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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Enhancing Systems Engineering Education Through Case Study Writing

Developing and refining methods for teaching systems engineering is part of Systems Engineering grand challenges and agenda for research in the SE research community. Retention of systems engineering knowledge is a growing concern in the United States as the baby boom generation continues to retire and the faster pace of technology development does not allow for younger generations to gain experiential knowledge through years of practice. Government agencies, including the National Aeronautics and Space Administration (NASA), develop their own curricula and SE leadership development programs to "grow their own" systems engineers. Marshall Space Flight Center (MSFC) conducts its own Center-focused Marshall Systems Engineering Leadership Development Program (MSELDP), a competitive program consisting of coursework, a guest lecture series, and a rotational assignment into an unfamiliar organization engaged in systems engineering. Independently, MSFC developed two courses to address knowledge retention and sharing concerns: Real World Marshall Mission Success course and its Case Study Writers Workshop and Writers Experience. Teaching case study writing and leading students through a hands-on experience at writing a case study on an SE topic can enhance SE training and has the potential to accelerate the transfer of experiential knowledge. This paper is an overview of the pilot experiences with teaching case study writing, its application in case study-based learning, and identifies potential areas of research and application for case study writing in systems engineering education.

Stevens, Jennifer Stenger↗
NASA NTRS2018

Internship in Augmented and Virtual Reality - Rapid Model Import Tool

The integration of virtual and augmented reality, sometimes called mixed reality, is an emerging technology which will likely skyrocket overnight much in the way smartphones did a decade ago. Kennedy Space Center's Augmented and Virtual Reality (AVR) Lab is developing a Rapid Model Import Tool (RMIT) to create a quick and efficient way to bring NASA's complex engineering 3D models into virtual and augmented environments. The long-term objective is to create a tool that will ultimately benefit KSC engineers. Its various uses within NASA can potentially span from astronaut training, to marketing, to public outreach, to name a few. Unity is a prolific cross-platform game engine that allows users to build high quality 2D and 3D games for desktop, mobile, web, and game console platforms. It is perhaps also the most widely used software for virtual reality game development. At the AVR lab, we are looking at alternative uses of Unity to build tools for NASA engineers to perform design, development, testing, and training on spacecraft, rocket delivery systems, ground support equipment, and facilities at KSC. As an intern for the RMIT project, I am charged with the task of performing research on Unity-compatible file types to develop an efficient, affordable, preservative process to bring models from CATIA 3D engineering software into the Unity environment. With a tool called the NASA Enterprise Visualization Application (NEVA), developed by the Boeing Design Visualization group at KSC, we are able to easily convert CATIA's design models to. DAE (also known as COLLADA) and .OBJ file formats. I first reduce the polygon count of the model within CATIA itself, make any necessary tweaks to reduce the model further, and then export using NEVA. The .OBJ or. DAE files that I am left with are then converted by another intern to a Unity-compatible file format using a custom Python script. I have generated extensive documentation of this process in a NEVA User Guide. By the end of this semester, we will have built a solid framework for RMIT based on a thorough understanding of virtual reality specifications and file requirements, allowing future software development teams to go forward with development on the custom tool.

Leap Motion↗
NASA NTRS2018

Preparing to Assure Mission Success

Space flight missions are inherently risky. The mission operations assurance discipline grew out of strategies to control error and evolved into what today is an independent technical authority dedicated to achieving mission success. After the Mars Climate Orbiter loss, NASA made a set of recommendations for missions. Two of those recommendations led to the creation of today’s Mission Operations Assurance Manager role: (1) require an independent Mission Assurance representative during the operational phase of every flight project, and (2) require all flight projects to report and track post-launch anomalies. Since then, the MOAM role has been continually refined by lessons learned from its practice on more than 20 concurrent missions. The MOAM role requires significant operations experience as well as an additional set of skills. In the interests of developing a cadre of potential MOAMs, the MOAM training program was developed. It is a 14-unit set of presentations/discussions covering the 11 categories of MOAM tasks. It is offered to new MOAMs and interested systems engineers with an eye to preparing both types of personnel for present and future assignments, as well as building relationships among personnel of different organizations. MOAM training sessions include presentations on the various tools and processes employed by MOAMs, interspersed with mindset lessons illustrated via “war story” discussions of previous missions.

