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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2025 TEM Workshop

The TEM Data Management Workshop will take place on August 26 from 9 a.m. to 12 p.m. MT, and will be held virtually on TEAMS. The primary goal of this workshop is to engage NSUF users and stakeholders in discussions about the data needs for the utilization of AI and ML in the analysis of TEM data. Key topics to be covered include data storage, data sharing, data tagging, metadata inclusion, standardized data formats, data augmentation, and annotated training datasets. Additionally, the workshop will provide valuable insights into resources such as the Nuclear Research Data System (NRDS) for data storage and sharing, as well as open-source codes for data analysis.

Bachhav, Mukesh↗
DOE OSTI2023

ReclaimingSTEM: A healing-centered counterspace model for inclusive science communication and policy training

The dominant U.S. cultural norms shape science, technology, engineering, and math (STEM), and in turn, these norms shape science communication, further perpetuating oppressive systems. Despite being a core scientific skill, science communication research and practice lack inclusive training spaces that center marginalized identities. We address this need with a healing-centered counterspace grounded in the key principles of inclusive science communication: ReclaimingSTEM. ReclaimingSTEM is a science communication and science policy training space that centers the experiences, needs, and wants of people from marginalized communities. ReclaimingSTEM problematizes and expands the definitions of “what counts” as science communication. We organize ReclaimingSTEM with intentionality, emphasizing inclusion at every part of the process. Since initiating in 2018, five ReclaimingSTEM workshops have been held in multiple locations, both in-person and virtually, reaching more than 700 participants from all over the globe. In this paper, we share our model for ReclaimingSTEM, reflections of workshop participants and speakers, barriers faced during organizing, and recommendations for creating truly inclusive practices in science communication spaces.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1988

Spacecraft flight simulation: A human factors investigation into the man-machine interface between an astronaut and a spacecraft performing docking maneuvers and other proximity operations

The anticipated increase in rendezvous and docking activities in the various space programs in the Space Station era necessitates a renewed interest in manual docking procedures. Ten test subjects participated in computer simulated docking missions in which the influence of initial velocity was examined. All missions started from a resting position of 304.8 meters (1000 feet) along the space station's +V-bar axis. Test subjects controlled their vehicle with a translational hand controller and digital auto pilot which are both virtually identical to their space shuttle counterparts. While the 0.1 percent rule (range rate is equal to 0.1 percent of the range) used by space shuttle pilots is comfortably safe, it is revealed to be extremely inefficient in terms of time and not justifiable in terms of marginal safety. Time is worth money, not only because of training and launch costs, but because the sooner a pilot and spacecraft return from a mission, the sooner they can begin the next one. Inexperienced test subjects reduced the costs of simulated docking by close to a factor of 2 and achieved safe dockings in less than 4 percent of the time the baseline approach would entail. This reduction in time can be used to save lives in the event of an accident on orbit, and can tremendously reduce docking costs if fuel is produced from waste water on orbit.

Brody, Adam R.↗
NASA NTRS2012

Motion-Capture-Enabled Software for Gestural Control of 3D Models

Current state-of-the-art systems use general-purpose input devices such as a keyboard, mouse, or joystick that map to tasks in unintuitive ways. This software enables a person to control intuitively the position, size, and orientation of synthetic objects in a 3D virtual environment. It makes possible the simultaneous control of the 3D position, scale, and orientation of 3D objects using natural gestures. Enabling the control of 3D objects using a commercial motion-capture system allows for natural mapping of the many degrees of freedom of the human body to the manipulation of the 3D objects. It reduces training time for this kind of task, and eliminates the need to create an expensive, special-purpose controller.

Norris, Jeffrey S.↗
DOE OSTI2021

CSISAR--Complete System Integrated SAR

The CSISAR tool is GUI based and very simple to use. The algorithms are robust, and the unique processing flows, that the user is stepped through, virtually eliminate the possibility of error in GEOINT production. An integrated data manager is a key part of the CSISAR system. This data manager keeps track of the data available to a user and informs the user of what data is available and what can be done with that data. This keeps the user from having to be trained in the nuances of the algorithms. CSISAR also has an integrated product manager, which helps the user identify, view and manage previously made products. CSISAR was originally developed in 2010-2011 as a Windows based system. It was updated in 2015 to be a Linux based system. This SAND report is intended to make the Product Description and User’s Guide for CSISAR (originally included within the software) more widely available. New is a brief addition of Linux-specific installation details.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Virtual sensing of wind turbine hub loads and drivetrain fatigue damage, Virtuelle Sensoren für die Messung von Hauptwellenlasten und Ermüdungsschäden im Antriebstrang von Windenergieanlagen

