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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 1,549 records · Page 86

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NASA NTRS2001

Recent Troposheric Ozone Observations from Satellite and In-Situ Measurements: An Overview

The past 4-5 years have seen an unprecedented increase in tropospheric ozone (and related) observations thanks to focused campaigns, new satellite data, and to innovative approaches to climatology (commercial aircraft sampling, networks). The result has been new insights into the complex chemistry and dynamics affecting tropospheric ozone in the non-urban environment: global pollution, subtropical stratospheric folds, disturbances associated with the 1997 El-Nino. A synthesis based on selected examples of field and satellite data will be presented.

Thompson, A.↗

NASA NTRS2001

New Insights into Tropospheric Ozone from Satellites and Soundings

Thompson, A.↗

NASA NTRS1993

Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for Fast Global Optimization

A new method for unconstrained global function optimization, acronymed TRUST, is introduced.

terminal repellers subenergy tunneling function ar↗

NASA NTRS2006

NASA Tech Briefs, December 2006

Topic include: Inferring Gear Damage from Oil-Debris and Vibration Data; Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs; User Interactive Software for Analysis of Human Physiological Data; Representation of Serendipitous Scientific Data; Automatic Locking of Laser Frequency to an Absorption Peak; Self-Passivating Lithium/Solid Electrolyte/Iodine Cells; Four-Quadrant Analog Multipliers Using G4-FETs; Noise Source for Calibrating a Microwave Polarimeter; Hybrid Deployable Foam Antennas and Reflectors; Coating MCPs with AlN and GaN; Domed, 40-cm-Diameter Ion Optics for an Ion Thruster; Gesture-Controlled Interfaces for Self-Service Machines; Dynamically Alterable Arrays of Polymorphic Data Types; Identifying Trends in Deep Space Network Monitor Data; Predicting Lifetime of a Thermomechanically Loaded Component; Partial Automation of Requirements Tracing; Automated Synthesis of Architecture of Avionic Systems; SSRL Emergency Response Shore Tool; Wholly Aromatic Ether-Imides as n-Type Semiconductors; Carbon-Nanotube-Carpet Heat-Transfer Pads; Pulse-Flow Microencapsulation System; Automated Low-Gravitation Facility Would Make Optical Fibers; Alignment Cube with One Diffractive Face; Graphite Composite Booms with Integral Hinges; Tool for Sampling Permafrost on a Remote Planet; and Special Semaphore Scheme for UHF Spacecraft Communications.

Source record↗

NASA NTRS2016

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

passive microwave↗

NASA NTRS2013

Environmentally Friendly Rotorcraft Concepts: 2011-2014: US/France MoA Helicopter Aeromechanics - Task 5

Motivation: Greenhouse gas concentrations have increased well beyond their pre-industrial levels due to human activities; Aircraft emissions are becoming regulated in industrialized nations, so future rotorcraft will need to be designed for minimal environmental impact. Objectives: Determine suitable metrics to measure environmental impact of rotorcraft, focusing first on air pollution; Integrate performance metrics with rotorcraft design codes; Generate rotorcraft designs with minimal environmental impact. Outcomes: Obtain an understanding of how targeting reduced emissions affects the rotorcraft design process; Advance the capability of existing rotorcraft design tools to include environmental performance metrics. Tools: Evaluation and predesign - NDARC (NASA Design and Analysis of Rotorcraft) and CREATION (Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network); Flight simulation - CAMRAD II (Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics) and HOST (Helicopter Overall Simulation Tool).

Environmentally Friendly↗

NASA NTRS2017

Juno Gravity Science: Preparing for Data Collection at Jupiter

One of the primary goals of the Juno mission is to investigate Jupiter’s interior by mapping its gravitational field with the gravity science instrument. The Juno spacecraft has two radio science components that comprise the gravity science instrument: the X-band telecommunications system for a X-up/X-down link and a Ka-band Translator for a Ka-up/Ka-down link. The Deep Space Network’s DSS-25 beam waveguide antenna at the Goldstone Deep Space Communications Complex in California provides the X- and Ka-band uplink alongside an Advanced Water Vapor Radiometer to calibrate tropospheric effects. X-band and Ka-band downlink data are collected with both open-loop and closed-loop receivers located at the complex. Utilization of Ka-band provides scientific benefit to the Doppler measurements, but also adds operational challenges. Pointing of the uplink and downlink Ka-band signals requires additional systems to be calibrated and operated by the Deep Space Network; and the higher frequency of Ka-band means the signal dynamics are increased by a factor of four over X-band signals. Due to the spacecraft’s elliptical orbit and 4000 kilometer perijove altitude, it accelerates at an extreme rate as it approaches Jupiter, inducing large dynamic ranges in Doppler range of approximately 6 MHz over 3 hours at Ka-band. After the installation of a new Ka-band transmitter at DSS-25 for Juno was completed in 2015, end-to-end testing was conducted to ensure readiness for operations at Jupiter and provide an initial assessment of the performance. Cruise testing was conducted in the same operational configuration that the system will be used in during perijove passes. Processed open-loop data yielded uncalibrated Doppler residuals of 1.9 mHz at X-band and 6.0 mHz at Ka-band with 5-second compression time. Conduction of these tests has prepared the instrument and the operations team for science data collection during the science phase of the mission.

