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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 NTRS2012

NASA Ames DEVELOP Interns Collaborate with the South Bay Salt Pond Restoration Project to Monitor and Study Restoration Efforts using NASA's Satellites

In the past, natural tidal marshes in the south bay were segmented by levees and converted into ponds for use in salt production. In an effort to provide habitat for migratory birds and other native plants and animals, as well as to rebuild natural capital, the South Bay Salt Pond Restoration Project (SBSPRP) is focused on restoring a portion of the over 15,000 acres of wetlands in California's South San Francisco Bay. The process of restoration begins when a levee is breached; the bay water and sediment flow into the ponds and eventually restore natural tidal marshes. Since the spring of 2010 the NASA Ames Research Center (ARC) DEVELOP student internship program has collaborated with the South Bay Salt Pond Restoration Project (SBSPRP) to study the effects of these restoration efforts and to provide valuable information to assist in habitat management and ecological forecasting. All of the studies were based on remote sensing techniques -- NASA's area of expertise in the field of Earth Science, and used various analytical techniques such as predictive modeling, flora and fauna classification, and spectral detection, to name a few. Each study was conducted by a team of aspiring scientists as a part of the DEVELOP program at Ames.

Newcomer, Michelle E.↗
NASA NTRS2018

Particle Size Distributions in Chondritic Meteorites: Evidence for Pre-Planetesimal Histories

Magnesium-rich silicate chondrules and calcium-, aluminum-rich refractory inclusions (CAIs) are fundamental components of primitive chondritic meteorites. It has been suggested that concentration of these early-formed particles by nebular sorting processes may lead to accretion of planetesimals, the planetary bodies that represent the building blocks of the terrestrial planets. In this case, the size distributions of the particles may constrain the accretion process. Here we present new particle size distribution data for Northwest Africa 5717, a primitive ordinary chondrite (ungrouped 3.05) and the well-known carbonaceous chondrite Allende (CV3). Instead of the relatively narrow size distributions obtained in previous studies (Ebel et al., 2016; Friedrich et al., 2015; Paque and Cuzzi, 1997, and references therein), we observed broad size distributions for all particle types in both meteorites. Detailed microscopic image analysis of Allende shows differences in the size distributions of chondrule subtypes, but collectively these subpopulations comprise a composite "chondrule" size distribution that is similar to the broad size distribution found for CAIs. Also, we find accretionary 'dust' rims on only a subset (approximately 15-20 percent) of the chondrules contained in Allende, which indicates that subpopulations of chondrules experienced distinct histories prior to planetary accretion. For the rimmed subset, we find positive correlation between rim thickness and chondrule size. The remarkable similarity between the size distributions of various subgroups of particles, both with and without fine grained rims, implies a common size sorting process. Chondrite classification schemes, astrophysical disk models that predict a narrow chondrule size population and/or a common localized formation event, and conventional particle analysis methods must all be critically reevaluated. We support the idea that distinct "lithologies" in NWA 5717 are nebular aggregates of chondrules. If greater than or equal to centimeter-sized aggregates of chondrules can form it will have implications for planet formation and suggests the sticking stage is where the preferential size physics is operating.

chondritic meteorites↗
NASA NTRS2024

Automatic Detection of Large-scale Flux Ropes and Their Geoeffectiveness with a Machine-learning Approach

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential, since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary classification machine-learning models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of autodetected FRs. Using the properties of the southward magnetic field, the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% of large-scale ICMEs (duration 1day) observed at 1au by the Wind and the Solar TErrestrial RElations Observatory missions, respectively. While testing with continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during the 2008–2014 period (recall = 0.875), but also many false positives (precision = 0.56), as we do not take into account any additional solar wind properties other than the magnetic properties. We find an accuracy of 0.88 when estimating the geoeffectiveness of the autodetected FRs using our method. Thus, in space-weather nowcasting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and their geoeffectiveness.

