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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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NASA NTRS1993

Temporal and spatial mapping of surface albedo and atmospheric dust opacity on Mars

The Mariner 9 and Viking provided abundant evidence that eolian processes are active over much of the surface of Mars. Past studies have demonstrated that variations in regional albedo and wind-streak patterns are indicative of sediment transport through a region, while thermal inertia data (derived from the Viking Infrared Thermal Mapper (IRTM) dataset) are indicative of the degree of surface mantling by dust deposits. The visual and thermal data are therefore diagnostic of whether net erosion or deposition of dust-storm fallout is taking place currently and whether such processes have been active in a region over the long term. These previous investigations, however, have not attempted to correct for the effects of atmospheric dust loading on observations of the martian surface, so quantitative studies of current sediment transport rates have included large errors due to uncertainty in the magnitude of this 'atmospheric component' of the observations. We have developed a radiative transfer model that allows the atmospheric dust opacity to be determined from IRTM thermal observations. Corrections for the effects of atmospheric dust loading on observations of surface albedo can also be modeled. This approach to determining 'dust-corrected surface albedo' incorporates the atmospheric dust opacity, the single-scattering albedo and particle phase function of atmospheric dust, and the bidirectional reflectance of the surface, and it accounts for variable lighting and viewing geometry.

Lee, S. W.↗

NASA NTRS2011

Accessing Recent Trend of Land Surface Temperature from Satellite Observations

Land surface temperature (Ts) is an important element to measure the state of terrestrial ecosystems and to study surface energy budgets. In support of the land cover/land use change-related international program MAIRS (Monsoon Asia Integrated Regional Study), we have collected global monthly Ts measured by MODIS since the beginning of the missions. The MODIS Ts time series have approximately 11 years of data from Terra since 2000 and approximately 9 years of data from Aqua since 2002, which makes possible to study the recent climate, such as trend. In this study, monthly climatology from two platforms are calculated and compared with that from AIRS. The spatial patterns of Ts trends are accessed, focusing on the Eurasia region. Furthermore, MODIS Ts trends are compared with those from AIRS and NASA's atmospheric assimilation model, MERRA (Modern Era Retrospective-analysis for Research and Applications). The preliminary results indicate that the recent 8-year Ts trend shows an oscillation-type spatial variation over Eurasia. The pattern is consistent for data from MODIS, AIRS, and MERRA, with the positive center over Eastern Europe, and the negative center over Central Siberia. The calculated climatology and anomaly of MODIS Ts will be integrated into the online visualization system, Giovanni, at NASA GES DISC for easy use by scientists and general public.

Shen, Suhung↗

NASA NTRS1962

Sequential Phenomena in Psychophysical Judgments: A Theoretical Analysis

This paper deals with an analysis of psychophysical detection experiments designed to assess the limit of a human observer's level of sensitivity. A mathematical theory or the detection process is introduced that, in contrast to previous theories, provides an analysis of the sequential effects observed in psychophysical data. Two variations of the detection task are considered: information feedback and no-information feedback. In the feedback situation the subject is given information concerning the correctness of his responses, whereas in the no-feedback situation he is not. Data from a visual detection experiment with no-information feedback, and from an auditory detection experiment with information feedback are analyzed in terms of the theory. Finally, some general results are derived concerning the relationship between performance in the feedback situation and the no-feedback situation.

Atkinson, R. C.↗

DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Development and validation of a quantitative PCR assay method of assessing relative resistance of oat ( Avena sativa ) to crown rust ( Puccinia coronata f. sp. avenae )

Evaluation of oat crown rust resistance is usually based on visual assessment of disease severity or infection types. Visual assessment is subjective, prone to rater bias and requires expert knowledge. PCR‐based quantitative assays can overcome challenges associated with visual assessment. New TaqMan primers and probes were designed from Puccinia coronata f. sp. avenae (Pca) sequences. The primer–probe sets were specific to Pca, amplified using as little as 0.5 pg fungal DNA (fDNA) and allowed for scaling to variation in sample total DNA quantity. The quantitative PCR (qPCR) assay was validated using oat recombinant inbred lines (RILs) from the Provena × 94197A1‐9‐2‐2‐2‐5 cross evaluated under a controlled environment. For comparison with fDNA load, inoculation with the Pca race LCBB provided segregation data on the hypersensitive response, while Pca race LSLG provided data on segregation for reduced pustule number. fDNA content was positively correlated with both pustule number and infection type (IT). Composite interval mapping identified two quantitative trait loci (QTLs) on oat linkage groups Mrg12 and Mrg20 using visual and qPCR assessments (pustule number, IT and fDNA). In this study a qPCR assay method that can be used to assess the relative resistance of oat to crown rust was refined and validated, and single nucleotide polymorphisms (SNPs) closely linked with two QTLs derived from the crown rust resistant line 94197A1‐9‐2‐2‐2‐5 were identified.

