This means it is possible to control and optimize the incident flux on equipment such as high temperature reactors (Guene Lougou et al., 2020, Sarwar et al., 2015a, Sarwar et al., 2015b, Sarwar et al., 2015c) and photovoltaic cells (Kang et al., 2018) placed on the focal plane.
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One advantage of a HFSS is the ability to concentrate the light to regions of different areas on the focal plane with Gaussian (Zhu et al., 2020) or top-hat (Jin et al., 2019) profiles. High flux solar simulators (HFSS) typically consist of multiple light sources coupled with ellipsoidal or parabolic reflectors (Dibowski, Sarwar et al., 2015b).
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We explored one such opportunity here: using machine learning algorithms to position lamps within a light concentrating facility. Simultaneously, they offer opportunities to explore complex control strategies (Rowe et al., 2018), material testing methods (Sarwar et al., 2018), optimization of new devices (Gadi, 2000, Sarwar et al., 2019), novel reactor design (Gu et al., 2019), and more. These new apparatuses illuminate previously unknown challenges, such as the observer effect (Rowe et al., 2017), and bear some typical industrial issues, such as the design of a suitable control system (Martínez-Manuel et al., 2018). The need for pilot-scale and well-controlled experimental conditions led to the development and construction of light concentrating facilities referred to as high flux solar simulators (Gallo et al., 2017), ranging from a few kWe to more than 300 kWe (Wieghardt et al., 2017).
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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.
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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.
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Light energy concentrating systems such as High Flux Solar Simulators (HFSS) offer notable advantages in renewable energy research.