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Performance Analysis of a Poultry Engineering Chamber Complex for Animal Environment, Air Quality, and Welfare Studies

A. Padavagod Shivkumar, L. Wang-Li, S. B. Shah, L. F. Stikeleather, M. Fuentes


Published in Transactions of the ASABE 59(5): 1371-1381 (doi: 10.13031/trans.59.11402). Copyright 2016 American Society of Agricultural and Biological Engineers.


Submitted for review in June 2015 as manuscript number PAFS 11402; approved for publication by the Plant, Animal, & Facility Systems Communityof ASABE in June 2016.

The authors are Aditya Padavagod Shivkumar, Graduate Student, Lingjuan Wang-Li, ASABE Member, Professor, Sanjay B. Shah, ASABE Member, Professor, and Larry F. Stikeleather, ASABE Member, Professor, Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, North Carolina; Montserrat Fuentes, Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina. Corresponding author: Lingjuan Wang-Li, 186 Weaver Labs, Campus Box 7625, North Carolina State University, Raleigh, NC 27695-7625; phone: 919-515-6762; e-mail: Lwang5@ncsu.edu.

Abstract. Studies of animal welfare and air quality require good understanding of the production environment. This study evaluated the performance of a dedicated poultry engineering chamber complex (PECC) designed to conduct studies for enhancement in poultry production, air quality, and animal welfare. The performance evaluation of the PECC was carried out by direct flow testing and computational fluid dynamics (CFD) modeling. The flow rate measurements at six different blower speeds () in each of the six chambers and the corresponding pressure drops across the system indicated the effects of structural geometry and components on the flow characteristics. There was no significant difference in mean flow rate among chambers (p = 0.956). The flow in the animal-occupied zone (core chamber) was simulated using CFD, and the results were validated using field measurements. The average air velocities at bird height at blower speeds of 600 and 1200  obtained using direct measurements were 0.794 ±0.15 m s-1 and 1.706 ±0.305 m s-1, respectively, and the average air velocities obtained using CFD models were 0.809 ±0.169 m s-1 and 1.642 ±0.395 m s-1, respectively. Error analysis at each measurement point indicated a maximum value of 13.59% at bird height due to the presence of feeders. Statistical analysis showed no significant difference between measured and simulated results (p = 0.5415). The normalized mean square error was 0.007, indicating good agreement of simulated results with measurements. Certain regions in the animal-occupied zone had lower air velocity on average and therefore higher mean surface temperature of bird models in those regions, caused by the flocking effect.

Keywords.Air velocity, Animal environment, CFD, Flow testing, Poultry engineering chamber, Ventilation.

In the agricultural industry, production depends on numerous factors. External factors, such as climate and season, influence production, but the local conditions also play an important role in production quality and quantity, especially when the production is indoors. Many studies have shown that the ability to maintain desired environmental conditions is highly dependent on the design and performance of ventilation and heating/cooling systems (Norton et al., 2007). This is invariably true because ventilation is the driving factor for temperature, humidity, and air quality in the local surroundings. In ventilated structures, the air exchange processes are mainly dominated by convective heat and mass transfers (Roy et al., 2002). The airflow patterns form the basis for the link between the external environment and the indoor climate, and thus a complete understanding of the airflow is necessary to achieve the conditions required for a specific application (Norton et al., 2007). Understanding air distribution is important for characterizing hot spots, heat distribution, spread of airborne diseases, and energy conservation. All these factors contribute to establishing sustainable and safe production systems.

The requirement of uniformity in temperature and other variables of the microenvironment is more stringent in livestock and poultry housing systems because the animals themselves contribute substantially to the heat inside the housing systems, and animals are sensitive to the environment around them. Thermoregulation is an important mechanism by which animals are able to balance heat production with heat loss while maintaining a constant body temperature when interacting with the environment (Hahn, 1990). The extensive use of mechanical ventilation in animal housing has helped increase production by minimizing mortality caused by heat stress. The U.S. poultry industry has an annual production income of over $40 billion (USDA, 2014). According to USDA statistics, the demand for broiler meat is increasing substantially year by year. This has resulted in large investments in technology to enhance broiler production. Consequently, increasing numbers of poultry houses are being established. However, this growth affects the environment for the broilers, especially in summer seasons in hot and humid parts of the country. Therefore, heat stress is an important problem in the poultry industry, with yearly economic losses in the U.S. in the range of hundreds of millions of dollars due to bird mortality. Assessment of the physiological response of broilers to high temperatures helps producers understand the impacts of heat stress, which will eventually help increase production and foster animal welfare through improved facility designs (Yanagi et al., 2002). Another issue that is gaining attention is agricultural air quality. Air emissions from animal feeding operations are under increasing scrutiny, and animal production facilities are under pressure to adopt mitigation strategies to reduce emissions (Gates et al., 2009; Wheeler et al., 2003). An environment with a high degree of control over RH, temperature, and airflow rates is required to address the air quality issue.

