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DOE OSTI2025

Two-Scale Neural Networks for Partial Differential Equations with Small Parameters

We propose a two-scale neural network method for solving partial differential equations (PDEs) with small parameters using physics-informed neural networks (PINNs). We directly incorporate the small parameters into the architecture of neural networks. The proposed method enables solving PDEs with small parameters in a simple fashion, without adding Fourier features or other computationally taxing searches of truncation parameters. Various numerical examples demonstrate reasonable accuracy in capturing features of large derivatives in the solutions caused by small parameters.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1997

Neural Network Control of a Magnetically Suspended Rotor System

Abstract Magnetic bearings offer significant advantages because of their noncontact operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

Choi, Benjamin↗

NASA NTRS2004

Monitoring space shuttle air quality using the Jet Propulsion Laboratory electronic nose

A miniature electronic nose (ENose) has been designed and built at the Jet Propulsion Laboratory (JPL), Pasadena, CA, and was designed to detect, identify, and quantify ten common contaminants and relative humidity changes. The sensing array includes 32 sensing films made from polymer carbon-black composites. Event identification and quantification were done using the Levenberg-Marquart nonlinear least squares method. After successful ground training, this ENose was used in a demonstration experiment aboard STS-95 (October-November, 1998), in which the ENose was operated continuously for six days and recorded the sensors' response to the air in the mid-deck. Air samples were collected daily and analyzed independently after the flight. Changes in shuttle-cabin humidity were detected and quantified by the JPL ENose; neither the ENose nor the air samples detected any of the contaminants on the target list. The device is microgravity insensitive.

manned↗

NASA NTRS1982

LANDSAT technology transfer to the private and public sectors through community colleges and other locally available institutions, phase 2 program

A program established by NASA with the Environmental Research Institute of Michigan (ERIM) applies a network where the major participants are NASA, universities or research institutes, community colleges, and local private and public organizations. Local users are given an opportunity to obtain "hands on" training in LANDSAT data analysis and Geographic Information System (GIS) techniques using a desk top, interactive remote analysis station (RAS). The RAS communicates with a central computing facility via telephone line, and provides for generation of land use and land suitability maps and other data products via remote command. During the period from 22 September 1980 - 6 March 1982, 15 workshops and other training activities were successfully conducted throughout Michigan providing hands on training on the RAS terminals for 250 or more people and user awareness activities such as exhibits and demonstrations for 2,000 or more participants.

Rogers, R. H.↗

NASA NTRS2017

Tracking Historical NASA EVA Training: Lifetime Surveillance of Astronaut Health (LSAH) Development of the EVA Suit Exposure Tracker (EVA SET)

During a spacewalk, designated as extravehicular activity (EVA), an astronaut ventures from the protective environment of the spacecraft into the vacuum of space. EVAs are among the most challenging tasks during a mission, as they are complex and place the astronaut in a highly stressful environment dependent on the spacesuit for survival. Due to the complexity of EVA, NASA has conducted various training programs on Earth to mimic the environment of space and to practice maneuvers in a more controlled and forgiving environment. However, rewards offset the risks of EVA, as some of the greatest accomplishments in the space program were accomplished during EVA, such as the Apollo moonwalks and the Hubble Space Telescope repair missions. Water has become the environment of choice for EVA training on Earth, using neutral buoyancy as a substitute for microgravity. During EVA training, an astronaut wears a modified version of the spacesuit adapted for working in water. This high fidelity suit allows the astronaut to move in the water while performing tasks on full-sized mockups of space vehicles, telescopes, and satellites. During the early Gemini missions, several EVA objectives were much more difficult than planned and required additional time. Later missions demonstrated that "complex (EVA) tasks were feasible when restraints maintained body position and underwater simulation training ensured a high success probability".1,2 EVA training has evolved from controlling body positioning to perform basic tasks to complex maintenance of the Hubble Space Telescope and construction of the International Space Station (ISS). Today, preparation is centered at special facilities built specifically for EVA training, such as the Neutral Buoyancy Laboratory (NBL) at NASA's Johnson Space Center ([JSC], Houston) and the Hydrolab at the Gagarin Cosmonaut Training Centre ([GCTC], Star City, outside Moscow). Underwater training for an EVA is also considered hazardous duty for NASA astronauts. This activity places astronauts at risk for decompression sickness and barotrauma as well as various musculoskeletal disorders from working in the spacesuit. The medical, operational and research communities over the years have requested access to EVA training data to better understand the risks. As a result of these requests, epidemiologists within the Lifetime Surveillance of Astronaut Health (LSAH) team have compiled records from numerous EVA training venues to quantify the exposure to EVA training. The EVA Suit Exposure Tracker (EVA SET) dataset is a compilation of ground-based training activities using the extravehicular mobility unit (EMU) in neutrally buoyant pools to enhance EVA performance on orbit. These data can be used by the current ISS program and future exploration missions by informing physicians, researchers, and operational personnel on the risks of EVA training in order that future suit and mission designs incorporate greater safety. The purpose of this technical report is to document briefly the various facilities where NASA astronauts have performed EVA training while describing in detail the EVA training records used to generate the EVA SET dataset.

