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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 NTRSDate not supplied

Derivation of Capabilities and Resources for Treating Medical Conditions in Deep Space

BACKGROUND: Medical care in spaceflight requires the adaptation of terrestrial standards to the constraints of the space environment. However, there is often conflict between the ideal resources required for treatment and diagnosis of a medical condition, and the constraints on their mass, volume, data needs, etc. This optimization of trades between medical risk and resources will be a significant challenge for deep space medical system design. METHODS: A team of physicians with a broad range of expertise reviewed the 120 medical conditions on the condition list for NASA’s Informing Mission Planning via analysis of Complex Tradespaces Medical Database (IMPACT-MD). Each team member was assigned a subset of conditions based on their experience and training. The assigned physician generated a proposed list of capabilities, definitions, and resources required to treat each condition based on terrestrial practice guidelines, medical literature, and subject matter expertise. Additional specialists were consulted for conditions where expertise was not present within the main group. The proposed list was then reviewed by the broader team and modified as needed to achieve consensus. Each capability and resource was then assigned parameters to define quantity required per medical event, necessity, training level required, and primacy order of any alternative resources. These capabilities and resources were placed into condition specific tables and delivered to a team of engineers who added mass and volume data for each specific resource and converted the table into a database for use as input to a computational model to simulate spaceflight (IMPACT) DISCUSSION: This process ensures that a minimum of three subject matter experts review and agree upon the scope of practice, medical diagnostic tools, and treatment modalities that would be necessary to address emergent and non-emergent conditions that may arise during spaceflight to inform requirements during the vehicle design phase. The method is scalable to any design reference mission and permits modification of the existing database as information, conditions or experience are added. By including specialists, generalists, and those with subject matter expertise in the spaceflight environment on the team, we ensure that the included capabilities represent a realistic and actionable foundation for planning deep space missions.

D R Levin↗
NASA NTRS1988

Carbon dioxide catastrophes: Past and future menace

Carbon dioxide is important in its role as coupler of the terrestrial biosphere to inorganic chemical processes and as the principal greenhouse gas controlling Earth's surface temperature. The hypothesis that atmospheric CO2 levels have diminished with time, with the resulting cooling effect offsetting an increase in the solar constant, seems firmly established, and it is shown that feedback mechanisms exist which can maintain the terrestrial surface in a relatively narrow temperature range over geological time. Of the factors involved in such CO2 variation, the oceanic reservoir appears the most important. Surface waters are probably in approximate equilibrium with regard to CO2 exchange with the ambient atmosphere in most regions, but data from deep-ocean water sampling indicates that such waters are somewhat undersaturated in the sense that they would tend to absorb CO2 from the atmosphere if brought to the surface without change in composition or temperature. If major impacts into the ocean can result in loss of a substantial portion of the atmospheric CO2 reservoir, then any such future event could imperil the continuation of most higher forms of life on Earth. The most likely candidate for an inverse Nyos global event in previous Earth history is the Cretaceous-Tertiary terminal extinction event. The Cretaceous was characterized by warm, equable temperatures presumably indicative of relatively high CO2 levels and an intense greenhouse heating. Cooling of the oceans in absence of massive transfer of CO2 to the oceanic reservoir in itself would promote a condition of CO2 undersaturation in abyssal waters, and this is made even more extreme by the pattern of ocean water circulation. It is possible to envision a situation in which deep ocean waters were at least occasionally profoundly undersaturated with regard to CO2. Turnover of a major fraction of such an ocean would then remove, on a very short time scale, as much as 90 percent of the atmospheric CO2 inventory.

