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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

BioSentinel: To the Moon or Beyond?

BioSentinel, an Artemis-1 secondary spacecraft, will carry a biology experiment into deep space for the first time in 50 years. A 6U CubeSat form factor was utilized for the spacecraft and included technologies newly developed or adapted for operations beyond Earth orbit. This is the maiden deep-space voyage for the radio, propulsion system, electrical power system, and BioSensor payload. The spacecraft carries onboard budding yeast, Saccharomyces cerevisiae, as an analog to human cells to test the biological response to deep space radiation. Flying a secondary payload beyond LEO comes with unique challenges with respect to trajectory uncertainty and mission operations planning. BioSentinel does not carry propulsion for trajectory maneuvers, so plans for Comms and Power need to be developed for all trajectories. The nominal plan is a lunar flyby followed by an insertion into Heliocentric orbit. However, some possible scenarios include lunar eclipses that could severely impact the power budget during that phase of the mission, while others could result in a “Retrograde” hyperbola at swingby resulting in the spacecraft traveling inward toward Earth or even towards a collision with the lunar surface. BioSentinel’s final trajectory will not be known until after launch and deployment so possible scenarios need to be planned for ahead of time. This paper discusses the operational scenarios that were planned for as well as the actual execution of the mission operations including: command pass scheduling with the Deep Space Network, selecting bandwidth limits, medium gain antenna versus low gain antenna usage, and conserving power prior to a lunar eclipse. Note: Artemis-1 is planned to launch in the Spring of 2022

BioSentinel↗
NASA NTRS1972

Tracking and data system support for the Mariner Mars 1971 mission. Prelaunch phase through first trajectory correction maneuver, volume 1

The DSN command system provided the capability to enter commands in a computer at the deep space stations for transmission to the spacecraft. The high-rate telemetry system operated at 16,200 bits/sec. This system will permit return to DSS 14 of full-resolution television pictures from the spacecraft tape recorder, plus the other science experiment data, during the two playback periods of each Goldstone pass planned for each corresponding orbit. Other features included 4800 bits/sec modem high-speed data lines from all deep space stations to Space Flight Operations Facility (SFOF) and the Goddard Space Flight Center, as well as 50,000 bits/sec wideband data lines from DSS 14 to the SFOF, thus providing the capability for data flow of two 16,200 bits/sec high-rate telemetry data streams in real time. The TDS performed prelaunch training and testing and provided support for the Mariner Mars 1971/Mission Operations System training and testing. The facilities of the ETR, DSS 71, and stations of the MSFN provided flight support coverage at launch and during the near-earth phase. The DSSs 12, 14, 41, and 51 of the DSN provided the deep space phase support from 30 May 1971 through 4 June 1971.

Laeser, R. P.↗
NASA NTRS2016

Intelligent (Autonomous) Power Controller Development for Human Deep Space Exploration

As NASAs Evolvable Mars Campaign and other exploration initiatives continue to mature they have identified the need for more autonomous operations of the power system. For current human space operations such as the International Space Station, the paradigm is to perform the planning, operation and fault diagnosis from the ground. However, the dual problems of communication lag as well as limited communication bandwidth beyond GEO synchronous orbit, underscore the need to change the operation methodology for human operation in deep space. To address this need, for the past several years the Glenn Research Center has had an effort to develop an autonomous power controller for human deep space vehicles. This presentation discusses the present roadmap for deep space exploration along with a description of conceptual power system architecture for exploration modules. It then contrasts the present ground centric control and management architecture with limited autonomy on-board the spacecraft with an advanced autonomous power control system that features ground based monitoring with a spacecraft mission manager with autonomous control of all core systems, including power. It then presents a functional breakdown of the autonomous power control system and examines its operation in both normal and fault modes. Finally, it discusses progress made in the development of a real-time power system model and how it is being used to evaluate the performance of the controller and well as using it for verification of the overall operation.

Power Systems↗
DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗
NASA NTRS1992

Application of Nuclear Well Logging Techniques to Lunar Resource Assessment

The use of neutron and gamma ray measurements for the analysis of material composition has become well established in the last 40 years. Schlumberger has pioneered the use of this technology for logging wells drilled to produce oil and gas, and for this purpose has developed neutron generators that allow measurements to be made in deep (5000 m) boreholes under adverse conditions. We also make ruggedized neutron and gamma ray detector packages that can be used to make reliable measurements on the drill collar of a rotating drill string while the well is being drilled, where the conditions are severe. Modern nuclear methods used in logging measure rock formation parameters like bulk density and porosity, fluid composition, and element abundances by weight including hydrogen concentration. The measurements are made with high precision and accuracy. These devices (well logging sondes) share many of the design criteria required for remote sensing in space; they must be small, light, rugged, and able to perform reliably under adverse conditions. We see a role for the adaptation of this technology to lunar or planetary resource assessment missions.

