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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

A Search for Metal Lines in the Spectra of DA White Dwarfs

A theoretical analysis was carried out in order to interpret the ultraviolet spectra of DB white dwarfs obtained earlier with the International Ultraviolet Explorer (IUE) satellite. Here the results of the IUE ultraviolet spectroscopy combined with visual data and model atmospheres of DB white dwarfs are reported. In particular, a search for spectra lines due to the element carbon using the ultraviolet was made. In no case is there a positive detection of carbon and from these data, and upper limits for carbon by number relative to helium are derived in the range of C: He 10 to the minus 5 power to 10 to the minus 7 power for the 16 DB stars with ultraviolet spectra in the temperature range 11400 K T sub EFF less than 2300 K. The low carbon abundances found in the atmospheres of the DB stars agree well with the hypothesis that the atmospheric carbon observed in the cooler DQ members of the helium-rich white dwarf sequence is produced by a convective dredging mechanism.

Wegner, G. A.↗
DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗
DOE OSTI2025

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗
NASA NTRS2005

Near-Infrared (0.67-4.7 microns) Optical Constants Estimated for Montmorillonite

Various models of the reflectance from particulate surfaces are used for interpretation of remote sensing data of solar system objects. These models rely upon the real (n) and imaginary (k) refractive indices of the materials. Such values are limited for commonly encountered silicates at visual and near-infrared wavelengths (lambda, 0.4-5 microns). Availability of optical constants for candidate materials allows more thorough modeling of the observations obtained by Earth-based telescopes and spacecraft. Two approaches for determining the absorption coefficient (alpha=2pik/lambda) from reflectance measurements of particulates have been described; one relies upon Kubelka-Munk theory and the other Hapke theory. Both have been applied to estimate alpha and k for various materials. Neither enables determination of the wavelength dependence of n, n=f(lambda). Thus, a mechanism providing this ability is desirable. Using Hapke-theory to estimate k from reflectance measurements requires two additional quantities be known or assumed: 1) n=f(lambda) and 2) d, the sample particle diameter. Typically n is assumed constant (c) or modestly varying with lambda; referred to here as n(sub 0). Assuming n(sub 0), at each lambda an estimate of k is used to calculate the reflectance and is iteratively adjusted until the difference between the model and measured reflectance is minimized. The estimated k's (k(sub 1)) are the final results, and this concludes the typical analysis.

Roush, T. L.↗
NASA NTRS2005

Near-infrared (0.67-4.7 micron) Optical Constants Estimated for Montmorillonite

Various models of the reflectance from particulate surfaces are used for interpretation of remote sensing data of solar system objects. These models rely upon the real (n) and imaginary (k) refractive indices of the materials. Such values are limited for commonly encountered silicates at visual and near-infrared wavelengths (lambda, 0.4-5 micron). Availability of optical constants for candidate materials allows more thorough modeling of the observations obtained by Earth-based telescopes and spacecraft. Two approaches for determining the absorption coefficient (alpha=2 pi k/lambda) from reflectance measurements of particulates have been described; one relies upon Kubelka-Munk theory and the other Hapke theory. Both have been applied to estimate alpha and k for various materials. Neither enables determination of the wavelength dependence of n, n=f(lambda). Thus, a mechanism providing this ability is desirable. Using Hapke-theory to estimate k from reflectance measurements requires two additional quantities be known or assumed: 1) n=f(lambda) and 2) d, the sample particle diameter. Typically n is assumed constant (c) or modestly varying with lambda; referred to here as n(sub 0). Assuming n(sub 0), at each lambda an estimate of k is used to calculate the reflectance and is iteratively adjusted until the difference between the model and measured reflectance is minimized. The estimated k's (k(sub l)) are the final results, and this concludes the typical analysis. Additional information is included in the original extended abstract.

Roush, T. L.↗
NASA NTRS1998

User Directed Tools for Exploiting Expert Knowledge in an Immersive Segmentation and Visualization Environment

