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By having its origin in analytical and continuum mechanics, as well as in computer science and applied mathematics, multibody dynamics provides a basis for analysis and virtual prototyping of innovative applications in many fields of contemporary engineering. With the utilization of computational models and algorithms that classically belonged to different fields of applied science, multibody dynamics delivers reliable simulation platforms for diverse highly-developed industrial products such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, smart structures, biomechanical applications and nano-technologies. The chapters of this volume are based on the revised and extended versions of the selected scientific papers from amongst 255 original contributions that have been accepted to be presented within the program of the distinguished international ECCOMAS conference. It reflects state-of-the-art in the advances of multibody dynamics, providing excellent insight in the recent scientific developments in this prominent field of computational mechanics and contemporary engineering.
Focuses on the second-order turbulence-closure model and its applications to engineering problems. Topics include turbulent motion and the averaging process, near-wall turbulence, applications of turbulence models, and turbulent buoyant flows.
This book gathers the latest advances, innovations, and applications in the field of multibody and mechatronic systems. Topics addressed include the analysis and synthesis of mechanisms; modelling and simulation of multibody systems; railway and vehicle dynamics; mechatronic systems for energy harvesting; robot design and optimization; and mechatronic design. It gathers the second volume of the proceedings of the 7th International Symposium on Multibody Systems and Mechatronics (MuSMe), virtually held in Cordoba, Argentina, on October 12-15, 2021, within the framework of the FEIbIM Commission for Robotics and Mechanisms and IFToMM Technical Committees for Multibody Dynamics and for Robotics and Mechatronics.
Fluid-structure interactions (FSI), i.e., the interplay of some moveable or deformable structure with an internal or surrounding fluid, are among the most widespread and most challenging coupled or multi-physics problems. Although much has been accomplished in developing good computational FSI methods and despite convincing solutions to a number of classes of problems including those presented in this book, there is a need for more comprehensive studies showing that the computational methods proposed are reliable, robust, and efficient beyond the classes of problems they have successfully been applied to.This volume of LNCSE, a sequel to vol. 53, which contained, among others, the first numerical benchmark for FSI problems and has received considerable attention since then, presents a collection of papers from the "First International Workshop on Computational Engineering - special focus FSI," held in Herrsching in October 2009 and organized by three DFG-funded consortia. The papers address all relevant aspects of FSI simulation and discuss FSI from the mathematical, informatical, and engineering perspective.
The MIT mission - "to bring together Industry and Academia and to nurture the next generation in computational mechanics is of great importance to reach the new level of mathematical modeling and numerical solution and to provide an exciting research environment for the next generation in computational mechanics." Mathematical modeling and numerical solution is today firmly established in science and engineering. Research conducted in almost all branches of scientific investigations and the design of systems in practically all disciplines of engineering can not be pursued effectively without, frequently, intensive analysis based on numerical computations.The world we live in has been classified by the human mind, for descriptive and analysis purposes, to consist of fluids and solids, continua and molecules; and the analyses of fluids and solids at the continuum and molecular scales have traditionally been pursued separately. Fundamentally, however, there are only molecules and particles for any material that interact on the microscopic and macroscopic scales. Therefore, to unify the analysis of physical systems and to reach a deeper understanding of the behavior of nature in scientific investigations, and of the behavior of designs in engineering endeavors, a new level of analysis is necessary. This new level of mathematical modeling and numerical solution does not merely involve the analysis of a single medium but must encompass the solution of multi-physics problems involving fluids, solids, and their interactions, involving multi-scale phenomena from the molecular to the macroscopic scales, and must include uncertainties in the given data and the solution results. Nature does not distinguish between fluids and solids and does not ever repeat itself exactly.This new level of analysis must also include, in engineering, the effective optimization of systems, and the modeling and analysis of complete life spans of engineering products, from design to fabrication, to possibly multiple repairs, to end of service.
Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces. This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing.