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.

Among the significant problems in the sago processing, the extraction of the starch from its trunk tops the list. The extraction process involves a debarking and rasping process that is very tedious, unsanitary and time consuming that dramatically affects the quality of starch production since the local processor relies mainly on conventional method of extraction. This paper presents the design, fabrication and performance evaluation of sago pith extraction machine. The design concept made was to rasp the sago pith using a rotating rasper without debarking the log to minimize the steps on the extraction process. Four different teeth spacing of the rasper were considered during the testing and replicated three times; 1 cm, 2 cm, 3 cm, and no teeth as controlled parameters, respectively. Results revealed that teeth spacing has a significant effect on the performance of the machine such as extraction capacity, extraction efficiency and fineness of rasped pith. Based on average, the highest extraction capacity was recorded at 217.47 kg/h using 3 cm teeth spacing, and the highest extraction efficiency was 96.12% using 2 cm teeth spacing and the finest rasped pith having the lowest fineness modulus of 2.15 was when 1 cm teeth spacing is used. Compared to manual and other existing mechanical rasper accounting the process of debarking, machine’s extraction capacity is higher, more efficient, safer and hygienic. Cost and benefit analysis supports that the machine is profitable and the financial efficiency is high having a 65% rate of return and 1.8 years payback period.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.