Volume 9, Issue 4 (12-2019)
J Health Saf Work 2019, 9(4): 346-362 |
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Rajabi-Vardanjani H, Asilian-Mahabadi H, Bayareh M, Sedehi M. Design of an Inhalation Chamber to Expose Laboratory Animals with Suspended Particulate Matter by Numerical Simulation Method. J Health Saf Work 2019; 9 (4) :346-362
URL: http://jhsw.tums.ac.ir/article-1-6221-en.html
URL: http://jhsw.tums.ac.ir/article-1-6221-en.html
1- Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
2- Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran ,Asilia_h@modares.ac.ir
3- Department of Mechanical Engineering, Faculty of Engineering, Shahrekord University, Shahrekord, Iran
4- Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
2- Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran ,
3- Department of Mechanical Engineering, Faculty of Engineering, Shahrekord University, Shahrekord, Iran
4- Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
Abstract: (3124 Views)
Introduction: Usually, in the toxicological studies of airborne particulate pollutants, inhalation exposure chambers are used for providing and distributing the test atmosphere uniformly and stability in the respiratory zone of laboratory animals. The purpose of this study was to design, evaluate and optimize a whole-body exposure chamber, specifically for small laboratory animals exposed to particulate matter.
Material and Methods: In the first, the papers and scientific resources which had provided the technical details and performance of the inhalation exposure chambers were studied, and the advantages, disadvantages and those factors affecting their performance were extracted. Then the assumptions of the initial design of the chamber were prepared with regard to the principles of fluid dynamics and the standard conditions of lab animal housing. To create a uniform distribution of particles inside the chamber, guide plates of flow were used in the upper cone. Numerical simulation and ANSYS Fluent software were used to optimize the initial design. Drawing geometry of the chambers was done using Design modeler software and meshing of the computational field using ANSYS meshing software. The particles used had a mean aerodynamic diameter of 10 μm, spherical, inert, and a density of 1,400 kg. m^-3 and entered the chamber at the carrier gas velocity. Particle concentration was measured in the chambers along the cylindrical radius at 10 cm intervals on the x-axis. Then the percentage of variation coefficient of the particle concentration for each line was calculated. In the final analysis of the results, the geometry design with the lowest coefficient of variation of particle concentration along the selected sampling line was selected as the best chamber design.
Results: The optimized inhalation chamber has a dynamical flow and consists of a cylinder with two upper and lower cones. The flow enters from the upper cone and after passes through the guide plates, distributes in the interior of the chamber and exits from the lower cone. The k-ε turbulence and Discrete Phase Models could have modeled this problem. Design No. 7 was optimal design with the lowest coefficient of variation of the concentration (4.08%).
Conclusion: The numerical simulation method for planning and optimizing of the chambers, at a much lower cost than the empirical methods, was able to provide comprehensive information on the solution field. The analysis of this information led to the selection of the best chamber design to provide uniform concentration of the particles in the respiratory region of the animals.
Material and Methods: In the first, the papers and scientific resources which had provided the technical details and performance of the inhalation exposure chambers were studied, and the advantages, disadvantages and those factors affecting their performance were extracted. Then the assumptions of the initial design of the chamber were prepared with regard to the principles of fluid dynamics and the standard conditions of lab animal housing. To create a uniform distribution of particles inside the chamber, guide plates of flow were used in the upper cone. Numerical simulation and ANSYS Fluent software were used to optimize the initial design. Drawing geometry of the chambers was done using Design modeler software and meshing of the computational field using ANSYS meshing software. The particles used had a mean aerodynamic diameter of 10 μm, spherical, inert, and a density of 1,400 kg. m^-3 and entered the chamber at the carrier gas velocity. Particle concentration was measured in the chambers along the cylindrical radius at 10 cm intervals on the x-axis. Then the percentage of variation coefficient of the particle concentration for each line was calculated. In the final analysis of the results, the geometry design with the lowest coefficient of variation of particle concentration along the selected sampling line was selected as the best chamber design.
Results: The optimized inhalation chamber has a dynamical flow and consists of a cylinder with two upper and lower cones. The flow enters from the upper cone and after passes through the guide plates, distributes in the interior of the chamber and exits from the lower cone. The k-ε turbulence and Discrete Phase Models could have modeled this problem. Design No. 7 was optimal design with the lowest coefficient of variation of the concentration (4.08%).
Conclusion: The numerical simulation method for planning and optimizing of the chambers, at a much lower cost than the empirical methods, was able to provide comprehensive information on the solution field. The analysis of this information led to the selection of the best chamber design to provide uniform concentration of the particles in the respiratory region of the animals.
Type of Study: Research |
Received: 2019/12/28 | Accepted: 2019/12/28 | Published: 2019/12/28
Received: 2019/12/28 | Accepted: 2019/12/28 | Published: 2019/12/28
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