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

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Swapping the N- and C-terminal domains of human apolipoprotein E3 and AI reveals insights into their structure/activity relationship

Apolipoprotein (apo) E3 and apoAI are exchangeable apolipoproteins that play a dominant role in regulating plasma lipoprotein metabolism. ApoE3 (299 residues) is composed of an N-terminal (NT) domain bearing a 4-helix bundle and a C-terminal (CT) domain bearing a series of amphipathic α-helices. ApoAI (243 residues) also comprises a highly helical NT domain and a less structured CT tail. The objective of this study was to understand their structural and functional role by generating domain swapped chimeras: apoE3-NT/apoAICT and apoAI-NT/apoE-CT. The bacterially overexpressed chimeras were purified by affinity chromatography and their identity confirmed by immunoblotting and mass spectrometry. Their α-helical content was comparable to that of the parent proteins. ApoE3-NT/apoAI-CT retained the denaturation profile of apoE3 NT domain, with apoAI CT tail eliciting a relatively unstructured state; its lipid binding ability improved dramatically compared to apoE3 indicative of a significant role of apoAI CT tail in lipid binding interaction. The LDL receptor interaction and ability to promote ABCA1-mediated cholesterol efflux of apoE3-NT/apoAI-CT was comparable to that of apoE3. In contrast, apoAI-NT/apoE-CT elicited an unfolding pattern and lipid binding ability that were similar to that of apoAI. As expected, DMPC/apoAI-NT/ apoE-CT discoidal particles did not elicit LDLr binding ability, and promoted SR-B1 mediated cellular uptake of lipids to a limited extent. However, apoAI-NT/apoE-CT displayed an enhanced ability to promote cholesterol efflux compared to apoAI, indicative of a significant role for apoE CT domain in mediating this function. Together, these results indicate that the functional attributes of apoAI and apoE3 can be conferred on each other and that NT-CT domain interactions significantly modulate their structure and function.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Automated Data Processing as an AI Planning Problem

NASA s vision for Earth Science is to build a "sensor web"; an adaptive array of heterogeneous satellites and other sensors that will track important events, such as storms, and provide real-time information about the state of the Earth to a wide variety of customers. Achieving his vision will require automation not only in the scheduling of the observations but also in the processing af tee resulting data. Ta address this need, we have developed a planner-based agent to automatically generate and execute data-flow programs to produce the requested data products. Data processing domains are substantially different from other planning domains that have been explored, and this has led us to substantially different choices in terms of representation and algorithms. We discuss some of these differences and discuss the approach we have adopted.

Golden, Keith↗
NASA NTRS1988

Airborne imaging spectrometer-2 - Radiometric spectral characteristics and comparison of ways to compensate for the atmosphere

The reduction of AIS data to spectral radiance utilizing the detector responsivity equations developed by a laboratory calibration of the instrument with a BaSO4-coated integrating sphere is described. Estimates of the signal-to-noise ratio for laboratory and flight conditions are obtained. In addition, the effective in-flight instrumental spectral sampling interval is estimated by using atmospheric CO2 absorption lines generated from LOWTRAN simulations.

Conel, James E.↗
DOE OSTI2014

Photon assisted tunneling through three quantum dots with spin-orbit-coupling

The effect of an ac electric field on quantum transport properties in a system of three quantum dots, two of which are connected in parallel, while the third is coupled to one of the other two, is investigated theoretically. Based on the Keldysh nonequilibrium Green's function method, the spin-dependent current, occupation number, and spin accumulation can be obtained in our model. An external magnetic flux, Rashba spin-orbit-coupling (SOC), and intradot Coulomb interactions are considered. The magnitude of the spin-dependent average current and the positions of the photon assisted tunneling (PAT) peaks can be accurately controlled and manipulated by simply varying the strength of the coupling and the frequency of the ac field. A particularly interesting result is the observation of a new kind of PAT peak and a multiple-PAT effect that can be generated and controlled by the coupling between the quantum dots. In addition, the spin occupation number and spin accumulation can be well controlled by the Rashba SOC and the magnetic flux.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Evaluation of AI-Enabled Digital Documented Safety Analysis: A Case Study

