Degree Type


Date of Award


Degree Name

Doctor of Philosophy


Agricultural and Biosystems Engineering


A computer model was developed to predict the thermal and electrical energy required to provide a controlled atmospheric environment for life-cycle swine production. Using short time-interval climatological data recorded in central Iowa, the simulation can estimate the total energy requirements of a swine facility based on building construction, floor type, and environmental management choices. The simulation considers building conduction, solar, and ventilation heat transfers as well as animal heat production. Ventilation is based on temperature control and recommended practice to maintain optimum animal performance;The model was used to predict January energy demands for nursery, finishing, and farrowing buildings having various insulation levels, ventilation rates, and floor types. January was chosen because it is the "coldest" month in the year and would result in the greatest supplemental heat requirements. Ten years (1964-1973) of 3-hour January weather data were applied to the model to determine the average quanities of supplemental heat and ventilation-fan operational hours. These parameters were converted to primary fuel quantities and summed to obtain an overall energy demand estimate;The results of this study showed that in an environmentally controlled swine building with moderate to good levels of insulation, the heat loss in the ventilation air much exceeds the heat loss through the building envelope. In addition, floor type plays an important role in determining the energy use. Slotted-floor buildings, ventilated at published recommended winter rates, show lower relative humidities than do buildings with solid floors. Increasing relative humidity through a decrease in air exchange would result in lowering total energy use;A commercially operated, fully slotted-floor nursery facility was monitored during the winter to determine the moisture removal rate of the ventilating air. Continuous recordings of the inside and outside air conditions and the airflow rate from the room were used to calculate room moisture production. Third-hour observations, drawn from the population of average hourly moisture production data, were used as dependent variables in the development of multiple regression models predicting room moisture production. A stepwise regression procedure determined the most important independent variables during model development. Resulting regression equations were compared to published moisture production data;The results of this investigation showed that body size and floor surface area with respect to temperature were the two most important parameters in predicting room moisture production. In addition, published moisture production data did not adequately predict the room moisture production observed in this experiment.



Digital Repository @ Iowa State University,

Copyright Owner

Raymond Leroy Huhnke



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178 pages