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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 sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Aerospace Meteorology Lessons Learned Relative to Aerospace Vehicle Design and Operations

Aerospace Meteorology came into being in the 1950s as the development of rockets for military and civilian usage grew in the United States. The term was coined to identify those involved in the development of natural environment models, design/operational requirements, and environment measurement systems to support the needs of aerospace vehicles, both launch vehicles and spacecraft. It encompassed the atmospheric environment of the Earth, including Earth orbit environments. Several groups within the United States were active in this area, including the Department of Defense, National Aeronautics and Space Administration, and a few of the aerospace industry groups. Some aerospace meteorology efforts were similar to those being undertaken relative to aviation interests. As part of the aerospace meteorology activities a number of lessons learned resulted that produced follow on efforts which benefited from these experiences, thus leading to the rather efficient and technologically current descriptions of terrestrial environment design requirements, prelaunch monitoring systems, and forecast capabilities available to support the development and operations of aerospace vehicles.

Vaughan, William W.↗
NASA NTRS1998

Explorers of the Universe: Metacognitive Tools for Learning Science Concepts

Much of school learning consists of rote memorization of facts with little emphasis on meaningful interpretations. Knowledge construction is reduced to factual knowledge production with little regard for critical thinking, problem solving, or clarifying misconceptions. An important role of a middle and secondary teacher when teaching science is to aid students' ability to reflect upon what they know about a given topic and make available strategies that will enhance their understanding of text and science experiments. Developing metacognition, the ability to monitor one's own knowledge about a topic of study and to activate appropriate strategies, enhances students' learning when faced with reading, writing and problem solving situations. Two instructional strategies that can involve students in developing metacognitive awareness are hierarchical concept mapping, and Vee diagrams. Concept maps enable students to organize their ideas and reveal visually these ideas to others. A Vee diagram is a structured visual means of relating the methodological aspects of an activity to its underlying conceptual aspect in ways that aid learners in meaningful understanding of scientific investigations.

Alvarez, Marino C.↗
DOE OSTI2019

Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning

Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has concluded in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, we propose using fully convolutional neural networks (FCNNs) to classify older adults at low or high risk of falling using inertial sensor data collected from a smartphone. Due to the limited nature of older adult inertial gait data sets, we first pre-train the FCNN models using a publicly available data set for pedestrian activity recognition. Then via transfer learning, we train the network for falls risk classification. We reveal that via transfer learning, our falls risk classifier obtains an area under the receiver operating characteristic curve of 93.3%, which is 10.6% higher than the equivalent model trained without the use of transfer learning. Moreover, we show that our method outperforms other standard machine learning classifiers trained on features developed in prior research.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1998

The Magnetic Roots of Enhanced Coronal Heating

Building a description of the sites of enhanced heating in the solar atmosphere has been a primary interest of the solar group at MSFC. For locations within and between active regions, as well as in the quiet Sun, this work shows that extended magnetic structures are heated from active compact magnetic structures at their feet. This talk will review what we have learned about the magnetic characteristics of these active footpoint sites, and the relationships of the intensity variations in the low-lying and higher structures, in the context of current theories and observations of magnetic reconnection and jets. One puzzling result is the poor correlation of intensity variations in the low and high loops.

Porter, J. G.↗
DOE OSTI2024

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Elucidating and predicting the dynamic evolution of water and land systems due to natural and energy-related forcings

Focal Area(s): 3. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI; & 1. Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: Interactions between water, land, and energy systems are complex and occur on a variety of scales, ranging from local to basinal to regional. Accurately predicting the behavior of ground water and surface water systems for 5-10 years and beyond requires an understanding of the current system and the ability to model both the natural system at scale and human-induced forcings related to energy and other activities. Artificial intelligence and machine learning (AI/ML) combined with modern compilation and integration efforts for U.S. groundwater and surface water systems present potential solutions to bolstering detailed physics-based models of these systems. Big data tied with ML and physics-based modeling can drive breakthroughs in understanding the earth system, but research is often impeded by data access (e.g., privacy issues), quality, formats, gaps, multi-source, multi-scale, integration, and spatiotemporal challenges. Effective integration of real data and simulated (synthetic) data that fill gaps is critical. Overcoming these complex data and model integration challenges will enable a transformational approach to acquiring enhanced understanding of environmental systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Artemis and Ethics Workshop Lessons Learned

As NASA lays out its Artemis activities, it expects to set precedents in spaceflight for decades to come. Including ethical and social considerations in Artemis planning will improve the likelihood that the future we create is one where humanity collectively wants to live. NASA’s Office of Technology, Policy, and Strategy (OTPS) hosted an Artemis and Ethics Workshop. This workshop was held at NASA’s Mary Jackson Headquarters in Washington, DC from April 12th -14th 2023 and brought together invited experts in social science, humanities, and technical fields to discuss ethical, legal, and social implications (ELSI) of Artemis and human exploration in general. The two key study questions that the workshop aimed to address were: 1) How should NASA consider the ELSI of the Artemis and Moon to Mars efforts?; and 2) What are the key ethical and societal implications that need consideration? After briefly summarizing the formal workshop report, this paper addresses the lessons learned from implementing an interdisciplinary workshop of this nature, and from reflecting on the complexities of societal implications long-term. Issues of breadth vs depth, preparing people to engage across cultures, and allowing for structured brainstorming of specific issues are of particular importance. We also reflect on the limits of a workshop full of experts for addressing these issues and note ways in which other participatory processes or adjustments to policy practices may be worthwhile.

Artemis↗
NASA NTRSDate not supplied

Artemis and Ethics Workshop Lessons Learned

As NASA lays out its Artemis activities, it expects to set precedents in spaceflight for decades to come. Including ethical and social considerations in Artemis planning will improve the likelihood that the future we create is one where humanity collectively wants to live. NASA’s Office of Technology, Policy, and Strategy (OTPS) hosted an Artemis and Ethics Workshop. This workshop was held at NASA’s Mary Jackson Headquarters in Washington, DC from April 12th-14th 2023 and brought together invited experts in social science, humanities, and technical fields to discuss ethical, legal, and social implications (ELSI) of Artemis and human exploration in general. The two key study questions that the workshop aimed to address were: 1) How should NASA consider the ELSI of the Artemis and Moon to Mars efforts?; and 2) What are the key ethical and societal implications that need consideration? After briefly summarizing the formal workshop report, this paper addresses the lessons learned from implementing an interdisciplinary workshop of this nature, and from reflecting on the complexities of societal implications long-term. Issues of breadth vs depth, preparing people to engage across cultures, and allowing for structured brainstorming of specific issues are of particular importance. We also reflect on the limits of a workshop full of experts for addressing these issues and note ways in which other participatory processes or adjustments to policy practices may be worthwhile.

Artemis↗
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Progress of Gas Injection EOR Surveillance in the Bakken Unconventional Play—Technical Review and Machine Learning Study

Although considerable laboratory and modeling activities were performed to investigate the enhanced oil recovery (EOR) mechanisms and potential in unconventional reservoirs, only limited research has been reported to investigate actual EOR implementations and their surveillance in fields. Eleven EOR pilot tests that used CO2, rich gas, surfactant, water, etc., have been conducted in the Bakken unconventional play since 2008. Gas injection was involved in eight of these pilots with huff ‘n’ puff, flooding, and injectivity operations. Surveillance data, including daily production/injection rates, bottomhole injection pressure, gas composition, well logs, and tracer testing, were collected from these tests to generate time-series plots or analytics that can inform operators of downhole conditions. A technical review showed that pressure buildup, conformance issues, and timely gas breakthrough detection were some of the main challenges because of the interconnected fractures between injection and offset wells. The latest operation of co-injecting gas, water, and surfactant through the same injection well showed that these challenges could be mitigated by careful EOR design and continuous reservoir monitoring. Reservoir simulation and machine learning were then conducted for operators to rapidly predict EOR performance and take control actions to improve EOR outcomes in unconventional reservoirs.

