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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sanford Underground Research Facility’s approach to school education, community activities, and public outreach

The Sanford Underground Research Facility (SURF) is the deepest underground science facility in the United States. SURF hosts world-leading experiments in neutrino, astroparticle and nuclear physics, as well as projects in biology, geology, and engineering, and is home to a major excavation project making space for Fermi National Accelerator Laboratory’s Long-Baseline Neutrino Facility (LBNF), which will power the Deep Underground Neutrino Experiment (DUNE). An emphasis on outreach and education is embedded in SURF’s mission statement: “to advance world-class science and inspire learning across generations.” To achieve this mission, SURF goes beyond established science communication methods, including operating an open-to-the-public visitor center, hosting multiple public outreach events per month, and an annual city-wide science festival. Furthermore, SURF is training K-12 science educators, developing school curriculum units, and providing classroom materials, based on science researched at the laboratory. The strategic approach, specific methods, and successful outcomes of these programs, which are based on SURF’s science, location, and community, may serve as examples for effective science education, public outreach, and community engagement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1972

Exploratory development and services for preparing and examining ultrathin polished sections of lunar rocks and particulates, part 1

Development of improved procedures is reported for three classes of lunar materials: dense rocks, breccias, and particulates. High quality ultrathin sections of these materials are obtained. Lists of equipment and supplies, procedures, photomicrographic documentation, and training are provided. Advantages of ultrathin polished sections for conventional and unconventional optical microscopy methods are described. Recommendations are provided for use of ultrathin sections in lunar rock studies, for further refinement of ultrathinning procedures, and for additional training efforts to establish a capability at the Manned Space Center. For Part 2, See N72-50754.

Beauchamp, R. H.↗
NASA NTRS1998

Computational Microbial Morphometry and NASA Astrobiology Initiatives

From the 12 known meteorites believed to have made their way to Earth from Mars, about 20 kg (44 lbs.) of material are suitable for searching for microfossil evidence. An automated neural network trained to identify common morphologies to distinguish organic and non-organic origins for rock fossils is described. The high success rate of classification by this computerized image analysis (85% on training data) moves toward a fully-automated search technique.

Noever, David A.↗
NASA NTRS1997

Physical examination during space flight

OBJECTIVE: To develop techniques for conducting a physical examination in microgravity and to describe and document the physiologic changes noted with use of a modified basic physical examination. DESIGN: On the basis of data gathered from physical examinations on KC-135 flights, three physical variables were assessed serially in astronauts during two shuttle missions (of 8- and 10-day duration, respectively). Preflight, in-flight, and postflight examinations were conducted by trained physician-astronauts or flight surgeons, who used this modified examination. MATERIAL AND METHODS: Five male and two female crewmembers participated in the "hands-on" physical examination of all physiologic systems except the genitourinary system. Level of edema, intensity of bowel sounds, and peripheral reflexes were assessed and graded. RESULTS: This investigation identified unique elements of a physical examination performed during space flight that will assist in the development of standard methods for conducting examinations of astronauts in weightlessness. In addition, demonstrable changes induced by microgravity were noted in most physiologic systems examined. CONCLUSION: The data support the hypothesis that the microgravity examination differs from that conducted on earth or in a 1g environment. In addition, alterations in the physiologic response can be detected with use of hands-on technique. These data are invaluable in the development of optimal medical care for humans in space.

STS Shuttle Project↗
NASA NTRS2014

Quantitative Risk Modeling of Fire on the International Space Station

The International Space Station (ISS) Program has worked to prevent fire events and to mitigate their impacts should they occur. Hardware is designed to reduce sources of ignition, oxygen systems are designed to control leaking, flammable materials are prevented from flying to ISS whenever possible, the crew is trained in fire response, and fire response equipment improvements are sought out and funded. Fire prevention and mitigation are a top ISS Program priority - however, programmatic resources are limited; thus, risk trades are made to ensure an adequate level of safety is maintained onboard the ISS. In support of these risk trades, the ISS Probabilistic Risk Assessment (PRA) team has modeled the likelihood of fire occurring in the ISS pressurized cabin, a phenomenological event that has never before been probabilistically modeled in a microgravity environment. This paper will discuss the genesis of the ISS PRA fire model, its enhancement in collaboration with fire experts, and the results which have informed ISS programmatic decisions and will continue to be used throughout the life of the program.

