# Exploring Ventilation Efficiency in Poultry Buildings: The Validation of Computational Fluid Dynamics (CFD) in a Cross-Mechanically Ventilated Broiler Farm

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## Abstract

**:**

^{−1}for CFD and 0.64 ± 0.54 m s

^{−1}for direct measurements. In conclusion, the air velocity was not affected by the methodology (CFD or direct measurements), and the CFD simulations were therefore validated to analyze indoor environment of poultry farms and its operations. A better knowledge of the indoor environment may contribute to reduce the demand of electric energy, increasing benefits and improving the thermal comfort of broilers.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Experimental Poultry Farm

#### 2.2. Test Sections and Multisensor System for Direct Measurements

Sensor number * | Section A | Section B | ||
---|---|---|---|---|

X-coordinate (m) | Y-coordinate (m) | X-coordinate (m) | Y-coordinate (m) | |

1–2 | 22.45 | 0.30 | 35.90 | 0.05 |

3–4 | 19.50 | 12.00 | 31.50 | 11.80 |

5–6 | 18.00 | 11.95 | 32.80 | 11.95 |

7–8 | 9.30 | 12.00 | 41.80 | 7.15 |

9–10 | 5.70 | 12.05 | 40.70 | 6.80 |

11–12 | 0.60 | 12.00 | 45.85 | 11.35 |

13–14 | 0.55 | 6.30 | 47.35 | 4.50 |

15–16 | 0.50 | 7.60 | 46.95 | 5.35 |

17–18 | 0.55 | 2.15 | 44.05 | 0.80 |

19–20 | 8.70 | 6.70 | 48.50 | 3.15 |

21–22 | 24.85 | 7.10 | 47.70 | 1.10 |

23–24 | 23.60 | 7.15 | 47.70 | 0.65 |

#### 2.3. CFD Background

^{−3}); t: time (s); x, x

_{i}, x

_{j}: length components (m); u

_{i}, u

_{j}: velocity component (m s

^{−1}); p: pressure (Pa); τ

_{ij}: stress tensor (Pa); g

_{i}: gravitational acceleration (m s

^{−2}); F

_{i}: external body forces in the i direction (N m

^{−3}); c: specific heat (W kg

^{−1}K

^{−1}); T: temperature (K); K: thermal conductivity (W m

^{−1}K

^{−1}); S

_{T}: thermal source term (W m

^{−3}).

#### 2.4. Turbulence Models and Boundary Conditions (BC)

^{2}s

^{−2}); μ: fluid viscosity (m

^{2}s); μ

_{t}: turbulent viscosity (m

^{2}s); σ

_{k}: turbulent Prandtl number for k; G

_{k}: the generation of kinetic energy due to the variations of the components of the average velocity of the flow (kg m

^{−1}s

^{−2}); G

_{b}: the generation of kinetic energy by boundary push (kg m

^{−1}s

^{−2}); ε: turbulent dissipation rate (m

^{2}s

^{−3}); Y

_{M}: contribution of the pulsatile expansion associated to the compressible turbulence (kg m

^{−1}s

^{−2}); σ

_{ε}: turbulent Prandtl number for ε; C

_{1ε}: constant; C

_{2ε}: constant; C

_{3ε}= tanh[u

_{1}/u

_{2}]; u

_{1}: velocity of flow parallel to g

_{i}(gravitational vector); u

_{2}: velocity of flow perpendicular to g

_{i}. Moreover, the constant values were C

_{1ε}= 1.44, C

_{2ε}= 1.92, σ

_{k}= 1.0 and σ

_{ε}= 1.3 [22,26].

_{max}= Largest angle in face or cell; θ

_{min}= Smallest angle in face or cell; θ

_{e}= Angle for equiangular face or cell.