Laughlin, Mitzi S.↗

NASA NTRS1982

Training aircraft design considerations based on the successive organization of perception in manual control

The thesis that pilot skill development in the Navy approach and landing task is very strongly tied to the aircraft closure rate and, therefore, that pilot training for this task should be based on an appropriate progression closure rate is considered. A rational and explicit determination of design point approach speeds as well as other important aerodynamic features for training aircraft is also considered. Two keys are discussed: recognition of the significance of transitioning from a purely compensatory control loop technique to one involving a pursuit crossfeed between throttle and pitch attitude, and addressing the terminal flight path adjustment in terms of range-to-go.

Heffley, R. K.↗

NASA NTRS1977

Crop identification and area estimation over large geographic areas using LANDSAT MSS data

The author has identified the following significant results. LANDSAT MSS data was adequate to accurately identify wheat in Kansas; corn and soybean estimates in Indiana were less accurate. Computer-aided analysis techniques were effectively used to extract crop identification information from LANDSAT data. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels. Training statistics were successfully extended from one county to other counties having similar crops and soils if the training areas sampled the total variation of the area to be classified.

Bauer, M. E.↗

NASA NTRS2004

The Training Process of the Organization Development and Training Office

The Organization Development and Training Office provides training and development opportunities to employees at NASA Glenn Research Center, as a division of the Office of Human Resources and Workforce Planning. Center-wide required trainings, new employee trainings, workshops and career development programs are organized by the OD&TO staff. They also arrange all academic, non-academic, headquarters, fellowship and learning center sponsored courses. They also service organizations wishing to work more effectively by facilitating teambuilding exercises. Equal Opportunity programs and upward mobility programs such as the STEP and GO programs for administrative staff. In working with my mentor I am very involved with Cuyahoga Community College classes, mandatory supervisory training and administrative staff workshops. My largest tasks are in the secretarial training category. The Supporting Organizations And Relationships workshop for administrative personnel, commonly known as SOAR, began last year and continued this summer with follow-up workshops. Months before a workshop or class is brought to Glenn, a need has to be realized. In this case, administrative staff did not feel they had an opportunity to receive relevant training and develop skills through teambuilding, networking and communication. A Statement of work is then created as several companies are contacted about providing the training. After the company best suited to meet the target group s needs is selected, the course is announced with an outline of all pertinent information. A reservation for a facility is made and applications or nominations, depending on the announcement s guidelines, are received from interested employees. Confirmations are sent to participants and final preparations are made but there are still several concluding steps. A training office staff member also assists the facilitator with setting up the facility and introducing the class. After the class, participants evaluations are read and summarized to determine the effectiveness of the class and instructor. In addition to the SOAR workshops, I have several projects and daily tasks to complete. Coding training applications, which require me to be familiar with Glenn s budgetary allocations and policies on training, is an ongoing process. It also requires verifying information reported by an employee via her C-478 form, more commonly known as the training application. I am also the point of contact for the Cuyahoga Community College Advising Sessions held here at NASA Glenn which involves coordinating counselors visits with employees schedules. Two databases had to be created. The first database holds information on administrative staff, and the other tracks supervisors training histories. Through these assignments I gained experience in Microsoft Access 2002 and spreadsheet creation, communicating with co-workers, and successfully facilitating a training to serve specific purposes. With trainings and evaluations to assessment them, the Organization Development and Training Office can assure a quality product and continued customer satisfaction.