Castillo, Theresa↗
DOE OSTI2026

Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential

Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, in this study, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under detonation. We discuss a strategy for constructing diverse training data needed to capture the complex chemistry of TATB. Our potential demonstrates strong transferability across a wide range of thermodynamic conditions and other explosives, enabling accurate and reliable chemical modeling of organic materials under extreme conditions. The efficiency of our approach allows for simulations over several nanoseconds and for large system sizes, providing detailed insights into the chemistry of shocked TATB. The model accurately reproduces experimental Hugoniot equation of state data, and our simulations reveal the rapid formation of nitrogen-rich carbon clusters following shock. The methods and datasets developed here offer a robust framework for accurate chemical modeling of other shocked organic energetic materials.

Chemistry↗
DOE OSTI2025

Performance Improvement of LTS Undulators for Synchrotron Light Sources

The joint expertise of ANL and FNAL has led to the production of $\text{Nb}_{3}\text{Sn}$ undulator magnets in operation in the ANL Advanced Photon Source (APS). These magnets showed performance reproducibility close to the short sample limit, and a design field increase of 20% at 820 A. However, the long training did not allow obtaining the expected 50% increase of the on-axis magnetic field with respect to the ∼1 T produced at 450 A current in the ANL NbTi undulator. To address this, 10-pole long undulator prototypes were fabricated, and CTD-101K was replaced as impregnation material with TELENE, an organic olefin-based thermosetting dicyclopentadiene resin produced by RIMTEC Corporation, Japan. Training and magnet retraining after a thermal cycle were nearly eliminated, with only a couple of quenches needed before reaching short sample limit at over 1,100 A. TELENE will enable operation of $\text{Nb}_{3}\text{Sn}$ undulators much closer to their short sample limit, expanding the energy range and brightness intensity of light sources. TELENE is Co-60 gamma radiation resistant up to 7–8 MGy, and therefore already applicable to impregnate planar, helical and universal devices operating in lower radiation environments than high energy colliders.

43 PARTICLE ACCELERATORS↗
NASA NTRSDate not supplied

ISS Payload Operations Training During the COVID-19 Pandemic: Impacts and Solutions

The onset of the COVID-19 pandemic brought a dramatic and rapid transformation to almost every aspect of humanity in 2020. The world’s space agencies and their missions were not immune to the wide-sweeping changes. One discipline principally affected was mission operations and the various groups supporting that function. Mission support teams, especially for complex and crewed missions like the International Space Station were forced to rapidly rethink how and where control center staff performed their vital work. Operations training – an essential element to mission ops, had unique hurdles to overcome. Operations training is responsible for preparing astronaut crews for their missions, providing training to flight controllers, as well as ensuring that new team members are ready to join their colleagues. Every element of training was impacted by COVID restrictions. From orientation and introductory classes for new controllers, simulations and advanced lessons, On the Job Training and final evaluations; all aspects faced new challenges. Trainers at NASA’s Marshall Space Flight Center in Huntsville, Alabama, were forced to become more efficient with trainees and resources to continue supporting payload operations. The pandemic started in the USA in March 2020. Immediately, NASA mandated that the support for ISS real-time operations was critical. As a result, physical access to key facilities was restricted. Trainers and trainees had to quickly shift to 100 percent remote learning. In the short term, this was not a problem. However, instructors discovered lessons that they were accustomed to delivering in a classroom environment did not translate to remote teaching. Another hurdle to operations training was the mandate that all simulations could only be held remotely. The logistics of even small simulations proved to be challenging due to IT restrictions and public internet limitations. With simulations essentially stopped, as well as the restrictions on most OJT, trainees were essentially stopped in their advancement towards certification. Once limitations were identified, trainers prioritized new options. Transitioning to all electronic learning materials was a relatively easy fix. Teaching to large groups took additional shifts in the training paradigm. Methods for preparing astronauts for their missions were revised. Simulation supervisors found more efficient techniques to provide realistic training experience. Communication and approvals from management was essential. In every case, the payload operations instructors found novel solutions to all functions listed. This paper will discuss the factors and solutions payloads operations trainers found to keep scientific research on the ISS flying forward to mission success.

