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Pseudo spectral methods offer an attractive alternative to finite element procedures for the solution of problems in elasticity. Especially for simple domains, questions involving both two and three dimensional elasticity (Navier's Equations or their non-linear generalisations) would seem to be reasonable candidates for a pseudo spectral approach. This paper examines some simple vibrational eigenvalue type problems, demonstrating how Navier's equations can be recast into pseudo- spectral format, including first derivative boundary conditions representing zero traction. Fourier---Chebyshev methods are shown to give solutions with typical spectral accuracy, with the addition of pole conditions being necessary for the case of a two dimensional disc. There is also consideration given to time-stepping solutions of elastodynamic problems, especially those involving non-linear friction effects, the authors particular interest being the study of disc brake noise. It is shown that, at least for relatively simple cases, it is possible to model systems in such a way that animated graphical output can be provided as the system of partial differential equations is numerically integrated. This provides a useful tool for engineers to rapidly examine the effect of parameter changes on a system model.

Discretely defined surfaces that exhibit vertical displacements across unknown fault lines can be difficult to approximate accurately unless a representation of the faults is known. Accurate representations of these faults enable the construction of constrained approximation models that can successfully overcome common problems such as over-smoothing. In this paper we review an existing method for detecting fault lines and present a new detection approach based on data triangulations and discrete Gaussian curvature (DGC). Furthermore, we show that if the fault line can be described non-parametrically, then accurate support vector machine (SVM) models can be constructed that are independent of the type of triangulation used in the detection algorithms. We shall also see that SVM models are particularly effective when the data produced by the detection algorithms are noisy. We compare the performances of the various new and established models.

A pseudo-spectral approach to 2D vibrational problems arising in linear elasticity is considerede using differentiation matrices. The governing partial differential equations and associated boundary conditions on regular domains can be translated into matrix eigenvalue problems. Accurate results are obtained to the precision expected in spectral-type methods. However, we show that it is necessary to apply an additional “pole” condition to deal with ther=0 coordinate singularity arising in the case of a 2D disc.

Laurent Pade-Chebyshev rational approximants,A m (z,z −1)/B n (z, z −1), whose Laurent series expansions match that of a given functionf(z,z −1) up to as high a degree inz, z −1 as possible, were introduced for first kind Chebyshev polynomials by Clenshaw and Lord [2] and, using Laurent series, by Gragg and Johnson [4]. Further real and complex extensions, based mainly on trigonometric expansions, were discussed by Chisholm and Common [1]. All of these methods require knowledge of Chebyshev coefficients off up to degreem+n. Earlier, Maehly [5] introduced Pade approximants of the same form, which matched expansions betweenf(z,z −1)B n (z, z −1)). The derivation was relatively simple but required knowledge of Chebyshev coefficients off up to degreem+2n. In the present paper, Pade-Chebyshev approximants are developed not only to first, but also to second, third and fourth kind Chebyshev polynomial series, based throughout on Laurent series representations of the Maehly type. The procedures for developing the Pade-Chebyshev coefficients are similar to that for a traditional Pade approximant based on power series [8] but with essential modifications. By equating series coefficients and combining equations appropriately, a linear system of equations is successfully developed into two sub-systems, one for determining the denominator coefficients only and one for explicitly defining the numerator coefficients in terms of the denominator coefficients. In all cases, a type (m, n) Pade-Chebyshev approximant, of degreem in the numerator andn in the denominator, is matched to the Chebyshev series up to terms of degreem+n, based on knowledge of the Chebyshev coefficients up to degreem+2n. Numerical tests are carried out on all four Pade-Chebyshev approximants, and results are outstanding, with some formidable improvements being achieved over partial sums of Laurent-Chebyshev series on a variety of functions. In part II of this paper [7] Pade-Chebyshev approximants of Clenshaw-Lord type will be developed for the four kinds of Chebyshev series and compared with those of the Maehly type.

In this paper we are interested in the approximation of continuous surface texture profiles defined only at discretely obtained points. A continuous representation is required in order to be able to apply filtration methods that assume uniform data spacing to practical data that in general is not uniformly spaced. By reconstructing surface profiles as natural cubic spline interpolants accurately using numerically stable fitting algorithms, we can provide a mathematically sound basis on which to compute surface texture profile parameters. As an important direct benefit, parameters involving the integrals of surface profiles can be calculated directly from the spline interpolant. Examples are given to illustrate the advantages of using this reconstruction method.

A set of discrete data (xk, f (xk)) (k = 1, 2, , m) may be fitted in any lp norm by a nonlinear form derived from a function g (L) of a linear form L = L(x). Such a nonlinear approximation problem may under appropriate conditions be (asymptotically) replaced by the fitting of g-1 (f) by L in any lp norm with respect to a weight function w = g (g-1 (f)). In practice this direct method can yield very good results, sometimes coming close to a best approximation. However, to ensure a near-best approximation, by using an iterative procedure based on fitting L, two algorithms are proposed in the l2 norm - one already established by Mason and Upton (1989) and one completely new, based on minimising the two algorithms and multiplicative combinations of errors, respectively. For a general g we prove they converge locally and linearly with small constants. Moreover it is established that they converge to different (nonlinear) Galerkin type approximations, the first based on making the explicit error f - g (L) orthogonal to a set of functions forming a basis for L, and the second based on making the implicit error * w(g-1 (f) - L) orthogonal to such a basis. Finally, and mainly for comparison purposes, the well known Gauss-Newton algorithm is adopted for the determination of a best (nonlinear) approximation. Illustrative problems are tackled and numerical results show how effective all of the algorithms can be. To add a further novel feature, L is here chosen throughout to be a radial basis function (RBF), and, as far as we are aware, this is one of the first successful uses of a (nonlinear) function of an RBF as an approximation form in data fitting. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

This paper, resulting from research collaboration with the UK National Physical Laboratory, is the first to present successfully a simple method for controlling the location parameters in univariate spline approximations. Traditional highly non-linear approaches are avoided by considering the parameters to be a function of a given density model. We present a number of density models for a range of data types, such as dominant local variability. This paper delivers to a scientific discipline applying polynomial spline approximations to recover discrete data to a high level of accuracy a method which avoids the need to construct complicated mathematical models.