In the analytical approach, the radiative flux distribution utilizes an analytical function in the form of a Gaussian function or convolution. These rays are then reflected by the heliostats and projected to the receiver surface. MCRT traces millions or even billions of rays that are emitted from the sun.
Therefore, an efficient and accurate algorithm for simulating this radiative flux distribution is a fundamental requirement for CRS research.Īt present, there are two approaches for simulating the radiative flux distribution on the receiver surface: one is Monte Carlo ray tracing (MCRT) (Veach and Guibas, 1995, Jensen and Christensen, 1995, Modest, 2013), and the other is the analytical approach. (2017), accompanied by available simulation tools. The reflected radiative flux can be influenced by many optical losses, which are elaborated by Li et al. The tasks mentioned above are related to the radiative flux distribution that is reflected onto the receiver surface by either a single heliostat or all heliostats in the heliostat field.
Issues as such these ones could threaten the construction and operations of a CRS, since they could greatly affect the economy, efficiency and safety of solar energy utilization. There are many concerns regarding the design and deployment of a heliostat field (Imenes et al., 2006), the aiming strategy for the heliostats (Wang et al., 2017), and the estimation of the yearly received energy (Islam et al., 2018). The concentrated radiation energy heats the transfer fluid in the central receiver, such as water or molten salt, for subsequent electricity generation (Conroy et al., 2018). The heliostats track the movement of the sun and concentrate the lights they reflect onto the surface of a central receiver, which is usually mounted on top of a tower (Behar et al., 2013). In this type of CRS, thousands of highly reflective mirrors, known as heliostats, are deployed to form a heliostat field. The most common central receiver systems (CRSs) (Behar et al., 2013, Li et al., 2016, Levêque et al., 2017) are power facilities for converting solar energy into electrical energy (Lovegrove and Stein, 2012, Duffie et al., 2013).
#Create opt files for soltrace software
QMCRT also has an advantage in terms of efficiency for CRS compared with two well-known simulation software tools, i.e., SolTrace and Tonatiuh.Įfforts regarding the development and utilization of solar energy are attracting increasing attention because of its clean and renewable nature. Compared with the state-of-the-art GPU-based grid ray tracing (GRT) approach, QMCRT is equally fast but generates a more accurate result. QMCRT is two orders of magnitude faster than the traditional MCRT method when addressing traditional one-reflection CRS case. The results obtained for both synthetic and real heliostats obtained using QMCRT are substantially in keeping with the results obtained using established computational tools. As a result, a stable MaxRF value approaching the reference value is obtained, while the total power remains almost unchanged. In QMCRT, the problem is solved by applying a trimmed mean smoothing operation to the generated radiative flux distribution. Second, in the traditional approaches, the simulated maximum radiative flux (MaxRF) is randomly higher than the reference value, even if tens of millions of rays are traced. This method also facilitates sunshape sampling and heliostat surface slope error sampling, which can achieve memory and run-time efficiency. First, QMCRT, as a bidirectional approach, can avoid unnecessary intersection calculations. In this paper, a GPU-based ray-tracing simulation method, namely, quasi-Monte Carlo ray tracing (QMCRT), is proposed to address problems of both efficiency and accuracy. MCRT is an effective method to describe the radiative flux distribution on the receiver surface reflected by either a single heliostat or all heliostats in a heliostat field. Monte Carlo ray tracing (MCRT) is a fundamental simulation method for central receiver systems(CRSs).