Johnson, Melissa S.↗

NASA NTRSDate not supplied

Immersive Technologies for Human-in-the-Loop Lunar Surface Simulations

NASA, the National Aeronautics and Space Administration, continually seeks innovative solutions to enhance its operations, particularly in the realms of testing, evaluation, and training for future missions. Immersive technologies, such as virtual, augmented, and mixed reality have proven to be powerful tools for realistic, interactive, and engaging environments. This paper explores how the Simulation and Graphics Branch at NASA’s Johnson Space Center (JSC) leverages immersive technology, modern commercial rendering engines, and physics-based systems simulations to develop human-in-the-loop systems for humanity’s return to the Moon through the Artemis program. When NASA returns to the Moon, astronauts will travel to the Moon’s South Pole where lighting conditions will cause a more complex operational environment. Human-in-the-loop testing plays a crucial role in NASA's mission planning, spacecraft and space systems development, and evaluation of operational scenarios. The development of immersive environments such as a lunar rover mockup at a video wall enables engineers and astronauts to simulate and experience mission scenarios, integrated spacecraft systems, and operational procedures in a relevant environment before deployment. By integrating realistic virtual environments, immersive technology allows for the visualization and interaction with virtual spacecraft models, mission landscapes, and complex operational tasks. This approach helps identify potential design flaws, operational challenges, and safety considerations. It also provides valuable insights for risk reduction and helps improve mission efficiency and effectiveness. With advanced motion tracking systems and custom virtual environments data can be gathered and evaluated to help NASA refine training protocols, develop specialized training procedures and optimize human-robotic interactions for future space missions. Furthermore, immersive technology offers opportunities for future training initiatives at NASA. The Virtual Reality Laboratory at JSC has pioneered training with Virtual Reality (VR) since the Hubble Space Telescope repair missions in the early 1990’s. Extended Reality (XR) simulations enable astronauts to rehearse complex spacewalks, spacecraft maneuvers, and extravehicular activities in a safe and controlled environment. By replicating the physical and cognitive challenges of space missions, immersive training experiences enhance astronauts' situational awareness, decision-making abilities, and adaptability to unexpected scenarios. Additionally, immersive technology facilitates collaborative training, allowing geographically dispersed crew and mission control personnel to engage in synchronized simulations, fostering teamwork and effective communication. The adoption of immersive technology in NASA's testing, evaluation, and future training programs has yielded significant benefits. By incorporating human-in-the-loop testing for studies involving Extra Vehicular Activities (EVA), surface mobility and landing systems, NASA can identify and mitigate risks, optimize operational procedures, and enhance mission success. Ultimately, immersive training experiences can empower astronauts to better navigate the complexities of space missions, ensuring their safety, productivity, and success in the dynamic and challenging environments they will experience at the Lunar South Pole.