Numerous studies have been conducted to study the responses of broilers to different environments and environmental variables (Bustamante et al., 2013). Huge investments have been made to establish sensor systems to study the microenvironment inside poultry houses (Bustamante et al., 2012). Blanes-Vidal et al. (2008) and Norton et al. (2007), among others, have clearly stated the limitations of experimental methods and have vouched for using numerical methods, such as computational fluid dynamics (CFD) modeling, as a reliable means of studying the microenvironment. However, it is imperative to validate such models with experimental data before using them to make decisions. Blanes-Vidal et al. (2008) used a multisensor system to quantify air temperature, pressure, and velocity and used the experimental results to validate CFD models. Other studies have been performed to elucidate the use of CFD in modeling ventilation systems, airflow distribution, temperature variation, and distribution of pollutants such as ammonia (Pawar et al., 2007; Bjerg et al., 2013).

The alternative commonly adopted to address issues with the microenvironment of poultry involves conducting studies in laboratories or research chambers. Brown-Brandl et al. (2003, 2005) described a “within-chamber” type of test facility. The outer room had an air unit that could create an air temperature range from 0°C to 100°C. Another system developed by Brown-Brandl et al. (2003, 2005) controlled both the temperature and humidity with air handling units. The chambers were thoroughly insulated using fiberglass-reinforced panels sandwiched with Styrofoam. Several other environmental chambers were developed to study the physiological conditions of poultry, mainly deep body temperature in response various external factors such as ambient temperature and relative humidity (Hamrita et al., 1998). Mitchell et al. (2001) and Hunter et al. (1999) included air velocity to represent the effect of ventilation in commercial houses and in chambers used on transport vehicles. Chepete and Xin (2000) used an air conditioning chamber to circulate fresh air. The chamber included a spray nozzle for sprinkling the birds, and sensors were placed at bird height for more accurate data. Yanagi et al. (2002) used a similar arrangement and included an air straightener and a variable-speed fan to control the air velocity. However, all these research chambers were designed either for measuring certain physiological stimuli alone or during transport of broilers. In addition, these chambers could accommodate very few birds, hence neglecting the effects of flock and bird activity. Most of the pre-existing chambers also failed to study the effects of uniform airflow.

Thus, considering the shortcomings of the existing chambers, a poultry engineering chamber complex (PECC) was designed and built at North Carolina State University for controlled studies of animal environment, air quality, and welfare (Wang-Li et al., 2013). The performance analysis of the PECC using direct measurements and CFD modeling was the main goal of this study.

Methodology

The PECC consists of a set of six identical chambers. Figure 1 shows a cross-section of a chamber. The chambers are well insulated and represent a physical environment (bedding, lighting, feeders, and drinker lines) similar to that in a commercial broiler grow-out house. The main components of the chamber include the following:

The dimensions of the core chamber air inlet and outlet openings are 2.43 m wide × 0.91 m high (8 ft × 3 ft). The core chamber is essentially a cube with a length of 2.43 m (8 ft) and capacity for housing a small flock of birds (i.e., 64 birds per chamber). The bedding material used was fresh wood chips, and the thickness of the bedding was approximately 6.3 cm (2.5 in.). The core chamber is equipped with four feeders, a water line with six drinkers and a water level regulator, and two fluorescent lights (19 W each) with automatic switches. The core chamber also has a set of thermocouples to measure dry and wet bulb temperatures continuously. The wet bulb temperature measurement was achieved with a wetted sock in a small water reservoir (fig. 2). A data acquisition system was used to process the signals from the sensors (e.g., type-T thermocouples) in all the chambers. The blower controls and data acquisition system were housed in a separate control room adjacent to the PECC.

Figure 1. Cross-section of a poultry chamber (Wang-Li et al., 2013).
Figure 2. Interior of core chamber.

Experimental Design

The project execution had two main components: experimental studies and computational studies. The experimental studies consisted of two main tasks: phase 1 testing and phase 2 testing. Figure 3 shows the flow of project execution (Padavagod Shivkumar, 2014).

