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The boundary element method for the eddy current problem (BEM-ECP) was proposed in a number of papers and is applicable to important tasks such as the problem of inductive heating and transmission of electromagnetic energy. BEM-ECP requires the construction of a system of linear algebraic equations in which the matrix is inherently dense and is constructed out of element matrices. For the process of the element matrix computation, two cases are normally considered: far-field interaction and near-field interaction, because the construction of element matrices requires integration of a singular function. In this article, we suggest a transform that allows computing the matrix components of the near-singular interaction part while implementing only the single and double layer potentials. The previously suggested modified double layer potential (MDLP) can be integrated by means of this transform, which simplifies the program implementation of BEM-ECP significantly. Solving model problems, we analyse the drawbacks of the previously suggested approach. This analysis includes the proof of the MDLP singularity that makes the integration of this potential a rather difficult task without the help of our transform. The previously suggested approach does not work well with surfaces that are not smooth. Our approach does consider such cases, which is its main advantage. We demonstrate this on the model problems with known analytical solutions.
Chapter 9 describes simulation or sampling methods for reliability assessment. The chapter begins by describing methods for generation of pseudorandom numbers for prescribed univariate or multivariate distributions. Next, the ordinary Monte Carlo simulation (MCS) method is described. It is shown that for small failure probabilities, which is the case in most structural reliability problems, the number of samples required by MCS for a given level of accuracy is inversely proportional to the failure probability. Thus, MCS is computationally demanding for structural reliability problems. Various methods to reduce the computational demand of MCS are introduced. These include the use of antithetic variates and importance sampling. For the latter, sampling around design points and sampling in half-space are presented, the latter for a special class of problems. Other efficient sampling methods described include directional sampling, orthogonal-plane sampling, and subset simulation. For each case, expressions are derived for a measure of accuracy of the estimated failure probability. Methods are also presented for computing parameter sensitivities by sampling. Finally, a method is presented for evaluating certain multifold integrals by sampling. This method is useful in Bayesian updating, as described in Chapter 10.
We study some properties of integro splines. Using these properties, we design an algorithm to construct splines
$S_{m+1}(x)$
of neighbouring degrees to the given spline
$S_m(x)$
with degree m. A local integro-sextic spline is constructed with the proposed algorithm. The local integro splines work efficiently, that is, they have low computational complexity, and they are effective for use in real time. The construction of nonlocal integro splines usually leads to solving a system of linear equations with band matrices, which yields high computational costs.
In the finite element formulation, the body is divided into elements of various types. This chapter describes mapping functions for the description of element geometry in the undeformed configuration and shape functions for the description of displacement and thus deformed geometry in the 2D and 3D domains. We introduce the "isoparametric" formulation in which mapping functions and shape functions are identical. This is followed by discussions on integration in the mapped domains and numerical integration.
We consider the problem of numerical integration when the sampling nodes form a stationary point process on the real line. In previous papers it was argued that a naïve Riemann sum approach can cause a severe variance inflation when the sampling points are not equidistant. We show that this inflation can be avoided using a higher-order Newton–Cotes quadrature rule which exploits smoothness properties of the integrand. Under mild assumptions, the resulting estimator is unbiased and its variance asymptotically obeys a power law as a function of the mean point distance. If the Newton–Cotes rule is of sufficiently high order, the exponent of this law turns out to only depend on the point process through its mean point distance. We illustrate our findings with the stereological estimation of the volume of a compact object, suggesting alternatives to the well-established Cavalieri estimator.
In this chapter, we discuss several topics of numerical analysis. The topics include numerical interpolation, differentiation, integration, and solution of linear systems of equations. These numerical techniques are essential in the finite element method. MATLAB functions and subroutines performing these numerical calculations in the implementation of FE codes for engineering analysis are presented. While numerical analysis is a broad area, this chapter focuses on the methods and techniques that are used in the development and solution of finite element formulations and models. It should be noted that, while MATLAB has many built-in functions for performing numerical analysis tasks, this book is intended to reveal the underlying theories and techniques of the numerical methods. Therefore, we explain various numerical methods from the basics and create the MATLAB functions from scratch.
