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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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At least 793 records · Page 44

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Increasing Reliability and Safety of Hydrogen Components - Reliability Data Collection

Come learn about the new Hydrogen Component Reliability Database (HyCReD) and participate in discussions on hydrogen component reliability data collection, collaboration, and analysis. Funded by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy under the Hydrogen and Fuel Cell Technologies Office, HyCReD is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety reliability for hydrogen facilities by integrating risk reduction methodologies and component reliability data taxonomies that support hydrogen infrastructure failure rate analysis.

component↗

NASA NTRS2006

NASA Tech Briefs, February 2006

Topics discussed include: Nearly Direct Measurement of Relative Permittivity; DCS-Neural-Network Program for Aircraft Control and Testing; Dielectric Heaters for Testing Spacecraft Nuclear Reactors; Using Doppler Shifts of GPS Signals To Measure Angular Speed; Monitoring Temperatures of Tires Using Luminescent Materials; Highly Efficient Multilayer Thermoelectric Devices; Very High-Speed Digital Video Capability for In-Flight Use; MMIC DHBT Common-Base Amplifier for 172 GHz; Modular, Microprocessor-Controlled Flash Lighting System; Generic Environment for Simulating Launch Operations; Modular Aero-Propulsion System Simulation; X-Windows Socket Widget Class; Infrastructure for Rapid Development of Java GUI Programs; Processing Raman Spectra of High-Pressure Hydrogen Flames; X-Windows Information Sharing Protocol Widget Class; Simulating Humans as Integral Parts of Spacecraft Missions; Analyzing Power Supply and Demand on the ISS; Polyimides From a-BPDA and Aromatic Diamines; Making Plant-Support Structures From Waste Plant Fiber; Large Deployable Reflectarray Antenna; Periodically Discharging, Gas-Coalescing Filter; Ion Milling On Steps for Fabrication of Nanowires; Neuro-Prosthetic Implants With Adjustable Electrode Arrays; Microfluidic Devices for Studying Biomolecular Interactions; Studying Functions of All Yeast Genes Simultaneously; Polarization Phase-Compensating Coats for Metallic Mirrors; Tunable-Bandwidth Filter System; Methodology for Designing Fault-Protection Software; and Ground-Based Localization of Mars Rovers.

Source record↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

DOE OSTI2025

Data-Driven Clustering and Classification of Outage Patterns with Insights into their Links to Extreme Events

At a global level extreme events have increased in both scale and impact. These events have the potential to affect the electrical grid infrastructure and cause a wide range of outages, which can lead to a disruption in daily patterns, cost millions of dollars and also the loss of life. Currently, to track these outage events there have been various approaches developed ranging from regional to national level quantifications for what defines an outage. However, this variation in methods can potentially lead to subjective decision-making and a lack of proper management in relation to the event. While previous work has made strides in determining spatio-temporal patterns, minimal attention has been given to the type and number of outages an area may be exposed to. The differences in incurred cost and the overall severity of an event between a transformer box malfunction and a hurricane are drastic, and by finding historical signals, we can allow for more efficient management, potentially saving lives and millions of dollars. Here, we leverage unsupervised machine learning techniques to delineate outage patterns among 22 counties within the United States and find that there are clear, segregated clusters (0.93 silhouette) of data which are related by event behavior and underlying cause. This finding will allow for energy stakeholders, policy makers, and researchers to gain a deeper understanding of the extent and severity of historic events and to better prepare for electrical grid infrastructure planning and management.

Koob, Benjamin [ORNL]↗

NASA NTRS2014

Robust Informatics Infrastructure Required For ICME: Combining Virtual and Experimental Data

With the increased emphasis on reducing the cost and time to market of new materials, the need for robust automated materials information management system(s) enabling sophisticated data mining tools is increasing, as evidenced by the emphasis on Integrated Computational Materials Engineering (ICME) and the recent establishment of the Materials Genome Initiative (MGI). This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and or multi-scale models requires both the processing of large volumes of test data and complex materials data necessary to establish processing-microstructure-property-performance relationships. Fortunately, material information management systems have kept pace with the growing user demands and evolved to enable: (i) the capture of both point wise data and full spectra of raw data curves, (ii) data management functions such as access, version, and quality controls;(iii) a wide range of data import, export and analysis capabilities; (iv) data pedigree traceability mechanisms; (v) data searching, reporting and viewing tools; and (vi) access to the information via a wide range of interfaces. This paper discusses key principles for the development of a robust materials information management system to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets to enable ICME. In particular, NASA Glenn's efforts towards establishing such a database for capturing constitutive modeling behavior for both monolithic and composites materials

Mutli-scale models↗

DOE OSTI2020

Resilience of Urban Transport Network-of-Networks under Intense Flood Hazards Exacerbated by Targeted Attacks

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Well‐to‐wheels analysis of greenhouse gas emissions for passenger vehicles in Middle East and North Africa

