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Full-Text Articles in Mechanical Engineering
Deep Learning Of Nonlinear Dynamical System, Aditya Wagh
Deep Learning Of Nonlinear Dynamical System, Aditya Wagh
Dissertations, Master's Theses and Master's Reports
A data-driven approach, such as neural networks, is an alternative to traditional parametric-model methods for nonlinear system identification. Recently, long Short- Term Memory (LSTM) neural networks have been studied to model nonlinear dynamical systems. However, many of these contributions are made considering that the input to the system is known or measurable, which often may not be the case. This thesis presents a method based on LSTM for output-only modeling, identification, and prediction of nonlinear systems. A numerical study is performed and discussed on Duffing systems with various cubic nonlinearity.
Model Updating And Structural Health Monitoring Of Horizontal Axis Wind Turbines Via Advanced Spinning Finite Elements And Stochastic Subspace Identification Methods, Antonio Velazquez Hernandez
Model Updating And Structural Health Monitoring Of Horizontal Axis Wind Turbines Via Advanced Spinning Finite Elements And Stochastic Subspace Identification Methods, Antonio Velazquez Hernandez
Dissertations, Master's Theses and Master's Reports - Open
Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios …