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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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NASA NTRS1998

Nonlinear Insolation Forcing: A Physical Mechanism for Climate Change

This paper focuses on recent advances in the understanding of nonlinear insolation forcing for climate change. The amplitude-frequency resonances in the insolation variations induced by the Earth's changing obliquity are emergent and may provide a physical mechanism to drive the glaciation cycles. To establish the criterion that nonlinear insolation forcing is responsible for major climate changes, the cooperative phenomena between the frequency and amplitude of the insolation are defined as insolation pulsation. Coupling of the insolation frequency and amplitude variations has established an especially new and interesting series of insolation pulses. These pulses would modulate the insolation in such a way that the mode of insolation variations could be locked to generate the 100-kyr ice age cycle which is a long-time geophysical puzzle. The nonlinear behavior of insolation forcing is tested by energy balance and ice sheet climate models and the physical mechanism behind this forcing is explained in terms of pulse duration in the incoming solar radiation. Calculations of the solar energy flux at the top of the atmosphere show that the duration of the negative and positive insolation pulses is about 2 thousand years which is long enough to prolong glaciation into deep ice ages and cause rapid melting of large ice sheets in the high latitudes of the northern hemisphere. We have performed numerical simulations of climate response to nonlinear insolation forcing for the past 2 million years. Our calculated results of temperature fluctuations are in good agreement with the climate cycles as seen in the terrestrial biogenic silica (BDP-96-2) data as well as in the marine oxygen isotope (delta(sup 18)O) records.

Liu, H. S.↗

NASA NTRS2014

Preparing GMAT for Operational Maneuver Planning of the Advanced Composition Explorer (ACE)

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF.

Trajectory analysis↗

NASA NTRS2019

Preparing General Mission Analysis Tool for Operational Maneuver Planning of the Advanced Composition Explorer Mission

mission design↗

NASA NTRS2021

Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.

Image processing↗

DOE OSTI2021

Deep Learning Systems for Increased Safeguards Surveillance Review Productivity

Nuclear safeguards inspectors expend significant time and maintain intense focus in reviewing video surveillance for safeguards relevant events. To increase efficiency and reduce the time burden of safeguards inspectors performing surveillance data review, this paper presents a novel deep learning (DL) systems concept to integrate generalized DL models into the safeguards surveillance review workflow. The Agency is investigating several DL algorithms for object recognition, localization, tracking, and flagging relevant activities. The project team is working closely with nuclear safeguards inspectors to identify review use cases (based on specific safeguards objectives) and collect their associated requirements. We focused on CANDU and LWR Nuclear Power Plants (NPPs) and their associated dry storage areas as these present a particularly heavy burden on the inspector surveillance review process due to the number of these facilities under safeguards worldwide. Initial DL algorithm results on safeguards data are promising. Using a convolutional neural network, the team attained a mean average precision (mAP) of 92.9% identifying and localizing spent fuel (SF) casks from a 475 surveillance image dataset. Further, the team had initial success in training a recurrent neural network to identify reactor area activities in video clips, successfully indicating when SF casks enter or exit a pool. We discuss how such DL algorithms would be integrated into the Next Generation Surveillance Review (NGSR) software application. Another issue impacting review productivity is the long time inspectors may have to wait when running these algorithms in NGSR. We present a concept to pre-process remotely collected surveillance data with DL models as the data arrives to IAEA headquarters so that results are already available when starting a new review in NGSR. The proposed DL system concept shows a pathway and workflow for increasing an inspector’s surveillance review productivity by quickly and accurately identifying declared and undeclared safeguards relevant objects and activities in large quantities of surveillance imagery data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS2011

Comparison of Organ Dosimetry for Astronaut Phantoms: Earth-Based vs. Microgravity-Based Anthropometry and Body Positioning

