4.7 Article

Acceptance-Rejection Sampling Based Monte Carlo Ray Tracing in Anisotropic Porous Media

期刊

ENERGY
卷 199, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.117455

关键词

Monte Carlo ray tracing; Anisotropic porous media; Variable pore structure; Acceptance-rejection method; Optical performance

资金

  1. National Science Foundation of China [51509076]
  2. Fundamental Research Fund for Central Universities [B200202172]
  3. 111 Project of Renewable Energy and Smart Grid [B14022]

向作者/读者索取更多资源

In this paper, a Monte Carlo ray-tracing method for modeling the incident irradiation propagation in a porous absorber with linear variable pore structure is presented. An acceptance-rejection method (ARM) is employed to generate each step size of the photon's free path according to the specific radiative characteristics of the anisotropic porous medium. The method we proposed overcomes the limitation of the inverse transform method (ITM) by avoiding the integration process to obtain the cumulative distribution function. Using this method, the volumetric distribution in an absorber with a linear variable pore structure is determined. Three typical linear pore structure layouts-increasing (I-type), decreasing (D-type), and constant (C-type)-are analyzed. In general, the D-type layout achieves excellent optical efficiency and homogeneity of solar irradiation distribution. A sparse porous structure is beneficial for the in-depth propagation of photons, but it also increases the probability of photons scattering out of the medium. Therefore, increasing the density at the backside to intercept the ray effectively improves the optical efficiency. The model developed in this work is useful for understanding the propagation of solar irradiation distribution in a porous absorber with an anisotropic media, which is important for the thermal design of volumetric receivers. (C) 2020 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据