Advanced Methods For Processing And Visualizing The Renewable Energy
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Advanced Methods for Processing and Visualizing the Renewable Energy
This book is a collection of research work conducted by researchers at Centre for Smart Grid Energy Research (CSMER), Institute of Autonomous System, Universiti Teknologi PETRONAS (UTP), and Seismic Modelling and Inversion Group, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. The book covers topics in the field of renewable energy where visualization, artificial neural network and deep learning techniques have been applied to optimize the performance of various applications in energy-related industries. These examples include a natural gas vehicle (NGV), a single axis and a fixed axis solar tracker, seismic inversion enhanced oil recovery, viability of a PV system and construction of a septic B-spline tensor product scheme. Readers will benefit from these examples, which describe the current trend of energy optimization techniques in renewable energy applications making it a good reference for the researchers and industrial practitioners working in the field of renewable energy and optimization techniques.
Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration
The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.
Numerical Modeling of Nanoparticle Transport in Porous Media
Numerical Modeling of Nanoparticle Transport in Porous Media: MATLAB/PYTHON Approach focuses on modeling and numerical aspects of nanoparticle transport within single- and two-phase flow in porous media. The book discusses modeling development, dimensional analysis, numerical solutions and convergence analysis. Actual types of porous media have been considered, including heterogeneous, fractured, and anisotropic. Moreover, different interactions with nanoparticles are studied, such as magnetic nanoparticles, ferrofluids and polymers. Finally, several machine learning techniques are implemented to predict nanoparticle transport in porous media. This book provides a complete full reference in mathematical modeling and numerical aspects of nanoparticle transport in porous media. It is an important reference source for engineers, mathematicians, and materials scientists who are looking to increase their understanding of modeling, simulation, and analysis at the nanoscale. - Explains the major simulation models and numerical techniques used for predicting nanoscale transport phenomena - Provides MATLAB codes for most of the numerical simulation and Python codes for machine learning calculations - Uses examples and results to illustrate each model type to the reader - Assesses major application areas for each model type