Volumetric data sets have become common in medicine and many sciences through technologies such as computed x-ray tomography (CT), magnetic resonance (MR), positron emission tomography (PET), confocal microscopy and 3D ultrasound. When presented with 2D images humans immediately and unconsciously begin a visual analysis of the scene. The viewer surveys the scene identifying significant landmarks and building an internal mental model of presented information. The identification of features is strongly influenced by the viewers expectations based upon their expert knowledge of what the image should contain. While not a conscious activity, the viewer makes a series of choices about how to interpret the scene. These choices occur in parallel with viewing the scene and effectively change the way the viewer sees the image. It is this interaction of viewing and choice which is the basis of many familiar visual illusions. This is especially important in the interpretation of medical images where it is the expert knowledge of the radiologist which interprets the image. For 3D data sets this interaction of view and choice is frustrated because choices must precede the visualization of the data set. It is not possible to visualize the data set with out making some initial choices which determine how the volume of data is presented to the eye. These choices include, view point orientation, region identification, color and opacity assignments. Further compounding the problem is the fact that these visualization choices are defined in terms of computer graphics as opposed to language of the experts knowledge. The long term goal of this project is to develop an environment where the user can interact with volumetric data sets using tools which promote the utilization of expert knowledge by incorporating visualization and choice into a tight computational loop. The tools will support activities involving the segmentation of structures, construction of surface meshes and local filtering of the data set. To conform to this environment tools should have several key attributes. First, they should be only rely on computations over a local neighborhood of the probe position. Second, they should operate iteratively over time converging towards a limit behavior. Third, they should adapt to user input modifying they operational parameters with time.

Senger, Steven O.↗
DOE OSTI2025

The Analysis Description Language Ecosystem: Latest developments and physics applications

We present latest developments in Analysis Description Language (ADL), a declarative domain-specific language describing the physics algorithm of a HEP data analysis decoupled from software frameworks. Analyses written in ADL can be integrated into any framework for various tasks. ADL is a multipurpose construct with uses ranging from analysis design to preservation, reinterpretation, queries, visualisation, combination, etc. The most advanced infrastructure to execute ADL on events is the CutLang runtime interpreter. Recent technical developments include an automated interface with different data types, generation of the abstract syntax tree, a visualization tool that that auto-converts analysis flows to graphs, incorporation of trained machine learning models and a Jupyter-based plotting tool. We also report physics implications including a large scale LHC analysis implementation and validation effort for beyond the standard model reinterpretation purposes and studies with ATLAS and CMS open data.

Sekmen, Sezen [Kyungpook National Univ., Daegu (Ko↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
NASA NTRS1982

An experimental investigation of circulation control flow fields using holographic interferometry

Experiments are presented which were conducted on flow fields produced by a circulation control airfoil utilizing the Coanda effect at the trailing edge. The application of holographic interferometry to obtain both visualization and quantitative data on the flow field about a circulation control airfoil at transonic flow speed is covered. A brief description of the flow model and measurement techniques is given. The data reduction procedure, results, and interpretation are presented. The results have provided a good deal of information on the character of the flow field, particularly in the neighborhood of the trailing edge. As to the airfoil design, it is apparent that improved performance can be achieved if jet detachment is delayed. Another design improvement would involve the development of an optimum trailing-edge shape for the expected operating Mach and Reynolds number ranges.

Bachalo, William D.↗
DOE OSTI2024

Towards interpretable Cryo-EM: disentangling latent spaces of molecular conformations

Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

Vapor-screen flow-visualization experiments in the NASA Langley 0.3-m transonic cryogenic tunnel

The vortical flow on the leeward side of a delta-wing model has been visualized at several different tunnel conditions in the NASA Langley 0.3-Meter Transonic Cryogenic Tunnel using a vapor-screen flow-visualization technique. Vapor-screen photographs of the subject flow field are presented and interpreted relative to phenomenological implications. Results indicate that the use of nitrogen fog in conjunction with the vapor-screen technique is feasibile.

Selby, G. V.↗
NASA NTRSDate not supplied

Evaluating the Efficacy of Conditional Variational Autoencoders in Generating Synthetic Single Nuclei RNA-Seq Data for Space Biology Research

Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.

Sarah Golts↗
NASA NTRS1972

The measurement of driver describing functions in simulated steering control tasks

Measurements of driver describing functions in steering control tasks have been made using a driving simulator. The task was to regulate against a random crosswind gust input on a straight roadway, in order to stay in the center of the lane. Although driving is a multiloop task in general, the forcing function and situation were configured so that an inner-loop visual cue feedback of heading angle of heading rate would dominate, and the driver's response was interpreted to be primarily single-loop. The driver describing functions were measured using an STI describing function analyzer. Three replications for each subject showed good repeatability within a subject. There were some intersubject differences as expected, but the crossover frequencies, effective time delays, and stability margins were generally consistent with the prior data and models for similar manual control tasks. The results further confirm the feasibility of measuring human operator response properties in nominal control tasks with full (real-world) visual field displays.

