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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Gait Training Improves Performance in Healthy Adults Exposed to Novel Discordant Conditions

After they return to Earth, astronauts experience sensorimotor disturbances that disrupt their ability to walk. We have previously shown that training with a variety of sensorimotor adaptive challenges enhances the capability of adapting to novel sensorimotor conditions. We are currently developing a sensorimotor adaptability (SA) training program designed to facilitate recovery of function after gravitational transitions. The purpose of this study was to determine whether trained subjects could transfer learned skills from one discordant visuo-proprioceptive environment to another. During three sessions, subjects walked at 2.5 km/h on a treadmill mounted on a motion base platform. Ten subjects trained with a combination of lateral treadmill translation and superimposed sinusoidal lateral optic flow that was presented on a large screen positioned in front of them. Ten controls completed the same training schedule while viewing only the forward optic flow with no visual or physical oscillation. Twenty minutes after the final training session, all subjects completed a 2-minute trial with a novel combination of visual and treadmill roll perturbations not previously experienced during the training (Transfer Test). Compared to the untrained group, participants who received SA training showed faster reaction times and, based on a composite score derived from stride frequency, heart rate, and reaction time, an overall enhanced performance. Our results showed that an SA training program can improve overall walking performance when subjects are exposed to novel incongruent sensory environments. This training has application for both enhancing adaptive responses in astronauts and reducing fall and injury risk in the elderly.

Batson, Crystal D.↗
DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗
NASA NTRS1976

Correlation between multispectral photography and near-surface turbidities

Four-band multispectral photography obtained from an aerial platform at an altitude of about 10,000 feet has been utilized to measure near-surface turbidity at numerous sampling sites in the Ross Barnett Reservoir, Mississippi. Correlation of the photographs with turbidity measurements has been accomplished via an empirical mathematical model which depends upon visual color recognition when the composited photographs are examined on either an I squared S model 600 or a Spectral Data model 65 color-additive viewer. The mathematical model was developed utilizing least-squares, iterative, and standard statistical methods and includes a time-dependent term related to sun angle. This model is consistent with information obtained from two overflights of the target area - July 30, 1973 and October 30, 1973 - and now is being evaluated with regard to information obtained from a third overflight on November 8, 1974.

Wertz, D. L.↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2026

GridCoPilot for Thermal Events: An LLM-Based Platform for Power Grid Reliability Analysis

Large Language Models show promise for translating natural language into database queries, but deploying such systems in safety-critical domains requires high reliability. We present an application of GridCoPilot to thermal event analysis (heatwaves and coldwaves) that affect power grid reliability. Our approach uses a LangChain SQL Agent to translate natural language queries into auditable SQL statements, with deterministic visualization routines that parse the structured query results. We introduce structural framing as a design principle, we integrate a NERC-region-level event library with county-level meteorology and decompose the combined data into three relational tables (event metadata, county-level event details, and a county-to-NERC subregion mapping), using prompt-guided joins to direct the model toward correct multi-table queries. For two core analytical patterns (identifying worst events by region and by region-year), the system achieved 100% SQL accuracy across all 16 NERC subregions and both event types (64 queries total). These results validate the approach for target use cases, though performance on diverse natural language formulations requires further investigation. We discuss design trade-offs, failure modes including JSON output truncation, and pathways for extending this approach to other hazard domains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1996

Macular Preprocessing of Linear Acceleratory Stimuli: Implications for the Clinic

Three-dimensional reconstructions of innervation patterns in rat maculae were carried out using serial section images sent to a Silicon Graphics workstation from a transmission electron microscope. Contours were extracted from mosaicked sections, then registered and visualized using Biocomputation Center software. Purposes were to determine innervation patterns of type II cells and areas encompassed by vestibular afferent receptive fields. Terminals on type II cells typically are elongated and compartmentalized into parts varying in vesicular content; reciprocal and serial synapses are common. The terminals originate as processes of nearby calyces or from nerve fibers passing to calyces outside the immediate vicinity. Thus, receptive fields of the afferents overlap in unique ways. Multiple processes are frequent; from 4 to 6 afferents supply 12-16 terminals on a type II cell. Processes commonly communicate with two type II cells. The morphology indicates that extensive preprocessing of linear acceleratory stimuli occurs peripherally, as is true also of visual and olfactory systems. Clinically, this means that loss of individual nerve fibers may not be noticed behaviorally, due to redundancy (receptive field overlap). However, peripheral processing implies the presence of neuroactive agents whose loss can acutely or chronically alter normal peripheral function and cause balance disorders. (Platform presentation preferred - Theme 11)