The purpose of this study is to use NASA radiation transport codes to compare astronaut organ dose equivalents resulting from solar particle events (SPE), geomagnetically trapped protons, and free-space galactic cosmic rays (GCR) using phantom models representing Earth-based and microgravity-based anthropometry and positioning. Methods: The Univer sity of Florida hybrid adult phantoms were scaled to represent male and female astronauts with 5th, 50th, and 95th percentile heights and weights as measured on Earth. Another set of scaled phantoms, incorporating microgravity-induced changes, such as spinal lengthening, leg volume loss, and the assumption of the neutral body position, was also created. A ray-tracer was created and used to generate body self-shielding distributions for dose points within a voxelized phantom under isotropic irradiation conditions, which closely approximates the free-space radiation environment. Simplified external shielding consisting of an aluminum spherical shell was used to consider the influence of a spacesuit or shielding of a hull. These distributions were combined with depth dose distributions generated from the NASA radiation transport codes BRYNTRN (SPE and trapped protons) and HZETRN (GCR) to yield dose equivalent. Many points were sampled per organ. Results: The organ dos e equivalent rates were on the order of 1.5-2.5 mSv per day for GCR (1977 solar minimum) and 0.4-0.8 mSv per day for trapped proton irradiation with shielding of 2 g cm-2 aluminum equivalent. The organ dose equivalents for SPE irradiation varied considerably, with the skin and eye lens having the highest organ dose equivalents and deep-seated organs, such as the bladder, liver, and stomach having the lowest. Conclus ions: The greatest differences between the Earth-based and microgravity-based phantoms are observed for smaller ray thicknesses, since the most drastic changes involved limb repositioning and not overall phantom size. Improved self-shielding models reduce the overall uncertainty in organ dosimetry for mission-risk projections and assessments for astronauts

VanBaalen, Mary↗

DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗

DOE OSTI2024

Direct Observation of Circularly Polarized Nonlinear Optical Activities in Chiral Hybrid Lead Halides

Circularly polarized light emission is a crucial application in imaging, sensing, and photonics. However, utilizing low-energy photons to excite materials, as opposed to high-energy light excitation, can facilitate deep-tissue imaging and sensing applications. The challenge lies in finding materials capable of directly generating circularly polarized nonlinear optical effects. In this study, we introduce a chiral hybrid lead halide (CHLH) material system, R/S-DPEDPb 3 Br 8 ·H 2 O (DPED = 1,2-diphenylethylenediammonium), which can directly produce circularly polarized second harmonic generation (CP-SHG) through linearly polarized infrared light excitation, exhibiting a polarization efficiency as high as 37% at room temperature. To understand the spin relaxation mechanisms behind the high polarization efficiency, we utilized two models, so-called D’yakonov–Perel’ (DP) and Bir–Aronov–Pikus (BAP) mechanisms. The unique zigzag inorganic frameworks within the hybrid structure are believed to reduce the dielectric confinement and exciton binding energy, thus enhancing spin polarization, especially in regions with a high excitation pump fluence based on the DP mechanism. In the case of low excitation pump fluence, the BAP mechanism dominates, as evidenced by the observed decrease in the polarization ratio from CP-SHG measurement. Using density functional theory analysis, we elucidate how the distinctive 8-coordination environment of lead bromide building blocks effectively suppresses spin–orbit coupling at the conduction band minimum. This suppression significantly diminishes spin-splitting, thereby slowing the spin relaxation rate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Scenario Discovery Analysis of Drivers of Solar and Wind Energy Transitions Through 2050

Deep human-Earth system uncertainties and strong multi-sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM) and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid-century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non-CO 2 emissions. Globally and regionally, we found that solar and wind-related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid-century and discuss their tradeoffs in terms of other outcomes.

14 SOLAR ENERGY↗

DOE OSTI2025

High precision monitoring of outgassing species in model EUV photoresists with a cavity ring-down spectrometer

Extreme ultraviolet (EUV) photoresists play a pivotal role in advancing nanopatterning technologies by balancing image quality and sensitivity. The outgassing behavior of photoresist thin films under EUV and deep ultraviolet (DUV) exposure reveals chemical details relevant to their performance. This study focuses on utilizing an analytical technique not previously used in photolithography, the tabletop cavity ring-down spectrometer, to investigate outgassing dynamics in EUV photoresists, enabling precise chemical identification and deeper insights into resist processing. The spectrometer’s enhanced laser path length (~20 km) and broadband absorption capabilities in the CH overtone region allow for sensitive and temporally resolved detection of outgassed species. Using a model resist comprising a polymer matrix with a photoacid generator and quencher, we analyzed the influence of time delays between exposure and Post-Exposure Bake (PEB) as well as storage under varying environmental conditions. Suppression of isobutylene outgassing and thickness loss was observed with extended delays between exposure and PEB, potentially linked to water absorption and acid deactivation. The technique proved highly effective in distinguishing subtle chemical differences between processing stages. Delay times and their environmental conditions, particularly humidity, reduce outgassing and thickness loss of photoresists during PEB, suggesting decreased acid-driven deprotection. This can potentially impact sensitivity, defectivity, and roughness of resist patterns, necessitating precise monitoring and control.