Battery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO 2 eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2008

Hydrogen Research for Spaceport and Space-Based Applications: Hydrogen Sensors and Systems

The activities presented are a broad based approach to advancing key hydrogen related technologies in areas such as fuel cells, hydrogen production, and distributed sensors for hydrogen-leak detection, laser instrumentation for hydrogen-leak detection, and cryogenic transport and storage. Presented are the results from research projects, education and outreach activities, system and trade studies. The work will aid in advancing the state-of-the-art for several critical technologies related to the implementation of a hydrogen infrastructure. Activities conducted are relevant to a number of propulsion and power systems for terrestrial, aeronautics and aerospace applications. Sensor systems research was focused on hydrogen leak detection and smart sensors with adaptive feedback control for fuel cells. The goal was to integrate multifunction smart sensors, low-power high-efficiency wireless circuits, energy harvesting devices, and power management circuits in one module. Activities were focused on testing and demonstrating sensors in a realistic environment while also bringing them closer to production and commercial viability for eventual use in the actual operating environment.

Anderson, Tim↗

NASA NTRS2012

The NASA Heliophysics Active Final Archive at the Space Physics Data Facility

The 2009 NASA Heliophysics Science Data Management Policy re-defined and extended the responsibilities of the Space Physics Data Facility (SPDF) project. Building on SPDF's established capabilities, the new policy assigned the role of active "Final Archive" for non-solar NASA Heliophysics data to SPDF. The policy also recognized and formalized the responsibilities of SPDF as a source for critical infrastructure services such as VSPO to the overall Heliophysics Data Environment (HpDE) and as a Center of Excellence for existing SPDF science-enabling services and software including CDAWeb, SSCWeb/4D Orbit Viewer, OMNIweb and CDF. We will focus this talk to the principles, strategies and planned SPDF architecture to effectively and efficiently perform these roles, with special emphasis on how SPDF will ensure the long-term preservation and ongoing online community access to all the data entrusted to SPDF. We will layout our archival philosophy and what we are advocating in our work with NASA missions both current and future, with potential providers of NASA and NASA-relevant archival data, and to make the data and metadata held by SPDF accessible to other systems and services within the overall HpOE. We will also briefly review our current services, their metrics and our current plans and priorities for their evolution.

McGuire, Robert E.↗

NASA NTRS2015

Combining Solar Electric and Chemical Propulsion for Crewed Missions to Mars

This paper documents the results of an investigation of human Mars mission architectures that leverage near-term technology investments and infrastructures resulting from the planned Asteroid Redirect Mission, including high-power Solar Electric Propulsion (SEP) and a human presence in Lunar Distant Retrograde Orbit (LDRO). The architectures investigated use a combination of SEP and chemical propulsion elements. Through this combination of propulsion technologies, these architectures take advantage of the high efficiency SEP propulsion system to deliver cargo, while maintaining the faster trip times afforded by chemical propulsion for crew transport. Evolved configurations of the Asteroid Redirect Vehicle (ARV) are considered for cargo delivery. Sensitivities to SEP system design parameters, including power level and propellant quantity, are presented. For the crew delivery, liquid oxygen and methane stages were designed using engines common to future human Mars landers. Impacts of various Earth departure orbits, Mars loiter orbits, and Earth return strategies are presented. The use of the Space Launch System for delivery of the various architecture elements was also investigated and launch vehicle manifesting, launch scheduling and mission timelines are also discussed. The study results show that viable Mars architecture can be constructed using LDRO and SEP in order to take advantage of investments made in the ARM mission.

Percy, Tom↗

DOE OSTI2023

pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Advances in cyber–physical technologies have enabled real-time sensing and adaptive control of stormwater infrastructure. These smart stormwater systems allow for inexpensive, minimally-invasive stormwater control interventions in lieu of new construction. Importantly, however promising the area of smart stormwater control, there still remain barriers – for experts and novices alike – to access shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical and accessible, we present pystorms, an open-source Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. pystorms consists of a collection of real world-inspired smart stormwater control scenarios on which any number of control strategies can be applied and tested via an accompanying Python programming interface and coupled stormwater simulator. pystorms provides a framework for the rigorous and efficient evaluation of smart stormwater control methodologies across diverse watersheds with only a few lines of code.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗

DOE OSTI2021

Using Qualified On-Site Nuclear Power Plant Simulators in Human Factors Validations of Control Room Upgrades