Energy & Fuels↗
DOE OSTI2025

Machine Learning‐Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands

Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Chemistry↗
NASA NTRS2010

Solar Dynamics Observatory Lessons Learned

Mission is to study how solar activity is created and how space weather results from that activity. Atmospheric Imaging Assembly (AIA): High Resolution Images of 10 wavelengths every 10 seconds. Extreme Ultraviolet Variability Experiment (EVE): Measure Sun's brightness in EUV. Helioseismic and Magnetic Imager (HMI): Measures Doppler shift to study waves of the Sun. Launched February 11, 2010.

Rivera, Rachel↗
NASA NTRSDate not supplied

Artemis and Ethics Workshop Lessons Learned

As NASA lays out its Artemis activities, it expects to set precedents in spaceflight for decades to come. Artemis is a far more ambitious program than even Apollo, aimed at developing a sustained human presence on and around the lunar surface, in preparation for Mars exploration. As a result, considering ethical and social concerns in the discussion and design of Artemis is vital to ensuring the future worlds we create are ones where humanity collectively wants to live. By understanding how to reflect on these issues as NASA makes decisions related to Artemis, NASA can avoid unintended consequences stemming from cultural perceptions of human exploration of the solar system that may harm society. We recognize that a diverse range of groups need to be part of this conversation. There are many different views about the work that should happen in space. We recognize that NASA does not hold sole responsibility for determining the ethical path forward in space, as decisions made by commercial, international, and intergovernmental partners play a major role. Inclusive conversations help inform that work. Others have already argued that social scientists and engineers need to work together with inputs from stakeholders to design the future. To begin to answer this need, NASA’s Office of Technology, Policy, and Strategy (OTPS) hosted an Artemis and Ethics Workshop. This workshop was held in Washington, DC from April 12th-14th, 2023 and brought together invited experts in social science fields and NASA managers to discuss ethical, legal, and social aspects (ELSA) of Artemis and human exploration in general. The goal of the workshop was to discuss issues and build connections that NASA managers can consider across variety of ethical issues surrounding Artemis.

Katherine T McBrayer↗
NASA NTRSDate not supplied

Lessons Learned in Systems Engineering Availability and Recommendations for Mission Technical Leaders

In spaceflight missions at Goddard Space Flight Center (GSFC), the Mission System Engineer (MSE) is the technical leader of the overall engineering team and also is the Independent Engineering Technical Authority. The responsibility of this role includes the definition of the mission design architecture, concept of operations, and mission requirements, management of risk throughout the development, and verification and validation of the final system performance and function amongst other duties. This responsibility inherently requires time management, enabling focus on a balanced development with appropriate risk. Time is the most valuable resource of the MSE. The system engineer’s availability to interact with the development team (often product or component design leads and technicians, often in different worksites) to discover and mitigate mission risks during development is key to mission success. This paper presents examples from Lunar Reconnaissance Orbiter, Landsat 9, and Neutron star Interior Composition ExploreR (NICER) which represent in-house and out-of-house hardware builds. These examples demonstrate how interactions between the Mission Systems Engineers and other project and partner engineers result in discovery of critical risks, leading to early mitigation with significant cost and performance savings. These three missions would have suffered test failures or on-orbit failures had their MSEs not set aside time to visit engineers and technicians that were working on key pieces of space flight hardware. Availability is more than just time; it is openness to listen to concerns and questions. It begins by building a level of trust in the team that it is safe to ask questions or share concerns without the fear of blame or additional workload. It also requires enabling informal conversations (over lunch, coffee, in the clean room, or at the team members desk, etc.) where key information can be exchanged, and team members may even provide an easy-to-implement mitigation idea for another subsystem. Availability is a highly valuable commodity and completely non-obvious to protect and optimize. The natural tendency of engineers is to keep themselves busy with solving problems that they know about. This paper is encouraging MSEs to resist this tendency to try and solve all the complex problems themselves and actively devote daily time to learning and solving problems that are found with informal communications with other team members.

Lessons Learned↗