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通过有限元撕裂与互连方法对纳米尺度光镊进行高效预测与分析

Efficient Prediction and Analysis of Optical Trapping at Nanoscale via Finite Element Tearing and Interconnecting Method.

作者信息

Wan Ting, Tang Benliu

机构信息

College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.

State Key Laboratory of Millimeter Waves, Nanjing, 210096, China.

出版信息

Nanoscale Res Lett. 2019 Aug 27;14(1):294. doi: 10.1186/s11671-019-3131-7.

DOI:10.1186/s11671-019-3131-7
PMID:31456066
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6712131/
Abstract

Numerical simulation plays an important role for the prediction of optical trapping based on plasmonic nano-optical tweezers. However, complicated structures and drastic local field enhancement of plasmonic effects bring great challenges to traditional numerical methods. In this article, an accurate and efficient numerical simulation method based on a dual-primal finite element tearing and interconnecting (FETI-DP) and Maxwell stress tensor is proposed, to calculate the optical force and potential for trapping nanoparticles. A low-rank sparsification approach is introduced to further improve the FETI-DP simulation performance. The proposed method can decompose a large-scale and complex problem into small-scale and simple problems by using non-overlapping domain division and flexible mesh discretization, which exhibits high efficiency and parallelizability. Numerical results show the effectiveness of the proposed method for the prediction and analysis of optical trapping at nanoscale.

摘要

数值模拟在基于等离子体纳米光镊的光阱预测中起着重要作用。然而,等离子体效应的复杂结构和剧烈的局部场增强给传统数值方法带来了巨大挑战。本文提出了一种基于对偶原始有限元撕裂与互连(FETI-DP)和麦克斯韦应力张量的精确高效数值模拟方法,用于计算捕获纳米粒子的光力和光势。引入了一种低秩稀疏化方法以进一步提高FETI-DP模拟性能。该方法通过使用非重叠区域划分和灵活的网格离散化,可将大规模复杂问题分解为小规模简单问题,具有高效性和可并行性。数值结果表明了该方法在纳米尺度光阱预测与分析中的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/0b2e8976d54c/11671_2019_3131_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/63bfcb8ca898/11671_2019_3131_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/429e36a7354d/11671_2019_3131_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/3aef99d7e6e7/11671_2019_3131_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/34eaeb81d383/11671_2019_3131_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/0b2e8976d54c/11671_2019_3131_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/5e768b626089/11671_2019_3131_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/cc1068e3d70d/11671_2019_3131_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/d36acde9a71c/11671_2019_3131_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/3a3b4364f63f/11671_2019_3131_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/9b05f8f77a93/11671_2019_3131_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/b000ca05c515/11671_2019_3131_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/7ba636d59217/11671_2019_3131_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/d9325c1a21f4/11671_2019_3131_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/63bfcb8ca898/11671_2019_3131_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/429e36a7354d/11671_2019_3131_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/3aef99d7e6e7/11671_2019_3131_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/34eaeb81d383/11671_2019_3131_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c1e/6712131/0b2e8976d54c/11671_2019_3131_Fig13_HTML.jpg

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