Abstract
The optical properties of metallic nanoparticles are well known, but the study of their thermal behavior is in its infancy. However the local heating of surrounding medium, induced by illuminated nanostructures, opens the way to new sensors and devices. Consequently the accurate calculation of the electromagnetically induced heating of nanostructures is of interest. The proposed multiphysics problem cannot be directly solved with the classical refinement method of Comsol Multiphysics and a 3D adaptive remeshing process based on ana posteriorierror estimator is used. In this paper the efficiency of three remeshing strategies for solving the multiphysics problem is compared. The first strategy uses independent remeshing for each physical quantity to reach a given accuracy. The second strategy only controls the accuracy on temperature. The third strategy uses a linear combination of the two normalized targets (the electric field intensity and the temperature). The analysis of the performance of each strategy is based on the convergence of the remeshing process in terms of number of elements. The efficiency of each strategy is also characterized by the number of computation iterations, the number of elements, the CPU time, and the RAM required to achieve a given target accuracy.
Highlights
IntroductionGold nanoparticles, are known as efficient light absorbers [1,2,3]
Noble metals nanoparticles, gold nanoparticles, are known as efficient light absorbers [1,2,3]
The increase of temperature in gold nanoparticles has a variety of applications in nanotechnology, biology, chemistry, and medicine [4, 8] and the drug delivery with the remote release of drugs from a capsule containing gold nanoparticles when excited by a laser source [9,10,11]
Summary
Gold nanoparticles, are known as efficient light absorbers [1,2,3]. A h-adaptive remeshing process uses a physical error estimator based on an a posteriori p1-interpolation of the physical solution to increase the accuracy of the solution while ensuring the convergence of calculation. Such an a posteriori estimator uses the interpolation of the physical solution computed at the previous step to construct a new mesh that respects the geometry of objects defined in the physical problem.
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