Bryant, Larry↗
DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗
DOE OSTI2024

Lion Cub: Minimizing Communication Overhead in Distributed Lion

Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet intercon nects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techniques have been explored for SGD and Adam, the Lion optimizer has the distinct advantage that its update vectors are the output of a sign operation, enabling straightforward quantization. However, simply compressing updates for communication and using techniques like majority voting fails to lead to end-to-end speedups due to inefficient communication algorithms and reduced convergence. We analyze three factors critical to distributed learning with Lion: optimizing communication methods, identifying effective quantization methods, and assessing the necessity of momentum synchronization. Our findings show that quantization techniques adapted to Lion and selective momentum synchronization can significantly reduce communication costs while maintaining convergence. We combine these into Lion Cub, which enables up to 5x speedups in end-to-end training compared to Lion. This highlights Lion’s potential as a communication-efficient solution for distributed training.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Intelligent computer aided training systems in the real world: Making the technology accessible to the educational mainstream

Intelligent computer aided training systems hold great promise for the application of this technology to mainstream education and training. Yet, this technology, which holds such a vast potential impact for the future of education and training, has had little impact beyond the enclaves of government research labs. This is largely due to the inaccessibility of the technology to those individuals in whose hands it can have the greatest impact, teachers and educators. Simply throwing technology at an educator and expecting them to use it as an effective tool is not the answer. This paper provides a background into the use of technology as a training tool. MindLink, developed by HyperTech Systems, provides trainers with a powerful rule-based tool that can be integrated directly into a Windows application. By embedding expert systems technology it becomes more accessible and easier to master.

Kovarik, Madeline↗
DOE OSTI2024

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

We propose a framework to learn the time-dependent Hartree–Fock (TDHF) inter-electronic potential of a molecule from its electron density dynamics. Although the entire TDHF Hamiltonian, including the inter-electronic potential, can be computed from first principles, we use this problem as a testbed to develop strategies that can be applied to learn a priori unknown terms that arise in other methods/approaches to quantum dynamics, e.g., emerging problems such as learning exchange–correlation potentials for time-dependent density functional theory. We develop, train, and test three models of the TDHF inter-electronic potential, each parameterized by a four-index tensor of size up to 60 × 60 × 60 × 60. Two of the models preserve Hermitian symmetry, while one model preserves an eight-fold permutation symmetry that implies Hermitian symmetry. Across seven different molecular systems, we find that accounting for the deeper eight-fold symmetry leads to the best-performing model across three metrics: training efficiency, test set predictive power, and direct comparison of true and learned inter-electronic potentials. All three models, when trained on ensembles of field-free trajectories, generate accurate electron dynamics predictions even in a field-on regime that lies outside the training set. To enable our models to scale to large molecular systems, we derive expressions for Jacobian-vector products that enable iterative, matrix-free training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning interatomic potential for predicting the thermal properties of uranium nitride

We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential framework. The MLIP was trained on density functional theory (DFT) data and validated against various quantities including energies, forces, elastic constants, phonon dispersion, and defect formation energies, achieving excellent agreement with DFT calculations, prior experimental results, and our thermal conductivity measurement. The potential was then employed in molecular dynamics simulations to predict key thermal properties such as melting point, thermal expansion, specific heat, and lattice thermal conductivity. To further assess model accuracy, we fabricated a UN sample and performed new thermal conductivity measurements representative of single-crystal properties, which showed strong agreement with the MLIP predictions. This work confirms the reliability and predictive capability of the developed potential for determining the thermal properties of UN.

36 - MATERIALS SCIENCE↗
NASA NTRS2016

Top-of-the-Atmosphere Shortwave Flux Estimation from Satellite Observations: An Empirical Neural Network Approach Applied with Data from the A-Train Constellation

Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within 1% of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.

CERES data with high fidelity↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
NASA NTRS1989

Studies with spike initiators - Linearization by noise allows continuous signal modulation in neural networks

It is shown that noise can be an important element in the translation of neuronal generator potentials (summed inputs) to neuronal spike trains (outputs), creating or expanding a range of amplitudes over which the spike rate is proportional to the generator potential amplitude. Noise converts the basically nonlinear operation of a spike initiator into a nearly linear modulation process. This linearization effect of noise is examined in a simple intuitive model of a static threshold and in a more realistic computer simulation of spike initiator based on the Hodgkin-Huxley (HH) model. The results are qualitatively similar; in each case larger noise amplitude results in a larger range of nearly linear modulation. The computer simulation of the HH model with noise shows linear and nonlinear features that were earlier observed in spike data obtained from the VIIIth nerve of the bullfrog. This suggests that these features can be explained in terms of spike initiator properties, and it also suggests that the HH model may be useful for representing basic spike initiator properties in vertebrates.

Yu, Xiaolong↗
NASA NTRS2002

Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems

This document contains the proceedings of the Training Workshop on Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems held at NASA Langley Research Center, Hampton, Virginia, March 5 - 6, 2002. The workshop was jointly sponsored by Old Dominion University's Center for Advanced Engineering Environments and NASA. Workshop attendees were from NASA, other government agencies, industry, and universities. The objectives of the workshop were to give overviews of the diverse activities in hierarchical approach to material modeling from continuum to atomistics; applications of multiscale modeling to advanced and improved material synthesis; defects, dislocations, and material deformation; fracture and friction; thin-film growth; characterization at nano and micro scales; and, verification and validation of numerical simulations, and to identify their potential for future aerospace systems.

Noor, Ahmed K.↗
NASA NTRS2007

Revisiting Training and Verification Process Implementation for Risk Reduction on New Missions at NASA Jet Propulsion Laboratory

In 2003 we proposed an effort to develop a core program of standardized training and verification practices and standards against which the implementation of these practices could be measured. The purpose was to provide another means of risk reduction for deep space missions to preclude the likelihood of a repeat of the tragedies of the 1998 Mars missions. We identified six areas where the application of standards and standardization would benefit the overall readiness process for flight projects at JPL. These are Individual Training, Team Training, Interface and Procedure Development, Personnel Certification, Interface and procedure Verification, and Operations Readiness Testing. In this paper we will discuss the progress that has been made in the tasks of developing the proposed infrastructure in each of these areas. Specifically we will address the Position Training and Certification Standards that are now available for each operational position found on our Flight Operations Teams (FOT). We will also discuss the MGSS Baseline Flight Operations Team Training Plan which can be tailored for each new flight project at JPL. As these tasks have been progressing, the climate and emphasis for Training and for V and V at JPL has changed, and we have learned about the expansion, growth, and limitations in the roles of traditional positions at JPL such as the Project's Training Engineer, V and V Engineer, and Operations Engineer. The need to keep a tight rein on budgets has led to a merging and/or reduction in these positions which pose challenges to individual capacities and capabilities. We examine the evolution of these processes and the roles involved while taking a look at the impact or potential impact of our proposed training related infrastructure tasks. As we conclude our examination of the changes taking place for new flight projects, we see that the importance of proceeding with our proposed tasks and adapting them to the changing climate remains an important element in reducing the risk in the challenging business of space exploration.

verifications↗
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.↗
DOE OSTI2026

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
NASA NTRS2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, development of new coating systems is hindered by the large design space and difficulty in predicting properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate accurate prediction of the anisotropy of the CTE in good agreement with diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

rare earth silicates↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
NASA NTRS2010

Making Games Not Work: Paradoxes Embedded in Game-Based Training and Concepts for Overcoming Them

An interest in game-based training solutions is natural. All one has to do is watch someone fully engaged in a modern game to see the potential of harnessing that attention for training. However, the reality of game-based training has not fully satisfied these expectations. This paper explains two paradoxes that must be overcome for games to support training. These paradoxes are a result of the realities of the basic human condition clashing with the requirements of learning theory. 80th paradoxes arise from the concept of "engagement" that is central to games. The first comes from a more robust definition of engagement, which is the condition of Flow or Optimal Experience. Flow is the state game developers want to see in users. One aspect of Flow is loss of sense of self as the individual becomes immersed in the experience. The paradox arises because this loss of self directly contradicts the learning requirement of self-reflection. The second paradox comes from theories of play, which state in part that play requires a level of individual freedom. The contradiction arises when game-based play must be harnessed to an organizational training program or regimen. The paper will discuss these paradoxes in the context of an effort to design a game-based training modality to train combat medics and will close with a review of compensating strategies identified by the designers. The paper will provide information important to anyone interested in conceptualizing and designing game-based training.

Jones, Phillip N.↗