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Complex Effects in Large Eddy Simulations
  • Language: en
  • Pages: 440

Complex Effects in Large Eddy Simulations

The field of Large Eddy Simulations is reaching a level of maturity that brings this approach to the mainstream of engineering computations, while it opens opportunities and challenges. The main objective of this volume is to bring together leading experts in presenting the state-of-the-art and emerging approaches for treating complex effects in LES. A common theme throughout is the role of LES in the context of multiscale modeling and simulation.

Computational Modeling by Case Study
  • Language: en
  • Pages: 849

Computational Modeling by Case Study

Mathematical models power the modern world; they allow us to design safe buildings, investigate changes to the climate, and study the transmission of diseases through a population. However, all models are uncertain: building contractors deviate from the planned design, humans impact the climate unpredictably, and diseases mutate and change. Modern advances in mathematics and statistics provide us with techniques to understand and quantify these sources of uncertainty, allowing us to predict and design with confidence. This book presents a comprehensive treatment of uncertainty: its conceptual nature, techniques to quantify uncertainty, and numerous examples to illustrate sound approaches. Several case studies are discussed in detail to demonstrate an end-to-end treatment of scientific modeling under uncertainty, including framing the problem, building and assessing a model, and answering meaningful questions. The book illustrates a computational approach with the Python package Grama, presenting fully reproducible examples that students and practitioners can quickly adapt to their own problems.

Polynomial Chaos Methods for Hyperbolic Partial Differential Equations
  • Language: en
  • Pages: 217

Polynomial Chaos Methods for Hyperbolic Partial Differential Equations

  • Type: Book
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  • Published: 2015-03-10
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  • Publisher: Springer

This monograph presents computational techniques and numerical analysis to study conservation laws under uncertainty using the stochastic Galerkin formulation. With the continual growth of computer power, these methods are becoming increasingly popular as an alternative to more classical sampling-based techniques. The text takes advantage of stochastic Galerkin projections applied to the original conservation laws to produce a large system of modified partial differential equations, the solutions to which directly provide a full statistical characterization of the effect of uncertainties. Polynomial Chaos Methods of Hyperbolic Partial Differential Equations focuses on the analysis of stochas...

The Effects of Viscoelasticity on the Transitioning Cylinder Wake
  • Language: en
  • Pages: 166

The Effects of Viscoelasticity on the Transitioning Cylinder Wake

Using a newly developed three dimensional, time dependent finite volume code designed to compute non-Newtonian flows over a large range of Reynolds number (Re), we performed simulations of viscoelastic flow past a circular cylinder. Our goal was to elucidate elastic effects during transition to turbulence in a bluff body wake. Based on its ability to capture essential physical processes in turbulent drag reduction studies, the FENE-P rheological model was employed in the calculation, and the numerical method utilized was such that a large range of rheological parameters (polymer length L, dimensionless Weissenberg number (Wi), and polymer concentration (beta) in the FENE-P model) could be pr...

Foundations of Reinforcement Learning with Applications in Finance
  • Language: en
  • Pages: 658

Foundations of Reinforcement Learning with Applications in Finance

  • Type: Book
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  • Published: 2022-12-16
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  • Publisher: CRC Press

Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump...

Modeling, Simulation and Optimization for Science and Technology
  • Language: en
  • Pages: 252

Modeling, Simulation and Optimization for Science and Technology

  • Type: Book
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  • Published: 2014-06-18
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  • Publisher: Springer

This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problems with multiple criteria; four articles are on numerical methods for applied problems modeled with nonlinear PDEs; two contributions are on abstract estimates for error analysis; finally one paper deals with rare events in the context of uncertainty quantification. Applications include aerospace, glaciology and nonlinear elasticity. Herein is a selection of contributions from speakers at two conferences on applied mathematics held in June 2012 at the University of Jyväskylä, Finland. The first...

Uncertainty Quantification in Computational Fluid Dynamics
  • Language: en
  • Pages: 347

Uncertainty Quantification in Computational Fluid Dynamics

Fluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space. The methods are also complemented by concrete applications such as flows around aerofoils and rockets, problems of aeroelasticity (fluid-structure interactions), and shallow water flows for propagating water waves. The wealth of numerical examples provide evidence on the suitability of each proposed method as well as comparisons of different approaches.

Aerospace Science and Engineering
  • Language: en
  • Pages: 209

Aerospace Science and Engineering

The Aerospace PhD Days are organized by the Italian Association of Aeronautics and Astronautics, AIDAA, and are open to PhD students working on Aerospace Science and Engineering topics. The 2024 proceedings edition has 42 presentations, with authors from more than ten institutions, including delegates from China, Germany, Lithuania, and Switzerland. Many aerospace disciplines and topics were covered, such as fluid dynamics, structures, stratospheric balloons, maintenance and operations, UAV, dynamics and control, space systems, sustainability of aeronautics and space, aeroelasticity, multiphysics, space debris, aeroacoustics, navigation and traffic management, additive manufacturing, and human-machine interaction. Keywords: Luid Dynamics, Structures, Stratospheric Balloons, Maintenance and Operations, UAV, Dynamics and Control, Space Systems, Sustainability of Aeronautics and Space, Aeroelasticity, Multiphysics, Space Debris, Aeroacoustics, Navigation and Traffic Management, Additive Manufacturing, Human-Machine Interaction.

Deep Learning for Fluid Simulation and Animation
  • Language: en
  • Pages: 173

Deep Learning for Fluid Simulation and Animation

This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods – and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader – more specifically, researchers of more traditional areas of mathematical modeling – about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches.

Aeronautics and Astronautics
  • Language: en
  • Pages: 805

Aeronautics and Astronautics

These conference proceedings present 165 papers in all scientific and aerospace engineering fields, including materials and structures, aerodynamics and fluid dynamics, propulsion, aerospace systems, flight mechanics and control, space systems, and missions. Keywords: Aerospace Shell Structures, MCAST's Aerospace Program, Sandwich Structures, Thermal Buckling, Simulation of Elastodynamic Problems. Statically Deflected Beam, Meshes with Arbitrary Polygons, Variable Stiffness Composite Panels, Mechanical Response of Composites, 3D Printing Technique, Hygrothermal Effects in Composite Materials, Freeze-Thaw Cycling, Polymer Matrices, Morphing Aileron, Thermo-Elastic Homogenization of Polycrysta...