Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “deep potential”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Kilopower: Small and Affordable Fission Power Systems for Space

The Nuclear Systems Kilopower Project was initiated by NASA's Space Technology Mission Directorate Game Changing Development Program in fiscal year 2015 to demonstrate subsystem-level technology readiness of small space fission power in a relevant environment (Technology Readiness Level 5) for space science and human exploration power needs. The Nuclear Systems Kilopower Project centerpiece is the Kilopower Reactor Using Stirling Technology (KRUSTY) test, which consists of the development and testing of a fission ground technology demonstrator of a 1 kWe-class fission power system. The technologies to be developed and validated by KRUSTY are extensible to space fission power systems from 1 to 10 kWe, which can enable higher power future potential deep space science missions, as well as modular surface fission power systems for exploration. The Kilopower Project is cofounded by NASA and the Department of Energy National Nuclear Security Administration (NNSA).KRUSTY include the reactor core, heat pipes to transfer the heat from the core to the power conversion system, and the power conversion system. Los Alamos National Laboratory leads the design of the reactor, and the Y-12 National Security Complex is fabricating it. NASA Glenn Research Center (GRC) has designed, built, and demonstrated the balance of plant heat transfer and power conversion portions of the KRUSTY experiment. NASA MSFC developed an electrical reactor simulator for non-nuclear testing, and the design of the reflector and shielding for nuclear testing. In 2016, an electrically heated non-fissionable Depleted Uranium (DU) core was tested at GRC in a configuration identical to the planned nuclear test. Once the reactor core has been fabricated and shipped to the Device Assembly Facility at the NNSAs Nevada National Security Site, the KRUSTY nuclear experiment will be assembled and tested. Completion of the KRUSTY experiment will validate the readiness of 1 to 10 kWe space fission technology for NASAs future requirements for sunlight-independent space power. An early opportunity for demonstration of In-Situ Resource Utilization (ISRU) capability on the surface of Mars is currently being considered for 2026 launch. Since a space fission system is the leading option for power generation for the first Mars human outpost, a smaller version of a planetary surface fission power system could be built to power the ISRU demonstration and ensure its end-to-end validity. Planning is underway to start the hardware development of this subscale flight demonstrator in 2018.

Space nuclear power↗
NASA NTRS2020

Kilopower Project: The KRUSTY Fission Power Experiment and Potential Missions

The Kilopower Project was initiated by NASA’s Space Technology Mission Directorate/Game Changing Development Program in fiscal year 2015 to demonstrate subsystem-level technology readiness of small space fission power in a relevant environment (Technology Readiness Level 5) for space science and human exploration power needs. The Kilopower Project centerpiece is the Kilowatt Reactor Using Stirling TechnologY (KRUSTY) test, which consists of the development and testing of a ground technology demonstrator of a 1-kW(electric)–class fission power system (FPS). The technologies to be developed and validated by KRUSTY are extensible to space FPSs from 1 to 10 kW(electric), which can enable modular surface FPSs for human exploration as well as higher-power future potential deep space science missions. The KRUSTY demonstration is cofunded by NASA and the U.S. Department of Energy National Nuclear Security Administration. The KRUSTY demonstration in the National Critical Experiment Research Center’s Device Assembly Facility was completed in the first quarter of 2018.

Space nuclear reactor↗
NASA NTRS2022

The Co2 Profile and Analytical Model for the Pioneer Venus Large Probe Neutral Mass Spectrometer

We present a significantly updated CO2 altitude profile for Venus (64.2–0.9 km) and provide support for a potential deep lower atmospheric haze of particles (≤17 km). We extracted this information by developing a new analytical model for mass spectra obtained by the Pioneer Venus Large Probe (PVLP) Neutral Mass Spectrometer (LNMS). Our model accounts for changes in LNMS configuration and output during descent and enables the disentanglement of isobaric species via a data fitting routine that adjusts for mass-dependent changes in peak shape. The model yields CO2 in units of density (kg m−3), isotope ratios for 13C/12C and 18O/16O, and 14 measures of CO2 density across 55.4–0.9 km, which represents the most complete altitude profile for CO2 at ≤60 km to date. The CO2 density profile is also consistent with the pressure, temperature, and volumetric gas measurements from the PVLP and VeNeRa spacecraft. Nominal and low-noise operations for the LNMS mass analyzer are supported by the behaviors (e.g., ionization yields, fragmentation yields, and peak shapes) of several internal standards (e.g., CH3+, CH4+, 40Ar+, 136Xe2+, and 136Xe+), which were tracked across the descent. Lastly, our review of the CO2 profile and LNMS spectra reveals hitherto unreported partial and rapidly clearing clogs of the inlet in the lower atmosphere, along with several ensuing data spikes at multiple masses. Together, these observations suggest that atmospheric intake was impacted by particles at ≤17 km and that rapid particle degradation at the inlet yielded a temporary influx of mass signals into the LNMS.