Ross, M. D.↗
DOE OSTI2021

Carbohydrate-Containing Conjugated Polymers: Solvent-Resistant Materials for Greener Organic Electronics

Organic semiconducting polymers are exciting materials for electronic applications because of their good mechanical and optoelectronic properties. A major advantage of organic semiconductors is their solution processability. This allows access to a variety of simple and cost-effective device fabrication methods compared to the expensive, high-temperature processing methods required for silicon-based electronics. However, these materials often have low solubility, which limits their processing to toxic halogenated solvents. Also, their limited solubility often leads to interfacial mixing during device fabrication. Herein, this work explores the incorporation of environmentally friendly carbohydrate side chains in conjugated polymers to enhance processability in eco-friendly solvents. Moreover, a mild postprocessing treatment was designed to enable solvent resistance. Isoindigo-based polymers with varied ratios of acetyl-protected galactose side chains were synthesized to improve solubility in o-anisole in the protected state, while inducing solvent resistance through intramolecular hydrogen bonding in the deprotected state. Solvent resistance was confirmed both visually upon submersion in various solvents and using UV–visible spectroscopy. Importantly, the mild basic treatment to achieve solvent resistance has no negative impact on the electronic performance of these materials in organic field-effect transistors, even after subsequent submersion in various solvents, making them a valuable platform for the production of green processable multilayer electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Direct Multimodal Nanoscale Visualization of Early Phosphorus-Based Antiwear Tribofilm Formation

Understanding the mechanism of antiwear (AW) tribofilm formation and how to tune surface chemistry to control functionality is essential for the development of the next generation of oil lubricants. In particular, understanding and optimizing early AW tribofilm formation can increase the energy efficiency of mechanical systems. However, the mechanism for how these films form is not well understood. The majority of prior work has focused on analyzing only end-of-test surfaces long after the film has formed. In this work, we develop an in situ multimodal chemical imaging methodology to directly visualize the early formation of AW films on steel surfaces. We investigate an oil formulation containing a phosphorus-based additive commonly used to protect surfaces from wear and fatigue processes in machine elements, such as gears, bearings, and sliding contacts. Using nanoscale multimodal chemical imaging on combined platforms of atomic force microscopy (AFM) coupled directly with in situ nano-infrared (nano-IR) spectroscopy, and further combined ex situ with time-of-flight secondary ion mass spectrometry (ToF-SIMS), we demonstrate a direct correlation between changes in friction and local surface chemistry. In these experiments, the AFM probe acts as a single asperity contact to generate the tribofilm as well as a tool to analyze it in situ as it is forming. To verify our in situ measurements, we compare these results to the ex situ ToF-SIMS of macroscale block-on-ring tribometer-formed samples. We report the understanding gained here on how AW films form and how film properties can be modified by tuning the chemistry of the additives will facilitate developing transmission fluids to meet increasing demands for vehicle performance and efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2004

Developing parallel GeoFEST(P) using the PYRAMID AMR library

The PYRAMID parallel unstructured adaptive mesh refinement (AMR) library has been coupled with the GeoFEST geophysical finite element simulation tool to support parallel active tectonics simulations. Specifically, we have demonstrated modeling of coseismic and postseismic surface displacement due to a simulated Earthquake for the Landers system of interacting faults in Southern California. The new software demonstrated a 25-times resolution improvement and a 4-times reduction in time to solution over the sequential baseline milestone case. Simulations on workstations using a few tens of thousands of stress displacement finite elements can now be expanded to multiple millions of elements with greater than 98% scaled efficiency on various parallel platforms over many hundreds of processors. Our most recent work has demonstrated that we can dynamically adapt the computational grid as stress grows on a fault. In this paper, we will describe the major issues and challenges associated with coupling these two programs to create GeoFEST(P). Performance and visualization results will also be described.

adaptive mesh refinement (AMR)↗
NASA NTRS2011

Development of a Rover Simulation to Assess Operational Proficiency Following Long Duration Spaceflights

