Accurate iteration-free mixed-stabilised formulation for laminar incompressible Navier–Stokes: Applications to fluid–structure interaction

Stabilised mixed velocity–pressure formulations are one of the widely-used finite element schemes for computing the numerical solutions of laminar incompressible Navier–Stokes. In these formulations, the Newton–Raphson scheme is employed to solve the nonlinearity in the convection term. One fundamental issue with this approach is the computational cost incurred in the Newton–Raphson iterations at every load/time step. In this paper, we present an iteration-free mixed finite element formulation for incompressible Navier–Stokes that preserves second-order temporal accuracy of the generalised-alpha and related schemes for both velocity and pressure fields. First, we demonstrate the second-order temporal accuracy using numerical convergence studies for an example with a manufactured solution. Later, we assess the accuracy and the computational benefits of the proposed scheme by studying the benchmark example of flow past a fixed circular cylinder. Towards showcasing the applicability of the proposed technique in a wider context, the inf–sup stable P2–P1 pair for the formulation without stabilisation is also considered. Finally, the resulting benefits of using the proposed scheme for fluid–structure interaction problems are illustrated using two benchmark examples in fluid-flexible structure interaction.     

Newton's method on Riemannian manifolds: covariant alpha theory

In this paper, Smale's theory is generalized to the contextof intrinsic Newton iteration on geodesically complete analyticRiemannian and Hermitian manifolds. Results are valid for analyticmappings from a manifold to a linear space of the same dimension,or for analytic vector fields on the manifold. The invariant is defined by means of high-order covariant derivatives. Boundson the size of the basin of quadratic convergence are given.If the ambient manifold has negative sectional curvature, thosebounds depend on the curvature. A criterion of quadratic convergencefor Newton iteration from the information available at a pointis also given.     

On the Superlinear Convergence of the Successive Approximations Method

The Ostrowski theorem is a classical result which ensures the attraction of all the successive approximations x _k+1 = G(x _k ) near a fixed point x*. Different conditions [ultimately on the magnitude of G(x*)] provide lower bounds for the convergence order of the process as a whole. In this paper, we consider only one such sequence and we characterize its high convergence orders in terms of some spectral elements of G(x*); we obtain that the set of trajectories with high convergence orders is restricted to some affine subspaces, regardless of the nonlinearity of G. We analyze also the stability of the successive approximations under perturbation assumptions.     

The Newton Bracketing Method for Convex Minimization

An iterative method for the minimization of convex functions f :ⁿ , called a Newton Bracketing (NB) method, is presented. The NB method proceeds by using Newton iterations to improve upper and lower bounds on the minimum value. The NB method is valid for n = 1, and in some cases for n > 1 (sufficient conditions given here). The NB method is applied to large scale Fermat–Weber location problems.     

Improved RGF method to find saddle points

The predictor-corrector method for following a reduced gradient (RGF) to determine saddle points [Quapp, W. et al., J Comput Chem 1998, 19, 1087] is further accelerated by a modification allowing an implied corrector step per predictor but almost without additional costs. The stability and robustness of the RGF method are improved, and the new version in addition reduces the number of gradient and Hessian calculations.     

Numerical experience with the truncated Newton method for unconstrained optimization

The truncated Newton algorithm was devised by Dembo and Steihaug (Ref. 1) for solving large sparse unconstrained optimization problems. When far from a minimum, an accurate solution to the Newton equations may not be justified. Dembo's method solves these equations by the conjugate direction method, but truncates the iteration when a required degree of accuracy has been obtained. We present favorable numerical results obtained with the algorithm and compare them with existing codes for large-scale optimization.     

Robert Hooke’s Seminal Contribution to Orbital Dynamics

During the second half of the seventeenth century, the outstanding problem in astronomy was to understand the physical basis for Keplers laws describing the observed orbital motion of a planet around the Sun. In the middle 1660s,Robert Hooke (1635–1703) proposed that a planets motion is determined by compounding its tangential velocity with the change in radial velocity impressed by the gravitational attraction of the Sun, and he described his physical concept to Isaac Newton (1642–1726) in correspondence in 1679. Newton denied having heard of Hookes novel concept of orbital motion, but shortly after their correspondence he implemented it by a geometric construction from which he deduced the physical origin of Keplers area law,which later became Proposition I, Book I, of his Principia in 1687.Three years earlier, Newton had deposited a preliminary draft of it, his De Motu Corporum in Gyrum (On the Motion of Bodies), at the Royal Society of London, which Hooke apparently was able to examine a few months later, because shortly there-after he applied Newtons construction in a novel way to obtain the path of a body under the action of an attractive central force that varies linearly with the distance from its center of motion (Hookes law). I show that Hookes construction corresponds to Newtons for his proof of Keplers area law in his De Motu. Hookes understanding of planetary motion was based on his observations with mechanical analogs. I repeated two of his experiments and demonstrated the accuracy of his observations.My results thus cast new light on the significance of Hookes contributions to the development of orbital dynamics, which in the past have either been neglected or misunderstood.Michael Nauenberg is Professor Emeritus of Physics at the University of California, Santa Cruz. His primary research has been in theoretical physics, but he also has written several articles and coedited a book on the historical development of dynamics by Huygens, Newton, and Hooke.     

Incomplete Orthogonal Distance Regression

A common method of fitting curves and surfaces to data is to minimize the sum of squares of the orthogonal distances from the data points to the curve or surface, a process known as orthogonal distance regression. Here we consider fitting geometrical objects to data when some orthogonal distances are not available. Methods based on the Gauss–Newton method are developed, analyzed and illustrated by examples. AMS subject classification (2000) 65D10, 65K05.     

Newton polyhedra, unstable faces and the poles of Igusa's local zeta function

In this paper we examine when the order of a pole of Igusa's local zeta function associated to a polynomial is smaller than ``expected'. We carry out this study in the case that is sufficiently non-degenerate with respect to its Newton polyhedron , and the main result of this paper is a proof of one of the conjectures of Denef and Sargos. Our technique consists in reducing our question about the polynomial to the same question about polynomials , where are faces of depending on the examined pole and is obtained from by throwing away all monomials of whose exponents do not belong to . Secondly, we obtain a formula for Igusa's local zeta function associated to a polynomial , with unstable, which shows that, in this case, the upperbound for the order of the examined pole is obviously smaller than ``expected'.