Venus↗
DOE OSTI2023

Overview of the Pathways to CO2 Utilization and Storage for the Intermountain West Region

The topic of the webinar focused on findings from the first phase of I-WEST initiative, specifically related to CO2 utilization and storage as one of the many potential deep decarbonization pathway available to the region. Specifically, the seminar presented topics like: an overview of CCUS technology and benefits; a highlight of a variety of CO2 storage and utilization approaches available to the region, relevant governing regulations, and technical and non-technical grand challenges associated with each option; findings from an assessment of opportunities for CCUS deployment at significant scale within the Intermountain West; and context to help listeners better understand the technical aspects of CCUS and how they interface with economic, social, and policy aspects of decarbonization applicable to the I-WEST geographic region.

Vikara, Derek↗
DOE OSTI2021

Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials

Within the framework of Kohn–Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob’s ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric–isothermal ensemble. To generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained by using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed by using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water toward the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. However, some important discrepancies remain. The H-bond network predicted by the SCAN0 functional is still slightly overstructured compared to the experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Modeling the Solvation and Acidity of Carboxylic Acids Using an Ab Initio Deep Neural Network Potential

Formic and acetic acid constitute the simplest of carboxylic acids, yet they exhibit fascinating chemistry in the condensed phase such as proton transfer and dimerization. The go-to method of choice for modeling these rare events have been accurate but expensive ab-initio molecular dynamics simulations. Here, we present a deep neural network potential trained using accurate ab-initio data that can be used in tandem with enhanced-sampling methods to perform an efficient exploration of the free-energy surface of aqueous solutions of weak carboxylic acids. In particular, we show that our model captures proton dissociation and provides a good estimate of the pK a , as well as the dimerization of formic and acetic acid. This provides a suitable starting point for applications in different research areas where computational efficiency coupled with the accuracy of ab-initio methods is required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2024

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, the development of new coating systems is hindered by the large design space and difficulty in predicting the 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 the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the 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 the accurate prediction of the anisotropy of the CTE in good agreement with the 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.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Potential Uses of Deep Space Cooling for Exploration Missions

Nearly all exploration missions envisioned by NASA provide the capability to view deep space and thus to reject heat to a very low temperature environment. Environmental sink temperatures approach as low as 4 Kelvin providing a natural capability to support separation and heat rejection processes that would otherwise be power and hardware intensive in terrestrial applications. For example, radiative heat transfer can be harnessed to cryogenically remove atmospheric contaminants such as carbon dioxide (CO2). Long duration differential temperatures on sunlit versus shadowed sides of the vehicle could be used to drive thermoelectric power generation. Rejection of heat from cryogenic propellant could avoid temperature increase thus avoiding the need to vent propellants. These potential uses of deep space cooling will be addressed in this paper with the benefits and practical considerations of such approaches.

Chambliss, Joseph↗
NASA NTRS2012

Potential Uses of Deep Space Cooling for Exploration Missions

Nearly all exploration missions envisioned by NASA provide the capability to view deep space and thus to reject heat to a very low temperature environment. Environmental sink temperatures approach as low as 4 Kelvin providing a natural capability to support separation and heat rejection processes that would otherwise be power and hardware intensive in terrestrial applications. For example, radiative heat transfer can be harnessed to cryogenically remove atmospheric contaminants such as carbon dioxide (CO2). Long duration differential temperatures on sunlit versus shadowed sides of the vehicle could be used to drive thermoelectric power generation. Rejection of heat from cryogenic propellant could counter temperature increases thus avoiding the need to vent propellants. These potential uses of deep space cooling will be addressed in this paper with the benefits and practical considerations of such approaches.

