Feasibility Study of Solar Driven Underground Cooling System

In the United States the peak electrical use occurs during the summer. In addition, the building sector consumes a major portion of the annual electrical energy consumption. One of the main energy consuming components in the building sector is the Heating, Ventilation, and Air-Conditioning (HVAC) systems. This research studies the feasibility of implementing a solar driven underground cooling system that could contribute to reducing building cooling loads. The developed system consists of an Earth-to-Air Heat Exchanger (EAHE) coupled with a solar chimney that provides a natural cool draft to the test facility building at the Solar Energy Research Test Facility in Omaha, Nebraska. Two sets of tests have been conducted: a natural passively driven airflow test and a forced fan assisted airflow test. The resulting data of the tests has been analyzed to study the thermal performance of the implemented system. Results show that: The underground soil proved to be a good heat sink at a depth of 9.5ft, where its temperature fluctuates yearly in the range of (46.5°F-58.2°F). Furthermore, the coupled system during the natural airflow modes can provide good thermal comfort conditions that comply with ASHRAE standard 55-2004. It provided 0.63 tons of cooling, which almost covered the building design cooling load (0.8 tons, extreme condition). On the other hand, although the coupled system during the forced airflow mode could not comply with ASHRAE standard 55-2004, it provided 1.27 tons of cooling which is even more than the building load requirements. Moreover, the underground soil experienced thermal saturation during the forced airflow mode due to the oversized fan, which extracted much more airflow than the EAHE ability for heat dissipation and the underground soil for heat absorption. In conclusion, the coupled system proved to be a feasible cooling system, which could be further improved with a few design recommendations.

Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship

Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population- level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achieve these improvements. Its statistical properties are first described. The method constrains the weighted sum of all the covariate-specific associations (partial effects) of the regressors on the dependent variable to equal the overall association of one or more regressors, where the latter is known exactly from the population data. We refer to those regressors whose bivariate or limited multivariate relationships with the dependent variable are constrained by population data as being ‘‘directly constrained.’’ Our study investigates the improvements in the estimation of directly constrained variables as well as the improvements in the estimation of other regressor variables that may be correlated with the directly constrained variables, and thus ‘‘indirectly constrained’’ by the population data. The example application is to the marital fertility of black versus white women. The difference between white and black women’s rates of marital fertility, available from population-level data, gives the overall association of race with fertility. We show that the constrained MLE technique both provides a far more powerful statistical test of the partial effect of being black and purges the test of a bias that would otherwise distort the estimated magnitude of this effect. We find only trivial reductions, however, in the standard errors of the parameters for indirectly constrained regressors.