Narrow Results

The Lorentz–Drude model incorporated Maxwell equations are simulated by using the three-dimensional finite difference time domain (FDTD) method and the method is parallelized on multiple graphics processing units (GPUs) for plasmonics applications. The compute unified device architecture (CUDA) is used for GPU parallelization. The Lorentz–Drude (LD) model is used to simulate the dispersive nature of materials in plasmonics domain and the auxiliary differential equation (ADE) approach is used to make it consistent with time domain Maxwell equations. Different aspects of multiple GPUs for the FDTD method are presented such as comparison of different numbers of GPUs, transfer time in between them, synchronous, and asynchronous passing. It is shown that by using multiple GPUs in parallel fashion, significant reduction in the simulation time can be achieved as compared to the single GPU.

An efficient parallel elastoplastic reanalysis method is suggested. The main backbone of the suggested method is based on combined approximation (CA) reanalysis. GPU parallel computation is used to accelerate assembling the stiffness matrix. Assembling process is divided into the offline part for strain matrix and online part for element stiffness matrix, which makes the structure of the program more reasonable and efficient. Pseudo elastic analysis is introduced and extended to load increment method to make the CA method more feasible. The numerical examples show that the suggested method can improve the efficiency of elastoplastic analysis significantly and the accuracy of results can also be ensured.