Unit step and impulse function equations to simplify the solution of engineering problems

A unit step equation is proposed that when differentiated by elementary calculus yields the impulse function and when the resulting impulse function equation is integrated by elementary calculus yields the proposed unit step equation. Using these two equations, a two stage methodology is presented for the simplification of the solution of problems involving the impulse function

Simulating water-entry/exit problems using Eulerian-Lagrangian and fully-Eulerian fictitious domain methods within the open-source IBAMR library

In this paper we employ two implementations of the fictitious domain (FD) method to simulate water-entry and water-exit problems and demonstrate their ability to simulate practical marine engineering problems. In FD methods, the fluid momentum equation is extended within the solid domain using an additional body force that constrains the structure velocity to be that of a rigid body. Using this formulation, a single set of equations is solved over the entire computational domain. The constraint force is calculated in two distinct ways: one using an Eulerian-Lagrangian framework of the immersed boundary (IB) method and another using a fully-Eulerian approach of the Brinkman penalization (BP) method. Both FSI strategies use the same multiphase flow algorithm that solves the discrete incompressible Navier-Stokes system in conservative form. A consistent transport scheme is employed to advect mass and momentum in the domain, which ensures numerical stability of high density ratio multiphase flows involved in practical marine engineering applications. Example cases of a free falling wedge (straight and inclined) and cylinder are simulated, and the numerical results are compared against benchmark cases in literature.Comment: The current paper builds on arXiv:1901.07892 and re-explains some parts of it for the reader's convenienc

hp-FEM for Two-component Flows with Applications in Optofluidics

This thesis is concerned with the application of hp-adaptive finite element methods to a mathematical model of immiscible two-component flows. With the aim of simulating the flow processes in microfluidic optical devices based on liquid-liquid interfaces, we couple the time-dependent incompressible Navier-Stokes equations with a level set method to describe the flow of the fluids and the evolution of the interface between them

Synergies between Numerical Methods for Kinetic Equations and Neural Networks

The overarching theme of this work is the efficient computation of large-scale systems. Here we deal with two types of mathematical challenges, which are quite different at first glance but offer similar opportunities and challenges upon closer examination. Physical descriptions of phenomena and their mathematical modeling are performed on diverse scales, ranging from nano-scale interactions of single atoms to the macroscopic dynamics of the earth\u27s atmosphere. We consider such systems of interacting particles and explore methods to simulate them efficiently and accurately, with a focus on the kinetic and macroscopic description of interacting particle systems. Macroscopic governing equations describe the time evolution of a system in time and space, whereas the more fine-grained kinetic description additionally takes the particle velocity into account. The study of discretizing kinetic equations that depend on space, time, and velocity variables is a challenge due to the need to preserve physical solution bounds, e.g. positivity, avoiding spurious artifacts and computational efficiency. In the pursuit of overcoming the challenge of computability in both kinetic and multi-scale modeling, a wide variety of approximative methods have been established in the realm of reduced order and surrogate modeling, and model compression. For kinetic models, this may manifest in hybrid numerical solvers, that switch between macroscopic and mesoscopic simulation, asymptotic preserving schemes, that bridge the gap between both physical resolution levels, or surrogate models that operate on a kinetic level but replace computationally heavy operations of the simulation by fast approximations. Thus, for the simulation of kinetic and multi-scale systems with a high spatial resolution and long temporal horizon, the quote by Paul Dirac is as relevant as it was almost a century ago. The first goal of the dissertation is therefore the development of acceleration strategies for kinetic discretization methods, that preserve the structure of their governing equations. Particularly, we investigate the use of convex neural networks, to accelerate the minimal entropy closure method. Further, we develop a neural network-based hybrid solver for multi-scale systems, where kinetic and macroscopic methods are chosen based on local flow conditions. Furthermore, we deal with the compression and efficient computation of neural networks. In the meantime, neural networks are successfully used in different forms in countless scientific works and technical systems, with well-known applications in image recognition, and computer-aided language translation, but also as surrogate models for numerical mathematics. Although the first neural networks were already presented in the 1950s, the scientific discipline has enjoyed increasing popularity mainly during the last 15 years, since only now sufficient computing capacity is available. Remarkably, the increasing availability of computing resources is accompanied by a hunger for larger models, fueled by the common conception of machine learning practitioners and researchers that more trainable parameters equal higher performance and better generalization capabilities. The increase in model size exceeds the growth of available computing resources by orders of magnitude. Since $2012$, the computational resources used in the largest neural network models doubled every $3.4$ months\footnote{\url{https://openai.com/blog/ai-and-compute/}}, opposed to Moore\u27s Law that proposes a $2$-year doubling period in available computing power. To some extent, Dirac\u27s statement also applies to the recent computational challenges in the machine-learning community. The desire to evaluate and train on resource-limited devices sparked interest in model compression, where neural networks are sparsified or factorized, typically after training. The second goal of this dissertation is thus a low-rank method, originating from numerical methods for kinetic equations, to compress neural networks already during training by low-rank factorization. This dissertation thus considers synergies between kinetic models, neural networks, and numerical methods in both disciplines to develop time-, memory- and energy-efficient computational methods for both research areas