Baur, Mario E.↗
NASA NTRS2016

Investigation of Secondary Neutron Production in Large Space Vehicles for Deep Space

Future NASA missions will focus on deep space and Mars surface operations with large structures necessary for transportation of crew and cargo. In addition to the challenges of manufacturing these large structures, there are added challenges from the space radiation environment and its impacts on the crew, electronics, and vehicle materials. Primary radiation from the sun (solar particle events) and from outside the solar system (galactic cosmic rays) interact with materials of the vehicle and the elements inside the vehicle. These interactions lead to the primary radiation being absorbed or producing secondary radiation (primarily neutrons). With all vehicles, the high-energy primary radiation is of most concern. However, with larger vehicles, there is more opportunity for secondary radiation production, which can be significant enough to cause concern. In a previous paper, we embarked upon our first steps toward studying neutron production from large vehicles by validating our radiation transport codes for neutron environments against flight data. The following paper will extend the previous work to focus on the deep space environment and the resulting neutron flux from large vehicles in this deep space environment.

Rojdev, Kristina↗
NASA NTRS2015

Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

Slaba, T. C.↗
NASA NTRS2019

Solar Power System and Radioisotope Thermoelectric Generation Technologies at Jupiter-Saturn-Uranus Environments: New Insights and Paradigms

Power system selection for outer planet destinations, such as Jupiter, Saturn, and Uranus and beyond, is complex, involving and dependent on many interdisciplinary factors such as power system mass, specific power, cost, mechanical and electrical integration, and natural radiation environment. Low solar irradiance at Jupiter, Saturn, and Uranus systems (i.e., 50, 15, and 4 W/m2 , respectively) makes solar power systems challenging in mechanical / electrical integration and accommodating radiation environments. More costly radioisotope thermoelectric generator (RTG) systems can help proposed missions overcome radiation environment and spacecraft control challenges at Jupiter, Saturn, and Uranus. NASA’s Jet Propulsion Laboratory (JPL) has recently made significant strides in demonstrating high-efficiency, radiation-hard solar cell technologies for low-irradiance, low-temperature (LILT) applications, and high-efficiency thermoelectric (TE) materials and modules for higher-specific-power RTGs. Stateof-art multi-junction solar cells now routinely demonstrate high efficiencies of 30-34% at LILT (9.5AU and -165°C), making solar arrays a viable option for many near-term Saturn mission concepts. Emerging technologies like LILToptimized solar cells have recently demonstrated even higher efficiencies of 37% at 9.5AU and -165°C and 30% lower mass than the state-of-art, offering the prospect of ~3W/kg array-level, end-of-life specific powers under Saturn conditions. Having already demonstrated the tremendous utility of RTGs on Mars and in deep-space missions (e.g., Galileo at Jupiter, New Horizons at Pluto), NASA is now developing and demonstrating new TE materials and modules (e.g., skutterudites, La3-x Te4, and Zintls) for increasing RTG specific power (up to >8.5 W/kg), which strongly impacts an RTG’s mass, fuel utilization, and modularity in the power system trade domain. New accomplishments in both areas highlight the renewed requisite for updated comparisons and trade-offs in power output, specific power and mass, cost, mechanical and electrical integration, new technology timelines, and natural radiation impacts between new LILT-optimized photovoltaic technologies and next-generation RTG technologies. This work discusses and demonstrates how new LILT-based technologies are now allowing one to consider and design solar power systems for Saturn orbit and beyond, and are changing the potential cost-mass trade-offs between emerging solar power technologies and newly-envisioned RTG technologies. Key updated system mass and cost trade-offs between high-performance LILT solar technologies and new RTG technologies are presented, reinforcing and refining power selection criteria supporting possible future NASA deep-space science and exploration missions to Mars, the Jupiter system (Europa, Ganymede), the Saturn system (Titan, Enceladus), Uranus, and beyond. Key trade-offs in other above-mentioned interdisciplinary factors between these two power technologies are also discussed.

Bairstow, Brian K.↗
DOE OSTI2022

Towards Low-Overhead Resilience for Data Parallel Deep Learning

Data parallel techniques have been widely adopted both in academia and industry as a tool to enable scalable training of deep learning models. At scale, DL training jobs can fail due to software or hardware bugs, may need to be preempted or terminated due to unexpected events, or may perform suboptimally because they were misconfigured. Under such circumstances, there is a need to recover and/or reconfigure data-parallel DL training jobs on-the-fly, while minimizing the impact on the accuracy of the DNN model and the runtime overhead. In this regard, state-of-art techniques adopted by the HPC community mostly rely on checkpoint-restart, which inevitably leads to loss of progress, thus increasing the runtime overhead. In this paper we explore alternative techniques that exploit the properties of modern deep learning frameworks (overlapping of gradient averaging and weight updates with local gradient computations through pipeline parallelism) to reduce the overhead of resilience/elasticity. To this end we introduce a failure simulation framework and two resilience strategies (immediate mini-batch rollback and lossy forward recovery), which we study compared with checkpoint-restart approaches in a variety of settings in order to understand the trade-offs between the accuracy loss of the DNN model and the runtime overhead.

data-parallel training↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

How to Model Batteries (with PV, Stand-Alone, or Hybrids) in SAM and PySAM

This tutorial will be a deep dive into considerations for battery modeling and demonstrating how to model them in SAM, including battery chemistry, thermal modeling, degradation/lifetime, dispatch, interconnection limits and curtailment, and their associated impacts on project profits and battery lifetime. By the end of the tutorial attendees will know how to size and model both behind-the-meter and front-of-meter battery systems, including financial analysis and pairing with other PV models (including pvlib) via PySAM.

25 ENERGY STORAGE↗
DOE OSTI2025

External Radiation and Magnetic-Field Effects on the Coherence and Stability of Transmon Qubits

Superconducting transmon qubits are a central building block of modern quantum computing architectures, yet their coherence properties remain sensitive to external influences that can limit performance or introduce temporal instabilities. In this talk, I will discuss two experimental studies aimed at quantifying these effects. First, I will examine how ionizing radiation impacts qubit relaxation. Using the same transmon device operated at two locations with dramatically different radiation backgrounds—the above-ground SQMS facility at Fermilab (USA) and the deep-underground Gran Sasso Laboratory (Italy)—we observe a higher rate of radiation-induced decay events above ground, even though intrinsic noise remains the dominant source of single-shot errors. I will also discuss the detection efficiency of a radiation detector made using such a device. Second, I will present results on how small magnetic fields, applied either during cooldown or at millikelvin temperatures, influence device stability. We find that fixed-frequency transmons maintain robust coherence up to approximately 600 mG of trapped out-of-plane field, and that controlled application of magnetic field can reduce temporal fluctuations in T1. Together, these studies provide insight into how external environments shape transmon coherence and offer potential pathways for improving qubit stability in scalable quantum processors.

Roy, Tanay [Fermilab]↗
NASA NTRS1992

A history of the Lonar crater, India: An overview

The origin of the circular structure at Lonar, India, described variously as cauldron, pit, hollow, depression, and crater, has been a controversial subject since the early nineteenth century. A history of its origin and other aspects from 1823 to 1990 are overviewed. The structure in the Deccan Trap Basalt is nearly circular with a breach in the northeast, 1830 m in diameter, 150 m deep, with a saline lake in the crater floor. Over the years, the origin of the Lonar structure has risen from volcanism, subsidence, and cryptovolcanism to an authentic meteorite impact crater. Lonar is unique because it is probably the only terrestrial crater in basalt and is the closest analog with the Moon's craters. Some unresolved questions are suggested. The proposal is made that the young Lonar impact crater, which is less than 50,000 years old, should be considered as the best crater laboratory analogous to those of the Moon, be treated as a global monument, and preserved for scientists to comprehend more about the mysteries of nature and impact cratering, which is now emerging as a fundamental ubiquitous geological process in the evolution of the planets.