Lüttgenau, Bernhard↗

NASA NTRS2022

Agent-Based Modeling of Microbes in Space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal gravity, and much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER growth curves recapitulate experimental results, and allow comparison between direct effects (DNA damage) and indirect effects (reactive oxygen species generation, metabolic impairment) of radiation.

microbiology↗

DOE OSTI2024

Deep Image Prior Enabled Full Waveform Inversion (Final Technical Report)

MS Student Naveen Gupta worked on the problem of full waveform inversion (FWI) using neural networks as shown in Figure 1. Our goal was to learn a neural network to represent the subsurface velocity model, which when fed into the FWI module (implemented using a numerical forward model of wave equations) produces amplitude estimates that match with ground-truth observations of amplitude. We used neural networks to solve the inverse problem of estimating velocity distributions for a given seismic amplitude data such that, once trained, our neural network model can generate a distribution of velocity profiles for different random vectors fed as inputs to the neural network model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Rats use memory confidence to guide decisions

Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Multi-Wavelength Modeling and Analysis of the Center-to-Limb Effects of Solar Spectroscopy and Helioseismology

An accurate interpretation of observed solar dynamics with different instruments requires modeling solar magnetoconvection indifferent regimes, as well as taking into account center-to-limb effects, magnetic fields, and turbulence. Realistic 3D radiative MHD modeling of the solar magnetoconvection and atmosphere allows us to generate synthetic observables that directly link the physical properties of the solar plasma to spectroscopic and helioseismic observables. In this work, we investigate the influence of the center-to-limb effects for a wide range of wavelengths, which correspond to the operational lines of HMI/SDO, Hinode, DKIST, and other instruments. In particular, we discuss the wavelength-dependency of the center-to-limb helioseismic observations of acoustic travel times that are used for diagnostics of the deep meridional circulation, as well as 'concave' Sun effect. The presented study will support interpretation of helioseismic inversion results by taking into account realistic coupling of subsurface and atmosphere, and geometry-related effects. In particular, it allows us to improve accuracy of solar subsurface measurements from the SoHO and SDO missions, and resolve the long-standing problem of the meridional circulation and evolution with the solar cycle.

SMD↗

NASA NTRS1992

DSS-13 26-meter antenna upgraded radiometer system

The Deep Space Station (DSS)-13 26-m antenna radiometer system was upgraded with an IBM-compatible computer-controlled configuration with improved supporting hardware and software. Software was generated to analyze results and correct for antenna mispointing, tropospheric loss, and other observing errors. This total power radiometer configuration provides a prototype for the new DSS-13 34-m antenna. The radiometer system is described in terms of the theory, instrumentation hardware, computer configuration, and operational features and performance. The system is used to obtain antenna efficiency and pointing model data and is useful for radio source calibrations required for radio astronomy. Some recent results are given.

Stelzried, C. T.↗

NASA NTRS1994

Chemical models of the deep atmospheres of Jupiter and Saturn

New and updated chemical kinetic data, elemental abundances, and thermodynamic data are used for thermochemical equilibrium and, where relevant, thermochemical kinetic calculations of gas abundances and condensate stability in the hot, deep atmospheres of Jupiter and Saturn. Over 2000 compounds of all naturally occurring elements in the periodic table are considered. The calculations range from 298 to 2000 K and are done for adiabatic models of the two planetary atmospheres. The results predict the abundances of many gases which are potentially observable by the Galileo probe to Jupiter, by the Cassini mission to Saturn, and by Earth-based and Earth-orbital telescopes. In addition, the results also predict many new species which are potentially observable by a new generation of entry probes capable of penetrating deeper into the atmospheres of Jupiter and Saturn.

