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Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach.

Authors :
Küçüktopçu, Erdem
Cemek, Bilal
Simsek, Halis
Source :
Animals (2076-2615). Feb2024, Vol. 14 Issue 3, p501. 21p.
Publication Year :
2024

Abstract

Simple Summary: The aim of this review is to provide researchers with a guide to simulating the environment of poultry houses using computational fluid dynamics. Through an extensive review of the literature in this area, it provides comprehensive insights into the common challenges encountered when applying this method, as well as a discussion of planned future research efforts. In recent years, computational fluid dynamics (CFD) has become increasingly important and has proven to be an effective method for assessing environmental conditions in poultry houses. CFD offers simplicity, efficiency, and rapidity in assessing and optimizing poultry house environments, thereby fueling greater interest in its application. This article aims to facilitate researchers in their search for relevant CFD studies in poultry housing environmental conditions by providing an in-depth review of the latest advancements in this field. It has been found that CFD has been widely employed to study and analyze various aspects of poultry house ventilation and air quality under the following five main headings: inlet and fan configuration, ventilation system design, air temperature–humidity distribution, airflow distribution, and particle matter and gas emission. The most commonly used turbulence models in poultry buildings are the standard k-ε, renormalization group (RNG) k-ε, and realizable k-ε models. Additionally, this article presents key solutions with a summary and visualization of fundamental approaches employed in addressing path planning problems within the CFD process. Furthermore, potential challenges, such as data acquisition, validation, computational resource requirements, meshing, and the selection of a proper turbulence model, are discussed, and avenues for future research (the integration of machine learning, building information modeling, and feedback control systems with CFD) are explored. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20762615
Volume :
14
Issue :
3
Database :
Academic Search Index
Journal :
Animals (2076-2615)
Publication Type :
Academic Journal
Accession number :
175373635
Full Text :
https://doi.org/10.3390/ani14030501