A comparative study of methods for estimating intercept factor of parabolic trough collectors
Date Issued
1996
Abstract
One of the parameters used for the evaluation of a parabolic trough collector performance is optical efficiency.
This depends on the properties of the various materials employed in the construction of the collector, the
collector dimensions, the angle of incidence and the intercept factor (γ). The intercept factor depends on the
size of the receiver, the surface angle errors of the parabolic mirror, and on solar beam spread. A ray-trace
computer code called EDEP (Energy DEPosition computer code) is used by Guven and Bannerot (1985) to
calculate the intercept factor. The intercept factor can also be calculated by a closed-form expression
developed by Guven and Bannerot (1985). This expression considers both random and non-random errors.
These errors are encountered in the construction and/or in the operation of the collector. An artificial neural
network was trained to learn the γ-values based on the input data of collector rim angle, random and nonrandom
errors, and the EDEP results. The output is compared with the EDEP results which are considered to
be the most accurate, the results of a simple program developed by Guven (1987) using the trapezoidal
integration method, and a multiple linear regression analysis. From all the above it is shown that the results
obtained by the artificial neural network system approximates the results of the ray-trace model, extremely
well with an R2-value equal to 0.999.
This depends on the properties of the various materials employed in the construction of the collector, the
collector dimensions, the angle of incidence and the intercept factor (γ). The intercept factor depends on the
size of the receiver, the surface angle errors of the parabolic mirror, and on solar beam spread. A ray-trace
computer code called EDEP (Energy DEPosition computer code) is used by Guven and Bannerot (1985) to
calculate the intercept factor. The intercept factor can also be calculated by a closed-form expression
developed by Guven and Bannerot (1985). This expression considers both random and non-random errors.
These errors are encountered in the construction and/or in the operation of the collector. An artificial neural
network was trained to learn the γ-values based on the input data of collector rim angle, random and nonrandom
errors, and the EDEP results. The output is compared with the EDEP results which are considered to
be the most accurate, the results of a simple program developed by Guven (1987) using the trapezoidal
integration method, and a multiple linear regression analysis. From all the above it is shown that the results
obtained by the artificial neural network system approximates the results of the ray-trace model, extremely
well with an R2-value equal to 0.999.
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