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Computational Aerodynamic Modeling of Aerospace Vehicles

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ISBN: 9783038976103 Year: Pages: 294 DOI: 10.3390/books978-3-03897-611-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Transportation
Added to DOAB on : 2019-03-08 11:42:05
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Abstract

Currently, the use of computational fluid dynamics (CFD) solutions is considered as the state-of-the-art in the modeling of unsteady nonlinear flow physics and offers an early and improved understanding of air vehicle aerodynamics and stability and control characteristics. This Special Issue covers recent computational efforts on simulation of aerospace vehicles including fighter aircraft, rotorcraft, propeller driven vehicles, unmanned vehicle, projectiles, and air drop configurations. The complex flow physics of these configurations pose significant challenges in CFD modeling. Some of these challenges include prediction of vortical flows and shock waves, rapid maneuvering aircraft with fast moving control surfaces, and interactions between propellers and wing, fluid and structure, boundary layer and shock waves. Additional topic of interest in this Special Issue is the use of CFD tools in aircraft design and flight mechanics. The problem with these applications is the computational cost involved, particularly if this is viewed as a brute-force calculation of vehicle’s aerodynamics through its flight envelope. To make progress in routinely using of CFD in aircraft design, methods based on sampling, model updating and system identification should be considered.

Keywords

wake --- bluff body --- square cylinder --- DDES --- URANS --- turbulence model --- large eddy simulation --- Taylor–Green vortex --- numerical dissipation --- modified equation analysis --- truncation error --- MUSCL --- dynamic Smagorinsky subgrid-scale model --- kinetic energy dissipation --- computational fluid dynamics (CFD) --- microfluidics --- numerical methods --- gasdynamics --- shock-channel --- microelectromechanical systems (MEMS) --- discontinuous Galerkin finite element method (DG–FEM) --- fluid mechanics --- characteristics-based scheme --- multi-directional --- Riemann solver --- Godunov method --- bifurcation --- wind tunnel --- neural networks --- modeling --- unsteady aerodynamic characteristics --- high angles of attack --- hypersonic --- wake --- chemistry --- slender-body --- angle of attack --- detection --- after-body --- S-duct diffuser --- flow distortion --- flow control --- vortex generators --- aeroelasticity --- reduced-order model --- flutter --- wind gust responses --- computational fluid dynamics --- convolution integral --- sharp-edge gust --- reduced order aerodynamic model --- geometry --- meshing --- aerodynamics --- CPACS --- MDO --- VLM --- Euler --- CFD --- variable fidelity --- multi-fidelity --- aerodynamic performance --- formation --- VLM --- RANS --- hybrid reduced-order model --- quasi-analytical --- aeroelasticity --- flexible wings --- subsonic --- wing–propeller aerodynamic interaction --- p-factor --- installed propeller --- overset grid approach

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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ISBN: 9783039214099 / 9783039214105 Year: Pages: 254 DOI: 10.3390/books978-3-03921-410-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

Keywords

parameter-dependent model --- surrogate modeling --- tensor-train decomposition --- gappy POD --- heterogeneous data --- elasto-viscoplasticity --- archive --- model reduction --- 3D reconstruction --- inverse problem plasticity --- data science --- model order reduction --- POD --- DEIM --- gappy POD --- GNAT --- ECSW --- empirical cubature --- hyper-reduction --- reduced integration domain --- computational homogenisation --- model order reduction (MOR) --- low-rank approximation --- proper generalised decomposition (PGD) --- PGD compression --- randomised SVD --- nonlinear material behaviour --- machine learning --- artificial neural networks --- computational homogenization --- nonlinear reduced order model --- elastoviscoplastic behavior --- nonlinear structural mechanics --- proper orthogonal decomposition --- empirical cubature method --- error indicator --- symplectic model order reduction --- proper symplectic decomposition (PSD) --- structure preservation of symplecticity --- Hamiltonian system --- reduced order modeling (ROM) --- proper orthogonal decomposition (POD) --- enhanced POD --- a priori enrichment --- modal analysis --- stabilization --- dynamic extrapolation --- computational homogenization --- large strain --- finite deformation --- geometric nonlinearity --- reduced basis --- reduced-order model --- sampling --- Hencky strain --- microstructure property linkage --- unsupervised machine learning --- supervised machine learning --- neural network --- snapshot proper orthogonal decomposition

Methods in Computational Biology

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ISBN: 9783039211630 / 9783039211647 Year: Pages: 214 DOI: 10.3390/books978-3-03921-164-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-08-28 11:21:27
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Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measurement

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