Engineering Commons^™

Artificial Intelligence Approaches For Structural Health Monitoring Of Aerospace Structures, Kimberly A. Cardillo

Theses and Dissertations

Structural health monitoring (SHM) and non-destructive evaluation (NDE) have been a significant research topic to help with damage detection in aerospace structures. SHM and NDE techniques are based on extracting damage sensitive features to determine the criticality of damage and lifetime of a structure. Acoustic emission (AE) signal detection is an important technique in SHM and NDE especially for fatigue crack growth. AE signals for thin aerospace structures consist of ultrasonic guided Lamb waves that propagate through the structure. This thesis focuses on AE signal repeatability, load at which AE signals occur, feature extraction, artificial intelligence and electro-mechanical impedance of …

Short Term Energy Forecasting For A Microgird Load Using Lstm Rnn, Akhil Soman

Masters Theses

Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting.

In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as …

Development Of Horizontal Axis Hydrokinetic Turbine Using Experimental And Numerical Approaches, Abdulaziz Abutunis

Doctoral Dissertations

“Hydrokinetic energy conversion systems (HECSs) are emerging as viable solutions for harnessing the kinetic energy in river streams and tidal currents due to their low operating head and flexible mobility. This study is focused on the experimental and numerical aspects of developing an axial HECS for applications with low head ranges and limited operational space. In Part I, blade element momentum (BEM) and neural network (NN) models were developed and coupled to overcome the BEM’s inherent convergence issues which hinder the blade design process. The NNs were also used as a multivariate interpolation tool to estimate the blade hydrodynamic characteristics …