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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Heat flow in eastern Egypt - The thermal signature of a continental breakup

Data on the heat flow in eastern Egypt were collected to provide information related to the mode and the mechanism of the Red Sea opening. The data indicate a general increase in heat flow towards the Red Sea (75-100 mW/sq m in a zone within 40 km of the coast compared with 35-55 mW/sq m inland). Moderately high heat flow (about 70 mW/sq m) was found for the Gulf of Suez. Heat production data indicate that the coastal thermal anomaly is not primarily related to crustal radiogenic heat production. Possible causes of the anomaly (one of which could be a high mantle heat flow causing a lithospheric thinning centered beneath the Red Sea) are discussed.

Morgan, P.↗
DOE OSTI2025

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

AI↗
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

To date, 48 projects, consisting of 139 wells, are currently under review with the Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program for Class VI – wells used for geologic sequestration of carbon dioxide. The number of applications submitted is expected to increase in coming years with the increase of the 45Q tax credit available to projects that initiate construction prior to 2033. The amount of data collected to submit a Class VI permit is vast, and often disparate, coming from state, federal, and commercial entities, as well as field-specific data collected within an area of interest. When preparing for site selection and permitting, the initial aggregation of relevant public data can be time intensive. The Carbon Storage Site Mapping Inquiry tool (MapIT) was created to support and accelerate the discovery and accessibility of open-source data and information available across the USA. Data was aggregated and organized based on data types described within the EPA UIC Class VI permit documentation. The online tool enables users to explore hundreds of geospatial data layers and connect to additional external resources, leveraging API and REST services where possible to ensure updates to data in real time. MapIT enables users to explore state and federal data related to geologic, geophysical, structural, hydrologic, and contextual information. In addition to displaying spatial data and linking to external resources, MapIT leverages custom widgets to ensure that internal data and external data are discoverable and accessible. The widgets connect users to resources such as the USGS publications and the USGS Earthquake Catalog based on a user-defined location. This talk will describe data aggregation workflows, data types, data preparation, and tool development for MapIT. The Carbon Storage Site Mapping Inquiry Tool and underlying database are valuable, intuitive resources that empower government, academic, commercial and industry stakeholders to explore, analyze, and acquire carbon storage related data.

Morkner, Paige↗
NASA NTRSDate not supplied

Open Science for Life in Space: Bioimaging, Data Sharing, and Tools for Knowledge Discovery

Precious space-flown biological experiments have both multi-omic and phenotypic data which NASA strives to make maximally open access for reuse. Currently a number of these space-relevant bioimaging datasets are being reused for AI/ML approaches. NASA Ames Life Science Data Archive and NASA GeneLab are working to make all current and future bioimaging data even more accessible and reusable. Standards for collection and curation are being implemented to enable scientists worldwide access to these data for further discovery and use.

data science↗
NASA NTRS2016

Using 3-D Numerical Weather Data in Piloted Simulations

This report describes the process of acquiring and using 3-D numerical model weather data sets in NASA Langley's Research Flight Deck (RFD). A set of software tools implement the process and can be used for other purposes as well. Given time and location information of a weather phenomenon of interest, the user can download associated numerical weather model data. These data are created by the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR) model, and are then processed using a set of Mathworks' Matlab(TradeMark) scripts to create the usable 3-D weather data sets. Each data set includes radar re ectivity, water vapor, component winds, temperature, supercooled liquid water, turbulence, pressure, altitude, land elevation, relative humidity, and water phases. An open-source data processing program, wgrib2, is available from NOAA online, and is used along with Matlab scripts. These scripts are described with sucient detail to make future modi cations. These software tools have been used to generate 3-D weather data for various RFD experiments.