Sanchita Pal↗
DOE OSTI2023

Hierarchical Data-Driven Protection for Microgrid with 100% Renewable Penetration: Preprint

The accurate detection and isolation of faults is critical for the reliable operation of microgrids (MGs). Traditional protection approaches are even more challenged for 100% renewable MGs because inverter-based resources (IBRs) are the only sources for fault current which are usually low and unpredictable/non-uniform. This calls for new protection scheme that can identify IBR fault responses and detect faults in MGs. Data-driven based protection can learn the pattern of IBR fault responses and make the correct decision to identify faults. Therefore, this paper presents a data-driven approach for fault localization in island MGs. The approach builds a training dataset of comprehensive fault scenarios that can be used to learn fault characteristics from processed measurements. The localization task is modeled as a binary classification problem at each relay, which simplifies the learning process. Then, a hierarchical decision mechanism is used to identify the fault location. The proposed approach is assessed using an exemplary MG with several grid-forming (GFM) and grid-following (GFL) inverters, where accurate estimation of fault location is achieved. The data-driven based protection approach developed in this paper provides a generic framework and useful guidance for power system protection engineers to achieve reliable protection for MGs with 100% renewables.

artificial intelligence↗
DOE OSTI2022

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Tibbetts, Jake↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2025

A Morphological Model to Separate Resolved–Unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream

Separating resolved and unresolved sources in large imaging surveys is a fundamental step to enable downstream science, such as searching for extragalactic transients in wide-field time-domain surveys. Here we present our method to effectively separate point sources from the resolved, extended sources in the Dark Energy Spectroscopic Instrument (DESI) Legacy Surveys (LS). We develop a supervised machine learning model based on the Gradient Boosting algorithm XGBoost. The features input to the model are purely morphological and are derived from the tabulated LS data products. We train the model using ∼2 × 10 5 LS sources in the COSMOS field with HST morphological labels and evaluate the model performance on LS sources with spectroscopic classification from the DESI Data Release 1 (∼2 × 10 7 objects) and the Sloan Digital Sky Survey Data Release 17 (∼3 × 10 6 objects), as well as on ∼2 × 10 8 Gaia stars. A significant fraction of LS sources are not observed in every LS filter, and we therefore build a “Hybrid” model as a linear combination of two XGBoost models, each containing features combining aperture flux measurements from the “blue” (gr) and “red” (iz) filters. The Hybrid model shows a reasonable balance between sensitivity and robustness, and achieves higher accuracy and flexibility compared to the LS morphological typing. With the Hybrid model, we provide classification scores for ∼3 × 10 9 LS sources, making this the largest ever machine learning catalog separating resolved and unresolved sources. The catalog has been incorporated into the real-time pipeline of the La Silla Schmidt Southern Survey (LS4), enabling the identification of extragalactic transients within the LS4 alert stream.

astrostatistics↗
DOE OSTI2021

Pushing automated morphological classifications to their limits with the Dark Energy Survey

ABSTRACT We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

Models of an In-Situ Propellant Production Plant for Mars Exploration

An in-situ propellant production system (ISPP) is designed to make rocket fuel from chemicals in the Martian atmosphere in order to reduce the amount of materials that would need to be brought from Earth to support Mars missions. We have developed a description of a hypothetical ISPP system that we would like to make available to researchers who are interested in the problem of automatically diagnosing failures in complex NASA systems. This problem description will help researchers to investigate problems of interest to NASA. We would like to make the following material publicly available: (1) a 'common sense' model of an ISPP system; (2) low- and medium-fidelity simulations of the ISPP system written in Microsoft Excel and HCC; and (3) previously published data and diagrams concerning ISPP components. We do not believe there are any export considerations on these materials for the following reasons: (1) These models are not useful for guidance and real time control of vehicles, encrpytion, or any other software purpose categorized under the Export Control Classification Numbers; and (2) The models are very high level and would not by themselves enable real-time control of a real hardware system. The models are at the level of common sense. They capture, for example, that if a heater is turned on an increase in temperature should result(see the attached excerpt). We do not believe there is any commercial value to this material, given the low commercial demand for propellant plants on mars. We have spoken to acting Code IC Division Chief Dan Clancy, and he concurs with our desire to make these materials publicly available via a technical report.