Nayak, V. K.↗
NASA NTRS1993

Elemental abundance anomalies in the late Cenomanian extinction interval - A search for the source(s)

Measurements of elemental abundances by neutron activation methods across the Cenomanian-Turonian extinction interval in samples collected from 16 sites in the Western Interior Basin of North America from 12 widely separated locations around the globe, including six ODP/DSDP sites, are reported. In most Western Interior Basin sites, in Colombia, and in Western Europe, two closely spaced elemental abundance peaks occur in the upper Cenomanian (about 92 m.y.), spanning the ammonite zones of Sciponoceras gracile through Neocardioceras juddii. Elements with anomalously high concentrations include Sc, Ti, V, Cr, Mn, Co, Ni, Ir, Pt, and Au. The lower peak coincides with the disappearance (extinction) of the foraminifer Rotalipora cushmani. In North American sections R. greenhornensis also disappears at or just below this horizon, but in Europe it disappears considerably earlier than R. cushmani. Although the weak geochemical signal from comet impact(s) could be masked by the strong terrestrial-like overprint, these anomalies more likely resulted in the large late Cenomanian through early Turonian eustatic rise and deep-water opening of the South Atlantic.

Orth, Charles J.↗
NASA NTRS2015

A Journey with MOM

In late 2013, the Indian Space Research Organization (ISRO) launched its "Mars Orbiter Mission" (MOM). ISRO engaged NASA's Jet Propulsion Laboratory (JPL) for navigation services to support ISRO's objectives of MOM achieving and maintaining Mars orbit. The navigation support included planning, documentation, testing, orbit determination, maneuver design /analysis, and tracking data analysis. Several of MOM's attributes had an impact on navigation processes, e.g., S -band telecommunications, Earth Orbit Phase maneuvers, and frequent angular momentum desaturation s (AMDs). The primary source of tracking data was NASA/ JPL's Deep Space Network (DSN); JPL also conducted a performance assessment of Indian Deep Space Network (IDSN) tracking data. Planning for the Mars Orbit Insertion (MOI) was complicated by a pressure regulator failure that created uncertainty regarding MOM's main engine and raised potential planetary protection issues. A successful main engine test late on approach resolved these issues; it was quickly followed by a successful MOI on 24-September - 2014 at 02:00 UTC. Less than a month later, Comet Siding Spring's Mars flyby necessitated plans to minimize potential spacecraft damage. At the time of this writing, MOM's orbital operations continue, and plans to extend JPL 's support are in progress. This paper covers the JPL 's support of MOM through the Comet Siding Spring event.

Mars orbiter↗
DOE OSTI2024

"Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test"

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Jang, Kathy↗
NASA NTRS1993

Effect of Earth and Mars departure delays on human missions to Mars

This study determines the impact on the initial mass in low-Earth orbit (IMLEO) for delaying departure from Mars and Earth by 5, 15, and 30 days, once a nominal mission to Mars has been selected. Additionally, the use of a deep space maneuver (DSM) is attempted to alleviate the IMLEO penalties. Three different classes of missions are analyzed using chemical and nuclear thermal propulsion systems in the 2000-2025 time-frame: opposition, conjunction, and fast-transfer conjunction. The results indicate that Mars and Earth delays can lead to large IMLEO penalties. Opposition and fast-transfer conjunction class missions have the highest IMLEO penalties, upwards of 432.4 mt and 1977.3 mt, respectively. Conjunction class missions, on the other hand, tend to be insensitive to Mars and Earth delays having IMLEO penalties under 103.5 mt. As expected, nuclear thermal propulsion had significantly lower IMLEO penalties as compared to chemical propulsion. The use of a DSM is found not to have a significant impact on reducing the IMLEO penalties. Through this investigation, the effect of off-nominal departure conditions on the overall mission (i.e., IMLEO) can be gained, enabling mission designers to incorporate the influence of off-nominal departure conditions of the interplanetary trajectory in the overall conceptual design process of a Mars transfer vehicle.

Desai, Prasun N.↗
DOE OSTI2023

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

Potnis, Abhishek↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