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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems

Active distribution networks are being challenged by frequent and rapid voltage violations due to renewable energy integration. Conventional model-based voltage control methods rely on accurate parameters of the distribution networks, which are difficult to achieve in practice. This paper proposes a novel physical-model-free two-timescale voltage control framework for active distribution systems. To achieve fast control of PV inverters, the whole network is first partitioned into several subnetworks using voltage-reactive power sensitivity. Then, the scheduling of PV inverters in the multiple sub-networks is formulated as Markov games and solved by a multi-agent soft actor-critic (MASAC) algorithm, where each subnetwork is modeled as an intelligent agent. All agents are trained in a centralized manner to learn a coordinated strategy while being executed based on only local information for fast response. For the slower time-scale control, OLTCs and switched capacitors are coordinated by a single agent-based SAC algorithm using the global information with considering control behaviors of the inverters. Particularly, the two-level agents are trained concurrently with information exchange according to the reward signal calculated from the data-driven surrogate model. Comparative tests with different benchmark methods on IEEE 33-and 123-bus systems and 342-node low voltage distribution system demonstrate that the proposed method can effectively mitigate the fast voltage violations and achieve systematical coordination of different voltage regulation assets without the knowledge of accurate system model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1999

Aerospace Engineering Systems and the Advanced Design Technologies Testbed Experience

Continuous improvement of aerospace product development processes is a driving requirement across much of the aerospace community. As up to 90% of the cost of an aerospace product is committed during the first 10% of the development cycle, there is a strong emphasis on capturing, creating, and communicating better information (both requirements and performance) early in the product development process. The community has responded by pursuing the development of computer-based systems designed to enhance the decision-making capabilities of product development individuals and teams. Recently, the historical foci on sharing the geometrical representation and on configuration management are being augmented: 1) Physics-based analysis tools for filling the design space database; 2) Distributed computational resources to reduce response time and cost; 3) Web-based technologies to relieve machine-dependence; and 4) Artificial intelligence technologies to accelerate processes and reduce process variability. The Advanced Design Technologies Testbed (ADTT) activity at NASA Ames Research Center was initiated to study the strengths and weaknesses of the technologies supporting each of these trends, as well as the overall impact of the combination of these trends on a product development event. Lessons learned and recommendations for future activities are reported.

VanDalsem, William R.↗
NASA NTRS2020

Kibo Robot Programming Challenge Final Activity Report

On October 8th, 2020 the Kibo Robot Programming Challenge (RPC) Finals Event took place. Student teams from Australia, Indonesia, Japan, Singapore, Taiwan, Thailand, and UAE were the finalists of the competition and became the first students to ever upload APKs to an Astrobee robot (Bumble) on the ISS. This was the culmination of a joint effort between JAXA’s Int-Ball developing team in Tsukuba, Japan and NASA’s Astrobee Facility team at NASA Ames Research Center\ in California, USA. The collaboration between JAXA and NASA started in late 2017. Throughout this time mutual visits by the engineering and management teams were organized. In July 2019 the Kibo RPC concept was formally defined and the competition started. Hundreds of students across the Asia Pacific region participated and during the final event they did so in real time with the participation of NASA’s astronaut, Chris Cassidy. Chris interacted with the students, JAXA personnel and with the robot during the activity. This report aims to summarize the lessons learned from the several crew and non-crew tended activities, the evaluations performed on ground, the coordination of resources both in the ground and at the International Space Station (ISS) as well as operations. It identifies the issues the teams faced and the solutions put in place to mitigate them.

Astrobee↗
NASA NTRS2022

Thinking Inside the Box: A Hands-on Student Activity for Building a Contamination Containment Glovebox to Encourage Problem Solving in a Collaborative Environment

Engineers from the National Aeronautics and Space Administration (NASA) and education experts from the Virginia Space Grant Consortium (VSGC) partnered together to create a hands-on student activity to teach students about problem solving, working in a collaborative environment, and about the unique career fields of contamination control and planetary protection. The activity focuses on contamination containment gloveboxes, which are sealed containers where operators outside the glovebox can safely manipulate hazardous or contamination-sensitive materials inside the glovebox through glove ports on the container. The activity utilizes common household materials and teams of students work together to design and build a glovebox using the materials provided. Once the glovebox has been constructed, students perform a task under a time constraint by using their glovebox to assemble a puzzle “contaminated” with corn starch. In a post-activity debrief, teams discuss lessons learned such as how the actual built glovebox differed from the sketched design, the challenge of managing a budget for materials, how the team dealt with surprises, and if their glovebox allowed enough room for the operator to perform the task. This activity has been part of VSGC’s Virginia Earth System Science Scholars (VESSS) summer academy program for high school students since 2016, and has been an engaging method to teach students teamwork, creativity, hands-on experimentation, communication, and reasoning skills while also teaching them about unique engineering fields such as contamination control and planetary protection.