Numerical modeling is a way of solving complex sets of equations that cannot be solved analytically.In finite difference modeling the infinitesimal step (e.g. dx) in the governing differential equation is replaced by a finite step (e.g. "∆x" ), and the variation over this step is assumed to be linear. Integration starts at a boundary and continues stepwise across the domain. In finite element and finite volume models, the domain is discretized into elements of unequal size and equations are written relating parameters on the boundaries of these elements. This results in a system of equations that must be solved simultaneously.Because the deformation rate in a glacier is a function of temperature, and conversely, full solutions require coupling of energy balance and momentum balance equations.Examples are given involving the role of subglacial permafrost in ice sheet behavior, estimation of prior ice sheet behavior from characteristics measured in ice cores and boreholes, and use of non-deterministic models to estimate sea level rise.
Using Mathematica and the Wolfram Language to engage with the calculus of functions of a single variable. Includes limits, continuity, differentiation, integration, sequences, and series.
Energy supplementation provides a means of reducing production risk of growing stocker cattle on winter wheat pasture. This study addresses the issue of risk aversion and energy supplement input use. Differences in supplementation practices induced by risk aversion and the effects of cattle and feed market conditions are examined. Results show that supplementation practices are likely to be similar across producers, irrespective of their risk attitudes. Cattle and feed market conditions, however, markedly affect supplementation practices. These findings provide information for assisting stockmen in identifying efficient supplementation strategies.
For a stationary Markov process the detailed balance condition is equivalent to thetime-reversibility of the process. For stochastic differential equations (SDE’s), the timediscretization of numerical schemes usually destroys the time-reversibility property.Despite an extensive literature on the numerical analysis for SDE’s, their stabilityproperties, strong and/or weak error estimates, large deviations and infinite-timeestimates, no quantitative results are known on the lack of reversibility of discrete-timeapproximation processes. In this paper we provide such quantitative estimates by using theconcept of entropy production rate, inspired by ideas from non-equilibrium statisticalmechanics. The entropy production rate for a stochastic process is defined as the relativeentropy (per unit time) of the path measure of the process with respect to the pathmeasure of the time-reversed process. By construction the entropy production rate isnonnegative and it vanishes if and only if the process is reversible. Crucially, from anumerical point of view, the entropy production rate is an a posterioriquantity, hence it can be computed in the course of a simulation as the ergodicaverage of a certain functional of the process (the so-called Gallavotti−Cohen (GC) action functional). We computethe entropy production for various numerical schemes such as explicit Euler−Maruyama and explicit Milstein’s forreversible SDEs with additive or multiplicative noise. In addition we analyze the entropyproduction for the BBK integrator for the Langevin equation. The order (in thetime-discretization step Δt) of the entropy production rate provides a tool toclassify numerical schemes in terms of their (discretization-induced) irreversibility. Ourresults show that the type of the noise critically affects the behavior of the entropyproduction rate. As a striking example of our results we show that the Euler scheme formultiplicative noise is not an adequate scheme from a reversibilitypoint of view since its entropy production rate does not decrease withΔt.
A simple method is proposed for constructing fourth-degree cubature formulae over general product regions with no symmetric assumptions. The cubature formulae that are constructed contain at most n2 + 7n + 3 nodes and they are likely the first kind of fourth-degree cubature formulae with roughly n2 nodes for non-symmetric integrations. Moreover, two special cases are given to reduce the number of nodes further. A theoretical upper bound for minimal number of cubature nodes is also obtained.
We present a high precision frequency determination method for digitized NMR FID signals. The method employs high precision numerical integration rather than simple summation as in many other techniques. With no independent knowledge of the other parameters of a NMR FID signal (phase ф, amplitude A, and transverse relaxation time T2) this method can determine the signal frequency f0 with a precision of if the observation time T ≫ T2. The method is especially convenient when the detailed shape of the observed FT NMR spectrum is not well defined. When T2 is +∞ and the signal becomes pure sinusoidal, the precision of the method is which is one order more precise than the ±1 count error induced precision of a typical frequency counter. Analysis of this method shows that the integration reduces the noise by bandwidth narrowing as in a lock-in amplifier, and no extra signal filters are needed. For a pure sinusoidal signal we find from numerical simulations that the noise-induced error in this method reaches the Cramer-Rao Lower Band (CRLB) on frequency determination. For the damped sinusoidal case of most interest, the noise-induced error is found to be within a factor of 2 of CRLB when the measurement time T is 2 or 3 times larger than T2. We discuss possible improvements for the precision of this method.