Experimental Studies

Phase 1 testing was performed to develop a set of flow curves that describe the operating characteristics of the chamber system. The flow rates and differential pressure across the chamber system were obtained for the operating configurations of the blower rotary speed (rpm) and damper opening combinations listed in table 1. The results were tabulated in the form of a database, and the flow curves were developed using the recorded data. Various pressure drops were created by using wooden strips to block the cross-section area of the flow (fig. 4). The strips were of equal size, and the pressure drop was adjusted by changing the flow area uniformly at the collimation screen in the bottom half of the conditioning chamber.

Figure 3. Project execution process.
Table 1. Phase 1 testing configuration.
Damper
Opening
(%)
Flow Area
Blocked
(%)
Blower Speed
(rpm)
100, 25, 50, 75200, 400, 600, 800, 1000, 1200
300, 25, 50, 75200, 400, 600, 800, 1000, 1200
500, 25, 50, 75200, 400, 600, 800, 1000, 1200
700, 25, 50, 75200, 400, 600, 800, 1000, 1200

The flow velocity was measured in the circular exhaust duct downstream of each core chamber in the top part of the conditioning chamber (fig. 1) using the velocity traverse method (ASHRAE, 2009). The smooth edges and the colli-mating screen at the duct entry provided a suitable measurement point. Airflow velocity was measured using a hot-wire anemometer (range 0 to 75 m s-1, accuracy ±2%, Dwyer Instruments, Michigan City, Ind.).

Figure 4. Blocking the cross-section area of the flow with wooden strips to create various pressure drops.
Figure 5. Core chamber velocity monitoring: LF = left front, LB = left back, RF = right front, RB = right back, and C = center. Flow inlet was defined as front, and flow direction was used to define left and right.
Table 2. Simulation of different cases.
CaseSpecificationsDescription
1General flow dynamicsRepresents the general flow dynamics as a consequence of the chamber geometry.
2Velocity contoursSpecific boundary conditions measured at inlet and outlet.
3Simulated birdSpecific boundary conditions measured at inlet and outlet, heat flux from bird, surface characteristics of bird.

In phase 2 testing, 3-D ultrasonic anemometers (model 81000, R.M. Young & Co, Traverse City, Mich.) were used to measure the airflow velocity inside the core chamber. The velocity was recorded using data loggers (Hobo U12-006, Onset Computer Corp., Pocasset, Mass.). Velocities in three directions were measured at five locations in the core chamber (fig. 5). At each location, velocity was monitored at three heights: 0.20 m (bird height), 0.63 m (plane of maximum flow), and 1.22 m above the bedding. Therefore, velocities were measured at a total of 15 coordinates inside the core chamber. The testing time was set to 6 min at 10 s intervals. Air temperature was also recorded by the anemometer. The airflow velocity was measured at blower speeds of 600 and 1200 rpm, which represented the middle and highest flow rates that could be achieved by the blower. Although wind speeds were recorded in three directions, only the velocity in the direction of flow (perpendicular to the inlet) was considered for analysis. In this article, “velocity” refers to airflow velocity in the direction perpendicular to the inlet.

Computational Studies

The core chamber of the PECC was considered the flow domain or control volume. Because the six core chambers are identical, only chamber 3 was used for simulation. The geometry of the flow domain (core chamber) was developed using Creo Parametric (version 2.0, PTC, Inc., Needham, Mass.). The accessory components, such as drinkers, regulators, and feeders, inside the core chamber were considered in the geometry.

A three-dimensional simulation of the indoor airflow was performed using the commercial code FloEFD for Creo. The inlet and outlet were modeled as rectangular openings. Specific properties such as roughness (3000 µm) were assigned to the bedding material, drinkers, and feeders. Only the core chamber was considered when running the simulations because it is where broilers are housed and is therefore the area of interest for characterizing the animal environment. This approach also makes the model more robust and less sensitive to air loss through leakages, which in turn helps to simulate the real-world environment more closely.

The simulations were carried out under steady-state conditions with three different cases to provide a comprehensive picture of the effectiveness of the chamber for animal welfare and air quality studies. In each case, the airflow was simulated for a set of operating configurations that were intended to be representative of the real world. The cases with their respective simulations are listed in table 2. The inlet and outlet boundary conditions for the three cases are listed in table 3 (Padavagod Shivkumar, 2014).