(i) Constant and computational settings | |||||

3D double precision Segregated Steady Turbulence model: Standard k-ε Wall treatment: Standard Wall Functions Pressure-velocity coupling: SIMPLE algorithm Discretization scheme: Pressure: standard; Momentum: Second order upwind; Turbulence kinetic energy: Second order upwind; Turbulence dissipation rate: Second order upwind; Energy: Second order upwind. Air properties: Density: 1.225 Kg m ^{−3}; C_{p}: 1006.43 J kg^{−1} K^{−1}; Thermal conductivity: 0.0242 W m^{−1} K^{−1}; Viscosity: 1.789·10^{−5} kg m^{−1}s^{−1}.Wall material: Density: 2400 Kg m ^{−3}; C_{p}= 1125 J kg^{−1} K^{−1}; Thermal conductivity: 1.2 W m^{−1} K^{−1}. Atmospheric pressure: 101,325 Pa.Gravitational acceleration: 9.81 m s ^{−2}. | |||||

(ii) Boundary Conditions | |||||

CFD Simulation | Assay Section | Scenario | Outlets (Fans) Mass Flux rate at each outlet (in kg s ^{−1})Air temperature at each outlet (in K) | Inlet Air (10% Turbulence Intensity (1)) Air velocity (in m s ^{−1}) Air temperature (in K) | Temperature at solid elements (in K) Floor North-Wall (2) South-Wall (2) East-Wall (2) West-Wall (2) East-Cover (2) West-Cover (2) |

I | Section A | I | Large = 9.60 Kg s^{−1} 303.7 KSmall = 0 | 6.62 m s^{−1} 304.5 K | 303.0 K 303.4 K 304.7 K 305.1 K 304.1 K 305.5 K 305.0 K |

II | Section A | II | Large = 9.03 Kg s^{−1} 301.9 KSmall = 3.2 Kg s ^{−1} 301.9 K | 7.70 m s^{−1} 303.3 K | 303.0 K 302.5 K 303.0 K 303.5 K 302.0 K 303.5 K 302.0 K |

III | Section A | III | Large = 8.17 Kg s^{−1} 303.7 KSmall = 0 | 9.01 m s^{−1} 304.5 K | 302.0 K 303.4 K 304.6 K 306.6 K 303.1 K 305.7 K 305.0 K |

IV | Section A | IV | Large = 7.82 Kg s^{−1} 301.9 KSmall = 2.78 Kg s ^{−1} 301.9 K | 10.67 m s^{−1} 303 K | 303.0 K 302.5 K 303.0 K 304.0 K 302.0 K 303.5 K 302.0 K |

V | Section B | I | Large = 9.60 Kg s^{−1} 304.8 KSmall = 0 | 4.66 m s^{−1} 305.6 K | 305.0 K 305.0 K 306.0 K 307.0 K 303.0 K 305.0 K 304.0 K |

(ii) Boundary Conditions | |||||

VI | Section B | II | Large = 9.03 Kg s^{−1} 305.1 KSmall = 3.2 Kg s ^{−1} 305.1 K | 5.93 m s^{−1} 305.8 K | 304.0 K 304.0 K 306.0 K 307.3 K 303.5 K 305.7 K 304.6 K |

VII | Section B | III | Large = 8.17 Kg s^{−1} 304.9 KSmall = 0 | 6.35 m s^{−1} 305.8 K | 304.0 K 304.5 K 306.0 K 307.2 K 303.2 K 305.5 K 304.3 K |

VIII | Section B | IV | Large = 7.89 Kg s^{−1} 305.1 KSmall = 2.78 Kg s ^{−1} 305.1 K | 8.23 m s^{−1} 306.2 K | 304.0 K 304.0 K 306.0 K 307.5 K 303.5 K 305.3 K 304.7 K |

_{fluct}, to the mean flow velocity, u

_{aver}; (2) According to the main orientation reached by the walls and covers.

^{−1}) of each outlet (fan), air velocity at inlets and temperature at solid elements were BC used to carry out the CFD simulations. The air temperature was also measured at inlets and at outlets, as the temperature fluctuations at inlets and at outlets were negligible in each scenario (operation), one average from air temperature at inlets and another average from air temperature at outlets was introduced as indicates in Table 2. In the same way, two averages of mass flux rate (one average from each type of fans) and another average of air velocity at inlets from each scenario were introduced at CFD software as indicated in Table 2. These air velocity at inlets (m s

