seed separation

Abstract

To reduce the complexity of research into designing promising vibratory machines while minimizing the harmful effect of the aerodynamic factor, it is convenient to use regression models. With their help, a quantitative assessment of the effectiveness of separation (cleaning) of seed mixtures is carried out, depending on the design parameters and the mode of operation of vibratory machines.

This paper reports the results of research on the construction of regression models for parsnip seeds based on numerical modeling and full-scale experiment. Based on numerical modeling, a four-factor regression model of the second order was built, which takes into account the geometric characteristics of the aerodynamic screen, the design of the set of working surfaces, and the oscillation amplitude of a vibratory machine. Based on a full-scale experiment, a three-factor regression model of the second order was constructed for a constant gap between the working surfaces.

A comparative analysis of the resulting regression models suggests that numerical modeling provides satisfactory accuracy in assessing the influence of the aerodynamic factor. This estimate, when using a regression model based on a numerical experiment, exaggerates the estimate determined by the full-scale experiment by 5–15 % (depending on the regressate variation area localization).

Hence, the numerical model of the process of vibrational motion of light-weight seeds, taking into account the action of aerodynamic forces and moments, used to build a regression model of separation of parsnip seeds, can be considered adequate. Regression models (for parsnips and other plant crops), which are built on the basis of numerical modeling, should be used to solve problems of optimizing the parameters of vibratory machines according to the criterion of reducing the harmful effect of the aerodynamic factor.

Author Biographies

Аnton Nykyforov, State Biotechnological University

Senior Lecturer

Department of Mechatronics, Life Safety and Quality Management

Roman Antoshchenkov, State Biotechnological University

Doctor of Sciences, Professor, Head of Department

Department of Mechatronics, Life Safety and Quality Management

Ivan Halych, State Biotechnological University

PhD, Senior Lecturer

Department of Mechatronics, Life Safety and Quality Management

Liliia Kis-Korkishchenko, State Biotechnological University

Doctor of Philosophy, Senior Lecturer

Department of Equipment and Engineering of Processing and Food Industries

Viсtor Kis, Kharkiv Regional Trade Union Organization of Workers of the Agro-Industrial Complex of Ukraine

PhD, Associated Professor, Deputy Head of Trade Union Organization

Alla Dombrovska, O. M. Beketov National University of Urban Economy in Kharkiv

PhD, Associated Рrofessor

Department of Legal Support of Economic Activity

Inna Kilimnik, O. M. Beketov National University of Urban Economy in Kharkiv

PhD, Associated Рrofessor

Department of Legal Support of Economic Activity

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Regression models for assessing the efficiency of vibratory separation of parsnip seeds taking into account air dynamics based on numerical simulation and field experiment

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Published

2023-04-28

How to Cite

Nykyforov А., Antoshchenkov, R., Halych, I., Kis-Korkishchenko, L., Kis, V., Dombrovska, A., & Kilimnik, I. (2023). Regression models for assessing the efficiency of vibratory separation of parsnip seeds taking into account air dynamics based on numerical simulation and field experiment . Eastern-European Journal of Enterprise Technologies, 2(1 (122), 40–51. https://doi.org/10.15587/1729-4061.2023.275592

Issue

Vol. 2 No. 1 (122) (2023): Engineering technological systems

Section

Engineering technological systems

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Copyright (c) 2023 Аnton Nykyforov, Roman Antoshchenkov, Ivan Halych, Liliia Kis-Korkishchenko, Viсtor Kis, Alla Dombrovska, Inna Kilimnik

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