Case 3 aimed at qualitatively visualizing the effect of birds and flocking on the airflow regime. In case 3, the birds were modeled as solid spheres with specific heat flux and surface characteristics. Simulations were carried out with eleven and thirty birds modeled to analyze the effects of bird location and distribution on air velocity. Although the chamber was designed with a capacity of about 64 birds, the simulations used fewer birds because the main goal was to understand the qualitative effects of flocking and the presence of birds themselves on the microclimate. Each of the birds was modeled as a solid sphere of 0.21 m (8.5 in.) diameter such that the surface area was 0.15 m2. The surface rough-ness was assumed to be 3000 µm. No detailed literature was found regarding the surface roughness of broilers at different ages. Heat production was calculated using an empirical equation (Heinsohn and Cimbala, 2003) with an assumed bird mass of 2 kg. Heat flux (W m-2) was calculated and input into the model. Case 3 tended to give a qualitative understanding of the flocking effect. Hence, the simulated birds roughly represented physical birds. No data were available to validate the model. Therefore, it only provided a means of visualizing how flocking affects the surrounding microclimate.

Table 3. Boundary conditions for different cases.
CaseBoundary Condition[a]Blower Speed and
Damper Opening
Description
1Inlet: volume flow rate
Outlet: static pressure
600 rpm,
30% opening
Volume flow rate measured at exhaust duct;
static pressure measured at outlet.
2Inlet: volume flow rate
Outlet: static pressure
(BC-1)
600 and 1200 rpm,
30% opening
Volume flow rate measured at exhaust duct;
static pressure measured at outlet.
Inlet: volume flow rate
Outlet: static pressure
(BC-2)
600 and 1200 rpm,
30% opening
Velocity measured at inlet (44 monitoring points);
static pressure measured at outlet.
3Inlet: volume flow rate
Outlet: static pressure
(BC-2)
600 rpm (30 Hz) and 1200 rpm,
30% opening
Velocity measured at inlet (44 monitoring points);
static pressure measured at outlet.

    [a]   Case 1 assessed the general flow dynamics as a consequence of the chamber geometry; hence, only BC-1 boundary conditions were considered; cases 2 and 3 considered BC-1 and BC-2 boundary conditions.

Validation is essential for computational models to make sure that the modeled results conform to the measured values (Srebric and Chen, 2002). A multi-way ANOVA was performed to compare the computational results with the measured values. The data set included values at five different positions and two heights for each position. Two different blower speeds (rpm) were simulated to compare the results. An error analysis in terms of percentage of bulk velocity (Eb) was performed to determine the variation in computational results at each data point using equation 1:

      (1)

Table 4. Chamber system airflow rates (Q, m3 h-1) versus pressure drop (?P, Pa) at 30% damper opening.
Damper Opening
(%)
Chamber 1Chamber 2Chamber 3Chamber 4Chamber 5Chamber 6
?PQ?PQ?PQ?PQ?PQ?PQ
Blower speed = 400 rpm
0523556403446573760503723453461473500
25553687503612603648523653503544553512
50623677523588653661553401553412523474
75623593553398623566623692553313553391
Blower speed = 600 rpm
0132546110255231276018127564110551841075440
25137571011454621325792129557110952841125377
50134569511754691325671129512111250771125343
75139544412452211375605137545211950561195231

where Vcfd is the value predicted by CFD, Vmeasured is the measured value, and Vb is the bulk jet velocity (initial average inlet velocity). Further, normalized mean square error (NMSE), as presented by Saraz et al. (2010), was used to examine the validity of the model. An NMSE (eqs. 2 and 3) value less than 0.25 indicates that the model is in good agreement with the observed data (Saraz et al., 2010):

      (2)

      (3)

where Cp is model predictions, Co is observations, Cpm is mean of model predictions, and Com is mean of observations.

Results and Discussion

Table 4 shows the airflow rates and corresponding static pressure across the system for all chambers at blower speeds of 400 and 600 rpm with various damper openings. Airflow rates and corresponding static pressures at other blower speeds (200, 800, 1000, and 1200 rpm) and damper openings (10%, 30%, 50%, and 70%) are provided by Padavagod Shivkumar (2014). The average chamber-to-chamber variation was 5.06% at 600 rpm. The highest static pressure was 645 Pa (2.59 in. of water) at 1200 rpm at maximum area coverage with a damper opening of 10%. The static pressure increased substantially with the decrease in damper openings because the system is a recirculating system. In the initial testing, it was discovered that the flow rates and the corresponding static pressure drops in chambers 5 and 6 differed substantially from those of the other chambers. This was due to air leakage in the system flow path. Corrective action was taken immediately after the leakage problem was identified. Nevertheless, the flow rates in these chambers were 3.5% lower on an average than in the other chambers. Similar flow rates could be achieved by increasing the blower speeds for these two chambers so that all chambers had the same flow rate.