^{−1}) were obtained from thirty measurements (thirty seconds) at each inlet by means of a calibrated Testo 425 hot-wire anemometer [28]; then, the average of all inlets was calculated and introduced in CFD software. Introducing these single values reduces time consumption of CFD calculations; in this sense, some authors have calculated and assumed uniform velocities and airflow rates for inlets or outlets in their CFD simulations [18,29,30]. In this paper, the individual ventilation rate of each outlet was measured by [27]. This protocol of measurement [27] consist of ducted the exhaust air 50 cm from the fan and then by means of a hot wire anemometer measuring at 24 different locations in the section [31]. On the other hand, the surface temperature of internal solid elements (wall, floor or covers) was measured by means of a portable model Optex PT-3LF non-contact (infrared) thermometer and the measured values were indicated in the same Table 2. Evidently, the specifications of a correct functioning of this model of thermometer were according to the range of values reached of any poultry farm from broiler production (an accuracy ±1 of the reading value or ±2 °C ±1 digit in an ambient temperature 0 °C to 50 °C, ambient humidity 35% to 85% RH). The temperatures reached at solid elements were another BC requires by CFD program. Table 2 summarizes the main inputs and BC at CFD simulations.

**Figure 2.**Screen of geometry and meshed of poultry farm at GAMBIT (FLUENT). Orientation of walls and covers.

#### 2.5. Statistical Validation Model

- Y
_{ijk}: Air velocity in the section i at boundary conditions j at height k by the sensor l and by methodology n; - Z
_{i}: Measurement section (2); - B
_{j}: Boundary conditions (4); - H
_{k}: Height of the sensor (2); - SN
_{l}: Sensor l (24); - M
_{n}: Methodology: CFD vs. direct measurements using the multisensor system (2); - (Z X B)
_{ij}: Interaction between Section-Boundary (8); - (Z X H)
_{ik}: Interaction between Section-Height (4); - (Z X M)
_{in}: Interaction between Section-Methodology (4); - (Z X SN)
_{il}: Interaction between Section-Sensor (48); - (B X H)
_{jk}: Interaction between Boundary-Height (8); - (B X SN)
_{jl}: Interaction between Boundary-Sensor (96); - (B X M)
_{jn}: Interaction between Boundary-Methodology (8); - (H X M)
_{lk}: Interaction between Height-Methodology (4); - (SN X H)
_{lk}: Interaction between Sensor-Height (48); - (Z X B X H)
_{ijk}: Triple interaction between Section-Boundary-Height (16); - (Z X SN X M)
_{iln}: Triple interaction between Section-Sensor-Methodology (96); - (B X SN X M)
_{jln}: Triple interaction between Boundary-Sensor-Methodology (192); - (Z X B X H X M)
_{ijkn}: Four interaction between Section-Boundary-Height-Methodology (384); - ε
_{ijkln}: Error of the model

## 3. Results and Discussion

#### 3.1. CFD vs. Direct Measurements

^{−1}from measurements and 0.60 ± 0.30 m s

^{−1}from CFD.

**Table 3.**Air velocity in m s

^{−1}(mean ± standard deviation) in the field experiment by direct measurements and by CFD simulations. The number of data is indicated in parenthesis.

Scenario | Height | Methodology | Section A | Section B | Mean |
---|---|---|---|---|---|

I | 0.25 m | Measured | 0.62 ± 0.86 (12) | 0.37 ± 0.30 (12) | 0.50 ± 0.65 (24) |

CFD | 0.52 ± 0.68 (12) | 0.34 ± 0.29 (12) | 0.43 ± 0.52 (24) | ||

1.75 m | Measured | 0.37 ± 0.39 (12) | 0.66 ± 1.00 (12) | 0.52 ± 0.76 (24) | |

CFD | 0.35 ± 0.40 (12) | 0.62 ± 0.97 (12) | 0.49 ± 0.74 (24) | ||

Mean | Measured | 0.50 ± 0.67 (24) | 0.53 ± 0.74 (24) | 0.51 ± 0.70 (48) | |

CFD | 0.43 ± 0.55 (24) | 0.48 ± 0.71 (24) | 0.46 ± 0.63 (48) | ||

II | 0.25 m | Measured | 0.70 ± 0.35 (12) | 0.71 ± 0.29 (12) | 0.71 ± 0.33 (24) |

CFD | 0.60 ± 0.30 (12) | 0.74 ± 0.36 (12) | 0.67 ± 0.33 (24) | ||

1.75 | Measured | 0.47 ± 0.32 (12) | 0.68 ± 0.47 (12) | 0.58 ± 0.41 (24) | |

CFD | 0.41 ± 0.34 (12) | 0.68 ± 0.50 (12) | 0.55 ± 0.44 (24) | ||

Mean | Measured | 0.59 ± 0.35 (24) | 0.70 ± 0.38 (24) | 0.64 ± 0.37 (48) | |

CFD | 0.60 ± 0.30 (24) | 0.71 ± 0.43 (24) | 0.61 ± 0.39 (48) | ||

III | 0.25 m | Measured | 0.78 ± 0.41 (12) | 0.80 ± 0.31 (12) | 0.79 ± 0.35 (24) |