Daniels, Taumi S.↗
NASA NTRS2002

The NASA Scientific and Technical Information (STI) Program's Implementation of Open Archives Initiation (OAI) for Data Interoperability and Data Exchange

Interoperability and data-exchange are critical for the survival of government information management programs. E-government initiatives are transforming the way the government interacts with the public. More information is to be made available through web-enabled technologies. Programs such as the NASA's Scientific and Technical Information (STI) Program Office are tasked to find more effective ways to disseminate information to the public. The NASA STI Program is an agency-wide program charged with gathering, organizing, storing, and disseminating NASA-produced information for research and public use. The program is investigating the use of a new protocol called the Open Archives Initiative (OAI) as a means to improve data interoperability and data collection. OAI promotes the use of the OAI harvesting protocol as a simple way for data sharing among repositories. In two separate initiatives, the STI Program is implementing OAI In collaboration with the Air Force, Department of Energy, and Old Dominion University, the NASA STI Program has funded research on implementing the OAI to exchange data between the three organizations. The second initiative is the deployment of OAI for the NASA technical report server (TRS) environment. The NASA TRS environment is comprised of distributed technical report servers with a centralized search interface. This paper focuses on the implementation of OAI to promote interoperability among diverse data repositories.

Rocker, JoAnne↗
NASA NTRS2000

Microwave Sensing of Ocean Winds Including the Use of SAR Imagery

This paper presents aircraft measurements of near-surface atmospheric boundary layer wind roll signatures and radar-derived sea surface roughness. These data are completely coincident in space and time and this unique feature supports attempts to definitively link SAR backscatter signatures to boundary layer roll impacts. The open-ocean data were collected at an altitude of 20 m from NOAA's Long-EZ aircraft using its turbulence probe and down-looking Ka-band radar scatterometer. Several flight legs of 20-30 km were flown with a heading across the wind direction, which is also roughly perpendicular to the roll vortices. We find remarkable correlation between measured modulations in the along-wind component of wind speed and radar backscatter for the spatial scale of 1 to 1.5 km. Close agreement between normalized modulation amplitudes suggests the radar-inferred surface slope variance is changing linearly with wind speed. These data were collected within 30 minutes of a RADARSAT SAR overpass where apparent boundary layer impacts of the same orientation and spatial dimension are prevalent in the SAR backscatter image. Quantitative comparison between modulations in the aircraft and satellite radar data will be discussed.

Vandemark, Douglas↗
NASA NTRS2000

Measured Correlation Between Roll-Vortex Signatures and Radar-Inferred Sea Surface Roughness

This paper presents aircraft measurements of near-surface atmospheric boundary layer roll signatures and radar-derived sea surface roughness. These data are completely coincident in space and time and this unique feature supports attempts to definitively link SAR backscatter signatures to boundary layer roll impacts. The open-ocean data were collected at an altitude of 20 in from NOAA's Long-EZ aircraft using its turbulence probe and down-looking Ka-band radar scatterometer, Several flight legs of 20-30 km were flown with a heading across the wind direction, which is also roughly perpendicular to the roll vortices. We find remarkable correlation between measured modulations in the along-wind component of wind speed and radar backscatter for the spatial scale of I to 1.5 kin. Close agreement between normalized modulation amplitudes suggests the radar-inferred surface slope variance is changing linearly with wind speed. These data were collected within 30 minutes of a RADARSAT SAR overpass where apparent boundary layer impacts of the same orientation and spatial dimension are prevalent in the SAR backscatter image. Quantitative comparison between modulations in the aircraft and satellite radar data will be discussed.

Vandemark, Douglas↗
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 part due to a culture of open data sharing and reuse. AI/ML methodology is 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 key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. 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 Casaletto↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗
NASA NTRSDate not supplied

Data-Driven Art

In Fall 2023, Katie Baldwin (UAH) and Helen Parache (NASA) will follow up on their pilot activity from the spring that focused on collaboration between the arts and sciences at the UAH Art Department. Ms. Parache will present on open access data and artists that incorporate scientific data in their work, e.g. Tali Weinberg and Sarah Bryant (University of Alabama). Ms. Baldwin will demonstrate printmaking and mark making techniques. The students in Ms. Baldwin’s Book Arts class will participate in a series of generative activities and engage with data to develop content. The focus on the Art Department stems from the importance of Art as a cultural pillar. Tapping into the communication and social relevance of art could be an avenue to pursue Environmental Justice goals of interest to NASA. A creative perspective on data can bring about creative questions and solutions. The workshop incorporates changes based on feedback from the spring workshop.

data science↗
DOE OSTI2025

Transfer learning of neural surrogates on multifidelity groundwater simulations

Multifidelity data used in the paper published in Advances in Water Resources 206 (2025) 105140, https://doi.org/10.1016/j.advwatres.2025.105140 The code used to process the data is openly available on GitHub at https://github.com/Model-Reduction-and-UQ-Group/Transfer_Learning_K_reconstruction Computationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate’s robustness when interpreting low-quality inputs—such as interpolated maps or data affected by noise—which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget.

