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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Planet X probe: A fresh new look at an old familiar place

Planet X Probe utilizes a Get Away Special (GAS) payload to provide a large student population with a remote Earth sensing experimental package. To provide a cooperative as well as a competitive environment, the effort is targeted at all grade levels and at schools in different geographical regions. LANDSAT capability allows students to investigate the Earth, its physical makeup, its resources, and the impact of man. This project also serves as an educational device to get students to stand back and take a fresh look at their home planet. The key element is to treat the familiar Earth as an unknown planet with knowledge based only on what is observable and provable from the images obtained. Through participation, a whole range of experiences will include: (1) mission planning; (2) research and pilot projects to train teams; (3) identification and recruitment of scientific mentors and dialogue; (4) selection of a student advisory team to be available during the mission; (5) analysis of data and compilation of findings; (6) report preparation, constucted along sound scientific principles; and (7) presentation and defense of findings before a meeting of competitive student groups and scientist in the field.

Nicholson, James↗
NASA NTRS2002

STS-107 Crew Interviews: William McCool, Pilot

STS-107 Pilot William McCool is seen during this preflight interview, where he gives a quick overview of the mission before answering questions about his inspiration to become an astronaut and his background. McCool outlines his role in the mission in general, and discusses the scientific experiments which comprise the primary payloads for the mission. He provides details on the following instruments and experiments: MEIDEX (Mediterranean Israeli Dust Experiment), BIOPACK (Bacterial Physiology and Virulence on Earth and in Microgravity) and SOLSE (Shuttle Ozone Limb Sounding Experiment). McCool talks about the new SPACEHAB research module which doubles the amount of space available for scientific research projects. He also mentions the training for the mission, the astronauts working in dual shifts on the shuttle, and the importance of international cooperation in planning the mission.

Source record↗
NASA NTRS2004

Effect of Palagonite Dust Deposition on the Automated Detection of Carbonate Vis/NIR Spectra

Currently Mars missions can collect more data than can be returned. Future rovers of increased mission lifetime will benefit from onboard autonomous data processing systems to guide the selection, measurement and return of scientifically important data. One approach is to train a neural net to recognize spectral reflectance characteristics of minerals of interest. We have developed a carbonate detector using a neural net algorithm trained on 10,000 synthetic Vis/NIR (350-2500 nm) spectra. The detector was able to correctly identify carbonates in the spectra of 30 carbonate and noncarbonate field samples with 100% success. However, Martian dust coatings strongly affect the spectral characteristics of surface rocks potentially masking the underlying substrate rock. In this experiment, we measure Vis/NIR spectra of calcite coated with different thicknesses of palagonite dust and evaluate the performance of the carbonate detector.

Gilmore, Martha S.↗
NASA NTRS2020

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Center—we make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This year’s report features 10 of the Agency’s 16 Technology Areas, and I am proud of Marshall’s role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before— first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Center’s capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come.

Dankanich, John W.↗
NASA NTRSDate not supplied

Building Sustainable Capacity within Under-Represented Communities Through A Deep Partnership Model