The means and standard deviations (SD) of air velocity at different positions in the core chamber at 600 and 1200 rpm are listed in tables 5 and 6. The maximum air velocity was measured at 0.63 m. This is highly likely because that height almost coincided with the velocity centerline of the inlet jet. The velocities at 0.63 m were 0.89 and 1.92 m s-1 at 600 and 1200 rpm, respectively. Regarding the spatial locations, the velocities at positions LB and RB were relatively low. This was due to the feeders, which obstructed the flow. At bird height, the velocity was highest close to the inlet (LF and RF) and relatively high in the center of the chamber. Positions closer to the outlet had 30% to 60% lower velocity compared to positions near the inlet due to the feeders. In general, the velocity measured at the inlet (44 monitoring points) indicated the presence of velocity components in the plane of the inlet (y-axis) and in the downward direction (z-axis). This indicated a less uniform flow than was expected. These measured velocities were later used in the CFD simulations for case 2.

Table 5. Measured air velocities at different positions and heights.
Position[a]Blower Speed = 600 rpmBlower Speed = 1200 rpm
At 0.20 m
(bird height)
(m s-1)
At
0.63 m
(m s-1)
At 0.20 m
(bird height)
(m s-1)
At
0.63 m
(m s-1)
LB0.7360.7981.5371.815
LF0.9910.8722.0601.913
RB0.5680.9601.2801.926
RF0.8490.9741.8882.073
C0.8280.8681.7681.892
Mean
±SD
0.794
±0.156
0.894
±0.073
1.706
±0.3050
1.924
±0.0939

    [a]   Refer to figure 5 for measurement locations.

Table 6. Measured air velocity at inlet for BC-2 boundary conditions.
Blower Speed
(rpm)
Velocity at Inlet [a]
(m s-1) (n = 44)
Differential
Pressure at
Outlet (Pa)
600Vx = 0.98, Vy = -0.05, Vz = -0.064.98
1200Vx = 1.97, Vy = -0.07, Vz = -0.099.71

    [a]   x = direction perpendicular to inlet, y = plane of inlet (negative for east to west), and z = plane perpendicular to floor (positive for updraft).

Simulated Results

Case 1 represented the general flow characteristics with the average inlet velocity perpendicular to the plane of flow. The inlet velocity was a consequence of the volume rate inlet boundary condition. Figures 6 through 9 show cut plots at different planes corresponding to the experimental points and flow trajectories of the velocity field. The air entered through the inlet, followed by a backward step that reduced the velocity near the floor. The flow field resembled an inlet jet without significant vortices in the bird zone. The velocity decreased as it traveled through the chamber. The feeders affected the flow field, significantly reducing the velocity by almost 50% near the outlet. Higher velocities were observed in the center of the chamber and near the chamber walls. Due to flow expansion, large vortices were observed above the inlet height. This can be significant for modeling pollutant transport inside the chamber.

In case 2, boundary condition BC-1 was found to be less accurate due to air losses between the core chamber and the exhaust duct. Hence, BC-2 was considered more representative of the real conditions and was therefore used in further analysis.

In case 3, simulation with bird models provided a qualitative understanding of the effect of birds on the microclimate. It also helped visualize this effect. The heat generated by the birds contributed significantly to the fluid temperature. The average surface temperature of the bird models was 300.38 K at 600 rpm (30 Hz). Figures 10 through 13 show the velocity and temperature contours for 30 bird models simulated inside the chamber. Air velocity significantly affected the surface temperature of the bird models. There were significant differences in the surface temperatures of the bird models at different locations in the core chamber. The surface temperature of the bird models was largely affected by the feeders and flocking. Locations in the center of the chamber near the drinkers had higher velocities. Generally, birds tend to remain in the center due to higher velocities and the presence of drinkers. However, more birds in the center may result in lower airflow over the birds. Figure 11 shows the temperatures of the bird models at 600 rpm (30 Hz) with air temperature of 293.23 K (20°C). The bird models close to the feeders had higher temperatures compared to other locations. The mean surface temperature of the bird models behind the feeders was 2.4°C greater than the average surface temperature of the bird models. The bird models near the inlet had the lowest average surface temperature (2°C less than the mean surface temperature). The higher the inlet air temperature, the greater the surface temperature difference was. At air temperature of 303 K (about 30°C), the difference between the mean surface temperature and the highest average surface temperature was 2.9°C and was observed for the bird models behind the feeders. Figure 12 shows a cut plot of fluid temperature at bird height (0.20 m). Figure 13 shows the flow trajectories of the velocity adjusted to the inlet height. The flow above the inlet height resulted in the formation of vortices. The vortex formation was a consequence of the chamber geometry and had no effect on the animal-occupied zone, which is the main area of interest in this study.

