GPU-based Monte Carlo ray tracing simulation considering refraction for central receiver system

被引:13
作者
Lin, Xiaoxia [1 ]
He, Caitou [2 ]
Huang, Wenjun [3 ]
Zhao, Yuhong [3 ]
Feng, Jieqing [1 ]
机构
[1] Zhejiang Univ, State Key Lab CAD &CG, Hangzhou 310058, Peoples R China
[2] Wenzhou Univ, Coll Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China
[3] Zhejiang Univ, Inst Ind Proc Control, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China
基金
中国国家自然科学基金;
关键词
Central receiver system; Monte Carlo ray-tracing; Flux density distribution simulation; Refraction; GPU; FLUX-DENSITY; SOLAR; MODEL; DISTRIBUTIONS; SUNSHAPE;
D O I
10.1016/j.renene.2022.04.151
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The heliostat in the central receiver system usually adopts silvered-glass reflectors, where a glass layer covers a specular reflection layer. Previous flux density distribution simulation methods ignored the refraction effects caused by the glass layer. This paper proposes a more accurate Monte Carlo ray-tracing simulation method considering refraction and total internal reflection (TIR) effects caused by the ray transmission in the glass layer. The proposed simulation method is fully designed and implemented on a graphics processing unit (GPU), which enables the algorithm performance to remain effective even when the simulation is more consistent with the real situation. Experiments and simulations reveal that refraction has non-negligible effects on the simulation results. Compared with the classical Monte Carlo ray-tracing simulation method that only considers the ray's reflection, refraction reduces the maximum radiative flux and total energy by up to 80% and 50%, respectively. Refraction also causes the flux spot reflected on the receiver panel to spread greatly. In several extreme cases, the ray is trapped in the glass due to TIR.(c) 2022 Elsevier Ltd. All rights reserved.
引用
收藏
页码:367 / 382
页数:16
相关论文
共 50 条
[41]   GPU-accelerated Monte Carlo simulation of particle coagulation based on the inverse method [J].
Wei, J. ;
Kruis, F. E. .
JOURNAL OF COMPUTATIONAL PHYSICS, 2013, 249 :67-79
[42]   Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code [J].
Mettivier, Giovanni ;
Lai, Youfang ;
Jia, Xun ;
Russo, Paolo .
PHYSICS IN MEDICINE AND BIOLOGY, 2024, 69 (04)
[43]   Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images [J].
Ryden, T. ;
Lagerlof, J. Heydorn ;
Hemmingsson, J. ;
Marin, I. ;
Svensson, J. ;
Bath, M. ;
Gjertsson, P. ;
Bernhardt, P. .
EJNMMI PHYSICS, 2018, 5
[44]   Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images [J].
T. Rydén ;
J. Heydorn Lagerlöf ;
J. Hemmingsson ;
I. Marin ;
J. Svensson ;
M. Båth ;
P. Gjertsson ;
P. Bernhardt .
EJNMMI Physics, 5
[45]   Simulation of satellite, airborne and terrestrial LiDAR with DART (I): Waveform simulation with quasi-Monte Carlo ray tracing [J].
Gastellu-Etchegorry, Jean-Philippe ;
Yin, Tiangang ;
Lauret, Nicolas ;
Grau, Eloi ;
Rubio, Jeremy ;
Cook, Bruce D. ;
Morton, Douglas C. ;
Sun, Guoqing .
REMOTE SENSING OF ENVIRONMENT, 2016, 184 :418-435
[46]   A GPU-CUDA based direct simulation Monte Carlo algorithm for real gas flows [J].
Goldsworthy, M. J. .
COMPUTERS & FLUIDS, 2014, 94 :58-68
[47]   Acceptance-Rejection Sampling Based Monte Carlo Ray Tracing in Anisotropic Porous Media [J].
Wang, P. ;
Li, J. B. ;
Zhou, L. ;
Liu, D. Y. .
ENERGY, 2020, 199
[48]   Near-infrared star map simulation for starlight refraction sensor based on ray tracing [J].
Wang, Hongyuan ;
Wu, Shaochong ;
Wang, Bingwen ;
Yan, Zhiqiang ;
Yao, Shuai ;
Zeliu, Chuanjiang .
INFRARED PHYSICS & TECHNOLOGY, 2023, 132
[49]   gMCAP: a GPU-based Monte Carlo proton transport program for high-density tissues with precise nuclear reaction models [J].
Luo, Xi-Yu ;
Sun, Liang ;
Wu, Zhen ;
Qiu, Rui ;
Xu, Shou-Ping ;
Zhang, Hui ;
Li, Jun-Li .
NUCLEAR SCIENCE AND TECHNIQUES, 2025, 36 (05)
[50]   A detailed study on the optical performance of parabolic trough solar collectors with Monte Carlo Ray Tracing method based on theoretical analysis [J].
Zou, Bin ;
Dong, Jiankai ;
Yao, Yang ;
Jiang, Yiqiang .
SOLAR ENERGY, 2017, 147 :189-201