CFD | 0.73 ± 0.36 (12) | 0.89 ± 0.43 (12) | 0.75 ± 0.36 (24) | ||

1.75 m | Measured | 0.63 ± 0.34 (12) | 0.77 ± 0.50 (12) | 0.70 ± 0.42 (24) | |

CFD | 0.50 ± 0.32 (12) | 0.79 ± 0.55 (12) | 0.65 ± 0.47 (24) | ||

Mean | Measured | 0.71 ± 0.37 (24) | 0.79 ± 0.41 (24) | 0.75 ± 0.39 (48) | |

CFD | 0.62 ± 0.35 (24) | 0.84 ± 0.49 (24) | 0.73 ± 0.44 (48) | ||

IV | 0.25 m | Measured | 0.42 ± 0.26 (12) | 0.65 ± 0.59 (12) | 0.54 ± 0.46 (24) |

CFD | 0.37 ± 0.27 (12) | 0.54 ± 0.38 (12) | 0.46 ± 0.34 (24) | ||

1.75 m | Measured | 0.72 ± 0.69 (12) | 0.77 ± 0.87 (12) | 0.74 ± 0.75 (24) | |

CFD | 0.72 ± 0.87 (12) | 0.80 ± 1.04 (12) | 0.76 ± 0.94 (24) | ||

Mean | Measured | 0.57 ± 0.53 (24) | 0.71 ± 0.71 (24) | 0.64 ± 0.62 (48) | |

CFD | 0.54 ± 0.65 (24) | 0.67 ± 0.78 (24) | 0.61 ± 0.71(48) | ||

All | 0.25 m | Measured | 0.63 ± 0.53 (48) | 0.63 ± 0.41 (48) | 0.63 ± 0.47 (96) |

CFD | 0.55 ± 0.44 (48) | 0.63 ± 0.41 (48) | 0.59 ± 0.43 (96) | ||

1.75 m | Measured | 0.55 ± 0.47 (48) | 0.72 ± 0.71 (48) | 0.63 ± 0.61 (96) | |

CFD | 0.50 ± 0.53 (48) | 0.72 ± 0.78 (48) | 0.61 ± 0.67 (96) | ||

Mean | Measured | 0.59 ± 0.50 (96) | 0.68 ± 0.58 (96) | 0.63 ± 0.54 (192) | |

CFD | 0.52 ± 0.49 (96) | 0.68 ± 0.62 (96) | 0.60 ± 0.56 (192) |

#### 3.2. CFD-Air Velocity Results

**Figure 3.**Contours of air velocity in Planes 1 and 2 of the Section A in a trial scenario (Scenario II). Air velocity is expressed in m s

^{−1}.

**Figure 4.**Vectors of air velocity showing trajectories in Planes 1 and 2 of the Figure 3. Air velocity is expressed in m s

^{−1}.

#### 3.3. Results of the Validation Model

Parameter | DF | Sum of squares | Mean square | F-ratio | p-value |
---|---|---|---|---|---|