Buccino, Dustin↗

NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from Smart Spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs and mathematical formalisms to demonstrate operation. The proposed framework will be evaluated on a regional AAM flight scenario and will focus on two initial applications: (1) GPS-free navigation supporting precision approach and landing (PAL), and (2) surveillance and conformance monitoring of aircraft in vertiport airspaces. Such approaches show promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Structurally-Adaptive↗

NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Distributed sensing↗

DOE OSTI2021

Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems

We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders (VAEs) for learning nonlinear state space models from observations. We develop ways to incorporate geometric and topological priors through general manifold latent space representations. We investigate the performance of our methods for learning low dimensional representations for the nonlinear Burgers equation and constrained mechanical systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Direct Study of Changes in Catalyst Structure-Kinetic Properties During Redox Transitions

The Temporal Analysis of Products (TAP) pulse response methodology is a transient technique that provides the time resolution needed to deconvolve reaction steps from the complex networks typical in industrial catalytic processes. Traditionally, TAP measurements observe gas phase dynamics at the reactor exit but lack direct measurements of changes in the catalyst itself. Recently, a new operando technique was developed that couples gas phase transients to dynamic changes in metal centers with the precise TAP methodology for nanomole titration. Using an industrial CrOx/Al2O3 catalyst used for propane dehydrogenation, we demonstrate the capabilities of this unique device to reveal key catalytic processes: 1) total propane oxidation not associated with chromia centers, 2) reduction of Cr6+ to Cr3+ correlated with selective product formation, and 3) subsequent carbon accumulation. By utilizing incremental pulsing in a diffusion-only transport regime, the spectrokinetic device allows us to resolve detailed changes in catalyst structure, composition, and kinetic function that are otherwise indistinguishable in conventional operando devices. The unification of the TAP methodology with time-resolved spectroscopic measurements offers new and unique insights into the complex kinetic phenomena regulated by solid catalyst surfaces.

03 - NATURAL GAS↗

DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1993

Venus Interior Structure Mission (VISM): Establishing a Seismic Network on Venus

Magellan radar data show the surface of Venus to contain a wide range of geologic features (large volcanoes, extensive rift valleys, etc.). Although networks of interconnecting zones of deformation are identified, a system of spreading ridges and subduction zones like those that dominate the tectonic style of the Earth do not appear to be present. In addition, the absence of a mantle low-viscosity zone suggests a strong link between mantle dynamics and the surface. As a natural follow-on to the Magellan mission, establishing a network of seismometers on Venus will provide detailed quantitative information on the large scale interior structure of the planet. When analyzed in conjunction with image, gravity, and topography information, these data will aid in constraining mechanisms that drive surface deformation.

Stofan, E. R.↗

DOE OSTI2023

Dynamic conformational switching underlies TFIIH function in transcription and DNA repair and impacts genetic diseases

Transcription factor IIH (TFIIH) is a protein assembly essential for transcription initiation and nucleotide excision repair (NER). Yet, understanding of the conformational switching underpinning these diverse TFIIH functions remains fragmentary. TFIIH mechanisms critically depend on two translocase subunits, XPB and XPD. To unravel their functions and regulation, we build cryo-EM based TFIIH models in transcription- and NER-competent states. Using simulations and graph-theoretical analysis methods, we reveal TFIIH’s global motions, define TFIIH partitioning into dynamic communities and show how TFIIH reshapes itself and self-regulates depending on functional context. Our study uncovers an internal regulatory mechanism that switches XPB and XPD activities making them mutually exclusive between NER and transcription initiation. By sequentially coordinating the XPB and XPD DNA-unwinding activities, the switch ensures precise DNA incision in NER. Mapping TFIIH disease mutations onto network models reveals clustering into distinct mechanistic classes, affecting translocase functions, protein interactions and interface dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

