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Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

Technology: general issues --- History of engineering & technology --- self-healing grid --- machine-learning --- feature extraction --- event detection --- optimization techniques --- manta ray foraging optimization algorithm --- multi-objective function --- radial networks --- optimal power flow --- automatic P2P energy trading --- Markov decision process --- deep reinforcement learning --- deep Q-network --- long short-term delayed reward --- inter-area oscillations --- modal analysis --- reduced order modeling --- dynamic mode decomposition --- machine learning --- artificial neural networks --- steady-state security assessment --- situation awareness --- cellular computational networks --- load flow prediction --- contingency --- fuzzy system --- change detection --- data analytics --- data mining --- filtering --- optimization --- power quality --- signal processing --- total variation smoothing --- n/a

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

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

self-healing grid --- machine-learning --- feature extraction --- event detection --- optimization techniques --- manta ray foraging optimization algorithm --- multi-objective function --- radial networks --- optimal power flow --- automatic P2P energy trading --- Markov decision process --- deep reinforcement learning --- deep Q-network --- long short-term delayed reward --- inter-area oscillations --- modal analysis --- reduced order modeling --- dynamic mode decomposition --- machine learning --- artificial neural networks --- steady-state security assessment --- situation awareness --- cellular computational networks --- load flow prediction --- contingency --- fuzzy system --- change detection --- data analytics --- data mining --- filtering --- optimization --- power quality --- signal processing --- total variation smoothing --- n/a

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

In recent decades, the field of computational fluid dynamics has made significant advances in enabling advanced computing architectures to understand many phenomena in biological, geophysical, and engineering fluid flows. Almost all research areas in fluids use numerical methods at various complexities: from molecular to continuum descriptions; from laminar to turbulent regimes; from low speed to hypersonic, from stencil-based computations to meshless approaches; from local basis functions to global expansions, as well as from first-order approximation to high-order with spectral accuracy. Many successful efforts have been put forth in dynamic adaptation strategies, e.g., adaptive mesh refinement and multiresolution representation approaches. Furthermore, with recent advances in artificial intelligence and heterogeneous computing, the broader fluids community has gained the momentum to revisit and investigate such practices. This Special Issue, containing a collection of 13 papers, brings together researchers to address recent numerical advances in fluid mechanics.

History of engineering & technology --- fluid–structure interaction --- monolithic method --- Updated Lagrangian --- Arbitrary Lagrangian Eulerian --- computational aerodynamics --- Kutta condition --- compressible flow --- stream function --- non-linear Schrödinger equation --- cubic B-spline basis functions --- Galerkin method --- pressure tunnel --- hydraulic fracturing --- transient flow --- finite element method (FEM) --- Abaqus Finite Element Analysis (FEA) --- computational fluid dynamics --- RANS closures --- uncertainty quantification --- Reynolds stress tensor --- backward-facing step --- OpenFOAM --- large eddy simulations (LES) --- shock capturing --- adaptive filter --- explicit filtering --- jet --- proper orthogonal decomposition --- coherent structures --- turbulence --- vector flow fields --- PIV --- buildings --- urban area --- pollution dispersion --- Large Eddy Simulation (LES) --- multiple drop impact --- computational fluid dynamics (CFD) simulation --- volume-of-fluid --- crater dimensions --- vorticity --- transient incompressible Navier-Stokes --- meshless point collocation method --- stream function-vorticity formulation --- strong form --- explicit time integration --- wall layer model --- LES --- separated flow --- body fitted --- immersed boundary --- reduced order modeling --- Kolmogorov n-width --- Galerkin projection --- turbulent flows --- reduced order model --- closure model --- variational multiscale method --- deep residual neural network --- internal combustion engines --- liquid-cooling system --- heat transfer --- n/a --- fluid-structure interaction --- non-linear Schrödinger equation

Choose an application

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

In recent decades, the field of computational fluid dynamics has made significant advances in enabling advanced computing architectures to understand many phenomena in biological, geophysical, and engineering fluid flows. Almost all research areas in fluids use numerical methods at various complexities: from molecular to continuum descriptions; from laminar to turbulent regimes; from low speed to hypersonic, from stencil-based computations to meshless approaches; from local basis functions to global expansions, as well as from first-order approximation to high-order with spectral accuracy. Many successful efforts have been put forth in dynamic adaptation strategies, e.g., adaptive mesh refinement and multiresolution representation approaches. Furthermore, with recent advances in artificial intelligence and heterogeneous computing, the broader fluids community has gained the momentum to revisit and investigate such practices. This Special Issue, containing a collection of 13 papers, brings together researchers to address recent numerical advances in fluid mechanics.

fluid–structure interaction --- monolithic method --- Updated Lagrangian --- Arbitrary Lagrangian Eulerian --- computational aerodynamics --- Kutta condition --- compressible flow --- stream function --- non-linear Schrödinger equation --- cubic B-spline basis functions --- Galerkin method --- pressure tunnel --- hydraulic fracturing --- transient flow --- finite element method (FEM) --- Abaqus Finite Element Analysis (FEA) --- computational fluid dynamics --- RANS closures --- uncertainty quantification --- Reynolds stress tensor --- backward-facing step --- OpenFOAM --- large eddy simulations (LES) --- shock capturing --- adaptive filter --- explicit filtering --- jet --- proper orthogonal decomposition --- coherent structures --- turbulence --- vector flow fields --- PIV --- buildings --- urban area --- pollution dispersion --- Large Eddy Simulation (LES) --- multiple drop impact --- computational fluid dynamics (CFD) simulation --- volume-of-fluid --- crater dimensions --- vorticity --- transient incompressible Navier-Stokes --- meshless point collocation method --- stream function-vorticity formulation --- strong form --- explicit time integration --- wall layer model --- LES --- separated flow --- body fitted --- immersed boundary --- reduced order modeling --- Kolmogorov n-width --- Galerkin projection --- turbulent flows --- reduced order model --- closure model --- variational multiscale method --- deep residual neural network --- internal combustion engines --- liquid-cooling system --- heat transfer --- n/a --- fluid-structure interaction --- non-linear Schrödinger equation