Thomas Wick - Böcker

This monograph discusses modeling, adaptive discretisation techniques and the numerical solution of fluid structure interaction. An emphasis in part I lies on innovative discretisation and advanced interface resolution techniques. The second part covers the efficient and robust numerical solution of fluid-structure interaction. In part III, recent advances in the application fields vascular flows, binary-fluid-solid interaction, and coupling to fractures in the solid part are presented. Moreover each chapter provides a comprehensive overview in the respective topics including many references to concurring state-of-the art work. ContentsPart I: Modeling and discretizationOn the implementation and benchmarking of an extended ALE method for FSI problemsThe locally adapted parametric finite element method for interface problems on triangular meshesAn accurate Eulerian approach for fluid-structure interactions Part II: SolversNumerical methods for unsteady thermal fluid structure interactionRecent development of robust monolithic fluid-structure interaction solversA monolithic FSI solver applied to the FSI 1,2,3 benchmarks Part III: ApplicationsFluid-structure interaction for vascular flows: From supercomputers to laptopsBinary-fluid–solid interaction based on the Navier–Stokes–Cahn–Hilliard EquationsCoupling fluid-structure interaction with phase-field fracture: Algorithmic details

This monograph is centered on mathematical modeling, innovative numerical algorithms and adaptive concepts to deal with fracture phenomena in multiphysics. State-of-the-art phase-field fracture models are complemented with prototype explanations and rigorous numerical analysis. These developments are embedded into a carefully designed balance between scientific computing aspects and numerical modeling of nonstationary coupled variational inequality systems. Therein, a focus is on nonlinear solvers, goal-oriented error estimation, predictor-corrector adaptivity, and interface conditions. Engineering applications show the potential for tackling practical problems within the fields of solid mechanics, porous media, and fluidstructure interaction.

Dieses Lehrbuch behandelt zeitgemäß, anwendungsorientiert und ausführlich die theoretischen Grundlagen der Numerik. Dabei sind – zusätzlich zu den gängigen Inhalten – zahlreiche angewandte Beispiele und Praxis-Exkurse eingebunden, um das Verständnis nachhaltig zu fördern. Beweise werden sehr kleinteilig in vielen detailliert beschriebenen Schritten dargestellt. Auf die sich wiederholenden, zentralen Kernkonzepte der Numerik (z.B. Stabilität, Effizienz, Robustheit, Genauigkeit,…) wird explizit eingegangen, und diese Begriffe werden klar gegeneinander abgegrenzt. Außerdem werden Numerische Verfahren der Linearen Algebra und der Analysis getrennt dargestellt, was den Studierenden den Zugang zur Numerik – ausgehend von den beiden Grundvorlesungen des Mathematik-Studiums – deutlich erleichtert. Das Buch ist daher sowohl für Studierende der Mathematik als auch der Physik, der Informatik oder der Ingenieurwissenschaften bestens geeignet.Für die 2. Auflage wurde das Buch umfassend überarbeitet und erweitert. Unter anderem wurden Aspekte des maschinellen Lernens und neuronaler Netze aufgenommen, der Teil zu Krylow-Raum-Verfahren ausgebaut sowie angegebene Algorithmen durch konkrete Python-Codes ersetzt. Konkrete exemplarische Rechnungen werden nun auch mit diesen Python-Programmen durchgeführt.

This textbook presents the theoretical foundations of numerical mathematics in a modern, application-oriented, and comprehensive way. In addition to the standard content, it includes numerous examples and practical excursions to sustainably enhance understanding. Proofs are presented in a very detailed, step-by-step manner. The recurring core concepts of numerical mathematics like accuracy, efficiency, robustness and stability are explicitly addressed and clearly distinguished from one another. Specific example calculations for the described algorithms are carried out using provided Python codes. Numerical modeling for aspects of machine learning and neural networks are taken into consideration. Furthermore, numerical methods from linear algebra and analysis are presented separately, which significantly facilitates students’ access to numerical mathematics – based on the two main lectures within undergraduate studies in mathematics. Therefore, the book is ideally suited for students of mathematics, physics, computer science, or engineering. This book is a translation of the original German 2nd edition of “Einführung in die Numerische Mathematik” (Springer, 2024). The translation was done with the help of artificial intelligence. A subsequent revision was performed by the authors to further refine the work and to ensure that the translation is appropriate concerning content and scientific correctness.