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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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SNL and SunSpec were to create a System Validation Platform to verify the functionality of DERs with advanced grid functionality and utility communication capabilities. The Validation Platform software was to ensure DERs are compatible with new grid interconnection requirements for these advanced functions by communicating to equipment under test, data acquisition software, and test equipment.
Carbon capture is an integral part of the CO 2 mitigation efforts, and encompasses, among other measures, the demonstration of effective and inexpensive CO 2 capture technologies. The project demonstrated a novel platform—the amine-functionalized stellate mesoporous silica nanosphere (MSN)—for effective CO 2 absorption. The reported CO 2 absorption data are superior to the performance of other reported silica matrices utilized for carbon capture, featuring an amount of over 4 milimoles CO 2 /g sorbent at low temperatures (in the range of 30-45 ºC), selected for simulating the temperature of actual flue gas. The reported platform is highly resilient, showing recyclability and 85 % mass conservation of sorbent upon nine tested cycles. Importantly, the stellate MSNs show high CO 2 selectivity at room temperature, indicating that the presence of nitrogen in flue gas will not impair the CO 2 absorption performance. The results could lead to a simple and inexpensive new technology for CO 2 mitigation which could be implemented as measure of CO 2 mitigation in current fossil-fuel burning plants in the form of solid sorbent.
The objective of the Open and Extensible Control and Analytics (openECA) Platform for Phasor Data project was to develop an open source software platform that significantly accelerates the production, use, and ongoing development of real-time decision support tools, automated control systems, and off-line planning systems that (1) incorporate synchrophasor data and (2) enhance system reliability while enabling the North American Electric Reliability Corporation (NERC) operating functions of reliability coordinator, transmission operator, and/or balancing authority to be executed more effectively.
A robotic arm development platform will provide the tools for SRNL to increase its competency in this area and allow customer demonstrations to be performed so they can visualize how the technology relates to their application. The objective is to set up an adaptable development platform that can easily be re-configured for a variety of demonstrations for collecting data that will help engage the customer. Collaborative robots are designed with built in features allowing them to safely operate alongside employees.
In this study, we explore how solar photovoltaic (PV) installer strategies have changed over time with respect to PV financing products and how these strategies affect PV customer choice. We use a novel data set from obtained from a software platform that connects installers to institutional investors and helps installers generate financed quotes. This data set enables us to explore the behaviors of small and mid-scale installers, whose market strategies have largely been unknown owing to lack of data. We find that the financing platform extends access to financing to small- and mid-scale installers, that installers have shifted toward power purchase agreements over time, that installers appear to modify strategies in reaction to various policy and market drivers, and that installers tend to 'lock in' to a specific strategy.
Large-scale quantum systems with controllable interactions are important for understanding complex phenomena in nature, and are the basis for advanced quantum technologies. Realizing a controllable platform for controlling, understanding, and ultimately harnessing the entanglement is an outstanding challenge in quantum science. This project demonstrated reconfigurable arrays of individually-trapped ultracold atoms, thus realizing a platform that could demonstrate large-scale quantum entanglement with the addition of strong inter-atomic interactions. Arrays of more than 50 trap sites were formed via digital holography and a high-numerical aperture imaging system that featured in-situ trap diagnostics and single-atom imaging resolution. We further discovered a new implementation of a controlled-phase gate that utilized coherent excitation to Rydberg states. This method will enable robust entanglement protocols in many-atom systems such as the one developed here.
Advanced manufacturing and engineering are fundamental to the economic strength and national security of the United States. With the introduction of advanced modeling and simulation practices in manufacturing and engineering over the last several decades, we are producing better products, more quickly, and at decreased cost. Through the adoption of high-performance computing (HPC) enabled workflows, in small- to medium-sized manufacturers and engineering firms, which employ fewer than 500 people, is renewing United States manufacturing and engineering. Despite successes, there are significant barriers to widespread adoption of these tools. Our Phase I SBIR project, Next-Generation Analysis and Visualization Platform for Manufacturing and Engineering Workflows, aims to address several of these barriers. Our project helped us evaluate the feasibility and benefits and mitigate risks of asynchronous processing for analysis and visualization workflows. We evaluated the impact of asynchronous, non-blocking communication can have on the user experience in a complex visualization application. Besides the impact on users, we wanted to understand the impacts on developers (development application programming interfaces (APIs))and generated applications (robustness and stability). Any asynchronous platform implementation is bound to rely on multiple threads which can make the programming model extraordinarily complex and prone to bugs. If not managed carefully, the complexity and resulting instability would be an unacceptable evolution for widely used production tools like ParaView and putting the success of the Phase II project at risk.
Phase I research was conducted to investigate the technical feasibility of an integrated open source platform GeoLib to provide turnkey solutions for model-data integration for environmental systems science (ESS), using modern web technologies, advances in HPC and cloud computing and high-performance visualization. A key feature of our platform is that it minimizes and automates data movement between data storage and computing systems. Another critical feature of the design is that it enables users to perform high-fidelity post-processing by performing high-performance visualization server-side and in-situ on the compute cluster close to the data. The investigation, which was performed over a 12-month period ending April 2019, centered on developing prototype software to demonstrate the key enabling technologies required for this system. This included: (i) rapid access to data from multiple remote sources, via a unified data interface; (ii) software for scalable geospatial visualization of raster, vector, and point cloud data on the web; (iii) development of an easy-to-use API for creating simulation and modeling workflows; (iv) remote data visualization and processing on middle-tier servers; and (v) remote data processing on high-performance computing systems. A critical part of this work is that all of these capabilities be accessible to end users through standard desktop tools such as Project Jupyter, which is widely adopted for ESS simulation and modeling.
