Comparison of Three k-e Turbulence Models for Predicting Ventilation Air Jets

Published data on unconfined plane-wall jets and plane-free jets were reviewed and used to assess the accuracy of numerical simulations. A plane-free jet was numerically simulated using the standard k-e model and four nonuniform grid patterns (70 ¥ 32, 100 ¥ 52, 120 ¥ 60, and 120 ¥ 74). The solution for a plane-free jet with adequate grid resolution was in good agreement with the published data. A plane-wall jet was numerically simulated using five different grids (70 ¥ 32, 100 ¥ 52, 120 ¥ 60, 120 ¥ 74, and 120 ¥ 92) and three k-e turbulence models (the standard k-e model, Lam and Bremhorst low Reynolds number model, and Lam and Bremhorst low Reynolds number model with wall functions). The simulations predicted velocity decay and velocity profile well, but overpredicted the jet spread and entrainment ratio by 20 to 40%, indicating the need for a better turbulence model for wall jet predictions

Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings

The quantification of diurnal and seasonal gas (NH3, H2S, and CO2) and PM10 concentrations and emission rates (GPCER) from livestock production facilities is indispensable for the development of science-based setback determination methods and evaluation of improved downwind community air quality resulting from the implementation of gas pollution control. The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions. The statistical results revealed that the BPNN and GRNN models were successfully developed to forecast hourly GPCER with very high coefficients of determination (R2) from 81.15% to 99.46% and very low values of systemic performance indexes. These good results indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. It was also found that the process of constructing, training, and simulating the BPNN models was very complex. Some trial-and-error methods combined with a thorough understanding of theoretical backpropagation were required in order to obtain satisfying predictive results. The GRNN, based on nonlinear regression theory, can approximate any arbitrary function between input and output vectors and has a fast training time, great stability, and relatively easy network parameter settings during the training stage in comparison to the BPNN method. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling

HVAC Techniques for Modern Livestock and Poultry Production Systems

Thermal modification for housed livestock and poultry production (HLPP) systems has evolved from outside raised or uncontrolled naturally ventilated building systems into sophisticated computer-controlled cloud-analyzed complexes in the quest for producing a safe, reliable, sustainable, and efficient protein supply for our ever-growing population. This chapter discusses a few of the various HLPP systems used in the USA and details the design process in quantifying the needs for our housed livestock and poultry. Specific emphasis is placed on general building characteristics, general ventilation design features, heat stress control, and systems designed to address animal welfare