Following long-duration space transits, adaptive changes in sensorimotor and cognitive function may impair the crew s ability to safely control pressurized rovers designed to explore the new environment. We describe a rover simulation developed to quantify post-flight decrements in operational proficiency following International Space Station expeditions. The rover simulation consists of a serial presentation of discrete tasks to be completed as quickly and accurately as possible. Each task consists of 1) perspective taking using a map that defines a docking target, 2) navigation toward the target around a Martian outpost, and 3) docking a side hatch of the rover to a visually guided target. The simulator utilizes a Stewart-type motion base (CKAS, Australia), single seat cabin with triple scene projection covering approximately 150 horizontal by 40 vertical, and joystick controller. The software was implemented using Unity3 with next-gen PhysX engine to tightly synchronize simulation and motion platform commands. Separate C# applications allow investigators to customize session sequences with different lighting and gravitational conditions, and then execute tasks to be performed as well as record performance data. Preliminary tests resulted in low incidence of motion sickness (<15% unable to complete first session), with only negligible after effects and symptoms after familiarization sessions. Functionally relevant testing early post-flight will develop evidence regarding the limitations to early surface operations and what countermeasures are needed. This approach can be easily adapted to other vehicle designs to provide a platform to safely assess how sensorimotor and cognitive function impact manual control performance.

DeDios, Y. E.↗
DOE OSTI2026

ACDC (Automated Campbell Diagram Code) [SWR-26-042]

This application provides a web-based graphical user interface to generating Campbell Diagrams and visualizing mode shapes for OpenFAST turbine models. Determining the aeroelastic stability and dynamic characteristics of wind turbines is a critical step in turbine design and analysis. Historically, extracting natural frequencies and mode shapes from OpenFAST—the industry-standard whole-turbine simulation code—has been a fragmented and tedious process. It required manual model configuration, command-line linearization execution, and complex post-processing via proprietary scripts to handle rotating-frame dynamics. To address these workflow bottlenecks, we present the Automated Campbell Diagram Code (ACDC), an open-source graphical software tool developed by the National Laboratory of the Rockies (NLR) under the DOE-funded Distributed Wind Aeroelastic Modeling (dWAM) project. ACDC streamlines the end-to-end linearization and stability analysis workflow into a single, intuitive cross-platform application. The software guides users through OpenFAST model configuration, definition of operating points, and the automated execution of steady-state trim and linearization simulations. Under the hood, ACDC automates the complex mathematical post-processing steps required for rotating systems, including Multi-Blade Coordinate (MBC) transformations, eigenanalysis, and advanced modal tracking utilizing the Modal Assurance Criterion (MAC) and spectral clustering. Finally, ACDC processes these results to automatically generate Campbell diagrams and features a robust 3D visualization engine to animate full-system mode shapes. By eliminating the reliance on external post-processing environments and manual data manipulation, ACDC significantly accelerates dynamic analysis and lowers the barrier to entry for wind energy researchers and engineers.

Summerville, Brent [National Laboratory of the Roc↗
DOE OSTI2022

Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks

Abstract A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface

Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag–H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner.

Chemistry↗
DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2024

Circuit QED Pulse Control Interface

The Circuit Quantum Electrodynamics (Circuit QED) Pulse Control Interface provides a robust platform for designing and testing control signals in transmon-cavity systems. Utilizing a PyQt5-based drag-and-drop GUI, this tool facilitates the implementation of two key protocols: Selective Number-dependent Arbitrary Phase (SNAP)-Displacement and Echoed Conditional Displacement (ECD). It offers real-time pulse visualization and supports CSV exports and imports for integrating custom signals, thereby enhancing circuit QED experimental workflows. Simulations performed using QuTiP confirm the high fidelity of quantum state preparations and the effectiveness of the implemented controls. Future developments will focus on interfacing the tool with hardware and enhancing signal generation accuracy.

Lin, Yuqing↗
DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

The Mars Exploration Rover Instrument Positioning System

During Mars Exploration Rover (MER) surface operations, the scientific data gathered by the in situ instrument suite has been invaluable with respect to the discovery of a significant water history at Meridiani Planum and the hint of water processes at work in Gusev Crater. Specifically, the ability to perform precision manipulation from a mobile platform (i.e., mobile manipulation) has been a critical part of the successful operation of Spirit and Opportunity rovers. As such, this paper describes the MER Instrument Positioning System that allows the in situ instruments to operate and collect their important science data using a robust, dexterous robotic arm combined with visual target selection and autonomous software functions.

in situ instruments↗