A density-functional theory study of the Al/AlOx/Al tunnel junction

The aluminum oxide tunnel junction is a key component of the majority of superconducting quantum devices. For high-quality, reproducible, and scalably manufacturable qubits, the ability to fabricate Josephson junctions (JJs) with a targeted critical current and high uniformity is essential. In this study, we use first-principles modeling to assess fundamental aspects of the atomic structure of both amorphous and crystalline aluminum oxide tunnel junctions and relate the structure to predicted performance metrics. We use modified ab initio molecular dynamics to develop realistic models of the tunnel junction, from which interface roughness and local thickness fluctuations are analyzed in an unbiased manner by training a neural network to identify the boundary between metal and oxide. We show that the effective thickness of the insulating part of the junction can be different from the apparent physical thickness. We calculate the rate of Cooper pair tunneling for the atomically resolved electrostatic potential using direct numerical solution in 3D, which shows a channeling effect that impacts the junction critical current. The predicted critical current is a useful JJ design parameter that can be accessed from the ab initio calculations without fitting parameters. To assess the limits of uniformity and fabrication choices (e.g., oxidation vs epitaxy), we compare the amorphous junctions to crystalline models, which show order of magnitude more efficient tunneling compared to the amorphous case, underlining the connection between atomistic structure and Cooper pair tunneling efficiency. Further, this work provides a foundation for ab initio materials design and evaluation to help accelerate future development of improved tunnel junctions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2010

Implementing and Simulating Dynamic Traffic Assignment with Intelligent Transportation Systems in Cube Avenue

As urban populations and traffic congestion levels increase, effective use of information and communication tools and intelligent transportation systems as becoming increasingly important in order to maximize the efficiency of transportation networks. The appropriate placement and employment of these tools within a network is critical to their effectiveness. This presentation proposes and demonstrates the use of a commercial transportation simulation tool to simulate dynamic traffic assignment and rerouting to model route modifications as a result of traffic information.

Foytik, Peter↗

DOE OSTI2023

Microgrid Modeling for Stability Analysis

Here this document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid (MG) Dynamic Modeling, IEEE Power and Energy Society, Tech. Rep. PES-TR106, 2023. In this paper, the major issues and challenges in microgrid modeling for stability analysis are discussed, and a review of state-of-the-art modeling approaches and trends is presented. In the context of the IEEE 1547 standard, the document covers issues associated with component models for MG dynamic studies and simulations, including generator and grid modeling, full and average converter models, unbalanced and balanced system conditions, dynamic and static loads, protection requirements, and detailed and simplified controls considering communications delays, packet losses, and security issues. Considering the future integration of grids and MGs to form broad integrated networks, a discussion is presented of the use of phasor vis-à-vis electromagnetic transient simulation tools for MG dynamic stability studies, as well as modeling scale-up issues and MG equivalent models. Specifically white-, grey-, and black-box models, are presented. This TF paper and companion report constitute a modeling guide for R&D groups working on developments and standards of MGs with a focus on stability issues.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Exchangeable Liquid Crystalline Elastomers: Enabling Rapid Processing and Enhanced Actuation Stability through On-Demand Deactivation

Exchangeable liquid crystalline elastomers (xLCEs) bearing dynamic covalent bonds are promising candidates for soft actuators due to their unique capability to adjust both network structure and liquid crystalline (LC) alignment after polymerization. While current xLCEs with low exchange temperatures are convenient for processing, they suffer from issues such as creep and loss of LC alignment during repeated thermal actuation. Herein, we present an effective solution using dynamic anhydride chemistry within a thiol-ene-based xLCE. This approach enables a catalyst-free, low-temperature bond exchange of the xLCE after polymerization, allowing for the adjustment of LC orientation under mild conditions. More importantly, it enables on-demand deactivation of the bond exchange via anhydride hydrolysis, effectively eliminating creep and enhancing actuation stability for over 100 cycles. Furthermore, the hydrolysis process results in the formation of carboxylic acid groups, which can be converted into carboxylates via alkali treatment, thereby providing the xLCE with humidity responsiveness. Finally, these findings highlight the use of dynamic anhydride bonds in the fabrication and optimization of xLCEs with enhanced durability and functionality, which is expected to facilitate significant advancements in their applications in soft actuators and robotics.

36 MATERIALS SCIENCE↗