The Effect of a Coarse Doppler Resolution on the Performance of a Vertical SAR Navigation System -- Presentation
Explore the source record for details and available documents.
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Abstract not provided.
Explore the source record for details and available documents.
A tremendous commitment of resources is needed to acquire, understand and apply battery data in terms of performance and aging behavior. There are many state of performance (SOP) and state of health (SOH) metrics that are useful to guide alignment of batteries to end-use, yet how these metrics are measured or extracted can make the difference between usable, valuable datasets versus data that lacks the necessary integrity to meet baseline confidence levels for SOP/SOH quantification. This work will speak to 1) types of data that support SOP and SOH evaluations on mechanistic terms, 2) measurement conditions needed to assure high data integrity, 3) equipment limitations that can compromise data high fidelity, and 4) the impact of cell polarization on data quality. A common goal in battery research and field use is to work from a data platform that supports economical paths of data capture while minimizing down-time for battery diagnostics. An ideal situation would be to utilize data obtained during normal daily use (“pulses or cycles of convenience”) without stopping the daily duty cycles to perform dedicated SOP/SOH diagnostic routines. However, difficulties arise in trying to make use of daily duty cycle data (denoted as cycle-by-cycle, CBC) that underscores the need for standardization of conditions: temperature and duty cycles can vary over the course of a day and throughout a week, month and year; polarization can develop within an immediate cycle and throughout successive cycles as a hysteresis. If CBC data is envisioned as a data source to determine performance and aging trends, it should be recognized that polarization is a frequent consequence of CBC and thus makes it difficult to separate reversible and irreversible components to metrics such as capacity loss and resistance increase over aging. Since CBC conditions can have a major impact on data usability, we will devote part of this paper to CBC data conditioning and management. Differential analyses will also be discussed as a means to detect changing trends in data quality. Our target cell chemistries will be lithium-ion types NMC/graphite and LMO/LTO.
As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.
Nuclear energy is increasingly being considered for such targeted energy applications as data centers in light of their high capacity factors and low carbon emissions. This paper focuses on assessing the tradeoffs between economies of scale versus mass production to identify promising reactor sizes to meet data center demands. A framework is then built using the best cost estimates from the literature to identify ideal reactor power sizes for the needs of the given data center. Results should not be taken to be deterministic but highlight the variability of ideal reactor power output against the required demand. While certain advocates claim that with the gigawatts of clean, firm energy needed, large plants are ideal, others advocate for SMRs that can be deployed in large quantities and reap the benefits from learning effects. The findings of this study showcase that identifying the optimal size for a reactor is likely more nuanced and dependent on the application and its requirements. Overall, the study does show potential economic promise for coupling nuclear reactors to data centers and industrial heat applications under certain key conditions and assumptions.
Abstract: Flash droughts, characterized by their rapid onset, sharply contrast with the typically gradual development of traditional droughts. These events are triggered by a combination of low rainfall and high evaporation rates, driven by elevated temperatures, making them particularly challenging to predict and prepare for. As a relatively new concept, flash drought is not well understood, which introduces significant epistemic uncertainties regarding their nature and detection methods. This under-detection hinders planners' ability to effectively manage these events. Despite these uncertainties, flash droughts can have significant impacts, raising the question: how can decision-makers prepare for such events given the current knowledge gaps? To address this, we propose a methodological framework aimed at enhancing flash drought preparedness by guiding the selection of appropriate indicators based on their detection capabilities and the decision-makers' level of risk aversion. Our approach involves evaluating six different flash drought indicators and analyzing the level of agreement among them. Additionally, we consider the decision-makers' risk aversion, distinguishing between those who require consensus across all methods (risk-takers) and those who act based on a single method's indication (risk-averse). The insights gained from this study offer a pathway towards more informed decision-making processes regarding flash droughts, potentially mitigating their adverse effects through better preparedness and response strategies.
Abstract not provided.
Abstract not provided.
Explore the source record for details and available documents.
Abstract not provided.
Abstract not provided.
As the climate changes, so will water availability and other ambient conditions, threatening the reliability of today's power system and tomorrow's clean energy grid. That's why researchers at the National Renewable Energy Laboratory (NREL) - along with those at Sandia National Laboratories, Oak Ridge National Laboratory, the City University of New York, the Electric Power Research Institute, and the National Energy Technology Laboratory - are studying both regional and national climate and hydrologic changes to provide a comprehensive assessment of climate and water impacts and risks to the U.S. power grid.
Abstract not provided.
Cities and communities continue to expand pedestrian and bicycle infrastructure as part of their sustainable mobility and post-pandemic recovery plans. However, the emergence of micro-mobility (e.g., electric bicycles) has created new challenges for urban transport planning. As the popularity of micro-mobility modes has grown, so have safety concerns due to rising injuries, thus challenging many cities to come up with regulatory measures that enable efficient access while minimizing negative impacts from micro-mobility. Leveraging the Open-Source Tool (Mobility Energy Productivity metric), powered by an open-source dataset (OpenStreetMap), and enhanced through the incorporation of perceived discomfort (level-of-traffic stress), this research study focuses on evaluating the accessibility implications of infrastructure planning and regulatory measures for micro-mobility. Five scenarios pertaining to level of traffic stress, sidewalk access, traffic calming, and bike lane coverage were tested in the Denver-Aurora region in Colorado. Maximum improvements in energy-efficient access are realized when allowing sidewalks for micro-mobility use. Cities and planning agencies could leverage this information to assess sidewalk use policies for micro-mobility, while ensuring pedestrian safety and ADA access. Results indicate that expansion of bicycle lane coverage yields 11% accessibility benefits for micro-mobility compared to implementing traffic calming measures yielding 3% accessibility improvements. Further, it was observed most of the population in the Denver-Aurora region is experiencing lower accessibility in-part due to presence of a high-stress connections in the network. Although not generalizable, the use of open-source data and access calculation methodology, make this analysis reproducible and transferable to other locations.
Nuclear sites across the Unites States and other nations in the world, are prone to extreme radiation effects. To understand the radiation hazards, plan the cleanup efforts; and to meet the quality standards and guidelines, it is important to accurately characterize any nuclear facility. One of the current methods in characterizing facilities is the use of advanced autonomous systems. These are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. Conventional methods of taking radiation measurements by hand within or around the containment areas, and analyzing the collected data to obtain the result are: Ineffective, Puts scientists at risk of Radiation, Not cost-effective. The platform uses the Robotic Operating Systems (ROS) to integrate multiple sensors seamlessly. It can operate near real time by using a distributed processing system to handle large amounts of data: Surrounding cameras, A multi-channel 3D Lidar, Manipulator arm, High-performance gamma-ray spectrometer.
Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) provided a comprehensive analysis of pathways for Puerto Rico to achieve its goal of 100% renewable energy by 2050, based on extensive stakeholder input. The study illuminates immediate and longer-term investments needed to achieve reliability while pursuing Puerto Rico's energy goals and addressing critical energy needs. Expected benefits of these investments include improvements in safety, security, health, and economic opportunity.