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The design and operation of ventilation systems for animal housing is a crucial component in maintaining a suitable environment for both animals and workers by removing heat, moisture, and gas species. However, important design and operational criteria, such as profiles of velocity and extent of mixing within animal housing, can be difficult to study experimentally. Thus, a computational fluid dynamics (CFD) model of a commercial dairy holding area, including the generation of transport of heat and gas species within the domain, was developed. The animal-occupied zone (AOZ) was modeled using porous media. The model was evaluated with experimental data of velocity and gas concentration. Results indicated that the airflow uniformity and air speed could be increased within the holding area by modifying the configuration of the side curtain openings and using concrete walls to guide airflow; a 20% increase in average velocity within the AOZ was achieved. Results of the CFD model have shown it to be an effective tool for gaining insight into the complex mixing patterns within holding areas to improve the design of animal housing. [ABSTRACT FROM AUTHOR]

The increased global demand for milk and other dairy products over the past decade has heightened concerns about the potential for increased environmental impacts. Accurate measurement of gas emissions from dairy cows is essential to assess the effects of cow diets and other management practices on both the composition and rate of gas emissions. In this article, methodologies are described to instrument, calibrate, and assess the uncertainty of gas emissions by cows housed in chambers that simulate production settings. The supply and exhaust ducts of each chamber were equipped with pitot tubes, temperature and relative humidity probes, and gas samplers to monitor airflow rates, gas composition, and gas emission rates. A Fourier transform infrared spectroscopy (FTIR) instrument was used to quantify gaseous concentrations in the gas samples on a semi-continuous basis. The measurement uncertainty of the rate of gaseous emission from the chambers was quantified, and gas concentration and differential pressure, as measured by the pitot tubes, were identified as the primary parameters contributing to gas emission uncertainties. Mass recovery tests determined that the recovery of methane from each chamber was within 10% of the released mass. Fan operating curves were experimentally determined to identify optimum differential chamber pressures to minimize gas leakage from the chambers. A computational fluid dynamics model was developed to assess air mixing patterns and define steady-state conditions. The model was validated with experimental data of air velocity within each chamber. These procedures will facilitate accurate measurement of gas emissions from housed dairy cows and provide a laboratory to test various gas mitigation treatments. [ABSTRACT FROM AUTHOR]

Biofuels derived from algae are becoming an increasingly viable alternative to petroleum-based fuels; however, research and development in the field must continue to advance the technology before biofuels can be produced in an economical and environmentally friendly manner. Unlike with photobioreactors, there is no generally accepted model for evaluating the growth of algae in open raceways because algal growth involves a large number of variables. For this reason, computational fluid dynamics (CFD) could prove to be a valuable and effective tool for the design, optimization, and operation of large-scale raceway ponds under local environmental conditions. CFD can elucidate and quantify the complex sets of variables that govern heat, mass, and flow patterns within the pond and provide spatiotemporal data concerning algal concentration and other water quality variables, such as light and temperature. The corresponding outcomes will enable designers to create more efficient ponds and more accurately predict growth under a variety of scenarios, as well as optimize the pond's operation in order to produce the maximum amount of biomass possible within a given locality. The present study focuses on developing user-defined functions capable of capturing key parameters, verifying the CFD outcomes against existing experimental data, providing computational solutions, and assessing the sensitivity of the model. [ABSTRACT FROM AUTHOR]

Forage harvest is a time and energy intensive process requiring the coordination of multiple pieces of equipment. Detailed characterizations of the time spent in each work state for each piece of equipment would increase the understanding of process inefficiencies and aid in development of optimization tools. Geospatial and controller area network (CAN) machine data were recorded on forage harvesters and transport equipment, during two types of harvest operations, to quantify utilization of harvesters and transports as well as transport productivity. The data collection and processing method was successful in identifying work states for forage harvesters and transports. The results indicated that overall utilization of the harvester for harvesting was 61% and dependent on transport availability. The portion of total operational time spent in the idle work state (idle utilization) was 10% to 20% for transports and 18% to 23% for harvesters. A new metric for transport productivity was developed and found to be highly dependent on transport capacity ranging from 125 to 49 Mg km h-1 for semi-trucks and smaller transports, respectively. The proposed data collection methods and productivity metrics could be used to optimize the forage harvest process to reduce idle time and maintain crop quality. [ABSTRACT FROM AUTHOR]