Albats, P.↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2024

CubeSat Interplanetary Exploration: A Deep Dive into Nuclear Propulsion and Astrodynamics for Small Satellites

CubeSats are small satellites most commonly used by universities, however, nowadays places like NASA are looking to CubeSats for interplanetary missions. Because of their universal form fit it makes them cost effective, economical, and therefor desirable for deep space. To achieve deep space exploration, we must explore the applications of propulsion and astrodynamics on small satellites. This paper will touch on one scaling Ion and chemical propulsion as well as nuclear kilo reactors. Two choreographing a flight path for these small satellites and lastly designing an efficient cost-effective platform for CubeSats to get to deep space territory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Superionic iron oxide–hydroxide in Earth’s deep mantle

Water ice becomes a superionic phase under the high pressure and temperature conditions of deep planetary interiors of ice planets such as Neptune and Uranus, which affects interior structures and generates magnetic fields. The solid Earth, however, contains only hydrous minerals with a negligible amount of ice. Here we combine high pressure and temperature electrical conductivity experiments, Raman spectroscopy and first-principles simulations to investigate the state of hydrogen in the pyrite-type FeO 2 H x (x ≤ 1), which is a potential H-bearing phase near the core–mantle boundary. We find that when the pressure increases beyond 73 GPa at room temperature, symmetric hydroxyl bonds are softened and the H + (or proton) becomes diffusive within the vicinity of its crystallographic site. Increasing temperature under pressure, the diffusivity of hydrogen is extended beyond the individual unit cell to cover the entire solid, and the electrical conductivity soars, indicating a transition to the superionic state, which is characterized by freely moving protons and a solid FeO 2 lattice. Furthermore, the highly diffusive hydrogen provides fresh transport mechanisms for charge and mass, which dictate the geophysical behaviours of electrical conductivity and magnetism, as well as geochemical processes of redox, hydrogen circulation and hydrogen isotopic mixing in Earth’s deep mantle.

58 GEOSCIENCES↗
DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Deep Learning Reconstruction of Daily Soil CO 2 Efflux Reveals Biogeochemical Insights and Reduces Annual Estimate Uncertainty Despite Limited Daily Predictability

Soil CO 2 efflux is commonly measured monthly or seasonally, leaving daily dynamics poorly resolved and contributing to global estimation uncertainty. We trained a single Long Short-Term Memory (LSTM) model to predict daily soil CO 2 efflux across 82 globally distributed sites in COSORE, with 0.2%–46.9% daily data coverage from 2003 to 2020. Despite using far fewer sites than are typically used to train a single deep learning model, with observations biased toward temperate mesic sites, the LSTM model performed well at approximately one-third of sites, reconstructed nearly 2 decades of daily efflux, and outperformed commonly used approaches for estimating daily efflux when applied to the same data set. Performance was weakest at pronounced peaks and troughs and at non-temperate sites with <1.5 years of observations and irregular data patterns. Nevertheless, annual efflux from reconstructed daily data had <40% error even at underperforming sites, substantially improving estimates derived from monthly and seasonal sampling (maximum errors of 95% and 136%, respectively). Temperature sensitivity (Q 10 ) estimated from reconstructed daily predictions closely matched estimates from daily observations, whereas Q 10 values derived from monthly or seasonal observations deviated substantially, suggesting that coarse temporal sampling may contribute to uncertainty in reported Q 10 values. Consistent daily reconstructions further enabled trend analyses for well-performing, predominantly temperate sites and showed increasing soil CO 2 efflux at most sites from 2003 to 2020, with more variable summer trends. Despite limitations, these results demonstrate the potential of LSTM models to reconstruct daily soil CO 2 efflux and reduce estimation uncertainties from sparse observations.

Smykalov, Valerie [Pennsylvania State University, ↗
DOE OSTI2025

Northern peatland microbial communities exhibit resistance to warming and acquire electron acceptors from soil organic matter