Goodrich, Charlie↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗
DOE OSTI2023

Lorentz group equivariant autoencoders

Abstract There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $$\textrm{SO}^+(3,1)$$ SO + ( 3 , 1 ) , with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Star cluster classification using deep transfer learning with PHANGS- HST

Currently available star cluster catalogues from the Hubble Space Telescope (HST) imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. Here, the time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models that accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D ≲ 20 Mpc). Here, we use HST ultraviolet (UV)–optical imaging of over 20 000 sources in 23 galaxies from the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: (i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12, 14–18, and 18–24 Mpc), and (ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (~60–80 per cent) and shows improvement by a factor of 2 in classifying asymmetric and multipeaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and spectral energy distribution (SED)-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ~ 200 000) for public release.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1998

Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and

Ranson, K. Jon↗
DOE OSTI2026

Towards Anomaly Detection at the CMS High-Level Trigger System

Traditional trigger strategies in CMS typically rely on model-dependent selections or rigid kinematic cuts, risking the omission of unexpected exotic signatures. To address this, we propose a novel anomaly detection (AD) algorithm for the High-Level Trigger (HLT), designed to serve as a complementary second layer of filtering to the Level-1 AXOL1TL AD algorithm. We employ a transformer-based foundation model trained on a diverse ensemble of Standard Model processes. By combining a joint contrastive and classification objective, and using particle kinematics as inputs, the model learns to map events to a physics-informed latent space where anomalous events are isolated from dominant backgrounds. Preliminary results show that this strategy enhances the signal-to-background ratio across a range of rare SM and BSM scenarios. Furthermore, this work constitutes foundational R&D for the potential implementation of an analogous AD algorithm in the Level-1 trigger system for Phase-2.

Cruz, Roy [U. Wisconsin, Madison (main)] (ORCID:00↗
NASA NTRS1981

Modeling the effects of various radiant transfers in mountainous terrain on sensor response

A model is developed for the study of remote sensing spectral data problems encountered in mountainous terrain, where slope orientation with respect to sources of incident radiation must be considered for accurate classification of multispectral data. The model simulates the effects of solar, sky and adjacent slope irradiance fluxes and the bidirectional reflectance properties of the target surface on sensor response for various slope orientations. The errors associated with two practical techniques used to account for topographic effects on sensor response were evaluated in light of the model, and it was found that the simple cosine correction of the direct solar source showed reflectance errors greater than 11% while the technique considering both direct solar and diffuse sky sources was 50% more accurate.

Kimes, D. S.↗
DOE OSTI2025

Deploying Adversarial Attacks in Super-Resolution Models

Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRSDate not supplied

Multi-Class Anomaly Detection in Flight Data using Semi-Supervised Explainable Deep Learning Model

Identifying precursor for safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach, in practice, leverages domain expertise to define expected tolerances in system’s behavior and alarm exceedance from such safety margins. However, this approach is incapable of identifying unknown risk and vulnerabilities. Machine learning has been long studied and deployed to identify precursors for such anomalies, with the great challenge of the need for sufficient labelled set of data to achieve a reliable and accurate performance. In this article, we develop an explainable deep semi-supervised model for anomaly detection in aviation, building upon recent advancements in the machine learning literature. The proposed model combines feature engineering and classification in the feature space, while leveraging all available data (labelled and unlabeled). Validating on two case studies of anomaly detection in take-off and landing phases of commercial aircraft, we show that our model is able to outperform state-of-the-art supervised anomaly detection model and reach significantly high accuracy and low false alarm with minimum amount of available labelled data.

Anomaly Detection↗