Admassu‐Yimer, B.↗

NASA NTRS1973

Stereo study as an aid to visual analysis of ERTS and Skylab images

The author has identified the following significant results. The parallax on ERTS and Skylab images is sufficiently large for exploitation by human photointerpreters. The ability to view the imagery stereoscopically reduces the signal-to-noise ratio. Stereoscopic examination of orbital data can contribute to studies of spatial, spectral, and temporal variations on the imagery. The combination of true stereo parallax, plus shadow parallax offer many possibilities to human interpreters for making meaningful analyses of orbital imagery.

Vangenderen, J. L.↗

NASA NTRS1973

Stereo study as an aid to visual analysis of ERTS and Skylab images

Vangenderen, J. L.↗

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 NTRS2008

Scene Context Dependency of Pattern Constancy of Time Series Imagery

A fundamental element of future generic pattern recognition technology is the ability to extract similar patterns for the same scene despite wide ranging extraneous variables, including lighting, turbidity, sensor exposure variations, and signal noise. In the process of demonstrating pattern constancy of this kind for retinex/visual servo (RVS) image enhancement processing, we found that the pattern constancy performance depended somewhat on scene content. Most notably, the scene topography and, in particular, the scale and extent of the topography in an image, affects the pattern constancy the most. This paper will explore these effects in more depth and present experimental data from several time series tests. These results further quantify the impact of topography on pattern constancy. Despite this residual inconstancy, the results of overall pattern constancy testing support the idea that RVS image processing can be a universal front-end for generic visual pattern recognition. While the effects on pattern constancy were significant, the RVS processing still does achieve a high degree of pattern constancy over a wide spectrum of scene content diversity, and wide ranging extraneousness variations in lighting, turbidity, and sensor exposure.

Woodell, Glenn A.↗

DOE OSTI2018

Following the Morphological Disruption by an Electrolyte of a Buried Interface

A challenge of broad interest in both materials science and biology is the study of interfaces that are buried within a structure, particularly multilayer structures. Despite the enormous costs of corrosion and many decades of corrosion research, details of the mechanisms of various sorts of corrosion are still not clear, in part due to the difficulty in interrogating the interface between the corroding metal and an organic coating, which is typically used to mitigate corrosion. Generally, the performance of such coatings is evaluated by visual inspection after exposure or by modeling impedance data, which is a process not straightforwardly connected to physical interface structures. "Rocking-curve" X-ray scattering measurements provide a means of probing such interfaces due to the ability of X-rays to penetrate materials. Here, variations in the morphology of an interface between a protective coating and a metal substrate due to exposure to an electrolyte are derived from analysis of rocking-curve data in conjunction with atomic force microscopy imaging of the outer coating surface. The interfaces of cross-linked epoxy coatings with aluminum are irreversibly changed after 12 h of contact between the electrolyte solution and the face of the coating. The character of this change varies with the molecule used to cross-link the coating. Since X-ray off-specular scattering is sensitive to changes on the nanometer scale, it is also able to register interface degradation on time scales shorter than those probed by many other techniques, potentially expediting the evaluation of coatings for protection against degradation of the interface.

36 MATERIALS SCIENCE↗

NASA NTRS1994

The real-time display of interferometry data for Goldstone radar astronomy data acquisition

A method for visualizing radar interferometry data in real time has been developed for the Goldstone radar astronomy ranging data-acquisition system. The presentation is similar in appearance to a vector field display or data-based grid. This form was selected to facilitate the recognition of characteristic patterns of local variation in the phase and magnitude of complex elements in a two-dimensional data array. The design emphasized efficiency under the demands of real-time processing and remote monitoring. The interferometry 'phase-magnitude' presentation, as it has come to be called, has been used to monitor radar interferometry experiments on three targets, beginning with the asteroid 4179 Toutatis, and continuing with Mars and Mercury.

Howard, S. D.↗

DOE OSTI2022

SDSS IV MaNGA: visual morphological and statistical characterization of the DR15 sample

ABSTRACT We present a detailed visual morphological classification for the 4614 MaNGA galaxies in SDSS Data Release 15, using image mosaics generated from a combination of r band (SDSS and deeper DESI Legacy Surveys) images and their digital post-processing. We distinguish 13 Hubble types and identify the presence of bars and bright tidal debris. After correcting the MaNGA sample for volume completeness, we calculate the morphological fractions, the bi-variate distribution of type and stellar mass M* – where we recognize a morphological transition ‘valley’ around S0a-Sa types – and the variations of the g − i colour and luminosity-weighted age over this distribution. We identified bars in 46.8 per cent of galaxies, present in all Hubble types later than S0. This fraction amounts to a factor ∼2 larger when compared with other works for samples in common. We detected 14 per cent of galaxies with tidal features, with the fraction changing with M* and morphology. For 355 galaxies, the classification was uncertain; they are visually faint, mostly of low/intermediate masses, low concentrations, and discy in nature. Our morphological classification agrees well with other works for samples in common, though some particular differences emerge, showing that our image procedures allow us to identify a wealth of added value information as compared to SDSS-based previous estimates. Based on our classification, we also propose an alternative criteria for the E–S0 separation, in the structural semimajor to semiminor axis versus bulge to total light ratio (b/a − B/T) and concentration versus semimajor to semiminor axis (C − b/a) space.