Safety basis documentation development and review under U.S. Department of Energy (DOE) authorization have emerged as critical constraint throttling deployment of advanced nuclear reactors, with traditional processes demanding extraordinary resource investment that delays the delivery of these technologies. Traditional Documented Safety Analysis (DSA) processes rely on static documents with limited traceability [U.S. DOE]. The regulatory review and engagement processes are similarly constrained, often requiring significant effort and extensive manual verification. The scale of this challenge is exemplified by the U.S. Nuclear Regulatory Commission (NRC) review of the NuScale application, which required over 250,000 staff hours and the evaluation of approximately two million pages of documentation [Bergman 2021]. The volume and complexity of information within nuclear licensing applications or authorization reviews demands innovative approaches to document generation and data management.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

End-User Assessment of the NASA SPoRT Lightning AI Product

The NASA Short-term Prediction Research and Transition (SPoRT) Center has begun to develop products to address operational challenges and enhance safety during the lifecycle of lightning activity, and conduct evaluations in concentrated R2O/O2R efforts. The product evaluated for this study is Lightning Artificial Intelligence (A.I.), which predicts the probability of lightning out to 15 minutes in advance and spatially maps it using filled color contours. Lightning A.I. uses reflectivity, differential reflectivity, and correlation coefficient data from a subset of radars within the NEXRAD network to generate lightning probabilities at approximately 2 km resolution. The domains, spanning 145 x 145 km are centered over NASA-affiliated centers across the CONUS and use radar data which are closest in proximity. Lightning A.I. was developed with intended use by emergency managers at NASA centers. However, National Weather Service (NWS) offices are also often tasked with monitoring and forecasting the threat for lightning within their County Warning Areas for various impact-based decision support services. These forecasts are typically provided for aviation operations and large-scale, outdoor events, which may have varying safety requirements for decision-making based on lightning proximity and recency. The NASA SPoRT center conducted an assessment of the operational uses of Lightning A.I by various collaborative NWS Offices from late July into early September. This assessment included feedback from participants on both the product itself as well as its accessibility within the new, interactive NASA SPoRT Lightning Viewer. This presentation will include background information about Lightning A.I. and highlight results from this assessment. The feedback from the assessment will be used to inform research on any necessary modifications to this and future lightning products to assist end users.

Kelley Murphy↗
DOE OSTI2022

Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

Recent progress in the application of machine learning (ML)/artificial intelligence (AI) algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, motional Stark effect, and kinetic reconstruction data has been generated for developments of EFIT model-order-reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. Furthermore, a neural-network MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian process Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the Magnetohydrodynamic Resistive Spectrum - Feedback (MARS-F) code for developments of 3D-MOR surrogate models.

Gaussian process↗
NASA NTRS1987

Geometric reasoning

Cognitive robot systems are ones in which sensing and representation occur, from which task plans and tactics are determined. Such a robot system accomplishes a task after being told what to do, but determines for itself how to do it. Cognition is required when the work environment is uncontrolled, when contingencies are prevalent, or when task complexity is large; it is useful in any robotic mission. A number of distinguishing features can be associated with cognitive robotics, and one emphasized here is the role of artificial intelligence in knowledge representation and in planning. While space telerobotics may elude some of the problems driving cognitive robotics, it shares many of the same demands, and it can be assumed that capabilities developed for cognitive robotics can be employed advantageously for telerobotics in general. The top level problem is task planning, and it is appropriate to introduce a hierarchical view of control. Presented with certain mission objectives, the system must generate plans (typically) at the strategic, tactical, and reflexive levels. The structure by which knowledge is used to construct and update these plans endows the system with its cognitive attributes, and with the ability to deal with contingencies, changes, unknowns, and so on. Issues of representation and reasoning which are absolutely fundamental to robot manipulation, decisions based upon geometry, are discussed here, not AI task planning per se.

Woodbury, R. F.↗
NASA NTRS1994

Free Flight Rotorcraft Flight Test Vehicle Technology Development

A rotary wing, unmanned air vehicle (UAV) is being developed as a research tool at the NASA Langley Research Center by the U.S. Army and NASA. This development program is intended to provide the rotorcraft research community an intermediate step between rotorcraft wind tunnel testing and full scale manned flight testing. The technologies under development for this vehicle are: adaptive electronic flight control systems incorporating artificial intelligence (AI) techniques, small-light weight sophisticated sensors, advanced telepresence-telerobotics systems and rotary wing UAV operational procedures. This paper briefly describes the system's requirements and the techniques used to integrate the various technologies to meet these requirements. The paper also discusses the status of the development effort. In addition to the original aeromechanics research mission, the technology development effort has generated a great deal of interest in the UAV community for related spin-off applications, as briefly described at the end of the paper. In some cases the technologies under development in the free flight program are critical to the ability to perform some applications.

