Various research fields exploit concentrated sunlight for applications such as electricity production (Renno et al., 2017), chemical synthesis (Rego de Vasconcelos and Lavoie, 2019), and CO 2 utilization (Khan and Tahir, 2019, Nguyen et al., 2020). While significant research has been conducted on materials and energy harvesting concepts (Cheng et al., 2020), there is a need for advanced testing methods with quick turnarounds and equipment with flexible control (Garg et al., 1985). One of the crucial engineering challenges of this time is to provide a cleaner source of energy (Abbas and Wan Daud, 2010) to power rapid industrial and population growth.
Remarkably, this achievement allows for full automation and better control of light concentrating facilities and the development of more innovative energy harvesting systems. This is comparable to the mechanical system’s accuracy, which allows the positioning of the light source with a Euclidian distance of approximately 0.24 mm. Consequently, the hypothesis was validated as the CNN accurately predicted the source position within 0.07, 0.11, and 0.07 mm in the x-, y-, and z-directions, respectively (a Euclidean distance of ~ 0.249 mm). Then, the optical modelling of the HFSS using Monte Carlo Ray Tracing was employed to generate more than 2,500 images for the training and validation of the CNN. First, the HFSS output was characterized in detail to set the overall study expectations and serve as the baseline metric. Specifically, the hypothesis proposed is that a CNN can predict three HFSS parameters (the relative ×, y, z position of the light source) using imaging and computer vision techniques with an accuracy equal to or better than the operator. Accordingly, this study investigated the development and performance of a machine learning model based on convolutional neural networks (CNNs) for HFSS operation and control. Despite their expanding usage, they face some operational challenges that provide opportunities for new designs and further exploration. It is found that reducing the tube diameter by a factor of two, reduces TPI significantly from 39.2% to 23.4%.Light energy concentrating systems such as High Flux Solar Simulators (HFSS) offer notable advantages in renewable energy research. Thermal performance index (TPI) is defined to study the effect of tube diameter, number of tubes, aspect ratio, and position of the receiver relative to the focal plane of the Fresnel lens. The efficiency drops from 52% to 38% at a flow rate of 150 L/min, when the HTF flow direction is changed from the downward to the upward flow configuration. A novel approach is adopted to solve the numerical model, which is validated with experimental results, to predict the heat transfer characteristics of the receiver. A numerical model for helical coil cavity receiver is developed considering conjugate heat transfer between the receiver and the surroundings.
The experiments are carried out using compressed air as heat transfer fluid (HTF) around solar noon in Bangalore, India for different flow rates and different directions of flow of HTF. In this paper, an experimental and numerical study is carried out to evaluate the thermal performance of a helical coil cavity receiver fitted with a Fresnel lens. Helical coil cavity receivers integrated with Fresnel lens can be used in decentralized solar power generation applications.