Xiaoqi Chen – författare

Laser Direct Energy Deposition (DED) 3D Printing of Superalloys synthesizes a decade of pioneering translational research. The book systematically tackles critical challenges in DED of superalloys towards industrial applications, including process modeling, microstructure control, defect suppression, and mechanical property optimization. It also presents a suite of innovative methodologies, such as physics-informed neural networks, gradient laser power deposition, ultrasonic vibration-assisted deposition, and heterogeneous alloy doping. Backed by validated models, the book serves as an indispensable resource. It is tailored for researchers, engineers, and graduate students aiming to master DED technology and its applications in aerospace, energy, and high-value component manufacturing.

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consider at the beginning of the GT modeling process, such as GT types and configurations, control system types and configurations, and modeling methods and objectivesHighlights research in the fields of white-box and black-box modeling, simulation, and control of GTs, exploring models of low-power GTs, industrial power plant gas turbines (IPGTs), and aero GTsDiscusses the structure of ANNs and the ANN-based model-building process, including system analysis, data acquisition and preparation, network architecture, and network training and validationPresents a noteworthy ANN-based methodology for offline system identification of GTs, complete with validated models using both simulated and real operational dataCovers the modeling of GT transient behavior and start-up operation, and the design of proportional-integral-derivative (PID) and neural network-based controllersGas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks not only offers a comprehensive review of the state of the art of gas turbine modeling and intelligent techniques, but also demonstrates how artificial intelligence can be used to solve complicated industrial problems, specifically in the area of GTs.

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consider at the beginning of the GT modeling process, such as GT types and configurations, control system types and configurations, and modeling methods and objectivesHighlights research in the fields of white-box and black-box modeling, simulation, and control of GTs, exploring models of low-power GTs, industrial power plant gas turbines (IPGTs), and aero GTsDiscusses the structure of ANNs and the ANN-based model-building process, including system analysis, data acquisition and preparation, network architecture, and network training and validationPresents a noteworthy ANN-based methodology for offline system identification of GTs, complete with validated models using both simulated and real operational dataCovers the modeling of GT transient behavior and start-up operation, and the design of proportional-integral-derivative (PID) and neural network-based controllersGas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks not only offers a comprehensive review of the state of the art of gas turbine modeling and intelligent techniques, but also demonstrates how artificial intelligence can be used to solve complicated industrial problems, specifically in the area of GTs.