CFD Simulation of Dynamic Temperature Variations Induced by Tunnel Ventilation in a Broiler House

Abstract

Maintaining the optimal microclimate in broiler houses is crucial for bird productivity, yet enabling efficient temperature control remains a significant challenge. This study developed and validated a computational fluid dynamics (CFD) model to predict temporal changes in indoor air temperature in response to variable ventilation operations in a commercial broiler house. The model accurately simulated air velocity and airflow distribution for different numbers of tunnel fans in operation, with air-velocity errors ranging from -0.22 to 0.32 m s^-1. The predicted airflow rates through inlets and cooling pads showed good agreement with measured values with an accuracy of up to 108.1%. Additionally, the CFD model effectively predicted temperature dynamics, accounting for chicken heat production and ventilation effect. The model successfully predicted the longitudinal temperature gradients and their variations during ventilation cycles, validating its reliability through comparison with experimental data. This study also explored different variable inlet configurations to mitigate the temperature gradient. The variable inlet adjustment showed the potential to relieve the high temperatures but may reduce overall ventilation efficiency or intensify temperature gradients, which confirms the importance of optimising ventilation strategies. This CFD model provides a valuable tool for evaluating and improving ventilation systems and contributes to enhanced indoor microclimates and productivity in poultry houses.

Keywords: CFD validation; inlet baffles; microclimate; poultry heat production; thermal gradients.

Figure 1

Schematic of the experimental broiler house and structure.

Figure 2

Airflow generated by circulation fans and mesh creation using an O-grid for these fans. The meshes in blue represent the lateral surface for air discharge.

Figure 3

Schematic illustrating the heat transfer between broiler chickens and the surrounding air.

Figure 4

Modelling of the inlet baffles using air resistance.

Figure 5

Mesh of Case 4, which satisfies the criteria for grid independence. The blue squares correspond to the exhaust fans, while the dark regions represent the fan frames.

Figure 6

Indoor-air velocity distribution one meter above the floor with five or fourteen tunnel fans operating. The tunnel fans are numbered from 1 to 14, and the tunnel fans not in operation are shown faintly.

Figure 7

Operational status of the tunnel fans and inlet baffles during the experiment.

Figure 8

Comparison of measured indoor temperatures and CFD model predictions for the six different zones.

Figure 9

Pathlines illustrating the indoor airflow from inlet baffles (results at 18:28).

Figure 10

Indoor temperature distribution from 18:25 to 18:28 for two variable inlet configurations: (a) Case 1, featuring more open inlet baffles in the zones near the tunnel fans, and (b) Case 2, with more open inlet baffles in the zones farther from the tunnel fans. Contours are displayed on a horizontal plane 0.6 m above the floor and three vertical planes along the length of the building. Tunnel ventilation was active during 18:27 and 18:28.

Figure 11

Indoor temperature changes in six zones due to variable inlets in Case 1 and Case 2.

Figure 12

Comparison of average computed indoor temperature across six zones for different variable inlet configurations.