Despite varied programmatic inducements and the documented benefits of Diversity Equity & Inclusion (DE&I), participation rates of underrepresented communities within NASA’s planetary sciences activities have remained low and nearly unchanged over the last decade. The lack of success of various efforts may be attributable to factors identified in social science research. First, the proportionality of under-represented groups has been shown to be critical to achieving the positive impacts of DE&I. When under-represented groups make up less than ~30%, group dynamics such as assimilation or tokenism routinely appear, with destructive and counter-productive results. Additionally, if participation only occurs within limited segments of an organization, benefits may be limited. Proportionality must extend across all levels or layers of the organization for the benefits of DE&I to accrue. Second, the inclusion of “Third Spaces” in the planning and support of DE&I efforts is critical to the long-term success or “stickiness” of DE&I activities. Third Spaces are those beyond home (First Space) and work (Second Space) where informal, community-building activities occur. Examples include conferences, after-work activities, and weekend social engagements. Without explicitly acknowledging and including “Third Space” interactions, DE&I efforts can fall short of their long-term goals. In 2014, the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado Boulder began a partnership with the United Arab Emirates to develop and fly a space mission to Mars. The scientific mission had an explicit goal of training scientists and engineers, and creating a sustainable ecosystem that would spark and accelerate the UAE’s space economy. To accomplish this, LASP and the UAE set up a program that matched scientists and engineers in one-on-one mentorship relationships, at all levels of the organization. Over the course of the roughly 4-year project, those partnering relationships fostered not only strong professional relationships and knowledge transfer, but also personal friendships that extended into “Third Spaces.” The UAE/LASP partnership now continues through the co-development of an asteroid mission currently in definition phase. In collaboration with NASA Ames Research Center, LASP is developing the conceptual frame work to extend this deep partnership model to domestic opportunities. By implementing a planetary science mission that employs an analogous one-on-one partnership model across all elements of the organization, this project will demonstrate how proportionality and Third Space relationships can enable the development of localized, sustainable communities that are explicitly and deeply engaged with the NASA ecosystem.

Capacity Building↗
NASA NTRS1980

Work of the cosmonaut

The necessity for the cosmonaut to receive broad training in many fields in order to carry out his multifaceted work is discussed. The work includes: scientific research, engineering, operator's work, participation in technical commissions and councils, training on simulators, and the study of technology and sports.

Demin, L. S.↗
NASA NTRS1998

Earth Remote Sensing: What is it Really? What to do with it?

NASA!s Earth Sciences Program supports a wide range of endeavors in basic Earth system scientific research, technology development to support that research, development of materials and training for educators and students based on that research and information, and increasingly practical applications. A brief overview of the scope of this scientific research and the key features of the necessary remote sensing instrumentation will be given. I will also describe available educational materials and training courses for a wide range of grade levels. Information will be provided on how to obtain educational materials or to participate in a training course. Finally, a few examples will be given to illustrate how Earth remote sensing effects our daily life.

Meeson, Blanche W.↗
DOE OSTI2021

Nuclear Enterprise Science & Technology (NEST) [Slides]

The NEST Certificate Program is designed to provide students with the skills and experience to qualify for entry-level positions in nuclear facilities as Fissile Material Handler and/or Glovebox Operator. The program goal is to provide for a technically qualified workforce who can execute a variety of programmatic work in modern nuclear materials handling and processing facilities. The program is designed to be completed in a minimum of 1 year (2 semesters) of university level courses (30 credit hours). A certificate will be offered and awarded through the University of New Mexico – Los Alamos (UNM-LA). NEST is unique in that it is an immersive education program. The NNSA-required training for nuclear material handlers and fissionable material handlers has been cross-walked with the educational Core Curricula. Academic content will be delivered that provides background scientific and engineering understanding of the fundamental concepts behind this training. It will be offered as a Pilot Program to an incumbent cohort to ensure that the Certificate meets programmatic needs. NEST is modelled after a Wharton County Junior College (Texas) program to attract and produce nuclear reactor operators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

The Chemistry Graduate Student Experience: Findings from an ACS Survey

Graduate training is a key element in producing a scientific workforce that reflects the nation’s diversity. This paper examines data from a 2013 American Chemical Society (ACS) survey of 2,544 chemistry masters and doctoral students and reveals barriers to reaching this goal. Multivariate statistical analyses indicate that women reported significantly less supportive relationships with advisors. Women were less likely to plan to finish their degrees, and for PhD students, the discrepancy was larger for students at the start of their graduate program. Women were also less likely to pursue the next level of training, and the gender difference related to postdoctoral plans was greater for those who identified with a racial-ethnic group traditionally underrepresented in chemistry (underrepresented minority, URM). URM students who were beyond the first year of their graduate program reported significantly less supportive relationships with peers. They were also less likely to have funding sufficient to meet their needs and more often used personal resources including loans. Despite these difficulties, URM students were more likely to definitely plan to finish their degrees, and men who identified as URM were more likely to plan to pursue postdoctoral work. Independent of gender and identification as URMs, students in more highly ranked schools reported less advisor support. Extensive open-ended comments indicated that large proportions of the students desired more attention and meaningful feedback from advisors and changes within their programs to promote support for students and advisor accountability. Suggestions for future research are given, and a companion commentary discusses needed directions for change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2008