Abstract: This paper presents a Digital Twin for virtual sensing of wind turbine aerodynamic hub loads, as well as monitoring the accumulated fatigue damage and remaining useful life in drivetrain bearings based on measurements of the Supervisory Control and Data Acquisition (SCADA) and the drivetrain condition monitoring system (CMS). The aerodynamic load estimation is realized with data-driven regression models, while the estimation of local bearing loads and damage is conducted with physics-based, analytical models. Field measurements of the DOE 1.5 research turbine are used for model training and validation. The results show low errors of 6.4% and 1.1% in the predicted damage at the main and the generator side high-speed bearing respectively. Zusammenfassung: In diesem Aufsatz wird ein digitaler Zwilling für Windenergieanlagen vorgestellt, welcher die virtuelle Erfassung der Hauptwellenlasten und die Zustandsüberwachung von Ermüdungschäden und der verbleibende Nutzungsdauer der Antriebsstranglager ermöglicht. Der digital Zwilling nutzt Messdaten des Supervisory Control and Data Acquisition (SCADA) Systems und des Zustandsüberwachungssystems des Antriebsstranges (CMS). Die Berechnung der Hauptwellenlasten ist mit datenbasierten Regressionsmodellen umgesetzt, während die Berechnung der Lagerkräfte und der Ermüdungsschaden mit physikbasierten Modelle durchgeführt wird. Für die Modellentwicklung und -validierung werden Feldmessdaten der DOE 1.5MW Turbine eingesetzt. Die Abweichungen in den Ermüdungsschäden am Hauptwellenlager und am Generatorwellenlager betragen lediglich 6,4% beziehungsweise 1,1%.

17 WIND ENERGY↗
NASA NTRS2004

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

McGlynn, Thomas↗
NASA NTRS1999

Electron Microscopy and Image Analysis for Selected Materials

This particular project was completed in collaboration with the metallurgical diagnostics facility. The objective of this research had four major components. First, we required training in the operation of the environmental scanning electron microscope (ESEM) for imaging of selected materials including biological specimens. The types of materials range from cyanobacteria and diatoms to cloth, metals, sand, composites and other materials. Second, to obtain training in surface elemental analysis technology using energy dispersive x-ray (EDX) analysis, and in the preparation of x-ray maps of these same materials. Third, to provide training for the staff of the metallurgical diagnostics and failure analysis team in the area of image processing and image analysis technology using NIH Image software. Finally, we were to assist in the sample preparation, observing, imaging, and elemental analysis for Mr. Richard Hoover, one of NASA MSFC's solar physicists and Marshall's principal scientist for the agency-wide virtual Astrobiology Institute. These materials have been collected from various places around the world including the Fox Tunnel in Alaska, Siberia, Antarctica, ice core samples from near Lake Vostoc, thermal vents in the ocean floor, hot springs and many others. We were successful in our efforts to obtain high quality, high resolution images of various materials including selected biological ones. Surface analyses (EDX) and x-ray maps were easily prepared with this technology. We also discovered and used some applications for NIH Image software in the metallurgical diagnostics facility.

Williams, George↗
NASA NTRS1989

Moments applied in the manual assembly of space structures - Ease biomechanics results from STS-61B

Measurements of the level and pattern of moments applied in the manual assembly of a space structure were made in extravehicular activity (EVA) and neutral buoyancy simulation (NBS). The Experimental Assembly of Structures in EVA program included the repeated assembly of a 3.6 m tetrahedral truss structure in EVA on STS-61B after extensive neutral buoyancy crew training. The flight and training structures were of equivalent mass and geometry to allow a direct correlation between EVA and NBS performance. A stereo photographic motion camera system was used to reconstruct in three dimensions rotational movements of structural beams during assembly. Moments applied in these manual handling tasks were calculated on the basis of the reconstructed movements taking into account effects of inertia, drag and virtual mass. Applied moments of 2.0 Nm were typical for beam rotations in EVA. Corresponding applied moments in NBS were typically up to five times greater. Moments were applied as impulses separated by several seconds of coasting in both EVA and NBS. Decelerating impulses were only infrequently observed in NBS.