Hodges, W. Todd↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

15 GEOTHERMAL ENERGY↗
NASA NTRS2002

Contingency Planning for Planetary Rovers

There has been considerable work in AI on planning under uncertainty. But this work generally assumes an extremely simple model of action that does not consider continuous time and resources. These assumptions are not reasonable for a Mars rover, which must cope with uncertainty about the duration of tasks, the power required, the data storage necessary, along with its position and orientation. In this paper, we outline an approach to generating contingency plans when the sources of uncertainty involve continuous quantities such as time and resources. The approach involves first constructing a "seed" plan, and then incrementally adding contingent branches to this plan in order to improve utility. The challenge is to figure out the best places to insert contingency branches. This requires an estimate of how much utility could be gained by building a contingent branch at any given place in the seed plan. Computing this utility exactly is intractable, but we outline an approximation method that back propagates utility distributions through a graph structure similar to that of a plan graph.

Dearden, Richard↗
NASA NTRS2003

Incremental Contingency Planning

There has been considerable work in AI on planning under uncertainty. However, this work generally assumes an extremely simple model of action that does not consider continuous time and resources. These assumptions are not reasonable for a Mars rover, which must cope with uncertainty about the duration of tasks, the energy required, the data storage necessary, and its current position and orientation. In this paper, we outline an approach to generating contingency plans when the sources of uncertainty involve continuous quantities such as time and resources. The approach involves first constructing a "seed" plan, and then incrementally adding contingent branches to this plan in order to improve utility. The challenge is to figure out the best places to insert contingency branches. This requires an estimate of how much utility could be gained by building a contingent branch at any given place in the seed plan. Computing this utility exactly is intractable, but we outline an approximation method that back propagates utility distributions through a graph structure similar to that of a plan graph.

Dearden, Richard↗
NASA NTRSDate not supplied

Using Artificial Intelligence (AI) and Machine Learning (ML) to conduct Space Missions Solid Waste Management Survey

The National Aeronautics and Space Administration (NASA) Solid Waste Management team has been focusing on technologies that can operate in microgravity. NASA aims to conduct both short and long-term transit and planetary missions on the lunar and Mars surfaces. Therefore, an updated waste survey is needed to explore technologies for operation in microgravity for transit missions and partial gravity for planetary missions. This paper will utilize Artificial Intelligence and Machine Learning techniques to conduct the survey and generate knowledge graphs for Spacecraft Waste Management.

Artificial Intelligence↗
DOE OSTI2013

Spectroscopy diagnostic of dual-frequency capacitively coupled CHF{sub 3}/Ar plasma

A combined spectroscopic method of absorption, actinometry, and relative optical emission intensity is employed to determine the absolute CF{sub 2} density, the relative F and H densities, H atom excitation temperature and the electron density in dual-frequency (60/2 MHz) capacitively coupled CHF{sub 3}/Ar plasmas. The effects of different control parameters, such as high-frequency (HF) power, low-frequency (LF) power, gas pressure, gap length and content of CHF{sub 3}, on the concentration of radical CF{sub 2}, F, and H and excitation temperature are discussed, respectively. It is found that the concentration of CF{sub 2} is strongly dependent on the HF power, operating pressure and the proportion of CHF{sub 3} in feed gas, while it is almost independent of the LF power and the gap length. A higher concentration ratio of F to CF{sub 2} could be obtained in dual-frequency discharge case. Finally, the generation and decay mechanisms of CF{sub 2} and F were also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Terahertz generation by beating two Langmuir waves in a warm and collisional plasma

Terahertz (THz) radiation generated by beating of two Langmuir waves in a warm and collisional plasma is discussed theoretically. The critical angle between the two Langmuir waves and the critical wave-length (wave vector) of Langmuir waves for generating THz radiation are obtained analytically. Furthermore, the maximum radiation energy is obtained. We find that the critical angle, the critical wave-length, and the generated radiation energy strongly depend on plasma temperature and wave-length of the Langmuir waves. That is, the THz radiation generated by beating of two Langmuir waves in a warm and collisional plasma can be controlled by adjusting the plasma temperature and the Langmuir wave-length.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