Section | 1 | 1.31 | 1.32 | 2.94 | 0.4895 |

Boundary | 3 | 3.16 | 1.05 | 1.87 | 0.4551 |

Height | 1 | 0.01 | 0.01 | 0.02 | 0.9184 |

Sensor | 22 | 34.83 | 1.58 | 0.89 | 0.6041 |

Methodology | 1 | 0.11 | 0.11 | 0.46 | 0.5271 |

Section × Boundary | 3 | 0.24 | 0.08 | 0.20 | 0.8884 |

Section × Height | 1 | 0.59 | 0.59 | 0.36 | 0.5574 |

Section × Methodology | 1 | 0.11 | 0.11 | –1.92 | - |

Section × Sensor | 22 | 30.31 | 1.38 | 51.55 | <0.0001 |

Boundary × Height | 3 | 2.41 | 0.80 | 1.15 | 0.3829 |

Boundary × Sensor | 66 | 26.77 | 0.40 | 33.98 | <0.0001 |

Boundary × Methodology | 3 | 0.01 | 0.003 | –0.05 | - |

Height × Methodology | 1 | 0.01 | 0.01 | –0.07 | - |

Sensor × Height | 22 | 0.66 | 0.03 | –0.47 | - |

Section × Boundary × Height | 3 | 1.20 | 0.40 | 22.12 | 0.0012 |

Section × Sensor × Methodology | 22 | 0.59 | 0.03 | 0.26 | 0.9997 |

Boundary × Sensor × Methodology | 66 | 0.79 | 0.01 | 0.12 | 1.0000 |

Section × Boundary × Height × Methodology | 6 | 0.11 | 0.02 | 0.17 | 0.9833 |

Error | 132 | 13.68 | 0.10 | - | - |

^{−1}and using the direct measurements using the multisensor system were 0.64 ± 0.54 m s

^{−1}. For comparing both data a linear regression in the 192 studied points of the poultry building of the measured air velocity and the calculated by CFD was done. As expected, in this regression the slope was near one and the independent term near zero. The coefficient of determination of the linear regression was 0.888 (Figure 5).

^{−1}(CFD) and 0.80 ± 0.31 m s

^{−1}(measured). This was obtained for section B in scenario III. On the contrary, the minimum velocity was 0.34 ± 0.29 m s

^{−1}(CFD) and 0.37 ± 0.31 m s

^{−1}(measured) in the section B, scenario I. As indicated in literature, fluctuations of air velocity may have crucial effects on broiler rearing and performance [8,9,10,11,12,13]. Considering that determining and evaluating this velocity pattern may help the climate control of farms, we have achieved a validation of CFD to determine this pattern of broiler buildings.

## 4. Conclusions

^{−1}for CFD techniques and 0.64 ± 0.54 m s

^{−1}for direct measurements using the multisensor system. The “methodology” variable was not significant (p-value < 0.5271), and the same was found for its interactions. Accordingly, it is indifferent using the CFD techniques or the direct measurements with the multisensor system used here. Then, CFD techniques have been validated by multisensor isotemporal direct measurements and they can be used to explore ventilation efficiency and to identify optimal poultry farm designs, as well as to assess their optimal management. On the other hand, from this work and the analysis of this typical geometry model of poultry farm, we can affirm that mechanical cross ventilation system is adequate under the most common weather conditions, but they do no prevent from episodes of mortality caused by heat stress, because they provide lower velocity values than those required by animals in these conditions. According to the results of this paper, new forced ventilation systems and other livestock buildings designs could be evaluated using both developed methodologies in order to improve the thermal comfort and diminish mortality of animals. In this way forced ventilation systems require electric energy to activate the fans and automatisms, which are not required in naturally ventilated livestock buildings. Finally, it must be noted that from the two analyzed methodologies to explore the ventilation efficiency in livestock buildings, CFD techniques provide more points of knowledge and a more general view of indoor climatic conditions of poultry farms through the graphics than direct measurements.

## Acknowledgments

## Conflict of Interest

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## Share and Cite

**MDPI and ACS Style**

Bustamante, E.; García-Diego, F.-J.; Calvet, S.; Estellés, F.; Beltrán, P.; Hospitaler, A.; Torres, A.G.
Exploring Ventilation Efficiency in Poultry Buildings: The Validation of Computational Fluid Dynamics (CFD) in a Cross-Mechanically Ventilated Broiler Farm. *Energies* **2013**, *6*, 2605-2623.
https://doi.org/10.3390/en6052605

**AMA Style**

Bustamante E, García-Diego F-J, Calvet S, Estellés F, Beltrán P, Hospitaler A, Torres AG.
Exploring Ventilation Efficiency in Poultry Buildings: The Validation of Computational Fluid Dynamics (CFD) in a Cross-Mechanically Ventilated Broiler Farm. *Energies*. 2013; 6(5):2605-2623.
https://doi.org/10.3390/en6052605

**Chicago/Turabian Style**

Bustamante, Eliseo, Fernando-Juan García-Diego, Salvador Calvet, Fernando Estellés, Pedro Beltrán, Antonio Hospitaler, and Antonio G. Torres.
2013. "Exploring Ventilation Efficiency in Poultry Buildings: The Validation of Computational Fluid Dynamics (CFD) in a Cross-Mechanically Ventilated Broiler Farm" *Energies* 6, no. 5: 2605-2623.
https://doi.org/10.3390/en6052605