Weir, D. H.↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2026

Ray-tracing image simulations of transparent objects with complex shape and inhomogeneous refractive index

Optical images of transparent three-dimensional objects can be different from a replica of the object’s cross section in the image plane, due to refraction at the surface or in the body of the object. Simulations of the object’s image are thus needed for the visualization and validation of physical models. We report ray tracing image simulations that achieved high physical fidelity, reproducing optical behaviors and image features not rendered in previous studies. We replicated brightfield microscopy images of drops with complex shapes, and images of pressure and shock waves traveling inside them. For high physical fidelity, the simulations must replicate the spatial and angular distribution of illumination rays, and both the experiment and the simulation must be designed for accurate optical modeling. The simulations are highly sensitive to the properties of the drops and can be used to diagnose and refine fluid dynamics models. The simulated images can also be optimized to extract multiple 3D properties from experimental images. Compared to specialized single-shot 3D imaging methods, this approach has the advantage that it preserves the experimental simplicity, the high resolution, and the visual interpretability characteristic to basic optical imaging. The techniques introduced here are directly applicable to optical microscopy, so they can be used in other fields, such as microfluidics and biology, to expand the type and the accuracy of three-dimensional information that can be extracted from basic optical images.

Cavitation↗
NASA NTRS1972

Heat-transfer testing procedures in phase B shuttle studies with emphasis on phase change data improvement

The procedures used in the application of the phase change technique to the phase B shuttle configuration are discussed along with factors which may affect data accuracy. These factors include variation of thermal properties of phase change model material, sensitivity of measured heat transfer coefficients to the assumed value of the adiabatic to total temperature ratio, and wall temperature effects. These sensitivities are illustrated in sample calculations for a shuttle geometry. Factors which may affect the visual clarity and interpretation of phase change data are discussed, and a method of improving photographic data quality through the use of polarized light is presented.

Throckmorton, D. A.↗
NASA NTRS2008

Visualization of Flowfield Modification by RCS Jets on a Capsule Entry Vehicle

Nitric oxide planar laser-induced fluorescence (NO PLIF) has been used to visualize the flow on the aft-body of an entry capsule having an activated RCS jet in NASA Langley Research Center's 31-Inch Mach 10 wind tunnel facility. A capsule shape representative of the Apollo command module was tested. These tests were performed to demonstrate the ability of the PLIF method to visualize RCS jet flow while providing some preliminary input to NASA's Orion Vehicle design team. Two different RCS nozzle designs - conical and contoured - were tested. The conical and contoured nozzles had area ratios of 13.4 and 22.5 respectively. The conical nozzle had a half-angle of 10 . Low- and high-Reynolds number cases were investigated by changing the tunnel stagnation pressure from 350 psi to 1300 psi, resulting in freestream Reynolds numbers of 0.56 and 1.8 million per foot respectively. For both of these cases, three different jet plenum pressures were tested (nominally 56, 250 and 500 psi). A single angle-of-attack was investigated (24 degrees). NO PLIF uses an ultraviolet laser sheet to interrogate a slice in the flow containing seeded NO; this UV light excites fluorescence from the NO molecules which is detected by a high-speed digital camera. The system has spatial resolution of about 200 microns (2 pixel blurring) and has flow-stopping time resolution (approximately 1 microsecond). NO was seeded into the flow two different ways. First, the RCS jet fluid was seeded with approximately 1-5% NO, with the balance N2. This allowed observation of the shape, structure and trajectory of the RCS jets. Visualizations of both laminar and turbulent flow jet features were obtained. Visualizations were obtained with the tunnel operating at Mach 10 and also with the test section held at a constant pressure similar to the aftbody static pressure (0.04 psi) obtained during tunnel runs. These two conditions are called "tunnel on" and "tunnel off" respectively. Second, the forebody flow was seeded with a very low flowrate (<100 standard cubic centimeters per minute) of pure NO. This trace gas was entrained into and allowed visualization of the shear layer forming between the expansion fan on the shoulder of the model and the recirculating separated flow in the wake of the model. This shear layer was observed to be laminar in the absence of the RCS jet operation and turbulent above a certain RCS jet flowrate. Furthermore, the operation of the RCS jet is seen to push the shear layer out away from the model, with a higher jet pressures resulting in larger deflections. Figures show some data from this test, partially processed. In the final paper, these images will be processed and rendered on a three dimensional visualization of the test hardware for clearer visualization and interpretation of the flowfields.

Danehy, P. M.↗