Cousins, D.↗
DOE OSTI2025

Black-box optimization of CT acquisition and reconstruction parameters: a reinforcement learning approach

Protocol optimization is critical in Computed Tomography (CT) for achieving desired diagnostic image quality while minimizing radiation dose. Due to the inter-effect of influencing CT parameters, traditional optimization methods rely on the testing of exhaustive combinations of these parameters. This poses a notable limitation due to the impracticality of exhaustive parameter testing. This study introduces a novel methodology leveraging Virtual Imaging Trials (VITs) and reinforcement learning to more efficiently optimize CT protocols. Computational phantoms with liver lesions were imaged using a validated CT simulator and reconstructed with a novel CT reconstruction Toolkit. The optimization parameter space included tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. The optimization process was done using a Proximal Policy Optimization (PPO) agent which was trained to maximize the Detectability Index (d’) of the liver lesion for each reconstructed image. Results showed that our reinforcement learning approach found the absolute maximum d’ across the test cases while requiring 79.7% fewer steps compared to an exhaustive search, demonstrating both accuracy and computational efficiency, offering a efficient and robust framework for CT protocol optimization. The flexibility of the proposed technique allows for use of varying image quality metrics as the objective metric to maximize for. Our findings highlight the advantages of combining VIT and reinforcement learning for CT protocol management.

Fenwick, David [Duke University Medical Center]↗
NASA NTRSDate not supplied

Using Earth-based Operational Field Tests as High-Fidelity Analogs for Planetary Surface Exploration

NASA is preparing to land the first woman and first person of color on the Moon within the next decade and establish a permanent sustainable human presence before sending humans onto Mars. To ensure the success of these missions, NASA has performed operational testing in terrestrial, aquatic, and laboratory analog environments that simulate Lunar and Martian environmental characteristics to evaluate exploration concepts of operations (ConOps), engineering design requirements, science support needs, mission operations techniques, and crew training. Terrestrial analogs include Desert Research and Technology Studies (D-RATS), Biologic Analog Science Associated with Lava Terrains (BASALT), and Next Space Technologies for Exploration Partnerships (NextSTEP)Habitat Ground Testing. Aquatic analogs include NASA Extreme Environment Mission Operations (NEEMO) and Pavilion Lake Research Project (PLRP).Laboratory analogs include the Neutral Buoyancy Laboratory (NBL), Active Response Gravity Offload System(ARGOS), rock yards, and virtual and hybrid reality simulation environments. While no single Earth-based analog environment is perfect for simulating all characteristics of other planetary surfaces, testing across multiple locations leverages the strengths of each to provide an integrated understanding of how to best conduct real spaceflight surface exploration missions.

B A Janoiko↗
DOE OSTI2021

Converting tabular data into images for deep learning with convolutional neural networks

Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1994

NASA Johnson Space Center Life Sciences Data System

The Life Sciences Project Division (LSPD) at JSC, which manages human life sciences flight experiments for the NASA Life Sciences Division, augmented its Life Sciences Data System (LSDS) in support of the Spacelab Life Sciences-2 (SLS-2) mission, October 1993. The LSDS is a portable ground system supporting Shuttle, Spacelab, and Mir based life sciences experiments. The LSDS supports acquisition, processing, display, and storage of real-time experiment telemetry in a workstation environment. The system may acquire digital or analog data, storing the data in experiment packet format. Data packets from any acquisition source are archived and meta-parameters are derived through the application of mathematical and logical operators. Parameters may be displayed in text and/or graphical form, or output to analog devices. Experiment data packets may be retransmitted through the network interface and database applications may be developed to support virtually any data packet format. The user interface provides menu- and icon-driven program control and the LSDS system can be integrated with other workstations to perform a variety of functions. The generic capabilities, adaptability, and ease of use make the LSDS a cost-effective solution to many experiment data processing requirements. The same system is used for experiment systems functional and integration tests, flight crew training sessions and mission simulations. In addition, the system has provided the infrastructure for the development of the JSC Life Sciences Data Archive System scheduled for completion in December 1994.

Rahman, Hasan↗
DOE OSTI2021

A Community Roadmap for Scientific Workflows Research and Development

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together. This paper reports on discussions and findings from two virtual “Workflows Community Summits” (January and April, 2021). The overarching goals of these workshops were to develop a view of the state of the art, identify crucial research challenges in the workflows community, articulate a vision for potential community efforts, and discuss technical approaches for realizing this vision. To this end, participants identified six broad themes: FAIR computational workflows; AI workflows; exascale challenges; APIs, interoperability, reuse, and standards; training and education; and building a workflows community. We summarize discussions and recommendations for each of these themes.

