Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This work presents an efficient solution procedure for the elastohydrodynamic (EHD) contact problem considering structural dynamics. The contact bodies are modeled using reduced finite element models. Singly diagonal implicit Runge-Kutta (SDIRK) methods are used for adaptive time integration. The structural model is coupled with the nonlinear Reynolds Equation using a monolithic coupling approach. Finally, a reduced order model of the complete nonlinear coupled problem is constructed.

EHD --- ehd --- Strukturdynamik --- Modellordnungsreduktion --- Zeitintegration --- Newton-Raphson method --- model order reduction --- structural dynamics --- Newton-Verfahren --- time integration --- EHD --- ehd --- Strukturdynamik --- Modellordnungsreduktion --- Zeitintegration --- Newton-Raphson method --- model order reduction --- structural dynamics --- Newton-Verfahren --- time integration

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This work presents an efficient solution procedure for the elastohydrodynamic (EHD) contact problem considering structural dynamics. The contact bodies are modeled using reduced finite element models. Singly diagonal implicit Runge-Kutta (SDIRK) methods are used for adaptive time integration. The structural model is coupled with the nonlinear Reynolds Equation using a monolithic coupling approach. Finally, a reduced order model of the complete nonlinear coupled problem is constructed.

EHD --- ehd --- Strukturdynamik --- Modellordnungsreduktion --- Zeitintegration --- Newton-Raphson method --- model order reduction --- structural dynamics --- Newton-Verfahren --- time integration

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is devoted to the latest advances in the area of electrothermal modelling of electronic components and networks. It contains eight sections by different teams of authors. These sections contain the results of: (a) electro-thermal simulations of SiC power MOSFETs using a SPICE-like simulation program; (b) modelling thermal properties of inductors taking into account the influence of the core volume on the efficiency of heat removal; (c) investigations into the problem of inserting a temperature sensor in the neighbourhood of a chip to monitor its junction temperature; (d) computations of the internal temperature of power LEDs situated in modules containing multiple-power LEDs, taking into account both self-heating in each power LED and mutual thermal couplings between each diode; (e) analyses of DC-DC converters using the electrothermal averaged model of the diode–transistor switch, including an IGBT and a rapid-switching diode; (f) electrothermal modelling of SiC power BJTs; (g) analysis of the efficiency of selected algorithms used for solving heat transfer problems at nanoscale; (h) analysis related to thermal simulation of the test structure dedicated to heat-diffusion investigation at the nanoscale.

Dual-Phase-Lag heat transfer model --- thermal simulation algorithm --- thermal measurements --- Finite Difference Method scheme --- Grünwald–Letnikov fractional derivative --- Krylov subspace-based model order reduction --- algorithm efficiency analysis --- relative error analysis --- algorithm convergence analysis --- computational complexity analysis --- finite difference method scheme --- BJT --- modelling --- self-heating --- silicon carbide --- SPICE --- IGBT --- DC–DC converter --- electrothermal model --- averaged model --- thermal phenomena --- diode–transistor switch --- power electronics --- multi-LED lighting modules --- device thermal coupling --- compact thermal models --- temperature sensors --- microprocessor --- throughput improvement --- inductors --- ferromagnetic cores --- thermal model --- transient thermal impedance --- thermal resistance --- electrothermal (ET) simulation --- finite-element method (FEM) --- model-order reduction (MOR) --- multicellular power MOSFET --- silicon carbide (SiC)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

With the growing emphasis on enhancing the sustainability and efficiency of industrial plants, process integration and intensification are gaining additional interest throughout the chemical engineering community. Some of the hallmarks of process integration and intensification include a holistic perspective in design, and the enhancement of material and energy intensity. The techniques are applicable for individual unit operations, multiple units, a whole industrial facility, or even a cluster of industrial plants. This book aims to cover recent advances in the development and application of process integration and intensification. Specific applications are reported for hydraulic fracturing, palm oil milling processes, desalination, reactive distillation, reaction network, adsorption processes, herbal medicine extraction, as well as process control.

input shaping --- n/a --- integrating --- flexibility index --- breakthrough --- mixing --- membrane distillation --- regulatory --- utilisation index --- experimental --- underdamped --- PMPS particles --- EDCs --- phytomedicines --- natural products --- reactive distillation --- optimisation --- optimization --- multiple steady state --- steady state simulation --- design --- CFD-simulation --- manufacturing --- compartmental modeling --- energy --- surrogate-based optimization --- adsorption --- feasible operating range analysis --- model order reduction --- competing reaction system --- desalination --- extraction --- water --- mathematical programming --- graphical approach --- hydraulic fracturing --- unstable --- humidification --- reaction conversion --- dehumidification --- TAME synthesis --- fixed-bed column --- predictive control