ISS↗
NASA NTRS2008

Integrating Space Flight Resource Management Skills into Technical Lessons for International Space Station Flight Controller Training

The Johnson Space Center s (JSC) International Space Station (ISS) Space Flight Resource Management (SFRM) training program is designed to teach the team skills required to be an effective flight controller. It was adapted from the SFRM training given to Shuttle flight controllers to fit the needs of a "24 hours a day/365 days a year" flight controller. More recently, the length reduction of technical training flows for ISS flight controllers impacted the number of opportunities for fully integrated team scenario based training, where most SFRM training occurred. Thus, the ISS SFRM training program is evolving yet again, using a new approach of teaching and evaluating SFRM alongside of technical materials. Because there are very few models in other industries that have successfully tied team and technical skills together, challenges are arising. Despite this, the Mission Operations Directorate of NASA s JSC is committed to implementing this integrated training approach because of the anticipated benefits.

Baldwin, Evelyn↗
DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2022

ISS Payload Operations Training Throughout the COVID-19 Pandemic: Impacts, Opportunities and Solutions

The onset of the COVID-19 pandemic brought a dramatic and rapid transformation to almost every aspect of humanity. The world’s space agencies and their missions were not immune to the wide-sweeping changes. One discipline principally affected was mission operations and the various groups supporting that function. Mission support teams, especially for complex and crewed missions like the International Space Station (ISS) were forced to rethink how and where control center staff performed their vital work. Operations training – an essential element to mission ops, had unique hurdles to overcome. Operations training is responsible for preparing astronaut crews for their missions, training and certifying flight controllers, as well as ensuring that new team members are ready to join their colleagues. Every element of training was impacted during the pandemic. From orientation and introductory classes for new controllers, simulations, and advanced lessons, On the Job Training (OJT) and final evaluations; all aspects faced challenges. Trainers at NASA’s Marshall Space Flight Center in Huntsville, Alabama were forced to become more efficient with trainees and resources to continue supporting ISS payload operations. The pandemic arrived in the USA in March 2020. Immediately, NASA mandated that the support for ISS real-time operations was critical. As a result, physical access to key facilities was restricted. Trainers and trainees had to quickly shift to 100% remote learning. In the short term, this was not a problem. However, instructors discovered lessons they were accustomed to delivering in a classroom environment often did not translate to remote teaching. Another hurdle to operations training was the mandate that all simulations could only be held remotely. The logistics of even small simulations proved to be challenging due to Information Technology (IT) restrictions and public internet limitations. With simulations essentially halted, as well as the restrictions on most OJT, trainees were essentially stopped in their advancement towards certification. Once limitations were identified, trainers prioritized new options. Transitioning to all electronic learning materials was a relatively easy fix. Teaching to large groups took additional shifts in the training paradigm. Methods for preparing astronauts for their missions were revised. Simulation supervisors found efficient techniques to provide realistic training experiences. Communication and coordination with management was essential. In every case, the payload operations instructors found novel solutions to all functions listed. This paper discusses the factors and solutions payloads operations trainers found to keep scientific research on the ISS flying forward to mission success.

ISS↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Developing Nontraditional Partnerships to Disseminate the Space Science Story

NASA Space Science Days (NSSD) was established in 2004 to bring the story of the Mars Exploration Rovers (MER) to a community far removed from areas NASA traditionally serves. The original NSSD invited 400 5th and 8th graders from the Texas Rio Grande Valley area to the University of Texas Brownsville campus to participate in a one day Saturday event filled with information about MER with related hands on activities. Currently the program has grown to over 700 5th and 8th grade participants who are mentored by trained university students from several Texas universities and community colleges and growing to include universities from throughout the U.S. A collaboration between three major institutions: The NASA Johnson Space Center (JSC) Astromaterials Research and Exploration Science Directorate (ARES), the Society of Hispanic Professional Engineers/Advancing Hispanic Excellence in Technology Engineering, Math, and Science, (SHPE/AHETEMS), and the University of Texas at Brownsville (UTB) has been established to enable the dissemination of Solar System related educational materials throughout the U.S. Already in its 8th year, UTB has developed and tested a NSSD model that has successfully disseminated space science materials to students throughout South Texas Rio Grande Valley. With this newly formed collaboration this model s expansion will allow trained SHPE students and professionals to conduct events throughout its established nation-wide delivery systems. Each year a new NSSD site will be established through an application process solicited from SHPE student and professional chapters. Once a chapter is awarded, upper-level high school and university students will travel to NASA- JSC for a two day workshop where students learn about the current year s theme and are trained to present hands-on activities related to the theme. Additional training by ARES and UTB occurs one month before the new event in their own communities. Both local middle school teachers and mentors are trained locally. This allows the teachers time to prepare their students with the background material for NSSD and give the mentors time to decide which activity they feel comfortable with to lead. Several events using this process have already been successful and an increase in interest from SHPE chapters wanting to establish their NSSD event is growing.

