Computational Fluid Dynamics on Solar Dish in a Concentrated Solar Power: A Bibliometric Review

Abstract

Concentrated solar power is an alternative renewable energy technology that converts solar energy into electrical energy by using a solar concentrator and a solar receiver. Computational fluid dynamics have been used to numerically design concentrated solar power. This is a powerful numerical analysis approach that is widely used in energy and environmental engineering applications. In this paper, we review previous work on the applications of computational fluid dynamics in the design of concentrated solar power technology. We performed a bibliometric analysis of journal articles relevant to applications to analyze the current trend of utilization of computational fluid dynamics in these technologies. Then, we conducted a comprehensive analysis focused on the design of solar dish technology using computational fluid dynamics. Furthermore, we reviewed in detail the optical modeling of solar concentrators and solar receivers. Of the 83 retrieved publications from Scopus database, 80 were journal articles, and only three were review papers. Among these 80 journal articles, only 54 were relevant to this study, and 23 were relevant to solar dish technology. The documents were analyzed according to their number of citations, journal sources, and keyword evolution and network map. The information presented in this paper is useful to further recognize the contributions of computational fluid dynamics to the development of concentrated solar power, particularly to solar dish technology. In addition, we also discuss the challenges and future research directions to make solar energy a more sustainable source of renewable energy.

Improving Deproteinization in Colombian Latex from Hevea brasiliensis: A Bibliometric Approximation

“…This consisted of a map in which the colors indicate the degree of correlation between two variables under the measure of a statistical metric of chi-square co-occurrence. On the numerical scale, the value of −10 showed that the observed result of co-occurrence is 10 times lower than expected, on the other hand, the value of 10 indicated that the observed co-occurrence is 10 times higher than the expected value [ 39 ]. Additionally, the matrix presented a negative relationship through the blue cells, while the red cells meant a strong relationship; likewise, the white cells indicated that the variables had no relationship (neutrality) [ 40 ].…”