Existing commercial nuclear power plants (NPPs) are valuable assets in the infrastructure portfolio of the United States (U.S.) because they safely and reliably generate about 1/5th of all the electricity used. The instrumentation and control (I&C) systems in commercial NPPs are the ‘eyes and ears' of the operator, allowing operators to maintain situation awareness, thereby allowing the plant to operate safely and efficiently for all phases of operation. Because the I&C systems in NPPs are still mostly based on analog technologies, which are reliable but not especially cost-effective, upgrading the existing I&C in com-mercial NPPs to new digital I&C is very important. Furthermore, simulators and simulation are critical human factors engineering tools for this I&C modernization work. NPP control room simulators are essential test beds to simulate normal, abnormal, and emergency operations that allow human factors researchers to safely and realistically evaluate early design phase prototypes of the upgraded digital I&C, and validate final as-built digital I&C systems that have been modernized deployed in NPPs. This paper describes research performed using qualified on-site NPP simulators to perform human factors validations of digital I&C control room upgrades.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

CIRCLES: Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing

The energy efficiency of today’s vehicular mobility relies on the un-integrated combination of i) control via static assets (traffic lights, metering, variable speed limits, etc.); and ii) onboard vehicle automation (adaptive cruise control (ACC), ecodriving, etc.). These two families of control were not co-designed and are not engineered to work in coordination. Recent studies have shown i) limitations of controls, and even ii) negative impacts of ACC. This project focused on the technology development, implementation and prototyping, and validation of Mobile Traffic Control (MTC). MTC can be viewed as an extension of classical traffic control (in which static infrastructure actuates traffic flow). In the MTC paradigm, automated vehicles actuate the entire flow via their behavior, offering enhanced possibilities to optimize the energy footprint of traffic, if designed correctly. We set out to demonstrate for the first time that considerably reduced fuel consumption of all vehicles in traffic can be achieved via distributed control of a small proportion of CAVs. Compared to baseline vehicular technologies, our work offers a significant design departure: control algorithms for the CAVs consider the impact one vehicle can have on overall traffic, improving resulting overall fuel consumption. We focus on using a few vehicles (as traffic controllers via CAV technology) to improve the energy efficiency of traffic flow to further optimize energy efficiency. A live-traffic demonstration in November 2022 featured the deployment of 100 specially-equipped CAVs, constituting an approximate local penetration rate upwards of 2%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Uncovering I/O demands on HPC platforms: Peeking under the hood of Santos Dumont

High-Performance Computing (HPC) platforms are required to solve the most diverse large-scale scientific problems in various research areas, such as biology, chemistry, physics, and health sciences. Researchers use a multitude of scientific softwares, which have different requirements. These include input and output operations, which directly impact performance due to the existing difference in processing and data access speeds. Thus, supercomputers must efficiently handle mixed workload when storing data from the applications. Understanding the set of applications and their performance running in a supercomputer is paramount to understanding the storage system's usage, pinpointing possible bottlenecks, and guiding optimization techniques. This research proposes a methodology and visualization tool to evaluate a supercomputer's data storage infrastructure's performance, taking into account the diverse workload and demands of the system over a long period of operation. As a study case, we focus on the Santos Dumont supercomputer, identifying inefficient usage, problematic performance factors, and providing guidelines on how to tackle those issues.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Abstract This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180 ) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

58 GEOSCIENCES↗

DOE OSTI2020

National Reactor Innovation Center NRC Early Site Permit Roadmap

The National Reactor Innovation Center (NRIC) at Idaho National Laboratory (INL) in Idaho Falls, Idaho, was authorized under the Nuclear Energy Innovation Capabilities Act (Public Law 115–248) to provide private sector technology developers with resources and infrastructure for testing, demonstration, and performance assessment to accelerate deployment of new advanced reactor technology concepts. NRIC’s mission is to enable clean, affordable, reliable energy by supporting U.S. government investments in nuclear energy research, development, demonstration, and commercialization of new nuclear energy systems. It intends to provide existing facilities and other undeveloped and previously developed sites at INL to advanced reactor vendors for prototype technology testing and deployment. Some of these demonstration projects may be subject to DOE authorization, while other projects may require approval by the U.S. Nuclear Regulatory Commission (NRC) prior to construction and operation. NRIC and INL are considering whether to pursue one or more ESPs to help facilitate new reactor deployment at the INL Site. An ESP would allow early investment in licensing infrastructure in advance of a reactor design being ready to deploy. Through the ESPs, NRIC would help reduce the cost and time required to deploy demonstration reactors at the INL Site. This also will allow INL to bring its nuclear and siting experience to the ESP development process allowing for better and more efficient improvements and utilization of the Site. A preapproved ESP at INL will also remove the uncertainty of NRC siting approvals from the demonstration project proponents, who may not be well positioned to complete the analyses in a timely manner. The purpose of this NRIC ESP roadmap is to provide rigor and understanding needed for INL and DOE decision-makers to make data-informed decisions on pursuing NRC ESPs in support of future advanced reactor demonstrations at INL. The roadmap provides a structured method for addressing potential challenges and identifying options for resolution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

National Reactor Innovation Center NRC Early Site Permit Roadmap

22 GENERAL STUDIES OF NUCLEAR REACTORS↗