Galindo, Charles↗
NASA NTRS1971

Pyrotechnic hazards classification and evaluation program. Electrostatic vulnerability of the E8 and XM15/XM165 clusters, phase 2

An investigation into the electrostatic phenomena associated with the manufacturing and handling of explosives is discussed. The testing includes measurement of the severity of the primary charge generation mechanism, triboelectric effects between dissimilar surfaces; refinement of equivalent circuits of the XM15/XM165 and E8 fuse trains; evaluation of the electrostatic spark discharge characteristics predicted by an equivalent circuit analysis; and determination of the spark ignition sensitivity of materials, components, junctions, and subassemblies which compose the XM15/XM165 and E8 units. Special studies were also performed. These special tests included ignition sensitivity of the complete XM15 fuse train when subjected to discharges through its entire length, measurement of electrostatic potentials which occur during the E8 foaming operation during fabrication, and investigation of the inadvertent functioning of an XM15 cluster during manufacturing. The test results are discussed and related to the effectiveness of suggested modification to reduce the electrostatic ignition sensitivity.

Source record↗
DOE OSTI2020

AGR-5/6/7 Test Train Disassembly & Tools Phases I & II

This document provides the process for qualification of the AGR-5/6/7 Test Train Disassembly equipment for remote operation in the HFEF Main Cell. The test train is composed of five welded stainless steel capsules that are expected to be transported in two sections. The first section will be Capsules 1 and 2. The second section will include Capsules 3, 4 and 5. The capsules range in length from about 9 inches to about 12 inches. Inside the capsules is a graphite holder that contains multiple fueled AGR compacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1997

School Science Comes Alive

Phase 3 of the School Science Comes Alive Program (S(sup 2)CAP) created an exciting, science - enrichment experience for third, fourth and fifth graders and their teachers and enhanced the science-teaching skills of teacher teams at each of four participating elementary schools on Virginia's Peninsula. The schools involved enroll a majority of Black students, many of whom are from economically disadvantaged households. Designed to build on the highly successful S(sup 2)CAP program of the preceding two years, this project brought college faculty together with classroom teachers and trained volunteers in a cooperative effort to make a lasting difference in the quality of science education at the four schools. In total, this program touched approximately 1000 the school children, more than half of whom are black, giving them direct and indirect exposure to the spirit of inquiry and adventure of the world-wide science community. In S(sup 2)CAP Phase 3, a large measure of responsibility was placed on the classroom teachers, thus creating a more sustainable partnership between college faculty and grade school teacher. Our college physics professors coached and supported teams of teachers from each school at intensive training workshops. A volunteer program provided each teacher with one or more trained volunteers to assist in class with the hands-on activities that have been central to the S2CAP program. Most of the equipment for these activities was constructed during the workshops by the teachers and volunteers from low cost materials provided by the program. Two types of volunteers were enlisted: science smart black college students and technically trained retirees (many of whom are ex-NASA employees). One goal of this program was to increase the numbers of minority students who see science as an interesting and exciting subject, to make the science period a time which students look forward to in the school day. Such an attitude is expected to translate naturally into a higher interest in science and engineering as a career for these students. A second goal was to create a sustainable improvement in the way science is taught at the elementary level. By the end of the program we expected that our teachers would be significantly more self reliant in using hands-on-activities as a part of their science curricula than they were prior to their involvement with S2CAP. In summary, S2CAP Phase 3 offered intensive training workshops for teachers and supporting volunteers followed by stimulating hands-on activities in the classroom for the children. These components combined to amplify the experience, enthusiasm, and ideas of our scientists in a way that complements the normal elementary school curriculum in each of the two school systems involved.

Hartline, Frederick F.↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