This project is investigating the application of Spread Spectrum Time Domain Reflectivity (SSTDR) to monitor operating strings of modules in large photovoltaic (PV) arrays. SSTDR can detect, and spatially localize, changes in the impedance of the system in real-time, including at high voltages and currents. This allows monitoring of intermittent and slowly-evolving degradation and failure modes, and potentially enables more efficient characterization of PV power plants, which will maximize future energy output, reduce the levelized cost of electricity, and increase bankability.
The objective of the project is to establish the foundation for Helmholtz networks, which are deep, generative, physics-informed neural networks that reconstruct ultrasonic wave propagation and locate ultrasonic sources. The Helmholtz networks are based on the fact that each frequency of a wave can be represented as the sum of a sparse number of spatial modes. The modes are constrained by the Helmholtz equation and this physical constraint ensures that the machine learning algorithm is trustworthy for system-critical engineered systems (e.g., health monitoring of an aircraft). Such physics-informed machine learning is an important (albeit not widely studied) topic for integrating advanced computation tools into real-time engineered systems.
The goal of the proposed work is to combine deep, model-based reinforcement learning and transfer learning to elucidate one of the most fundamental, yet poorly understood, mechanisms in materials science: abnormal grain growth. We hypothesize that abnormal grain growth is the result of highly anisotropic grain boundary character networks, where a unique combination of neighboring grain boundaries incentivize accelerated growth.
The objective of this proposal is to develop a multiscale simulation approach to study dislocation – grain boundary interactions and to employ this approach to advance criteria for slip transfer across grain boundaries to consider the grain boundary damage state. In addition, the proposed simulation approach will allow for an analysis of the role of dislocation core structures on slip transfer. The hypothesis of this proposal is that the mechanisms by which dislocations are obstructed, absorbed, and/or transmitted through grain boundaries are sensitive to the damage state of the grain boundary (defined as a departure from equilibrium atomic structure) in addition to slip geometry. Such details are critically important to understand how grain boundaries promote strengthening or act as sources/sinks for damage during high rate plastic deformation.
In this project we aim to use high-throughput computational screening, coupled with experimental characterization, DFT techniques and thermodynamic modeling to identify novel and efficient second generation thermochemical redox materials that operate isothermally or near isothermally below 1400 °C. We predict that we can improve performance (i.e. increase efficiency and decrease operating temperature) using identified materials compared to the state of the art material, ceria, by operating at high pO2 (> 10-6 atm) and perturbing the system from equilibrium with either rapid changes in pressure or temperature (only small deviations, or “near” isothermal) or a combination of the two, using engineered structures (e.g. porous scaffolds) that afford enhanced surface driven kinetics without requiring bulk heating and cooling. We aim to leverage the expertise of University of Florida faculty from Mechanical Engineering and Aerospace Engineering (Jonathan Scheffe) and Materials Science Engineering (Juan Nino and Simon Phillpot) alongside four DOE HydroGEN nodes.