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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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A development effort has been embarked on with the goal of improving the automation of some key components of NASA's Deep Space Network.
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The Photonic Systems for Antenna Applications Symposium (PSAA) is the primary conference devoted exclusively to the exchange of information on the technology and application of photonics in antenna, phased array, and sensor systems.
This design study was conducted to support the HABEX project. There are a number of companion papers at this conference which go into detail on what all the HABEX goals are. The objective of this paper is to establish a baseline primary mirror design which satisfies the following structural related requirements. The designs in this study have a high TRL (Technology Readiness Level), realistic manufacturing limits and performance in line with the HABEX mission. A secondary goal of the study was to evaluate a number competing criteria for the selection. Questions such as differences in the on axis versus off axis static and dynamic response to disturbances. This study concentrates on the structural behavior, companion papers cover thermal and long term stability aspects of the problem.
NASA has developed a suite of advanced arrival management technologies combining time-based scheduling with controller- and flight deck-based precision spacing capabilities that allow fuel-efficient arrival operations during periods of high throughput. An operational demonstration of these integrated technologies, i.e., the ATM Technology Demonstration #1 (ATD-1), is slated for 2016. Human-in-the-loop simulations were conducted to evaluate the performance of the ATD-1 system and validate operational feasibility. The ATD-1 system was found to be robust to scenarios with saturated demand levels and high levels of system delay. High throughput, 10 above baseline demand levels, and schedule conformance less than 20 seconds at the 75th percentile were achievable. The flight-deck interval management capabilities also improved the median schedule conformance at the final approach fix from 5 to 3 seconds with less variance.
Learn how the ATD-2 analytical team has used various sources and metrics to verify that the operational system follows a do no harm mandate while also providing efficiency and predictability benefits.
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Smart outlets control the flow of power to devices plugged into them and measure their energy use. The energy use data can be accessed via an online dashboard or smartphone application, allowing the user to turn power to plug-in devices on or off based on a schedule established in the dashboard or application. This four-page resource is a fact sheet meant to educate commercial building owners on smart outlets: what they are, why we use them, and how to use them in a way that will achieve energy savings while maintaining occupant comfort. The resource also describe how to procure a smart outlet system and how to fully capture their benefits over time. This resource was developed to support the Better Buildings Alliance Plug and Process Loads Technology Research Team.
Prioritizing battery energy storage system opportunities across a large real estate portfolio can be complex. The National Renewable Energy Laboratory used a phased approach including detailed REopt analysis to efficiently identify the most cost-effective projects across 80 U.S. Army installations.
To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.
To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.
Federated learning (FL) is a powerful framework that enables multiple distributed clients to collaborate without the need to transfer their data to a central server. However, FL does not inherently guarantee the level of privacy that clients often require. In our review of recent studies on privacy-enhancing techniques in FL, we found that frequency estimation (FE) methods remain underexplored. To address this gap, we developed and integrated FE techniques on the client side, further examining the effects of incorporating an adaptive range and a shuffled model. We also analyzed the impact of varying hyper-parameters on privacy preservation. Our results provide clear guidance on the algorithms and configurations that are most effective for enhancing privacy in FL, particularly when using long short-term memory (LSTM) architectures.