In this study, the optical and thermal performance of a Parabolic Trough Collector PTC system is investigated theoretically. A series of numerical simulations and theoretical analysis has been conducted to investigate the effect of the receiver geometry and location relative to the focal line on its optical performance. The examined receiver geometries are circular, square, triangular, elliptical and a new design of circular‐ square named as channel receiver. The thermal performance of PTC is studied for different flow rates from (0.27 to 0.6 lpm) theoretically. Results showed that the best optical design is the channel receiver with an optical efficiency of 84% while the worst is the elliptical receiver with an optical efficiency of 70%. Thermally the best design is the elliptical receiver with a thermal efficiency of 85% while the worst is the circular receiver with a thermal efficiency of 82%.
In this study, different methods were used for estimating location parameter and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment estimation (ME),and approximation estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile as estimation for distribution f
... Show MoreThe main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
... Show More