Numerical investigation of the optical efficiency of a parabolic trough collector at different concentration ratios

ABSTRACT

The Concentration Ratio (CR) is one of the first parameters defined before starting a Parabolic Trough Collector (PTC) construction project as it can easily provide access to the operating temperature and the determination of geometric dimensions. Few studies have been conducted to highlight the importance of the CR. Hence, this work aims to numerically analyze three models of PTC, A, B, and C, with different concentration ratios to perform a comparative analysis of impacts of this parameter in the size and optical efficiency of the models. We used the Monte Carlo Ray Tracing (MCRT) method through the SolTrace software to perform this study. From the results, we conclude that the CR has a considerable effect on the size and optical efficiency of the PTC. On average, the results presented showed that for every $1\mathrm{\%}$ gain in PTC optical efficiency, it would be necessary to increase the concentration rate by $2.69\mathrm{\%}$. However, it is noticed that these gains are not linear and decrease as the concentration rate increases. Also, the increase in the CR leads to an increase in the rim angle, in the collector aperture width, and in the solar energy flux density around the absorber. Finally, the results we found were investigated and validated, presenting excellent agreement with other works in the literature.

KEYWORDS:

• Parabolic trough collector
• optical and geometric parameters
• concentration ratio

Nomenclature and Abbreviations

Table

Acknowledgments

The main author thanks the PPGER (Postgraduate Program in Renewable Energy) of the Federal Institute of Education, Science and Technology of Ceará, for the research opportunity. He also thanks all those who have contributed to this work.

Additional information

Notes on contributors

Abdel-Farid Mamadou Idrissou

Abdel-Farid Mamadou Idrissou: Graduated in Mechanical Engineering from École Polytechnique of Abomey Calavi (2017), with an emphasis on energy. Master in Renewable Energies from the Federal Institute of Education, Science, and Technology of Ceará (2019). He is currently a professor in Production Engineering at Universidade Paulista (UNIP). He has experience in energy engineering, computational fluid dynamics, renewable energy, materials science, and artificial intelligence. Participates in development projects in Industrial Automation, Artificial Intelligence, and embedded systems at the Mechanical Testing Laboratory (LEM) at the Federal Institute of Education, Science, and Technology of Ceará.

Francisco Frederico dos Santos Matos

Francisco Frederico dos Santos Matos: Graduated in Mechanical Engineering from the Federal University of Ceará (1996) and Ph.D. in Mechanical Engineering from the Federal University of Santa Catarina (2002). He worked as Researcher II at Whirlpool S.A. for 4 years and 11 months, where he gained experience in the area of Energy Efficiency, with an emphasis on automatic valves that are driven by the flow. He is currently a Professor at the Federal Institute of Education, Science and Technology of Ceará, where he works in undergraduate courses in mechanical engineering and academic master's degree in renewable energies, teaching in the disciplines of Thermodynamics, Fluid Mechanics, Heat Transfer and Computational Fluid Mechanics, Refrigeration and Thermal Machines.

Auzuir Ripardo de Alexandria

Auzuir Ripardo de Alexandria: Graduated in Electrical Engineering (1993) and Bachelor of Computer Science (1994) from the Federal University of Campina Grande, master's degree (2005) and doctorate (2011) in Teleinformatics Engineering from the Federal University of Ceará. He is a professor at the Federal Institute of Education, Science and Technology of Ceará – IFCE, Fortaleza campus, Department of Industry, since 2003. He participates as a permanent teacher in the Postgraduate Programs in Telecommunications Engineering and Renewable Energies. As a researcher, he works in the fields of Computer Vision, Mobile Robotics, Biomedical Engineering, Artificial Neural Networks, and Industrial Automation.