Many problems in engineering sciences can be described by linear, inhomogeneous, m-th order ordinary differential equations (ODEs) with variable coefficients. For this wide class of problems, we here present a new, simple, flexible, and robust solution method, based on piecewise exact integration of local approximation polynomials as well as on averaging local integrals. The method is designed for modern mathematical software providing efficient environments for numerical matrix-vector operation-based calculus. Based on cubic approximation polynomials, the presented method can be expected to perform (i) similar to the Runge-Kutta method, when applied to stiff initial value problems, and (ii) significantly better than the finite difference method, when applied to boundary value problems. Therefore, we use the presented method for the analysis of engineering problems including the oscillation of a modulated torsional spring pendulum, steady-state heat transfer through a cooling web, and the structural analysis of a slender tower based on second-order beam theory. Related convergence studies provide insight into the satisfying characteristics of the proposed solution scheme.
This paper presents a ductile damage-gradient based nonlocal and fully coupledelastoplastic constitutive equations by adding a Helmholtz equation to regularize theinitial and boundary value problem (IBVP) exhibiting some damage induced softening. First,a thermodynamically-consistent formulation of gradient-regularized plasticity fullycoupled with isotropic ductile damage and accounting for mixed non linear isotropic andkinematic hardening is presented. For the sake of simplicity, only a simplified version ofthis model based on von Mises isotropic yield function and accounting for the singlenonlinear isotropic hardening is studied and implemented numerically using an in house FEcode. An additional partial differential equation governing the evolution of the nonlocalisotropic damage is added to the equilibrium equations and the associated weak formsderived to define the IBVP (initial and boundary value problem). After the time and spacediscretization, two algebraic equations: one highly nonlinear associated with theequilibrium equation and the second purely linear associated with the damage non localityequation are obtained. Over a typical load increment, the first equation is solvediteratively thanks to the Newton-Raphson scheme and the second equation is solved directlyto compute the nonlocal damage \hbox{$\Bar{{D}}$}D̅ at each node. All the constitutive equations are “strongly” affected bythis nonlocal damage variable transferred to each integration point. Some applicationsshow the ability of the proposed approach to obtain a mesh independent solution for afixed value of the length scale parameter. Comparisons between fully local and nonlocalsolutions are given.
Galerkin discretizations of integral equations in $\mathbb{R}^{d}$ requirethe evaluation of integrals $I = \int_{S^{(1)}}\int_{S^{(2)}}g(x,y){\rm d}y{\rm d}x$where S(1),S(2) are d-simplices and g has a singularityat x = y. We assume that g is Gevrey smooth for x$\ne$y andsatisfies bounds for the derivatives which allow algebraic singularitiesat x = y. This holds for kernel functions commonly occurring in integralequations. We construct a family of quadrature rules $\mathcal{Q}_{N}$ usingN function evaluations of g which achieves exponential convergence|I – $\mathcal{Q}_{N}$| ≤C exp(–rNγ) with constants r, γ > 0.
In this paper we look at the performance of trigonometric integrators applied to highly oscillatory differential equations. It is widely known that some of the trigonometric integrators suffer from low-order resonances for particular step sizes. We show here that, in general, trigonometric integrators also suffer from higher-order resonances which can lead to loss of nonlinear stability. We illustrate this with the Fermi–Pasta–Ulam problem, a highly oscillatory Hamiltonian system. We also show that in some cases trigonometric integrators preserve invariant or adiabatic quantities but at the wrong values. We use statistical properties such as time averages to further evaluate the performance of the trigonometric methods and compare the performance with that of the mid-point rule.
A new sharp L2 inequality of Ostrowski type is established, which provides some other interesting results as special cases. Applications in numerical integration are also given.
Weak local linear approximations have played a prominent role in the construction of effective inference methods and numerical integrators for stochastic differential equations. In this note two weak local linear approximations for stochastic differential equations with jumps are introduced as a generalization of previous ones. Their respective order of convergence is obtained as well.