Monte Carlo Modeling Method for Surface Light Source

Haisong Tang; Xianglong Mao; Zexin Feng; Haoran Li

doi:10.3788/AOS230880

Acta Optica Sinica, Volume. 43, Issue 21, 2122001(2023)

Monte Carlo Modeling Method for Surface Light Source

Haisong Tang^1,2, Xianglong Mao^3、*, Zexin Feng^1,2、**, and Haoran Li^1,2

¹Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China

²MOE Key Laboratory of Optoelectronic Imaging Technology and Systems, Beijing Institute of Technology, Beijing 100081, China

³The New Technology Laboratory of Space Photon Information, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi , China

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Figures & Tables(11)

Fig. 1. Schematic diagram of calculating the theoretical value of irradiance on the receiver radiated by the surface light source

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Fig. 2. XY-polynomial surface

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Fig. 3. Sampling results for the XY-polynomial surface light source. (a) (b) (c) Sampled points, directions, and rays of uniform sampling with equal weights; (d) (e) (f) sampled points, directions, and rays of uniform sampling in parameter space with weights

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Fig. 4. Theoretical irradiance distribution on the receiver formed by the XY-polynomial surface light source. (a) 3D; (b) 2D

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Fig. 5. Error analysis of Monte Carlo modeling results for XY-polynomial surface light source. (a) Theoretical value of irradiance distribution on the receiver formed by surface light source; (b) (c) irradiance distribution on the receiver simulated by uniform sampling with equals weights and its error; (d) (e) irradiance distribution on the receiver simulated by uniform sampling in parameter space and its error

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Fig. 6. NURBS surface and the grid points the surface pass through

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Fig. 7. Sampling results for the NURBS surface light source. (a) (b) (c) Sampled points, directions, and rays of uniform sampling with equal weights; (d) (e) (f) sampled points, directions, and rays of uniform sampling in parameter space with weights

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Fig. 8. Theoretical irradiance distribution on the receiver formed by the NURBS surface light source. (a) 3D; (b) 2D

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Fig. 9. Error analysis of Monte Carlo modeling results for NURBS surface light source. (a) Theoretical value of irradiance distribution on the receiver formed by surface light source; (b) (c) irradiance distribution on the receiver simulated by uniform sampling with equal weights and its error; (d) (e) irradiance distribution on the receiver simulated by uniform sampling in parameter space and its error

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Fig. 10. Comparison of error and time consumption of different modeling methods for surface light sources. (a) The error of different modeling methods varying with the number of rays; (b) the time consumption of different modeling methods varying with the number of rays

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Table 1. Parameters of the XY-polynomial surface of the surface light source
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Table 1. Parameters of the XY-polynomial surface of the surface light source
Polynomial term Parameter value Polynomial term Parameter value Polynomial term Parameter value
$c$ 0.0100 $c_{3} x^{2}$ 0.0050 $c_{7} x^{2} y$ 0.0013
$k$ -0.0800 $c_{4} x y$ -0.0100 $c_{8} x y^{2}$ -0.0008
$c_{1} x$ -0.1000 $c_{5} y^{2}$ 0.0040 $c_{9} y^{3}$ -0.0011
$c_{2} y$ -0.1000 $c_{6} x^{3}$ 0.0010

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Haisong Tang, Xianglong Mao, Zexin Feng, Haoran Li. Monte Carlo Modeling Method for Surface Light Source[J]. Acta Optica Sinica, 2023, 43(21): 2122001

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