Method and Apparatus for Enhanced in Vivo MRI Imaging
Techniques described herein combine conventional proton magnetic resonance imaging (MRI) with novel image processing and analysis tools to develop methods for evaluating localized disease state in patients with emphysema, fibrosis, edema, or any combination of these. The imaging techniques employed are spatially sensitive, allowing for more precise diagnosis than spirometry, and do not expose the patient to ionizing radiation. Emphysema will be detected through imaging and analysis that is sensitive to local compliance changes, while fibrosis and edema will be detected and classified through measurements of the distribution, density, and environment of water molecules. It is envisioned that both of these imaging techniques would be employed, if necessary, during a single imaging session. Combination of these techniques into one image/analysis package would provide a powerful MRI tool for disease evaluation and diagnosis. In addition to clinical application, these combined tools would be valuable for large-scale pre-clinical animal studies for assessment of: disease phenotype, severity, progression, and localization; thereby facilitating targeted tissue harvesting, guiding molecular analysis, and preventing blind sacrifice.
DISTRIBUTED HIERARCHICAL CONTROL ARCHITECTURE FOR INTEGRATING SMART GRID ASSETS DURING NORMAL AND DISRUPTED OPERATIONS
Distributed generation, demand response, distributed storage, smart appliances, electric vehicles, and other emerging distributed smart grid assets are expected to play a key part in the transformation of the American power system. Due to the variability and uncertainty associated with these resources, there is much trepidation from the part of system planners and operators about the controllability of such resources, and how they affect the stability of the grid infrastructure. It is proposed to develop a hierarchical, distributed control architecture, enabling smart grid assets to effectively contribute to grid operations in a controllable manner, while ensuring system stability and equitably rewarding their contribution. The architecture will unify the dispatch of these resources to provide both market-based and balancing services. A means to dynamically select and arm the autonomous responses from these assets, enabling them to offer significant reliability benefits under the full range of grid operating conditions, will be developed. Transmission-level controls will be integrated with new and existing distribution-level control strategies, within a market structure, under both normal and disrupted operations (disrupted communications and other unforeseen events).
METHODS AND SYSTEMS FOR EVALUATING AND IMPROVING DISTRIBUTION-GRID OBSERVABILITY
The invention is a means to quantify grid observability (the ability to determine a set of operating parameters such as voltages, currents, and real and reactive power flows) from a set of (possibly sparse) grid sensor readings and a distribution grid system model and to determine the "islands of observability" where such determination is possible, and by exclusion, where it is not possible (resulting in deficiencies in grid observability that may have to be remedied for proper grid operation). The primary innovations here are in the means to determine the relationships among the sensors and the grid parameters needed for observability for three phase unbalanced distribution circuits, given the sensor placement and grid system model (topology and admittance matrix, or just the topology alone); taking into account grid model uncertainty and communication system characteristics (transportability), and further to determine zones where full observability is possible("islands of observability") and where it is not; to allow for interactive placement of sensors with continual re-calculation of observability indices and observability islands, to provide automatic allocation of sensors to a grid model via sensor allocation strategies, again with calculation of indices and islands, and finally to automatically optimize sensor allocation and placement.
Thomas Wild
James Ang
James (Jim) Ang, is the Chief Scientist for Computing in the Physical and Computational Sciences Directorate at Pacific Northwest National Laboratory (PNNL).
Combinatorial Evaluation of Systems Including Decomposition of a System Representation Into Fundamental Cycles
We construct an algebraic-combination model of networks-of-networks. A Petri net is used to construct an initial representation of the decision-making network, which in turn defines a hyperdigraph. We observe that the linear algebraic structure of each hyperdigraph admits a canonical set of algebraic-combinatorial invariants that correspond to the information flow conservation laws governing a kinetic network. The linear algebraic structure of the hyperdigraph and its sets of invariants can be generalized to define a discrete algebraic-geometric structure, which is referred to as an oriented Matroid. Oriented matroids define a polyhedral optimization geometry that is used to determine optimal subpaths that span the nullspace of a set of kinetic equations. Sets of constrained submodular path optimizations on the hyperdigraph are objectively obtained as a spanning tree of minimum cycle paths. This complete set of subcircuits is used to identify the network pinch points and invariant flow subpaths. We demonstrate that this family of minimal circuits also characteristically identifies additional significant pattern features. We used several applications (including the biochemistry of the Krebs Cycle, the SOS Compartment A of the EGFR biochemical pathway, and economics-driven electric power grids) to develop and demonstrate the application of our algebraic-combinatorial mathematical modeling methodology.
Michael Kintner-Meyer
Michael Kintner-Meyer is a research engineer and systems analyst at Pacific Northwest National Laboratory.
HIGHLY STABLE PHENAZINE DERIVATIVES FOR AQUEOUS REDOX FLOW BATTERIES
In this report, rationally functionalized, highly water-soluble phenazine derivatives are disclosed as a new class of redox-active anolyte material for aqueous redox flow batteries. These compounds are compatible with basic electrolytes leading to relatively high rate performance. They have sufficiently low redox potential (-1V vs Ag/AgCl) in basic electrolytes, which can enable high voltage flow batteries systems. In addition, they have two electron transfers and this is very helpful to improve their energy density by double. When coupled with potassium ferrocyanide, the flow cell exhibited a relatively stable cycling for ~300 cycles at 20 mA/cm2. The great cyclability indicate that these compounds and their charged species are chemically very stable, promising for highly durable flow battery systems. Moreover, these compounds can be synthesized from very inexpensive precursors through simple one-step synthesis. This feature allows easy molecular engineering to enable high solubilities and can lead to high cost-effectiveness redox materials. Therefore, the organic phenazine derivative compounds are expected to be promising material candidates to achieve competitive aqueous redox flow batteries that have high voltage, high energy density, good power density, long durability, and low cost.
CONTROL FOR ENERGY RESOURCES IN A MICROGRID
This concept uses a slider setting for microgrid operations that allows a user to select between "more efficient" and "more resilient". This is similar to the slider setting concept for transactive control, except that they are influencing different technical values. As the slider is set to more efficient, the dispatch and droop values of the generators are adjusted to increase the operating efficiency of the system. This is achieved by moving the operating points of the generators to their most efficient points while still meeting the current load. As the slider is set to more resilient, the dispatch and droop values of the generators are adjusted to minimize the frequency deviation from an expected increase in load or loss of generation. The value of the slider setting could be set by a human operator, or determined as part of a more complex control system. For example, the slider value could be determined as the output of a neural network that is optimization the operation of multiple networked microgrids. In its current state the work is using a modified version of the IEEE-123 node test system with 2 diesel generators and 1 PV inverter. As the slider setting is varied the control system determines the set points for both diesel generators and the PV inverter. The values for each generator include their power outputs and their current droop values for controls. The result is that the single slider setting determines multiple set points on multiple generators. The method is scalable, but the optimization becomes computationally burdensome with large number of generators. This should not be an issue with most operational microgrids.