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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 NTRS1994

Communications in Space

This paper provides an overview of the imaging results of the JPL/NASA unmanned exploration missions to all the planets of the solar system (except Pluto) as well as the telecommunications capability which was developed to enable/support those missions. the telecommunications link improvements will be described to show increases in data rate capability resulting from the addition of ground antenna aperture, transmitter power, coding techniques, lower noise receivers, etc. This paper will also describe (briefly) the current and future flight missions. The presentation will be supported by a significant number of pictures taken during the planetary encounters as well as pictures of the Deep Space Network antennas.

imaging planets Deep Space Network telecommunicati↗
NASA NTRS2008

A Solar-Powered Enceladus Mission

We present the results of a concept design study for a New Frontiers or small Flagship-class mission to Enceladus, using solar power. By concentrating on the science objectives most critical for a Cassini follow-on, this mission maximizes the science return while maintaining a power consumption level that can be provided by a practical solar power system. The optimized instrument payload is the product of a broad science community-based Science Definition Team Study. The spacecraft and mission designs are the products of studies carried out by the GSFC Mission Design Lab and Ball Aerospace. In addition to the low isolation at Enceladus, its location deep in Saturn's gravity well makes it a challenging target to reach, meaning that careful consideration must be given to spacecraft mass and the potential mission types. This presentation summarizes the mission science objectives and payload, the dynamical work, and the notional operations plan of this mission.

Simon-Miller, Amy A.↗
NASA NTRS2011

Time Synchronization and Distribution Mechanisms for Space Networks

This work discusses research on the problems of synchronizing and distributing time information between spacecraft based on the Network Time Protocol (NTP), where NTP is a standard time synchronization protocol widely used in the terrestrial network. The Proximity-1 Space Link Interleaved Time Synchronization (PITS) Protocol was designed and developed for synchronizing spacecraft that are in proximity where proximity is less than 100,000 km distant. A particular application is synchronization between a Mars orbiter and rover. Lunar scenarios as well as outer-planet deep space mother-ship-probe missions may also apply. Spacecraft with more accurate time information functions as a time-server, and the other spacecraft functions as a time-client. PITS can be easily integrated and adaptable to the CCSDS Proximity-1 Space Link Protocol with minor modifications. In particular, PITS can take advantage of the timestamping strategy that underlying link layer functionality provides for accurate time offset calculation. The PITS algorithm achieves time synchronization with eight consecutive space network time packet exchanges between two spacecraft. PITS can detect and avoid possible errors from receiving duplicate and out-of-order packets by comparing with the current state variables and timestamps. Further, PITS is able to detect error events and autonomously recover from unexpected events that can possibly occur during the time synchronization and distribution process. This capability achieves an additional level of protocol protection on top of CRC or Error Correction Codes. PITS is a lightweight and efficient protocol, eliminating the needs for explicit frame sequence number and long buffer storage. The PITS protocol is capable of providing time synchronization and distribution services for a more general domain where multiple entities need to achieve time synchronization using a single point-to-point link.

Woo, Simon S.↗
NASA NTRS2012

CO2 Flux Estimation Errors Associated with Moist Atmospheric Processes

Vertical transport by moist sub-grid scale processes such as deep convection is a well-known source of uncertainty in CO2 source/sink inversion. However, a dynamical link between vertical transport, satellite based retrievals of column mole fractions of CO2, and source/sink inversion has not yet been established. By using the same offline transport model with meteorological fields from slightly different data assimilation systems, we examine sensitivity of frontal CO2 transport and retrieved fluxes to different parameterizations of sub-grid vertical transport. We find that frontal transport feeds off background vertical CO2 gradients, which are modulated by sub-grid vertical transport. The implication for source/sink estimation is two-fold. First, CO2 variations contained in moist poleward moving air masses are systematically different from variations in dry equatorward moving air. Moist poleward transport is hidden from orbital sensors on satellites, causing a sampling bias, which leads directly to small but systematic flux retrieval errors in northern mid-latitudes. Second, differences in the representation of moist sub-grid vertical transport in GEOS-4 and GEOS-5 meteorological fields cause differences in vertical gradients of CO2, which leads to systematic differences in moist poleward and dry equatorward CO2 transport and therefore the fraction of CO2 variations hidden in moist air from satellites. As a result, sampling biases are amplified and regional scale flux errors enhanced, most notably in Europe (0.43+/-0.35 PgC /yr). These results, cast from the perspective of moist frontal transport processes, support previous arguments that the vertical gradient of CO2 is a major source of uncertainty in source/sink inversion.

