Computational Methods for Nanoscale Applications by Igor Tsukerman PDF

By Igor Tsukerman

ISBN-10: 038774777X

ISBN-13: 9780387747774

Computational tools for Nanoscale purposes: debris, Plasmons and Waves provides new views on glossy nanoscale difficulties the place basic technological know-how meets expertise and machine modeling. This booklet describes famous computational thoughts comparable to finite-difference schemes, finite point research and Ewald summation, in addition to a brand new finite-difference calculus of versatile neighborhood Approximation equipment (FLAME) that qualitatively improves the numerical accuracy in a number of difficulties. software components within the publication contain long-range particle interactions in homogeneous and heterogeneous media, electrostatics of colloidal platforms, wave propagation in photonic crystals, photonic band constitution, plasmon box enhancement, and metamaterials with backward waves and adverse refraction.

Computational tools for Nanoscale purposes is available to experts and graduate scholars in various components of nanoscale technology and know-how, together with physics, engineering, chemistry, and utilized arithmetic. moreover, a number of complicated issues may be of specific curiosity to the professional reader.

Key Features:

  • Utilizes a two-tiered sort of exposition with intuitive clarification of key rules within the first tier and extra technical information within the moment
  • Bridges the distance among physics and engineering and laptop technological know-how
  • Presents basics and functions of computational equipment, electromagnetic idea, colloidal structures and photonic structures
  • Covers "hot subject matters" in photonics, plasmonics, and metamaterials.

Show description

Read or Download Computational Methods for Nanoscale Applications PDF

Best computational mathematicsematics books

Download e-book for iPad: Orthogonal Polynomials: Computation and Approximation by Walter Gautschi

This can be the 1st booklet on optimistic equipment for, and purposes of orthogonal polynomials, and the 1st to be had selection of proper Matlab codes. The publication starts off with a concise advent to the idea of polynomials orthogonal at the actual line (or a component thereof), relative to a good degree of integration.

New PDF release: Numerical Modelling in Geomechanics

Describes theoretically and essentially the revolution within the research of geomechanics and geomaterials that numerical modelling has made attainable via examples of such components as chemical degradation, rock weathering, particles flows, and stream slides.

New PDF release: Computational Inelasticity

This booklet describes the theoretical foundations of inelasticity, its numerical formula and implementation. The subject material defined herein constitutes a consultant pattern of state-of-the- artwork method at present utilized in inelastic calculations. one of the quite a few issues lined are small deformation plasticity and viscoplasticity, convex optimization idea, integration algorithms for the constitutive equation of plasticity and viscoplasticity, the variational environment of boundary price difficulties and discretization by way of finite point equipment.

Additional resources for Computational Methods for Nanoscale Applications

Sample text

E. 58) To find this interpolation polynomial and then the BDF scheme itself, let us for convenience move the origin of the coordinate system to the midpoint of the stencil and set t0 = 0. 62) This is Gear’s second order method. The scheme is implicit – it constitutes a (generally nonlinear) equation with respect to y+1 or, in the case of a vector problem (y ∈ Rn ), a system of equations. In practice, iterative linearization by the Newton–Raphson method is used and suitable linear system solvers are applied in the Newton–Raphson loop.

1 The Runge–Kutta Methods This introduction to Runge–Kutta (R-K) methods follows the elegant exposition by E. Hairer et al. [HrW93]. The main idea dates back to C. Runge’s original paper of 1895. 30) t0 but also admits accurate approximations via numerical quadratures. For example, the midpoint rule gives y1 ≡ y(t1 ) ≈ y0 + ∆t0 f t0 + ∆t0 2 y2 ≡ y(t2 ) ≈ y1 + ∆t1 f t1 + ∆t1 2 and so on. , do not have to be equal. 4 Some Classic Schemes for Initial Value Problems 21 It is straightforward to verify that this numerical quadrature (that doubles as a time-stepping scheme) has second order accuracy with respect to the maximum time step.

To keep the notation simple, we consider the grid to be uniform along each axis; more generally, hx could vary along the x-axis and hy could vary along the y-axis, but the essence of the analysis would remain the same. Each node of the grid can be characterized in a natural way by two integer indices nx and ny corresponding to the x- and y-directions; 1 ≤ nx ≤ Nx + 1, 1 ≤ ny ≤ Ny + 1. 100), it is natural to approximate the x- and y- partial derivatives separately in exactly the same way as done in 1D.

Download PDF sample

Computational Methods for Nanoscale Applications by Igor Tsukerman

by Michael

Rated 4.78 of 5 – based on 36 votes