Student activity↗
NASA NTRSDate not supplied

Thinking Inside the Box: A Hands-On Student Activity for Building a Contamination Containment Glovebox to Encourage Problem Solving in a Collaborative Environment

Contamination containment gloveboxes are sealed containers where operators outside the glovebox can safely manipulate hazardous or contamination-sensitive materials inside the glovebox through glove ports on the container. Containment gloveboxes are used in the space industry to study sample materials returned from space such as lunar, asteroid, and comet materials, to manufacture sensitive instruments and space ight hardware, and to perform contamination-sensitive experiments. A hands-on activity has been developed by contamination control engineer and STEM education experts as part of the Virginia Earth System Science Scholars (VESSS) summer academy program to teach high school students about gloveboxes and about problem solving in a collaborative environment. In this activity, student teams are given a list of common household materials (e.g. plastic bags, plastic straws, aluminum foil, tape, disposable gloves, etc.) for building their own glovebox. Students work together to sketch and design their glovebox before being provided a kit of materials, which they must purchase along with any extra materials with a mock team budget. They are given 30 minutes to build their glovebox, however, they do not know what will be placed inside the glovebox or what operations they will need to perform within the glovebox. At the end of the build time, teams are given a small, sealed paper envelope which they place in the glovebox. Each team selects a team member to be designated as the operator, and the operator places their hands in the gloves of the glovebox. The operator uses their gloves to open the envelope in the glovebox, and they discover they have 20 minutes to assemble a jigsaw puzzle. However, the puzzle is \contaminated" with talcum powder. After the 20 minute time period, teams are judged on how well they completed and cleaned the contaminated puzzle. In a post-activity debrief, teams discuss lessons learned such as how the actual built box di ered from the sketched design, the challenge of managing a budget for materials, how the team dealt with surprises, and if their glovebox allowed enough room for the operator to perform the task. This activity has been part of the VESSS summer academy since 2016, and teaches students teamwork, creativity, hands-on experimentation, communication, and reasoning skills. In a demonstration at IAC, a volunteer from the audience will be chosen to assemble a glovebox during the presentation using the same kit of materials and will demonstrate handling puzzle pieces in the completed glovebox.

Elaine Seasly↗
NASA NTRS1997

Multiple subclasses of Purkinje cells in the primate floccular complex provide similar signals to guide learning in the vestibulo-ocular reflex

The neural "learning rules" governing the induction of plasticity in the cerebellum were analyzed by recording the patterns of neural activity in awake, behaving animals during stimuli that induce a form of cerebellum-dependent learning. We recorded the simple- and complex-spike responses of a broad sample of Purkinje cells in the floccular complex during a number of stimulus conditions that induce motor learning in the vestibulo-ocular reflex (VOR). Each subclass of Purkinje cells carried essentially the same information about required changes in the gain of the VOR. The correlation of simple-spike activity in Purkinje cells with activity in vestibular pathways could guide learning during low-frequency but not high-frequency stimuli. Climbing fiber activity could guide learning during all stimuli tested but only if compared with the activity present approximately 100 msec earlier in either vestibular pathways or Purkinje cells.

Non-NASA Center↗
NASA NTRS1999

Aerospace Engineering Systems

Continuous improvement of aerospace product development processes is a driving requirement across much of the aerospace community. As up to 90% of the cost of an aerospace product is committed during the first 10% of the development cycle, there is a strong emphasis on capturing, creating, and communicating better information (both requirements and performance) early in the product development process. The community has responded by pursuing the development of computer-based systems designed to enhance the decision-making capabilities of product development individuals and teams. Recently, the historical foci on sharing the geometrical representation and on configuration management are being augmented: Physics-based analysis tools for filling the design space database; Distributed computational resources to reduce response time and cost; Web-based technologies to relieve machine-dependence; and Artificial intelligence technologies to accelerate processes and reduce process variability. Activities such as the Advanced Design Technologies Testbed (ADTT) project at NASA Ames Research Center study the strengths and weaknesses of the technologies supporting each of these trends, as well as the overall impact of the combination of these trends on a product development event. Lessons learned and recommendations for future activities will be reported.

VanDalsem, William R.↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Lessons Learned from the Clean Energy to Communities (C2C) Peer-Learning Cohort on Planning and Funding for Electric Vehicle Charging Infrastructure Deployment

From June to December 2023, the U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) partnered with World Resources Institute (WRI) to run a peer-learning cohort for local governments on Planning and Funding for Electric Vehicle Charging Infrastructure Deployment. This cohort effort, funded by DOE's Clean Energy to Communities Program, paired 15 local and regional governments with Clean Cities and Communities coalitions to translate cohort learnings to local context and conduct activities that brought participants closer developing of public electric vehicle charging infrastructure plans. This document shares key takeaways, lessons learned, and resources from the six-month cohort.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior, without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. Non-negative tensor factorization, on the other hand, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Herein, our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.

97 MATHEMATICS AND COMPUTING↗
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.↗
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↗