Level Set Method for Simulating the Dynamics of the Fluid-Fluid Interfaces: Application of a Discontinuous Galerkin Method

A discontinuous Galerkin (DG) method was applied for simulating the dynamics of the fluid-fluid interfaces. The numerical implementations were performed in the context of an available in-house code, BoSSS. The flow field was assumed to be governed by a single-set of the Navier-Stokes equation in terms of the phase-dependent density and viscosity fields. As in the discontinuous Galerkin method the variables in each cell are expressed in terms of a polynomial space, the solution may exhibit spurious oscillations in the presence of the steep variations such as the density jumps across the interface. In order to overcome this problem, a diffuse interface assumption was made, according to which a jump is approximated by a continuous variation employing a regularized heaviside function. The interface diffusion is supposed to take place in a region with a reasonable width. Therefore, in order to properly express the smoothed jumps in terms of a polynomial space of a certain degree, only jumps with limited hight could be considered. Otherwise, the grid needs to be highly refined in the interface diffusion region for preventing the non-physical spatial oscillation of the solution. Surface tension effects as well as gravity were also involved in the simulations by adding the corresponding source terms to the Navier-Stokes equation. The interface kinematics was simulated using the level set method. Taking the advantage of the discontinuous Galerkin method, a precise solution to the level set advection equation was achieved. As the regularized Heaviside and delta functions are commonly expressed in terms of the level set function, the level set function needs to remain signed distance in order to keep a uniform diffusion width. The signed distance property of a level set functions was recovered by solving the re-initialization equation. A Godunov's scheme was applied for approximating the Hamiltonian of the re-initialization equation, in order to obtain a solution with a monotonicity preserving behavior. A notable stability improvement was achieved by adding an artificial diffusion along the characteristic lines of the re-initialization equation. The solution showed an appropriate hp-convergence behavior and almost no spurious movement of the interface was detected. For solving the Navier-Stokes equation, an explicit-implicit stiffly stable time integration method was employed combined by a splitting method for decoupling the velocity and pressure fields within the DG framework. This solver, which had been priorly implemented for the single phase formulation of the equation, was used as a basis for implementing a new solver for the multiphase formulation. The multiphase flow solver was verified by considering a number of the test cases, such as a rising bubble

Isogeometric Analysis for High Order Geometric Partial Differential Equations with Applications

In this thesis, we consider the numerical approximation of high order geometric Partial Differential Equations (PDEs). We first consider high order PDEs defined on surfaces in the 3D space that are represented by single-patch tensor product NURBS. Then, we spatially discretize the PDEs by means of NURBS-based Isogeometric Analysis (IGA) in the framework of the Galerkin method. With this aim, we consider the construction of periodic NURBS function spaces with high degree of global continuity, even on closed surfaces. As benchmark problems for the proposed discretization, we propose Laplace-Beltrami problems of the fourth and sixth orders, as well as the corresponding eigenvalue problems, and we analyze the impact of the continuity of the basis functions on the accuracy as well as on computational costs. The numerical solution of two high order phase field problems on both open and closed surfaces is also considered: the fourth order Cahn-Hilliard equation and the sixth order crystal equation, both discretized in time with the generalized-alpha method. We then consider the numerical approximation of geometric PDEs, derived, in particular, from the minimization of shape energy functionals by L^2-gradient flows. We analyze the mean curvature and the Willmore gradient flows, leading to second and fourth order PDEs, respectively. These nonlinear geometric PDEs are discretized in time with Backward Differentiation Formulas (BDF), with a semi-implicit formulation based on an extrapolation of the geometry, leading to a linear problem to be solved at each time step. Results about the numerical approximation of the two geometric flows on several geometries are analyzed. Then, we study how the proposed mathematical framework can be employed to numerically approximate the equilibrium shapes of lipid bilayer biomembranes, or vesicles, governed by the Canham-Helfrich curvature model. We propose two numerical schemes for enforcing the conservation of the area and volume of the vesicles, and report results on benchmark problems. Then, the approximation of the equilibrium shapes of biomembranes with different values of reduced volume is presented. Finally, we consider the dynamics of a vesicle, e.g. a red blood cell, immersed in a fluid, e.g. the plasma. In particular, we couple the curvature-driven model for the lipid membrane with the incompressible Navier-Stokes equations governing the fluid. We consider a segregated approach, with a formulation based on the Resistive Immersed Surface method applied to NURBS geometries. After analyzing benchmark fluid simulations with immersed NURBS objects, we report numerical results for the investigation of the dynamics of a vesicle under different flow conditions