Assessment of the NASA Astrobiology Institute

Astrobiology is a scientific discipline devoted to the study of life in the universe--its origins, evolution, distribution, and future. It brings together the physical and biological sciences to address some of the most fundamental questions of the natural world: How do living systems emerge? How do habitable worlds form and how do they evolve? Does life exist on worlds other than Earth? As an endeavor of tremendous breadth and depth, astrobiology requires interdisciplinary investigation in order to be fully appreciated and examined. As part of a concerted effort to undertake such a challenge, the NASA Astrobiology Institute (NAI) was established in 1998 as an innovative way to develop the field of astrobiology and provide a scientific framework for flight missions. Now that the NAI has been in existence for almost a decade, the time is ripe to assess its achievements. At the request of NASA's Associate Administrator for the Science Mission Directorate (SMD), the Committee on the Review of the NASA Astrobiology Institute undertook the assignment to determine the progress made by the NAI in developing the field of astrobiology. It must be emphasized that the purpose of this study was not to undertake a review of the scientific accomplishments of NASA's Astrobiology program, in general, or of the NAI, in particular. Rather, the objective of the study is to evaluate the success of the NAI in achieving its stated goals of: 1. Conducting, supporting, and catalyzing collaborative interdisciplinary research; 2. Training the next generation of astrobiology researchers; 3. Providing scientific and technical leadership on astrobiology investigations for current and future space missions; 4. Exploring new approaches, using modern information technology, to conduct interdisciplinary and collaborative research among widely distributed investigators; and 5. Supporting outreach by providing scientific content for use in K-12 education programs, teaching undergraduate classes, and communicating directly with the public. The committee s assessment of the NAI's progress in these five areas is presented in Chapters 2 to 6, respectively.

Source record↗
DOE OSTI2022

Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e.g., law, healthcare, education, etc. However, only limited efforts have investigated the opportunities and limitations of applying these powerful models to science and security applications. In this work we develop foundation models of scientific knowledge for chemistry to augment scientists with the advanced ability to perceive and reason at scale previously unimagined. Specifically, we build large-scale (1.47B parameter) general-purpose models for chemistry that can be effectively used to perform a wide range of in-domain and out-of-domain tasks. Evaluating these models in a zero-shot setting, we analyze the effect of model and data scaling, knowledge depth, and temporality on model performance in context of model training efficiency. Our novel findings demonstrate that (1) model size significantly contributes to the task performance when evaluated in a zero-shot setting; (2) data quality (aka diversity) affects model performance more than data quantity; (3) similarly, unlike previous work (Luu et al., 2021) temporal order of the documents in the corpus boosts model performance only for specific tasks, e.g., SciQ; and (4) models pre-trained from scratch perform better on in-domain tasks than those tuned from general-purpose models like Open AI’s GPT-2.

Foundation Models, Chemistry↗
DOE OSTI2023

Evaluation of pre-training large language models on leadership-class supercomputers

