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The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies.
Research & information: general --- Physics --- doubly-fed variable-speed pumped storage --- Hopf bifurcation --- stability analysis --- parameter sensitivity --- pumped storage unit --- degradation trend prediction --- maximal information coefficient --- light gradient boosting machine --- variational mode decomposition --- gated recurrent unit --- high proportional renewable power system --- active power --- change point detection --- maximum information coefficient --- cosine similarity --- anomaly detection --- thermal-hydraulic characteristics --- hydraulic oil viscosity --- hydraulic PTO --- wave energy converter --- pumped storage units --- pressure pulsation --- noise reduction --- sparrow search algorithm --- hybrid system --- facility agriculture --- chaotic particle swarms method --- operation strategy --- stochastic dynamic programming (SDP) --- power yield --- seasonal price --- reliability --- cascaded reservoirs --- doubly-fed variable speed pumped storage power station --- nonlinear modeling --- nonlinear pump turbine characteristics --- pumped storage units (PSUs) --- successive start-up --- ‘S’ characteristics --- low water head conditions --- multi-objective optimization --- fractional order PID controller (FOPID) --- hydropower units --- comprehensive deterioration index --- long and short-term neural network --- ensemble empirical mode decomposition --- approximate entropy --- 1D–3D coupling model --- transition stability --- sensitivity analysis --- hydro power
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Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics.
Technology: general issues --- History of engineering & technology --- Mining technology & engineering --- sheet metal forming --- uncertainty analysis --- metamodeling --- machine learning --- hot rolling strip --- edge defects --- intelligent recognition --- convolutional neural networks --- deep-drawing --- kriging metamodeling --- multi-objective optimization --- FE (Finite Element) AutoForm robust analysis --- defect prediction --- mechanical properties prediction --- high-dimensional data --- feature selection --- maximum information coefficient --- complex network clustering --- ring rolling --- process energy estimation --- metal forming --- thermo-mechanical FEM analysis --- artificial neural network --- aluminum alloy --- mechanical property --- UTS --- topological optimization --- artificial neural networks (ANN) --- machine learning (ML) --- press-brake bending --- air-bending --- three-point bending test --- sheet metal --- buckling instability --- oil canning --- artificial intelligence --- convolution neural network --- hot rolled strip steel --- defect classification --- generative adversarial network --- attention mechanism --- deep learning --- mechanical constitutive model --- finite element analysis --- plasticity --- parameter identification --- full-field measurements --- n/a
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