Chambliss, Joe↗
NASA NTRSDate not supplied

A MULTI TARGETED DIETARY SUPPLEMENT AS A POTENTIAL COUNTERMEASURE FOR DEEP SPACE EXPLORATION

Future deep space exploration to the Moon and Mars and beyond are currently major goals of NASA. Space flight presents unique challenges to the human physiology from the combined effects of microgravity, social isolation, altered circadian rhythm and radiation. Oxidative stress has been implicated as a crucial factor in space environment induced injury. Elucidating these detrimental effects on the central nervous system and brain is a primary objective, as they may result in adverse changes in astronaut behavior, mood and performance of critical tasks. We were recently funded by HRP Human Factors Behavioral Performance (HFBP) Element to test the hypothesis that Galactic Cosmic Ray Simulation (GCRsim) Ionizing Radiation (IR), microgravity and social isolation combine synergistically to trigger an oxidative stress response that alters immune homeostasis, brain structure and function, and neurobehavioral and cognitive performance. This project will identify potential biomarkers for, and mechanisms underlying, structural and functional changes in the immune and nervous systems leading to behavioral/cognitive performance deficits, and its potential application to develop effective countermeasures to mitigate negative health effects of long duration space habitation. Countermeasures to offset neurological damaged and cognitive deficits are paramount for protecting astronauts during deep space transits, but many countermeasures have been found to be unsuitable in ground-based model studies. We have developed a multi targeted dietary supplement (MTDS) designed to simultaneously ameliorate oxidative stress, inflammatory processes, energetic shortfalls, and membrane and mitochondrial deterioration. Administration of this MTDS has improved cognition and longevity in age accelerated and normal aging mice. Specifically, the MTDS was found to prevent a decline in mitochondrial complex III activity and cell loss, as well as prevent an increase in protein carbonyls and mitochondrial 3-nitrotyrosine in the brain of these mice. Further, administration of the MTDS was also found to reduce bone marrow chromosomal aberrations, DNA damage, lymphocyte apoptosis after whole body exposure of 2 Gy γ radiation. Mice exposed to a high dose cranially (10 Gy) had decreased cognitive responses (seen as increased latency time to uncover buried food and decreased novel object recognition), increased plasma 8-OHdG levels, increased markers of cell stress in the brain, decreased hippocampal brain-derived neurotrophic factor (BDNF) protein and decreased plasma levels of cytokines, all of which were ameliorated with treatment of the MTDS. As we have shown the supplement is protective in models of cognitive damage similar space environment stressors, particularly increased oxidative stress, we propose it may be a suitable countermeasure for upcoming deep space exploration.

Stephanie Puukila↗
DOE OSTI2021

A deep learning interatomic potential developed for atomistic simulation of carbon materials

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. As a result, the excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Methane emission hotspots in a boreal forest-fen mosaic potentially linked to deep taliks

Permafrost thaw is transforming boreal forests into mosaics of wetlands and drier uplands. Topographic controls on hydrological and ecological conditions impact methane (CH 4 ) fluxes, contributing to uncertainty in local and regional CH 4 budgets and underlying drivers. The objective of this study was to explore CH 4 fluxes and their drivers in a transitioning boreal forest-fen ecosystem (Goldstream Valley, Alaska, USA). This landscape is characterized by thawing discontinuous permafrost and heterogeneous mosaics of fens, collapse-scar channels, and small mounds of permafrost soils. From a survey in July 2021, observed chamber CH4 fluxes included fen areas with intermediate to very high emissions (29.8–635.3 mg CH 4 m −2 d −1 ), clustered locations with CH 4 uptake (−2.11 to −0.7 mg CH 4 m −2 d −1 ), and three anomalous emission hotspots (342.4–772.4 mg CH 4 m −2 d −1 ) that were located near samples with lower emissions. Some surface and near-surface variables partially explained the spatial variation in CH 4 flux. Log-transformed CH 4 flux had a positive linear relationship with soil moisture at 20 cm depth ( R 2 = 0.31, p -value < 1e-5) and negative linear relationships with microtopography ( R 2 = 0.13, p -value < 0.006) and slope ( R 2 = 0.28, p -value < 2e-5). Methane emissions generally occurred in flat, wet, graminoid-dominated fens, whereas CH 4 uptake occurred on permafrost mounds dominated by feather mosses and woody vegetation. However, the CH 4 hotspots occurred on drier, slightly sloped locations with low or undetectable near-surface methanogen abundance, suggesting that CH 4 was produced in deeper soils. When the hotspot samples were omitted, log-transformed CH 4 flux had a positive linear relationship with near-surface methanogen abundance ( R 2 = 0.29, p -value = 0.0023), and stronger linear relationships with soil moisture, slope, and soil macronutrient concentrations. Our findings suggest that some CH 4 emission hotspots could arise from CH 4 in deep taliks. The inference that methanogenesis occurs in deep taliks was strengthened by the identification of intrapermafrost taliks across the study area using low-frequency geophysical induction. This study assesses surface spatial heterogeneity in the context of subsurface permafrost conditions and highlights the complexity of CH 4 flux patterns in transitioning forest-wetland ecosystems. To better inform regional CH 4 budgets, further research is needed to understand the spatial distribution of terrestrial CH 4 hotspots and to resolve their surface, near-surface, and subsurface drivers.

boreal↗