A modified spectral method for simulating arbitrary directional radiative intensity in participating media with graded refractive index. (March 2017)

• Record Type: Journal Article
• Authors: Wei, Lin-Yang; Qi, Hong; Ren, Ya-Tao; Ruan, Li-Ming
• Is Part Of: International journal of heat and mass transfer. Volume 106(2017:Mar.)
• Page Start: 167
• Page End: 176
• Publication Date: 2017-03
• Subjects: Modified spectral method -- Spectral method -- Arbitrary directional radiative intensity -- Inversion of refractive index -- Stochastic particle swarm optimization; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2016.10.060
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: A modified SM is proposed to calculate arbitrary directional radiative intensity. MSM can simulate directional radiative intensity accurately and efficiently. GRI can be reconstructed accurately with forward MSM simulation. Abstract: Accurate simulation of the arbitrary directional radiative intensity in participating media with graded refractive index (GRI) is important for solving the inverse radiation problem. The conventional spectral method (SM) can be applied to solve the radiative transfer problem of graded index media with limit-fixed discrete directions, which cannot obtain arbitrary directional radiative intensities required in inverse estimation. From the conventional SM, a modified spectral method (MSM) is proposed to simulate the arbitrary directional radiative intensity of participating media with GRI accurately. The detailed derivation process of MSM is illustrated and verified by solving the radiative transfer problem involving three different kinds of GRI distributions. All results show that the arbitrary directional radiative intensity can be calculated accurately based on the fixed directional radiative intensities of SM. Furthermore, the GRI distribution was reconstructed accurately by using the stochastic particle swarm optimization with noisy data and the MSM as forward method.