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Rotating detonation Engines (RDE) are a novel technique in pressure gain combustion. In this type of combustion the theoretical efficiency gain achievable is not possible using traditional combustion techniques. The dynamics of the rotating detonation engines is complex, and high dimensional. Reduced order modelling are tools that can be used to reduce the high dimensional nature of these systems. Dynamic Mode Decomposition is one such technique. Dynamics in the rotating detonation engine manifests itself in two major forms. The first is a spinning wave form in which a single detonation wave that travels around the annulus of the engine at a very fast rate. The second is a clapping wave form which is obtained when two counterrotating waves interact with each other around the annulus. A robust dynamic mode decomposition algorithm is developed and it is benchmarked using synthetic data generated using mathematical formulations. Synthetic data is generated to mimic the dynamics present in the rotating detonation engine. Once the algorithm has been sufficiently tested and validated it is applied to the experimental data. High speed images of the aft end of the rotating detonation engine is used, the dynamic mode decomposition algorithm is then applied to this data and it is demonstrated that the dynamics of the system can be predicted using few dynamic modes and frequencies. However it was found that the error is still large and has to be reduced in order to create a reliable and accurate reduced order model of the engine. The images are then modified to isolate the region where the detonation wave operates. DMD on reduced set of images produces degenerate modes. The number of snapshot taken for analysis was shown to affect the accuracy and presence of so called degenerate modes. It was shown that the best accuracy value was achieved when the number of snapshots is equal to the number of the modes taken. It was further shown that for this test case the best accuracy was obtained using just 50 snapshots and 50 singular modes. Long term validity of the parameters obtained i.e. the number of snapshots and number of modes retained was demonstrated. It was seen that the frequencies and reconstruction error converge and oscillate around a mean. The maximum deviation of these parameters was within acceptable limits. The mode shapes also converge thus indicating that the parameters are valid for the whole test case. This also proved that there are no transients in the test case.

Dynamic Mode Decomposition, Rotating Detonation Engines, Reduced Order Modelling --- Ingénierie, informatique & technologie > Ingénierie aérospatiale

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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.

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

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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.

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

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This book is devoted to the teaching and learning of fluid mechanics. Fluid mechanics occupies a privileged position in the sciences; it is taught in various science departments including physics, mathematics, mechanical, chemical and civil engineering and environmental sciences, each highlighting a different aspect or interpretation of the foundation and applications of fluids. While scholarship in fluid mechanics is vast, expanding into the areas of experimental, theoretical and computational fluid mechanics, there is little discussion among scientists about the different possible ways of teaching this subject. We think there is much to be learned, for teachers and students alike, from an interdisciplinary dialogue about fluids. This volume therefore highlights articles which have bearing on the pedagogical aspects of fluid mechanics at the undergraduate and graduate level.

Technology: general issues --- fluid dynamics education --- damped pendulums --- fluid drag --- fluid-structure interaction --- computational fluid dynamics --- outcomes competences --- hydraulic engineering --- hydraulic teaching --- active methodology --- droplet impact --- undergraduate education --- applications of fluids --- vortex formation length --- wake --- vortex shedding --- practical engineering education --- fluid mechanics --- learning and teaching --- laboratories --- data assimilation --- variational and sequential methods --- Kalman filtering --- forward sensitivity --- measurements fusion --- reduced order models --- quasi-geostrophic equations --- closure models --- Navier-Stokes equations --- Leray-Hopf weak solutions --- existence --- inquiry-based instruction --- science education --- teaching-learning sequences --- didactic transformation --- primary level --- CFD --- Julia --- Blasius --- Hiemenz --- Homann --- Falkner–Skan --- boundary-layer --- open water tank --- education --- n/a --- Falkner-Skan

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This book is devoted to the teaching and learning of fluid mechanics. Fluid mechanics occupies a privileged position in the sciences; it is taught in various science departments including physics, mathematics, mechanical, chemical and civil engineering and environmental sciences, each highlighting a different aspect or interpretation of the foundation and applications of fluids. While scholarship in fluid mechanics is vast, expanding into the areas of experimental, theoretical and computational fluid mechanics, there is little discussion among scientists about the different possible ways of teaching this subject. We think there is much to be learned, for teachers and students alike, from an interdisciplinary dialogue about fluids. This volume therefore highlights articles which have bearing on the pedagogical aspects of fluid mechanics at the undergraduate and graduate level.

fluid dynamics education --- damped pendulums --- fluid drag --- fluid-structure interaction --- computational fluid dynamics --- outcomes competences --- hydraulic engineering --- hydraulic teaching --- active methodology --- droplet impact --- undergraduate education --- applications of fluids --- vortex formation length --- wake --- vortex shedding --- practical engineering education --- fluid mechanics --- learning and teaching --- laboratories --- data assimilation --- variational and sequential methods --- Kalman filtering --- forward sensitivity --- measurements fusion --- reduced order models --- quasi-geostrophic equations --- closure models --- Navier-Stokes equations --- Leray-Hopf weak solutions --- existence --- inquiry-based instruction --- science education --- teaching-learning sequences --- didactic transformation --- primary level --- CFD --- Julia --- Blasius --- Hiemenz --- Homann --- Falkner–Skan --- boundary-layer --- open water tank --- education --- n/a --- Falkner-Skan