Simulation Modeling Virtual Reality Immersive Tech↗

NASA NTRS2004

Results from an Investigation into Extra-Vehicular Activity (EVA) Training Related Shoulder Injuries

The number and complexity of extravehicular activities (EVAs) required for the completion and maintenance of the International Space Station (ISS) is unprecedented. The training required to successfully complete this magnitude of space walks presents a real risk of overuse musculoskeletal injuries to the EVA crew population. There was mounting evidence raised by crewmembers, trainers, and physicians at the Johnson Space Center (JSC) between 1999 and 2002 that suggested a link between training in the Neutral - Buoyancy Lab (NBL) and the several reported cases of shoulder injuries. The short- and long-term health consequences of shoulder injury to astronauts in training as well as the potential mission impact associated with surgical intervention to assigned EVA crew point to this as a critical problem that must be mitigated. Thus, a multi-directorate tiger team was formed in December of 2002 led by the EVA Office and Astronaut Office at the JSC. The primary objectives of this Tiger Team were to evaluate the prevalence of these injuries and substantiate the relationship to training in the NBL with the crew person operating in the EVA Mobility Unit (EMU). Between December 2002 and June of 2003 the team collected data, surveyed crewmembers, consulted with a variety of physicians, and performed tests. The results of this effort were combined with the vast knowledge and experience of the Tiger Team members to formulate several findings and over fifty recommendations. This paper summarizes those findings and recommendations as well as the process by which these were determined. The Tiger Team concluded that training in the NBL was directly linked to several major and minor shoulder injuries that had occurred. With the assistance of JSC flight surgeons, outside consultants, and the lead crewmember/physician on the team, the mechanisms of injury were determined. These mechanisms were then linked to specific aspects of the hardware design, operational techniques, and the training environment. During the 1999 to 2003 time frame many variables converged to make it impossible to determine with any accuracy which one or two root causes were primarily involved. Therefore a broad range of recommendations was established to prevent future injury to crewmembers training in the NBL in the near term. Many of these recommendations are lessons learned that are essentially timeless and therefore should be passed on to future EVA endeavors to ensure that hardware designs and operational techniques utilized in the future consider the demands of training on the human body here on earth.

Johnson, Brian J.↗

NASA NTRS2010

Simulated Service and Stress Corrosion Cracking Testing for Friction Stir Welded Spun Form Domes

Damage tolerance testing development was required to help qualify a new spin forming dome fabrication process for the Ares 1 program at Marshall Space Flight Center (MSFC). One challenge of the testing was due to the compound curvature of the dome. The testing was developed on a sub-scale dome with a diameter of approximately 40 inches. The simulated service testing performed was based on the EQTP1102 Rev L 2195 Aluminum Lot Acceptance Simulated Service Test and Analysis Procedure generated by Lockheed Martin for the Space Shuttle External Fuel Tank. This testing is performed on a specimen with an induced flaw of elliptical shape generated by Electrical Discharge Machining (EDM) and subsequent fatigue cycling for crack propagation to a predetermined length and depth. The specimen is then loaded in tension at a constant rate of displacement at room temperature until fracture occurs while recording load and strain. An identical specimen with a similar flaw is then proof tested at room temperature to imminent failure based on the critical offset strain achieved by the previous fracture test. If the specimen survives the proof, it is then subjected to cryogenic cycling with loads that are a percentage of the proof load performed at room temperature. If all cryogenic cycles are successful, the specimen is loaded in tension to failure at the end of the test. This standard was generated for flat plate, so a method of translating this to a specimen of compound curvature was required. This was accomplished by fabricating a fixture that maintained the curvature of the specimen rigidly with the exception of approximately one-half inch in the center of the specimen containing the induced flaw. This in conjunction with placing the center of the specimen in the center of the load train allowed for successful testing with a minimal amount of bending introduced into the system. Stress corrosion cracking (SCC) tests were performed using the typical double beam assembly and with 4-point loaded specimens under alternate immersion conditions in a 3.5% NaCl environment for 90 days. In addition, experiments were conducted to determine the threshold stress intensity factor for SCC (K1SCC) of Al-Li 2195 which to our knowledge has not been determined previously. The successful simulated service and stress corrosion testing helped to provide confidence to continue to Ares 1 scale dome fabrication.

