Nonlinear hyperelasticity-based mesh optimisation

In this work, various aspects of PDE-based mesh optimisation are treated. Different existing methods are presented, with the focus on a class of nonlinear mesh quality functionals that can guarantee the orientation preserving property. This class is extended from simplex to hypercube meshes in 2d and 3d. The robustness of the resulting mesh optimisation method allows the incorporation of unilateral boundary conditions of place and r-adaptivity with direct control over the resulting cell sizes. Also, alignment to (implicit) surfaces is possible, but in all cases, the resulting functional is hard to treat analytically and numerically. Using theoretical results from mathematical elasticity for hyperelastic materials, the existence and non-uniqueness of minimisers can be established. This carries over to the discrete case, for the solution of which tools from nonlinear optimisation are used. Because of the considerable numerical effort, a class of linear preconditioners is developed that helps to speed up the solution process

Computation and optimal perturbation of finite-time coherent sets for aperiodic flows without trajectory integration

Understanding the macroscopic behavior of dynamical systems is an important tool to unravel transport mechanisms in complex flows. A decomposition of the state space into coherent sets is a popular way to reveal this essential macroscopic evolution. To compute coherent sets from an aperiodic time-dependent dynamical system we consider the relevant transfer operators and their infinitesimal generators on an augmented space-time manifold. This space-time generator approach avoids trajectory integration, and creates a convenient linearization of the aperiodic evolution. This linearization can be further exploited to create a simple and effective spectral optimization methodology for diminishing or enhancing coherence. We obtain explicit solutions for these optimization problems using Lagrange multipliers and illustrate this technique by increasing and decreasing mixing of spatial regions through small velocity field perturbations

Quarklets: Construction and Application in Adaptive Frame Methods

This thesis is concerned with the construction and application of a new class of functions called quarklets. With the intention of constructing an adaptive hp-method based on wavelets, they do arise out of the latter through an enrichment with polynomials. The starting point for the construction is the real axis. There, we derive frames for the Sobolev space H^s(R). Through a boundary adaptation, tensorization and the application of a scale-dependent extension operator we are even able to construct quarklet frames on very general domains in multiple spatial dimensions. With these frames at hand we can discretize linear elliptic operator equations in a stable way. The discrete system can be handled with an adaptive numerical scheme. For this purpose it is necessary to show the compressibility of the stiffness matrix. We do this for the prototypical example of the Poisson equation independently of the dimension and in this way we are able to prove the optimality of the adaptive scheme. By the latter we mean that the approximation rate of the best n-term quarklet approximation is realized by the scheme. Finally, we carry out some numerical experiments in one and two spatial dimensions, where the theoretical findings are validated in practice and moreover, the value of the quarklets in the numerical scheme becomes visible by analysing the quarklet coefficients of the approximate solutions

A Wiener Chaos Based Approach to Stability Analysis of Stochastic Shear Flows

As the aviation industry expands, consuming oil reserves, generating carbon dioxide gas and adding to environmental concerns, there is an increasing need for drag reduction technology. The ability to maintain a laminar flow promises significant reductions in drag, with economic and environmental benefits. Whilst development of flow control technology has gained interest, few studies investigate the impacts that uncertainty, in flow properties, can have on flow stability. Inclusion of uncertainty, inherent in all physical systems, facilitates a more realistic analysis, and is therefore central to this research. To this end, we study the stability of stochastic shear flows, and adopt a framework based upon the Wiener Chaos expansion for efficient numerical computations. We explore the stability of stochastic Poiseuille, Couette and Blasius boundary layer type base flows, presenting stochastic results for both the modal and non modal problem, contrasting with the deterministic case and identifying the responsible flow characteristics. From a numerical perspective we show that the Wiener Chaos expansion offers a highly efficient framework for the study of relatively low dimensional stochastic flow problems, whilst Monte Carlo methods remain superior in higher dimensions. Further, we demonstrate that a Gaussian auto-covariance provides a suitable model for the stochasticity present in typical wind tunnel tests, at least in the case of a Blasius boundary layer. From a physical perspective we demonstrate that it is neither the number of inflection points in a defect, nor the input variance attributed to a defect, that influences the variance in stability characteristics for Poiseuille flow, but the shape/symmetry of the defect. Conversely, we show the symmetry of defects to be less important in the case of the Blasius boundary layer, where we find that defects which increase curvature in the vicinity of the critical point generally reduce stability. In addition, we show that defects which enhance gradients in the outer regions of a boundary layer can excite centre modes with the potential to significantly impact neutral curves. Such effects can lead to the development of an additional lobe at lower wave-numbers, can be related to jet flows, and can significantly reduce the critical Reynolds number.EPSR

The Structure of Isoperimetric Bubbles on Rn\mathbb{R}^n and Sn\mathbb{S}^n

The multi-bubble isoperimetric conjecture in $n$-dimensional Euclidean and spherical spaces from the 1990's asserts that standard bubbles uniquely minimize total perimeter among all $q-1$ bubbles enclosing prescribed volume, for any $q \leq n+2$. The double-bubble conjecture on $\mathbb{R}^3$ was confirmed in 2000 by Hutchings-Morgan-Ritor\'e-Ros, and is nowadays fully resolved for all $n \geq 2$. The double-bubble conjecture on $\mathbb{S}^2$ and triple-bubble conjecture on $\mathbb{R}^2$ have also been resolved, but all other cases are in general open. We confirm the conjecture on $\mathbb{R}^n$ and on $\mathbb{S}^n$ for all $q \leq \min(5,n+1)$, namely: the double-bubble conjectures for $n \geq 2$, the triple-bubble conjectures for $n \geq 3$ and the quadruple-bubble conjectures for $n \geq 4$. In fact, we show that for all $q \leq n+1$, a minimizing cluster necessarily has spherical interfaces, and after stereographic projection to $\mathbb{S}^n$, its cells are obtained as the Voronoi cells of $q$ affine-functions, or equivalently, as the intersection with $\mathbb{S}^n$ of convex polyhedra in $\mathbb{R}^{n+1}$. Moreover, the cells (including the unbounded one) are necessarily connected and intersect a common hyperplane of symmetry, resolving a conjecture of Heppes. We also show for all $q \leq n+1$ that a minimizer with non-empty interfaces between all pairs of cells is necessarily a standard bubble. The proof makes crucial use of considering $\mathbb{R}^n$ and $\mathbb{S}^n$ in tandem and of M\"obius geometry and conformal Killing fields; it does not rely on establishing a PDI for the isoperimetric profile as in the Gaussian setting, which seems out of reach in the present one.Comment: 90 pages, 14 figures. Incorporated comments by referee, added Figure 5 regarding quasi-centers, and Appendix B with a technical computation. Final version, to appear in Acta Mat

[Activity of Institute for Computer Applications in Science and Engineering]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science