Global variance reduction method based on volume and non-counting area corrections for global Monte Carlo simulations

Li Liu; Yinghong Zuo; Shengli Niu; Jinhui Zhu; Peng Shang

doi:10.1117/12.2692935

23 August 2023 Global variance reduction method based on volume and non-counting area corrections for global Monte Carlo simulations

Li Liu, Yinghong Zuo, Shengli Niu, Jinhui Zhu, Peng Shang

Author Affiliations +

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 1278412 (2023) https://doi.org/10.1117/12.2692935
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

A Global Variance Reduction (GVR) method based on volume and non-counting area corrections is proposed to improve the efficiency of the Monte Carlo simulation of the radiation field in the large space. The volume correction factor is introduced to modify the global weight window parameters to solve the over-splitting problem caused by the volume difference of counting grids. The GVR method based on volume correction yields a global figure of merit FOM_G about 39 times larger than the direct simulation, and the standard relative error σ is reduced by about two orders of magnitude. For the problem of the excessive computational resources occupied by the non-counting area, the non-counting area correction method is proposed, which results in a further 40% improvement of the FOM_G factor. The average error and maximum error of the radiation field in the large space obtained by three iterations of simulations using the corrected GVR method are 1.2% and 4.2%, respectively. And the FOM_G factor is improved by about 561 times compared with the direct simulation. The GVR method based on volume and non-counting area corrections can effectively guide the simulated particle transport, which improves the accuracy and efficiency of the global Monte Carlo simulation of the radiation field.

Citation Download Citation

Li Liu, Yinghong Zuo, Shengli Niu, Jinhui Zhu, and Peng Shang "Global variance reduction method based on volume and non-counting area corrections for global Monte Carlo simulations", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 1278412 (23 August 2023); https://doi.org/10.1117/12.2692935

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KEYWORDS

Particles

Monte Carlo methods

Computer simulations

Windows

Nuclear radiation

Scattering

Target detection

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