Stewart, Thomas J.↗

DOE OSTI2025

Anomaly Detection In DUNE Using AI/ML

We designed and built an AI/ML model to detect anomalies in the DUNE far detector data. The model has been trained on simulated radiological background (rbkg) data, which is the major background for supernova burst neutrinos. The trained model was evaluated on both new samples of radiological backgrounds and supernova burst neutrino events in the elastic scattering and charged current interaction channels. We found that the trained model can successfully identify supernova burst neutrino events as anomalies while identifying radiological backgrounds as nominal events.

Novello, Eric [Unlisted, US]↗

NASA NTRS2004

Ground Reaction Force and Mechanical Differences Between the Interim Resistive Exercise Device (iRED) and Smith Machine While Performing a Squat

Musculoskeletal unloading in microgravity has been shown to induce losses in bone mineral density, muscle cross-sectional area, and muscle strength. Currently, an Interim Resistive Exercise Device (iRED) is being flown on board the ISS to help counteract these losses. Free weight training has shown successful positive musculoskeletal adaptations. In biomechanical research, ground reaction forces (GRF) trajectories are used to define differences between exercise devices. The purpose of this evaluation is to quantify the differences in GRF between the iRED and free weight exercise performed on a Smith machine during a squat. Due to the differences in resistance properties, inertial loading and load application to the body between the two devices, we hypothesize that subjects using iRED will produce GRF that are significantly different from the Smith machine. There will be differences in bar/harness range of motion and the time when peak GRF occurred in the ROMbar. Three male subjects performed three sets of ten squats on the iRED and on the Smith Machine on two separate days at a 2-second cadence. Statistically significant differences were found between the two devices in all measured GRF variables. Average Fz and Fx during the Smith machine squat were significantly higher than iRED. Average Fy (16.82 plus or minus.23; p less than .043) was significantly lower during the Smith machine squat. The mean descent/ascent ratio of the magnitude of the resultant force vector of all three axes for the Smith machine and iRED was 0.95 and 0.72, respectively. Also, the point at which maximum Fz occurred in the range of motion (Dzpeak) was at different locations with the two devices.

Amonette, William E.↗

NASA NTRS2004

Real-Time Adaptive Color Segmentation by Neural Networks

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Duong, Tuan A.↗

NASA NTRS2016

Designing and Building a Crew-Centric Mobile Scheduling and Planning Tool for Exploring Crew Autonomy Concepts Onboard the International Space Station

During 2015, as part of our ongoing research on crew autonomy, our team adapted and extended the plan viewing tool, Playbook, into a mobile tool for exploring crew autonomy onboard the International Space Station (ISS). This work enables crewmembers to intuitively re-plan and schedule on their own with limited input from mission control. Designed for ease of use, re-planning is composed of simple drag and drop interactions and learning the tool requires little to no training. Playbook was successfully uplinked onto ISS as a technology demonstration and completed its onboard ground and crew checkouts in August and September 2015, respectively. We will discuss and describe how Playbook was able to integrate with actual ISS operational plans and operational procedures without disrupting real mission operations. This work will also outline the design and technical constraints required to build a collaborative tool that allows multiple users to simultaneously self-schedule and synchronize during communication dropouts in the restricted networking and computing environment onboard ISS. Finally, we will explore the aspects of crew autonomy that this technology enables as well as discuss our next steps and possible extensions.

ISS↗

NASA NTRSDate not supplied

Online Small-Sat Knowledge Repositories and Modeling Tools for Risk Reduction and Enhanced Mission Success

Developers of small satellites face the challenges of a short development schedule, incomplete information about parts from vendors, and limited budgets. The situation is complicated by the need to use commercial electronic parts rather than radiation hardened parts to meet performance and budget constraints. Fortunately, a number of organizations are standing up new modeling and data resources that are available for free online. Motivation for sharing models, part data, and lessons learned include shorter development times, increased mission success likelihood, and training the next generation of space professionals. The purpose of this paper is to make these resources and their complementary capabilities known to the small satellite community.

Small Satellites↗

DOE OSTI2024

Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Finding Real Uncertainties From Physical Simulations

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