Chiofalo, Alessia [University of Bologna] (ORCID:0↗
NASA NTRS1995

Standard methods for open hole tension testing of textile composites

Sizing effects have been investigated by comparing the open hole failure strengths of each of the four different braided architectures as a function of specimen thickness, hole diameter, and the ratio of specimen width to hole diameter. The data used to make these comparisons was primarily generated by Boeing. Direct comparisons of Boeing's results were made with experiments conducted at West Virginia University whenever possible. Indirect comparisons were made with test results for other 2-D braids and 3-D weaves tested by Boeing and Lockheed. In general, failure strength was found to decrease with increasing plate thickness, increase with decreasing hole size, and decreasing with decreasing width to diameter ratio. The interpretation of the sensitive to each of these geometrical parameters was complicated by scatter in the test data. For open hole tension testing of textile composites, the use of standard testing practices employed by industry, such as ASTM D5766 - Standard Test Method for Open Hole Tensile Strength of Polymer Matrix Composite Laminates should provide adequate results for material comparisons studies.

Portanova, M. A.↗
NASA NTRS2016

Preliminary Results from the AFRL-NASA W/V-Band Terrestrial Link Experiment in Albuquerque, NM

Atmospheric propagation models and the measurements that train them are critical to the design of efficient and effective space-ground links. As communication systems advance to higher frequencies in search of higher data rates and open spectrum, a lack of data at these frequencies necessitates new measurements to properly develop, validate, and refine the models used for link budgeting and system design. In collaboration with the Air Force Research Laboratory (AFRL), NASA Glenn Research Center has deployed the WV-band Terrestrial Link Experiment (WTLE) in Albuquerque, NM to conduct a measurement campaign at 72 and 84 GHz, among the first atmospheric propagation measurements at these frequencies. WTLE has been operational since October 1, 2015, and the system design shall be herein discussed alongside preliminary results and performance.

propagation↗
DOE OSTI2025

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data Sharing as a Catalyst for Expanding the Energy Frontier

As the energy landscape evolves to include technologies such as geothermal energy, comprehensive data become essential for driving innovation and scalability, particularly with the growing use of tools like machine learning and artificial intelligence. In emerging sectors, the cost of gathering high-quality data across large spatial areas can present a significant barrier. A key solution is leveraging existing data from well-established industries like oil and gas. However, the proprietary nature of data in these industries often hinders collaboration. This paper explores how cultivating a culture of data sharing can act as a catalyst for progress, fueling breakthroughs across both conventional and renewable energy sectors. Practical compromises that protect business interests while enabling data access are proposed, and real-world success stories are highlighted, demonstrating how collaboration has accelerated advancements in geothermal, carbon capture, and other innovative technologies.

15 GEOTHERMAL ENERGY↗
NASA NTRS1951

Wind-Tunnel Investigation of Air Inlet and Outlet Openings on a Streamline Body

In connection with the general problem of providing air flow to an aircraft power plant located within a fuselage, an investigation was conducted in the Langley 8-foot high-speed tunnel to determine the effect on external drag and pressure distribution of air inlet openings located at the nose of a streamline body. Air outlet openings located at the tail and at the 21-percent and 63-percent stations of the body were also investigated. Boundary layer transition measurements were made and correlated with the force and the pressure data. Individual openings were investigated with the aid of a blower and then practicable combinations of inlet and outlet openings were tested. Various modifications to the internal duct shape near the inlet opening and the aerodynamic effects of a simulated gun in the duct were also studied. The results of the tests suggested that outlet openings should be designed so that the static pressure of the internal flow at the outlet would be the same as the static pressure of the external flow in the vicinity of the opening.

Becker, John V↗