The response of microbial communities that regulate belowground carbon turnover to climate change drivers in peatlands is poorly understood. Here, we leverage a whole ecosystem warming experiment to elucidate the key processes of terminal carbon decomposition and community responses to temperature rise. Our dataset of 697 metagenome-assembled genomes (MAGs) represents the microbial community from the surface (10 cm) to 2 m deep into the peat column, with only 3.7% of genomes overlapping with other well-studied peatlands. Community composition has yet to show a significant response to warming after 3 years, suggesting that metabolically diverse soil microbial communities are resistant to climate change. Surprisingly, abundant and active methanogens in the genus Candidatus Methanoflorens, Methanobacterium , and Methanoregula show the potential for both acetoclastic and hydrogenotrophic methanogenesis. Nonetheless, the predominant pathways for anaerobic carbon decomposition include sulfate/sulfite reduction, denitrification, and acetogenesis, rather than methanogenesis based on gene abundances. Multi-omics data suggest that organic matter cleavage provides terminal electron acceptors, which together with methanogen metabolic flexibility, may explain peat microbiome composition resistance to warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Fresnel diffraction imaging of surface nanostructure using coherent resonant x-ray scattering

We investigated surface nanostructures on an antiferromagnet MnBi 2 Te 4 using a novel imaging technique, direct (real)-space and real time coherent x-ray imaging (direct-CXI). This technique has provided new insights into antiferromagnetic textures, including the formation of anti-phase antiferromagnetic (AFM) domains and thermal dynamics of AFM domains and domain walls. While this method produces real-space images of AFM textures without requiring a complex imaging retrieval process, its underlying imaging mechanism has not been fully understood, limiting a deep understanding of AFM textures and the information they contain. By investigating the well-defined structural characteristics of the nanostructures fabricated on MnBi 2 Te 4 ⁠, we elucidate the imaging principle of this novel technique. We find that the observed images can be well explained by the Fresnel diffraction integral. Using a simple model from classical optics, our calculations successfully reproduce the experimentally observed images of the nanostructures. This demonstrates that direct-CXI not only provides straightforward real-space imaging but also contains phase information through its Fresnel diffraction integral.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-X-point radiation and its dynamics in up/down asymmetry in plasma detachment regimes in Wendelstein 7-X

In Wendelstein 7-X (W7-X), stable plasma detachment in ECRH hydrogen plasmas is routinely achieved with the standard magnetic configuration, containing five magnetic islands in the scrape-off layer (SOL). Plasma detachment induced by intrinsic carbon impurities is characterized by a high radiation fraction (ƒ rad ) with significantly reduced divertor heat load and particle flux. In this work, we present the radiation dynamics during the detached plasma (DP) phase, such as the shift of radiation zones in radial and poloidal directions towards the X-points as ƒ rad increases (∼0.6–0.9), as well as the penetration of the radiation layer into the confinement region in the deep DP phase with ƒ rad approaching unity. In particular, a structure of multi-X-point radiation (multi-XPR) with an up/down asymmetry in the DP phase is highlighted, which is revealed by bolometer tomography and is further confirmed by video diagnostics. The multi-XPR structure forms helical 3D bands aligned with W7-X field periodicity. Field reversal experiments show that the brightest XPR displaces between the upper and lower SOL regions, suggesting the E× B drift effects. This paper presents the multi-XPR structure observed in the W7-X plasma in detail for the first time. A simplified model considering the influence of the poloidal E× B drift ( V d ) on the impurity flow in the SOL shows that the downstream drift toward the target or target shadow region ( V d < 0) decreases the impurity content, while the upstream drift toward the LCFS ( V d > 0) increases the impurity content. On this basis, the poloidal drift potentially leads to an up/down asymmetry of impurity density in the SOL despite the symmetry magnetic topology. The dynamics of the up/down asymmetry in the multi-XPR structure is also related to the magnitude V d /D (normalized to the impurity diffusivity), with an additional effect owing to the radial inward shift of the emission zone. These results provide new insights into impurity-induced detachment dynamics and provide a basis for improving 3D modeling of impurity transport, which typically does not consider drifts.

E × B drift↗
DOE OSTI2023

Multimodal Prediction of Tearing Instabilities in a Tokamak

Tokamak is a torus-shaped nuclear fusion device that uses magnetic fields to confine fusion fuel in the form of plasma. Tearing instability in plasma is a major issue in which the magnetic field breaks and recombines in tokamak. Here, this instability can lead to plasma disruption that terminates the fusion power generation and damages the plasma-facing wall materials. For a successful steady operation of a large-scale tokamak without disruption, it is required to predict and alarm the tearing instabilities well in advance to avoid them. In this work, we develop and validate a deep neural network-based multimodal prediction system that estimates the future tearing instability likelihood from multi-diagnostics signals in the DIII-D tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Deep-inelastic scattering with positron beams

Here we discuss several advantages of using positron beams in addition to electron beams for the study of parton distribution functions (PDFs) of the nucleon in deep-inelastic scattering. Specific topics include the flavor separation of the unpolarized PDFs as well as the spin-dependent distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Machine learning and deep learning for brain tumor MRI image segmentation

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

Research & Experimental Medicine↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