Fast Finite-difference Time-domain Research Articles

Characteristic Model and Efficient FDTD-SPM Algorithm for Fishnet Metasurfaces Analysis

Intelligent metasurfaces to create smart electromagnetic environments have attracted a surge of interest. The multiscale property of metasurfaces results in a huge computational challenge, further hindering their engineering development. Our previous paper has developed a fast finite difference time domain (FDTD) algorithm for patchlike metasurfaces analysis using the surface susceptibility model (SSM). This study extends that to efficient fishnetlike metasurfaces analysis using the surface porosity model (SPM), a useful complement to SSM. We introduce the SPM to FDTD, replacing brute force simulations, to accelerate fishnetlike metasurfaces analysis. At last, we conduct a series of numerical experiments, demonstrating that the proposed FDTD-SPM algorithm greatly reduces the number of computational meshing lattices and the consumed time.

Fast finite-difference time-domain (FDTD) method of two dimensional target scattering calculation by two-level hierarchical approach

This paper presents a fast finite-difference time-domain (FDTD) method to calculate the scattering of heterogeneous target by utilizing effective electrical properties of the material in local areas. The heterogeneous target sometimes can be a really large model, which may be time-consuming and requires significant computer resources to finish the scattering computation using FDTD method. For complex target, the grid must be very small to fit the detail of the material's distribution. To overcome this issue, we propose a two-level hierarchical approach to reduce the grid number of the model in scattering calculation. In level one, the model is partitioned to many small grids to fit the target's heterogeneous characteristics. Each grid is assigned a value of electrical property at that position. In level two, the model is then partitioned to big grids, each of which covers several small grids. The electrical property in each big grid is treated as homogeneous and the electrical value is obtained with the values of the small grids within it. These big grids are then applied in FDTD method to calculate the scattering of the target. Finite-difference method (FDM) and Bruggeman formula are utilized to calculate the effective property in each big grid. FDM can give the anisotropic properties of the material, while Bruggeman formula can only yield isotropic ones. Numerical experiment of a human head slice is conducted and the results show the accuracy and efficiency of the two-level hierarchical method.

Electromagnetic metamaterial simulations using a GPU-accelerated FDTD method

Metamaterials composed of artificial subwavelength structures exhibit extraordinary properties that cannot be found in nature. Designing artificial structures having exceptional properties plays a pivotal role in current metamaterial research. We present a new numerical simulation scheme for metamaterial research. The scheme is based on a graphic processing unit (GPU)-accelerated finite-difference time-domain (FDTD) method. The FDTD computation can be significantly accelerated when GPUs are used instead of only central processing units (CPUs). We explain how the fast FDTD simulation of large-scale metamaterials can be achieved through communication optimization in a heterogeneous CPU/GPU-based computer cluster. Our method also includes various advanced FDTD techniques: the non-uniform grid technique, the total-field/scattered-field (TFSF) technique, the auxiliary field technique for dispersive materials, the running discrete Fourier transform, and the complex structure setting. We demonstrate the power of our new FDTD simulation scheme by simulating the negative refraction of light in a coaxial waveguide metamaterial.

Finite‐Difference Time‐Domain Algorithm for Quantifying Light Absorption in Silicon Nanowires

AbstractWe introduce an accurate and fast finite‐difference time‐domain (FDTD) method for calculating light absorption in nanoscale optical systems. The dispersive FDTD update equations were derived from auxiliary differential equations (ADE), wherein dispersive media were fitted by various dispersion models including the Drude, Debye, Lorentz, and critical point models. Light absorption in the dispersive media was quantified by calculating polarization pole currents in the ADE. To verify this simulation method, the absorption spectrum of a 300 nm thick silicon film was calculated and compared to an analytic solution. In addition, the absorption cross‐section of a single silicon nanowire with a diameter of 300 nm was calculated using monochromatic and broadband light sources. We believe that this reformatted FDTD method is a powerful tool for the design of novel nanophotonic components, including nanowire photovoltaic devices.

3D Inversion-Based Interpretation of Marine CSEM Data

SummaryThe marine controlled-source electromagnetic (CSEM) method has been evolving into a geophysical imaging tool for increasingly complex geological settings in which multiple resistive bodies can be resolved. An advanced subsurface imaging workflow for 3D CSEM surveys is presented, which reproduces the resistivity distribution to within a spatial resolution determined by the frequencies included. The performance of our advanced-processing workflow is demonstrated using a case study from the Gulf of Mexico (GOM), where a dense 3D grid was acquired over an area of high-quality seismic data and well log control.At the core of our 3D workflow is an inversion methodology with approximate Hessian-based optimization and a fast finite-difference time-domain forward operator. The optimization matches the synthetic to the measured field within 100–200 iterations and is sufficiently robust in three dimensions to avoid expensive regularization schemes. The sensitivity of the gradient-based inversion to the starting model is addressed by investing considerable effort in building 1D inversion-based starting models. At the same time, 3D inversion algorithms for survey layouts, including azimuthal data, demand high-quality data conditioning for which we present a processing sequence from time-domain electromagnetic data acquired by seabed receivers to frequency-domain data and weights for inversion.Detection and delineation of reservoirs in the presence of salt is recognized as a major challenge to CSEM methods. In order to interpret 3D data accurately in such a complex environment, the true-resistivity cube is built from a sequence of constrained inversion-based interpretation steps. Using a data set acquired in 2008 in the GOM, we demonstrate the ability of our 3D technology to resolve small (2×2 km), low-resistivity pay (Δρ:5Ω·m) targets within the vicinity (< 1 km) of large salt bodies. In this case study, the 3D method converged within 1 week, running on 150 parallel nodes.

FDTD Modeling of Arrays and Grids of Lossy Conductors Based on Impedance Sheet Conditions

Fast finite-difference time-domain (FDTD) modeling techniques for arrays of parallel wires and wire meshes are developed. The analytical basis of the models lies in the use of impedance sheet conditions (Kontorovich conditions) for dense wire arrays and grids, connecting the tangential electric fields and the averaged currents in the plane of a wire array or grid. The proposed methods allow us to avoid the direct discretization of the fields inside the wires, greatly simplifying the modeling task. The conductor losses in the wires are accounted for assuming a strong or weak skin effect. The impedance sheet conditions are formulated in the time-domain using the convolution integral, which is evaluated recursively in FDTD. Numerical examples and comparisons with analytical results are provided to verify the proposed techniques.

Improved computational efficiency for the FDTD method

In this short article we present a new fast finite-difference timedomain (FDTD) method. Producing exactly the same solution to Maxwell's equations as Yee's classic FDTD method, the fast FDTD method offers up to a 50% reduction in the number of multiplications, which is a very important indicator of the computational complexity of an algorithm. Mathematical analysis and computer simulations are given in the article. © 1994 John Wiley & Sons, Inc.