Ferreira Da Silva, Rafael↗
NASA NTRSDate not supplied

The NASA Space Life Sciences Training Program (SLSTP)

The NASA Space Life Sciences Training Program (SLSTP) provides undergraduate students entering their junior or senior years with professional experience in space life science disciplines. This challenging ten-week summer program is hosted by NASA Ames Research Center. The primary goal of the program is to train the next generation of scientists and engineers, enabling NASA to meet future research and development challenges in the space life sciences. SLSTP Research Associates (RAs) work closely with NASA scientists and engineers on cutting-edge research and technology development. In addition to providing hands-on research experience and the ability to present their findings in a professional forum, SLSTP RAs attend technical lectures given by experts on a wide range of topics, tour NASA and other research facilities, participate in leadership and team building exercises, and develop a group project. Historically the SLSTP program has been held at Ames Research Center in Mountain View, California. However, due to the pandemic, both the 2020 and 2021 programs were restructured to be conducted virtually, enabling the program to continue despite the challenging times. For this presentation, we will highlight program processes, student recruitment, accomplishments, goals, and feedback from alumni and mentors since 2013. To date, 92 RAs from 64 different academic institutions in 29 states, 21 staffers, and 37 mentors have participated in the program. The SLSTP is funded by NASA’s Space Biology Program, which is part of the Biological and Physical Sciences Division of NASA. The SLSTP is managed by the Space Biology Project within the Science Directorate at Ames Research Center.

Training Space Biosciences Student↗
DOE OSTI2024

The Integrated Virtual Blast Furnace: Enabling Physics-Based Operational Guidance

As part of a DOE-supported research effort, Purdue University Northwest researchers are collaborating with Oak Ridge National Laboratory and United States Steel Corporation to develop a tool to provide blast furnace operators and engineers with process performance insight comparable to high-fidelity computational fluid dynamics modeling, accelerated to provide “what-if” scenarios at near-real-time speed. This is accomplished by pre-simulating a baseline case and a range of potential operating scenarios to establish how the furnace responds to changing inputs, then training a neural network-based Reduced Order Model to accelerate the speed at which predictions of key parameters can be generated.

Okosun, Tyamo↗
DOE OSTI2020

Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters

In this paper, a neural network adaptive critic design (ACD) method based on value gradient learning (VGL) is used to optimally control a grid-connected synchronverter. The main drawback of the traditional synchronverters is their infeasibility to face non-inductive grids. To be able to implement a synchronverter technique in any impedance angle, a neural network-based adaptive controller is used. The advantage of adaptive dynamic programing is its ability to adjust itself when it faces changes and uncertainties in the power system. The proposed VGL consists of two subnetworks: the critic network and the action network. The action network is trained during the operation, and the critic network can be pretrained offline or can be simultaneously trained with the action network. To compare the effectiveness of the traditional synchronverter to the VGL-based synchronverter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

We Are All Potential Role Models: How We Can Support Scientist Engagement with Youth and the General Public

We all have the opportunity to be positive STEM role models. Yet scientists rarely identify themselves as a possible role model. In the training, “Empowering Effective STEM Role Models,” scientists practice research based strategies for engaging youth, particularly girls. This type of training is complementary to science communication, but is focused on equipping scientists with the skills needed to successfully encourage youth to pursue STEM careers. This training has been provided to over 150 STEM professionals at NASA and 14 federal agencies participated in a Train-the-Trainer version, organized by the White House Council for Women and Girls in STEM Working Group. Participants in this training reported that learning and practicing how to connect with and inspire children in STEM made them more confident stepping into further engagement opportunities. Confident, inspiring scientists are a highlight of a successful engagement event, but other factors make a positive experience possible.It’s important that all partners are thoughtful and intentional in preparing for public engagement events. The logistics are crucial, especially now while many engagements have moved to virtual platforms. Additional technology considerations make preparation essential. Two examples of virtual engagement highlight the support provided that led to successful engagements. The GLOBE Mission Earth project engages scientists in virtual classroom visits. To ensure both teacher and scientist expectations are met, a coordinator facilitates up front preparation by providing slide templates for the scientist to customize and providing video and other resources to the teacher and students to prompt student questions. Another example leverages the power of social media, particularly NASA Facebook. The NASA Earth Science Education Collaborative organizes citizen science challenges and engages the public on social. For one event, scientists were asked to share their connection to the investigation, but without access to NASA’s video production studio, the plans shifted. Scientists prepared audio files instead of videos and collaborated with communications leads to help craft their message. This weekly series has been flexible, adjusting to scientist's comfort levels, and has resulted in fresh and creative engagement.

Jessica Elaine Taylor↗