This project is to develop a data management and analytics platform called Grid Data Crossing (GriD-Xing) that integrates data processing, storage, analysis, and visualization functionalities for users at industry (e.g., EMS and DMS) and universities. It intends to increase the applicability of large set of multi-source data (e.g., SCADA, PMU, μPMU, AMI, inverter, load/DER forecasting, and cyber data) and eventually improve the resilience, reliability, and security of critical energy infrastructure. GriD-Xing also intends to be a research platform to identify the usefulness of large multi-source data via case studies of interest to industry (e.g., 2011 southwest cold weather and 2016 Blue Cut fire events). In the first year, the team focused on the development and demonstration of large multi-source data (e.g., PMU, μPMU, SCADA, AMI, and cyber data) processing, storage, and analysis. Specifically, the team designed GriD-Xing framework in functional and structural aspects, developed GriD-Xing algorithms for large multi-source grid data processing, storage, and analysis, and finally demonstrated GriD-Xing prototype algorithms performance through a series of tests and case studies using real-world and simulation data.
The Seismo-Hydroacoustic Data Acquisition System (SHDAS) is undergoing evaluation in preparation for its engineering, development, and deployment by the U.S Navy as an ocean bottom seismic monitoring system. A prototype of the Underwater Platform has been deployed at the Pinedale Seismic Research Facility (PSRF) in Wyoming to determine how well it couples to the ground for the purpose of measuring ground motion. The evaluation was conducted during the summer of 2014 by the U.S. Navy, U.S. Air Force, RP Kromer Consulting, and other contractors. Sandia National Laboratories (SNL) was asked to analyze and interpret the collected data so as to comment on coupling of the Underwater Platform to the ground.
ATS platforms are some of the largest, most complex, and most expensive computer systems installed in the United States at just a few major national laboratories. This milestone describes our recent efforts to procure, install, and test a machine called Vortex at Sandia National Laboratories that is compatible with the larger ATS platform Sierra at LLNL. In this milestone, we have 1) configured and procured a machine with similar hardware characteristics as Sierra ATS, 2) installed the machine, verified its physical hardware, and measured its baseline performance, and 3) demonstrated the machine's compatibility with Sierra ATS, and capacity for useful development and testing of Sandia computer codes (such as SPARC), including uses such as nightly regression testing workloads.
The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.
An important part of a navigation system for a moving platform is the estimation of the rate of travel. This document presents a method for estimating the platform velocity in 3-dimensions using multiple antenna subarrays which could be used to augment navigation in a GPS-degraded environment. An advantage of this technique is that it does not require any knowledge of a positions of any landmarks. Results from radar data collected by the Sandia National Laboratories demonstration radar system are presented to illustrate the promise of this technique.
Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.
LANL has reinvested in a cylindrical implosion platform, enabling direct measurements of instability growth in convergent geometries. Scale-invariant hydrodynamic instability growth is observed in cylindrical implosions at OMEGA and NIF that varied in radial size and implosion time-scale by a factor of 3 (CR=2.25). Greatly improved NIF data at CR=5 are currently being analyzed. The cylindrical implosion platform is growing: Larger scale-4 cylinders will be fielded at the NIF; Point-projection sidelighters for the NIF implosions; Gas-filled designs should increase CR>10; Double cylinders are being pursued for FY22 and beyond; Results will aid in V&V efforts for 3D hydrodynamics in xRAGE.
Recent technology trends, such as the increasing focus on data management technologies and the emergence of sustainable on-demand computing and commercial cloud facilities provide initial steps and potential building blocks for creating such a compute facility. How do we fill the gap between what we have now and the capabilities we need to make the vision of an online data facility a reality? What research challenges need to be addressed – and can be addressed – in the coming 5-10 years? How should we adjust our vision if they are not solved? Is such a facility compatible with the existing and evolving exascale resources and/or how should they evolve to be compatible? How do “beyond Moore’s law” technologies enable advanced data analysis? Those are the challeges that the Future Platform workshop was organized to address. This report is organized as follows. Section 2 summarizes data challenges and patterns related to the exemplar use cases presented during the first half day of the workshop. Section 3 summarizes the requirements presented by those use cases as well as a synthesis of those requirements into a Future Platform definition achieved on the 3rd half day of the workshop (morning of the 2nd day). Section 4 includes area breakout reports from the areas on the 2nd half day of the workshop (afternoon of the 1st day). Finally Section 5 has a report on recommendation for building methodology and metrics. The report concludes in Section 6.
Hyperspectral imaging is a very promising technology for nuclear proliferation detection. However, due to size and weight restrictions, small hyperspectral platforms such as satellites and small drones lack the on-board computing resources for accurate, real-time analysis of the enormous flow of data that a continuously operating hyperspectral sensor generates. This severely limits satellite systems, which can collect far more data than what they can telemeter, and hinders the ability of all platforms to adapt their missions on the fly in response to observations. This program addresses the hyperspectral data processing challenge through development of new, fast and accurate algorithms that produce data products in real time. The algorithms circumvent the major computational bottlenecks in existing processing streams, and would be incorporated in lightweight, power-efficient single-board computer systems. The toolkit of fast algorithms will be immediately useful in current and future hyperspectral systems being built by the Government and by private industry, including drone-based systems and satellite constellations that acquire timely global imagery.
Los Alamos National Laboratory developed an ultra-high-throughput screening platform to engineer custom biocatalysts that enhance the rate of chemical reactions critical in pharmaceuticals, renewable energy, and environmental cleanup. Current methods to find biocatalysts are slow. This platform scans genetic variations to optimize a single enzyme or microbial cell to generate a product efficiently. It selects rare mutations needed for biocatalyst optimization orders of magnitude faster than current screening methods. A custom sensor reporter gene circuit causes cells to fluoresce when they are making the target product. When coupled to flow cytometry, a million biocatalyst variants can be screened in hours.