CFD Validation

The differences between the CFD-simulated air velocities and the measured velocities are shown in table 7. The highest absolute difference between the CFD-simulated and measured values was 0.23 m s-1. This was due to the feeders, which significantly affected the velocity flow field. In addition, the feeders were not rigidly fixed, and hence even a slight displacement may have a significant effect on the actual flow. On average, CFD simulation overestimated the velocities compared to the measured values. However, the multiway ANOVA results indicated that there was no difference between the simulated and measured results. Table 8 summarizes the ANOVA results.

While there was no evidence of any statistical difference between the simulated and measured values (p = 0.5415), validation of the CFD results was performed in terms of the distribution of air velocities at different positions and heights. The error between the simulated and measured val-ues as a percentage of the mean inlet velocity (Eb) was assessed. The Eb values, expressed as percentages, are summarized in table 7. Eighteen out of 20 points had Eb values of =10%, and ten points had Eb values of =5%. The LB position at 600 rpm had the highest Eb of 13.59%. This was certainly due to the feeders, which were located in the flow field. Similarly, the RB positions had greater Eb owing to the presence of feeders in the line of flow. The effect of feeders was most significant for points at bird height. However, the NMSE value was relatively very low with a value of 0.007, indicating good agreement between modeled and measured values.

Conclusion

A performance evaluation was carried out on a dedicated poultry chamber designed for animal environment, air quality, and animal welfare studies. Airflow tests were carried out at two blower speeds (rpm) and four damper openings in each of the six chambers. The average difference in airflow rates among the chambers was 5.06% at 600 rpm. The 3-D velocity measurements served two purposes: development of velocity contours such that the velocity variation across the core chamber could be visualized, and validation of the CFD results. Positions away from the inlet (LB and RB) had significantly lower velocities due to the presence of feeders in the line of flow. The 1.21 m height had higher velocities at positions away from the inlet due to the jet effect and expansion of the airflow jet at the inlet. The airflow in the animal-occupied zone was analyzed using CFD. Two blower rotary speeds were simulated. The simulated results were validated using accurate field measurements. There were no statistically significant differences between the modeled and measured air velocities. Greater variances were observed at bird height due to the presence of feeders in the line of flow.

This study bolsters the results of other studies in showing the utility of computational methods for analyzing indoor en-vironments and as an aid in decision making. CFD simulations were effectively used to predict the air velocity and hence the temperature inside the core chamber at different ventilation rates with negligible error. Further, the model results may be used in automating the system with feedback loops for establishing a controlled environment. This study showed that the poultry engineering chamber complex, with few corrective measures, is suitable for conducting poultry research and air quality studies. With such a system, a controlled environment can be established to carry out reliable research.

Table 8. Summary of ANOVA for simulated and measured air velocity.
SourceDFSum of
Squares
Mean
Square
F
Value
Pr > F
Model vs. experiment10.0030.0030.0920.5415
Height10.4040.40411.6070.00163
Wind velocity (rpm)18.9228.922256.32<2E-16
Error361.2540.035

Acknowledgements

This study was supported in part by USDA-NIFA-SRGP-002216 and NSF CAREER Award No. CBET-0954673. Individuals who provided great support and help with the chamber design, construction, and experimental data collection were Dr. David Beasley, Mr. Roberto Munilla, Mr. Mike Adcock, Mr. Carl Tutor, Mr. Steven Badawi, Mr. Richard Currin, Dr. Carm Parkhurst, Mr. Joseph Stuckey, and Mr. Phil Harris. Undergraduate summer internship student workers who helped with the chamber construction and airflow testing were Chen Hao, Chris White, Chris Buchannan, Greg Turner, Andrew MacNamara, Cheryl Odametey, and Symphony Roberts. Additional financial support from BAE-NCSU, the College of Agriculture and Life Science (CALS) at NCSU, and the Animal and Poultry Waste Management Center (APWMC) at NCSU is thankfully acknowledged.

References

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