Fegley, Bruce, Jr.↗

NASA NTRSDate not supplied

Seasonal and Dayurnal Planetary Albedo Variability from Six Years of DSCOVR EPIC Data

Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar radiation from the Lissajous orbital position near the Lagrangian L1 point provide continuous monitoring of the Earth’s sunlit hemisphere. CERES-based angle models were used to convert the near-hourly reflected radiances of the EPIC images into a climate-style planetary albedo data-point over the sunlit hemisphere. Integration over the sunlit hemispheres averages out the meteorological weather noise, but retains the seasonal and planetary-scale variability. As the Earth rotates, this generates variations in the Earth’s planetary albedo that are precisely aligned in longitude, which constitutes the dayurnal cycle. This dayurnal variability in the planetary albedo arises from planetary-scale changes in cloud radiative properties that can be directly compared to similarly sampled climate GCM output data. Six years of EPIC data have been analyzed, showing characteristic patterns in the seasonal and dayurnal variability of the Earth’s planetary albedo. Much of the seasonal change in planetary albedo is associated with the changing DSCOVR viewing geometry and the change in solar declination. But throughout the year, the highest planetary albedos are observed over the Central Asia (Iraq) longitude, while the lowest planetary albedos occur over the Central Pacific longitude. For these longitudes, the relative seasonal changes in the planetary albedo are slowly varying and anti-correlated. Dayurnal amplitude maxima tend to occur during the July-September timeframe, with April-May and December exhibit distinct minima in the dayurnal amplitude. West Africa and the West Pacific longitudes likewise exhibit anti-correlated seasonal variability, while they also undergo anti-correlated short period oscillations. On the other hand, in nearby longitudes, there are short period spikes in planetary albedo of a few-days duration, as well as longer period oscillations that may range from a week to several months, that generally tend to be correlated.

Planetary Albedo↗

NASA NTRSDate not supplied

Extending ISS Life Beyond 2030

The United States On-orbit Segment (USOS) of the International Space Station (ISS) was designed to meet a 15-year on-orbit life. Since the first hardware was launched in late 1998, the ISS would have reached its end of life in 2013. With the realization that the ISS would be needed well into the next decade, and beyond, a multi-disciplinary effort was undertaken to extend the ISS’ life through 2028 and to show that further extension to 2040 and beyond is not only feasible but achievable. Currently, NASA and the ISS international partners have agreed to extend its operations through 2030. This collaborative effort ensures that the ISS will continue to serve as a hub for scientific research, international cooperation, and educational endeavors for the next decade. Maintaining a continuous human presence in Low Earth Orbit (LEO) is desirable for testing new LEO, lunar, and deep-space technologies; conducting scientific research in micro-gravity for the benefit of life on Earth; and enabling a seamless transition of capabilities to one or more commercially owned and operated destinations. This paper provides an overview of the ISS life extension project with a particular focus on the analytical approach used to assess the primary structure. This analytical approach includes future operations planning, critical location screening, on-orbit dynamic load simulation, on-orbit optical property degradation studies, on-orbit thermal analyses, spectra generation, crack model idealization, fracture analyses, and post processing. Structural life results and identification of the most critical on-orbit events are presented. Also addressed are life extension approaches for other affected sub-systems, including: 1. Secondary Structure. 2. Materials. Evaluations consider environmental exposure to atomic oxygen, ionizing and gamma radiation, fluids, etc. Life limited materials, wear, and usage effects are also considered. 3. Environmental Control and Life Support Systems, including oxygen supply and generation, water recovery and management, and regenerative hardware. Evaluations are performed to determine which hardware can be run to failure and which are assessed for life extension. 4. Electrical power system. ISS is powered by eight channels of solar arrays and an electrical energy storage system providing 357 kWh power. Power generation and balance analyses are performed considering hardware degradation and increasing power demand. 5. Logistics and maintenance. Analyses are performed to determine critical spares required to maintain functionality. Consideration is given to supply chain health, obsolescence issues, onboard stowage availability, and up-mass capability. The successful life extension results have built confidence to safely operate, maintain and enhance the ISS well beyond the current decade. Extending the operational life of the ISS maintains an international presence in LEO and serves to avoid a gap in capability necessary to fulfill the exploration and research needs of NASA, international partners, and industry without interruption until a commercial space station is operational.

design life↗