moist atmospheric processes↗
NASA NTRS2016

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Ambur, Manjula Y.↗
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 NTRS2021

Positional Dependence of SNPP VIIRS Solar Diffuser BRDF Change Factor: An Empirical Approach

The Earth-observing Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs), primarily through observations of an onboard sunlit solar diffuser (SD). The on-orbit change of the SD bidirectional reflectance distribution function (BRDF) value, quantified by a numerical factor called the H-factor, is determined by the onboard SD stability monitor. Our previous study showed that the H-factor is solar angle and view direction dependent. In this study, we determine the dependence of the H-factor on the detector SD view footprint location. We fit an empirical model to the NASA Collection 1 SNPP VIIRS Level 1B (L1B) spectral reflectance difference across the detectors in an RSB over uniform Earth scenes of the Libya 4 desert and deep convective clouds. We apply the model predicted SD positional dependent H-factor to calibrate the RSBs. Under this new calibration scheme, the original unreal striping is removed from the homogeneous Libya 4 desert and the deep convective cloud images, as well as the original unreal striping from the Dunhuang desert image. The SD positional dependent H-factor has been used to calculate the SNPP VIIRS RSB radiometric correction factor for the NASA Collection 2.0 SNPP VIIRS L1B products.

SNPP VIIRS↗
NASA NTRS2023

Spacecraft Water Exposure Guidelines: Revision A

The enclosed table lists official Spacecraft Water Exposure Guidelines (SWEGs), which are guideline values set by the NASA/JSC Toxicology Group in cooperation with the National Research Council Committee on Toxicology (NRCCOT) or through publication in the peer‐reviewed scientific literature. Based on documented guidance (NRC, 2000), NASA has established SWEGs for 30 chemical compounds that are particularly relevant to water systems on the International Space Station (ISS) as well as on spacecraft for deep‐space exploration. Summaries of these SWEGs are presented in tabular form as part of this publication.

SWEGs↗
DOE OSTI2024

Star Truck : Interplanetary Bussing system

CubeSats are a standardized size of satellite. Used by elementary schools, universities, and hobbyists alike. CubeSats have made space exploration and research accessible to the general population. Utilizing CubeSats it is possible to make deep space exploration accessible as well. Using a flight path that takes advantage of gravitational assists and flybys we can use many forms of propulsion to get to Jupiter. However, to get farther nuclear power and propulsion can be utilized to bus hundreds of CubeSats at a time to interplanetary space. This craft is known as a Star Truck. The Star Truck will make interplanetary space assessable to the common man.