Large language models (LLMs) have arisen rapidly to the center stage of artificial intelligence as the foundation models applicable to many downstream learning tasks. However, how to effectively build, train, and serve such models for many high-stake and first-principle-based scientific use cases are both of great interests and of great challenges. Moreover, pre-training LLMs with billions or even trillions of parameters can be prohibitively expensive not just for academic institutions, but also for well-funded industrial and government labs. Furthermore, the energy cost and the environmental impact of developing LLMs must be kept in mind. Here, in this work, we conduct a first-of-its-kind performance analysis to understand the time and energy cost of pre-training LLMs on the Department of Energy (DOE)’s leadership-class supercomputers. Employing state-of-the-art distributed training techniques, we evaluate the computational performance of various parallelization approaches at scale for a range of model sizes, and establish a projection model for the cost of full training. Our findings provide baseline results, best practices, and heuristics for pre-training such large models that should be valuable to HPC community at large. We also offer insights and optimization strategies for using the first exascale computing system, Frontier, to train models of the size of GPT-3 and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2021

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1995

Helicopter Human Factors

Even under optimal conditions, helicopter flight is a most demanding form of human-machine interaction, imposing continuous manual, visual, communications, and mental demands on pilots. It is made even more challenging by small margins for error created by the close proximity of terrain in NOE flight and missions flown at night and in low visibility. Although technology advances have satisfied some current and proposed requirements, hardware solutions alone are not sufficient to ensure acceptable system performance and pilot workload. However, human factors data needed to improve the design and use of helicopters lag behind advances in sensor, display, and control technology. Thus, it is difficult for designers to consider human capabilities and limitations when making design decisions. This results in costly accidents, design mistakes, unrealistic mission requirements, excessive training costs, and challenge human adaptability. NASA, in collaboration with DOD, industry, and academia, has initiated a program of research to develop scientific data bases and design principles to improve the pilot/vehicle interface, optimize training time and cost, and maintain pilot workload and system performance at an acceptable level. Work performed at Ames, and by other research laboratories, will be reviewed to summarize the most critical helicopter human factors problems and the results of research that has been performed to: (1) Quantify/model pilots use of visual cues for vehicle control; (2) Improve pilots' performance with helmet displays of thermal imagery and night vision goggles for situation awareness and vehicle control; (3) Model the processes by which pilots encode maps and compare them to the visual scene to develop perceptually and cognitively compatible electronic map formats; (4) Evaluate the use of spatially localized auditory displays for geographical orientation, target localization, radio frequency separation; (5) Develop and flight test control/display concepts; (6) Quantify, model, predict, and improve pilots, workload-management strategies; and (7) Design computer-game trainers to reduce training time and cost.

Hart, Sandra G.↗
DOE OSTI2021

Laboratory earthquake forecasting: A machine learning competition

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

58 GEOSCIENCES↗
DOE OSTI2025

Investigating performance and variability of NIF ICF experiments with deep learning

The parameter space involved in designing an inertial confinement fusion shot at the National Ignition Facility (NIF) is massively multi-dimensional and the cost of a single shot makes a comprehensive set of sensitivity studies in the laboratory impractical. The use of machine learning to overcome these challenges has gained popularity and has had several successful applications by the scientific community. We extend on these efforts by training a neural network (NN) on information about the experimental design, engineering elements, and drive asymmetry to predict with uncertainty the neutron yield of an experiment. We find the measured and model predicted values are in good agreement, with an R 2 value of 0.91 for a randomly selected test dataset. Almost all the predicted 95% credible intervals contain the corresponding measured value for both training and test datasets. We identify correlations picked up by the NN between the shot design, yield, and variability and use them to motivate shot sensitivity studies. The first shot to exceed the Lawson-like ignition criteria (N210808) was conducted at the NIF and subsequent shots studied the design’s robustness. In a follow-up shot to N210808, our model predicts capsule quality to be the main performance degradation mechanism that prevented the shot from repeating previous performance levels. Shot N221204 was the first shot to exceed a target energy gain of 1. Our model predicts increased yield with reduced coast time for a N221204 study and greater variability for designs with lower peak powers at constant yield. The model’s fast prediction speed and uncertainty prediction are useful for identifying interesting design paths that could warrant further investigation with conventional simulations to search for robust high yield designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational autoencoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of denoising diffusion probabilistic models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method.

42 ENGINEERING↗