Vázquez-Mata, J. A. (ORCID:0000000186941204)↗

DOE OSTI2018

Understanding Dynamical Systems in High-Dimensional Parameter Spaces (Final Report)

In the last few decades, computational models, especially of the complex dynamical systems of greatest interest, i.e., climate, high energy density science, combustion, etc. have become increasingly accurate and sophisticated. Consequently, they are now routinely used to predict outcomes of experiments or to inform design decisions. However, many such simulations depend on a (large) number of parameters in order to, for example, approximate physical phenom whose exact solution is unknown or describe material properties in situations with little or no experimental data. Choosing these parameters and understanding how the outcomes of such a simulation changes with respect to the inputs are rapidly becoming some of the primary challenges in many application areas. Simulations have long been too complex for humans to fully comprehend and thus are often treated as black-box systems. The inputs to the systems are typically a collection of parameters with expert-selected ranges, and the outputs are various quantities of interest (QoIs) describing the performance of the system. Therefore, the fundamental challenge can be described as one of understanding a high-dimensional (many input parameters), multi-variate (many QoIs) function. Unfortunately, existing analysis tools are only of limited use for this task as most techniques can neither deal with many input dimensions nor offer opportunities to jointly study several QoIs. Furthermore, data analysis has long relied on an exploratory process in which scientist first visualize the available data, for example, using scatter plots or 3D renderings, form new hypotheses on some phenomena of interest, and subsequently test these hypotheses using dedicated analysis or statistics approaches. However, for high-dimensional functions this exploration is difficult if not impossible using existing tools as there are too many QoIs to simultaneously observe, and visualizations of high-dimensional spaces are abstract and difficult to understand if they exist at all. The goal of this project is to build new analysis techniques and tool that offer high level insights for exploring high-dimensional scientific data. And to extend existing approach beyond single scalar functions and instead consider multiple QoIs simultaneously.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Rheo-Structural Spectroscopy: Fingerprinting the In Situ Response of Fluids to Arbitrary Flow Fields

The objectives of this project were to develop new sample environments, measurement methodologies and associated modeling tools for characterizing the structural response to arbitrarily complex processing flows using small angle scattering, and to apply these new tools for understanding the fundamental physics governing the structuring of anisotropic particulate and polymeric materials under flow histories and conditions relevant to industrial processing flows. The research resulted in the development and implementation of a new sample environment, the fluidic four roll mill (FFoRM), for in situ small angle neutron and X-ray scattering (SANS/SAXS) measurements. These measurements are capable of generating large data sets that “fingerprint” how a complex fluid responds to a wide range of flow histories involving time variations in deformation type and rate. New modeling tools were developed to extract detailed microstructural information from such data sets, including orientation distribution functions and interparticle correlation functions, as well as reduced-order parametric descriptors of these high-dimensional functions that can be used to readily map, visualize and interpret a fluid’s structural response to its flow history. These new tools were applied to a range of model materials involving elongated particle suspensions in order to provide new insights into the physics of how flow couples with orientational and structural order in complex flows, particularly under non-dilute conditions for which no accurate theories currently exist. Using these investigations, we elucidated a number of new insights into the fundamental phenomena driving such process-structure-property relationships. These findings provide guidance for the further development of rheological models, and ultimately can inform the rational and model-based design of flow processes to achieve optimized orientational ordering that is key to the properties and function of a wide range of energy-relevant materials.

36 MATERIALS SCIENCE↗

NASA NTRS1981

Examination of silicon solar cells by means of the Scanning Laser Acoustic Microscope (SLAM)

The Scanning Laser Acoustic Microscope produces images of internal structure in materials. The acoustic microscope is an imaging system based upon acoustic rather than electromagnetic waves. Variations in the elastic propertis are primarily responsible for structure visualized in acoustic micrographs. The instrument used in these investigations is the SONOMICROSCOPE 100 which can be operated at ultrasonic frequencies of from 30 MHz to 500 MHz. The examination of the silicon solar cells was made at 100 MHz. Data are presented in the form of photomicrographs.

Vorres, C.↗

NASA NTRS1980

Land use in the Paraiba Valley through remotely sensed data

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results.

Dejesusparada, N.↗

NASA NTRS1991

Numerical simulation of flow past a tapered cylinder

The unsteady three-dimensional low Reynolds number flow past a tapered cylinder is computed. The spanwise variation in natural shedding frequency results in interesting three-dimensional flow phenomena. The computed hot-wire and spectral data are very similar to experimental results. The computation was done on the Connection Machine, a massively parallel computer; highlights of the capabilities of the Connection Machine for computation and visualization of three-dimensional unsteady flow fields are shown.

Jespersen, Dennis C.↗

NASA NTRS1990

Numerical Simulation of Flow Past a Tapered Cylinder

We have computed the unsteady three-dimensional low Reynolds number flow past a tapered cylinder. The spanwise variation in natural shedding frequency results in interesting three-dimensional flow phenomena. Our computed hot-wire and spectral data are very similar to experimental results. The computation was done on the Connection Machine, a massively parallel computer, we highlight the capabilities of the Connection Machine, for computation and visualization of three-dimensional unsteady flow fields.

Jespersen, Dennis↗