Belian, Olivia↗
DOE OSTI2024

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

Environmental Sciences & Ecology↗
NASA NTRS2022

Psyche Project Implementation During the COVID Pandemic

In January 2017, “Psyche: Journey to a Metal World” was selected for implementation as part of NASA’s Discovery program. The Psyche mission will utilize electric propulsion with SPT-140 Hall thrusters to rendezvous and orbit the metal-rich asteroid (16) Psyche, in the main asteroid belt between Mars and Jupiter. The Psyche spacecraft requires no chemical propulsion and, when launched in 2022, will be the first mission to use Hall thrusters beyond lunar orbit. The Psyche spacecraft is a hybrid development with Jet Propulsion Laboratory (JPL)-provided deep-space avionics and communications equipment mounted on a high-heritage MAXAR (formerly Space Systems Loral) Solar-Electric Propulsion (SEP) Chassis, based on their 1300 series of GEO communications satellites. The spacecraft is equipped to support a suite of science instruments, as well as a demonstration of the Deep Space Optical Communications (DSOC) technology. The spacecraft has sufficient onboard resources, autonomy, redundancy, and operability to complete a 3.5-year cruise to 16 Psyche, followed by a 20-month campaign of science investigations while orbiting the asteroid.The mission’s early concept design and progress through Preliminary Design Review (PDR) has been described in previous work. The paper will cover the recent mission progress from the Critical Design Review (CDR) through the start of Spacecraft Environmental Testing, which took place during the COVID pandemic.The authors will highlight the successful remote collaboration between the major partners: ASU, JPL, MAXAR, and the Payload teams that led to the initiation of the Assembly, Test, Launch, Operations Phase (ATLO) in early March 2021. Emphasis will be placed on the effects that the COVID-19 pandemic had on the work environment over the last 16+ months, including challenges to delivery of flight hardware and test equipment. In addition to the COVID-19 challenges, other significant anomalies discovered during design and test will be described along with any impacts to the overall science capability of the mission.

Lord, Peter↗
DOE OSTI2023

A robust estimator of mutual information for deep learning interpretability

Abstract We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Geothermal Reservoir Characterization at Wattenberg Area in Denver Basin

The geothermal potential of the Denver-Julesburg (D-J) Basin is largely overlooked. This study focuses on characterizing the geothermal potential in the greater Wattenberg area within the Denver Basin, using available well data and subsurface information and gas wells drilled to above 2500 m deep. The review reveals that the main structures comprise ENE-trending wrench faults, along with NNE, N, and NNW trending normal faults. A thermal anomaly is present bonded by Lafayette and Longmont wrench faults and overlying vitrinite reflectance anomaly. The geometry of thermal anomaly suggests that it is influenced by Longmont wrench fault and Lafayette fault, as well as the basin margin fault. Bottom hole temperatures are of 160 Degrees Celsius, with thermal gradients of and 62 Degrees Celsius/km. Reservoir temperature estimates from thermal gradients suggest a range of >200 Degrees Celsius at depths of 3 km.

Denver basin↗
DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

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

Narrowing the Phase Distribution of Quasi-2D Perovskites for Stable Deep-Blue Electroluminescence

Solution-processed quasi-2D perovskites contain multiple quantum wells with a broad width distribution. Inhomogeneity results in the charge funneling into the smallest bandgap components, which hinders deep-blue emission and accelerates Auger recombination. Here, a synthetic strategy applied to a range of quasi-2D perovskite systems is reported, that significantly narrows the quantum well dispersity. It is shown that the phase distribution in the perovskite film is significantly narrowed with controlled, simultaneous evaporation of solvent and antisolvent. Modulation of film formation kinetics of quasi-2D perovskite enables stable deep-blue electroluminescence with a peak emission wavelength of 466 nm and a narrow linewidth of 14 nm. Light emitting diodes using the perovskite film show a maximum luminance of 280 cd m –2 at an external quantum efficiency of 0.1%. This synthetic approach will serve in producing new materials widening the color gamut of next-generation displays.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Baseline for Global Weather and Climate Simulations at 1 km Resolution

In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four-month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well-tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large-scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden-Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Imaging Large Scale Structure in the X-ray Sky

We present the first results of a wide solid angle, moderately deep Chandra survey of the Lockman Hole North West Region. Our 9 ACIS-I fields cover an effective solid angle of 0.33 sq deg and reach a depth of 3 x 10(exp -16) erg/sq cm/s in the 0.4-2 keV band, and 3 x 10(exp -15) erg/sq cm/s in the 2-8 keV band. The best fit logN-logS for the entire field, the largest contiguous Chandra field yet observed, matches well onto that of the Chandra Deep Field North. This indicates that over 90% of the 2-8 keV X-ray background is resolved into point sources. We show that the full range of the 'cosmic variance' previously seen in different Chandra fields can be reproduced in a small region of the sky. The count-in-cells analysis shows that the 2-10 keV band sources have much larger angular correlation scale than that of the 0.4-2 keV band. We found the large difference is likely to be caused by a stronger spatial correlation in the hard band sources rather than the redshift effect.

Yang, Y.↗
DOE OSTI2021

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