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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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This page contains the datasets and code #O5097 Jupyter Notebook Code for “Data-Driven Insights to Accelerate Advanced Biomanufacturing”. Data literature-derived cultivation experiments for polyhydroxybutyrate (PHB) production in Synechocystis sp. PCC 6803 and were used for ML model development, interpretation, and experimental validation.
CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.
Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.
Savannah River National Laboratory has developed a monitor to measure plutonium and uranium concentrations in solutions of dissolved nuclear fuel. The monitor will be installed in the sample aisle location for the 6.3D Dissolver in the Savannah River Site’s H-Canyon and used in support of the electrolytic dissolution such as Fast Critical Assembly fuel. The monitor is based on the atomic absorbance of x-rays. Elements are differentiated by the appearance of absorbance features at specific energies of the x-ray spectrum that correspond to L-edge transitions of inner core electrons. Hence, the technique is called L-Edge X-Ray Absorbance Spectroscopy (L-XRAS). The technique is suitable for nuclear fuel processing due to its relative insensitivity to other components of the dissolver solution, such as nitric acid, transition metals (Fe, Cr, Ni, Mn) such as those from stainless steel, particulates, and catalysts and additives. The instrumentation consists of a commercially available x-ray source and detector, a sample cell designed to interface with the airlift sampler associated with H-Canyon Tank 6.3D, and a stainless steel enclosure. SRNL wrote instrument control software and developed chemometric models to interpret x-ray intensity spectra and estimate analyte concentrations and uncertainties in real time.
During the summertime, the high-latitude oceans come to life with green phytoplankton, which gain their energy from sunlight and are food for sea creatures small and large. When the phytoplankton are eaten or die, sulfur-rich gases are released under the ocean surface and mix into the air. Observations suggest that, over the Southern Ocean, frequent storms lift this air high into the atmosphere while raining out particulates like salt. As a result, the sulfur-rich air then spawns high concentrations of small ‘Aitken-mode’ aerosol particles. We hypothesize that these particles work their way down into the marine boundary layer, where they can replenish the supply of cloud-condensation nuclei scavenged by frequent precipitation. Further, this process maintains high concentrations of liquid cloud droplets in austral summer, promoting more sunlight to be reflected to space. We call this ‘Aitken buffering’. The primary objective of this project has been to document and test what role Aitken-mode aerosols play in clouds over the Southern Ocean and elsewhere. This effort has included three main components: 1) developing a computer model that realistically simulates the aerosol processes and the small-scale turbulent air motions that move aerosols around and create the clouds, 2) using that model to interpret and extend these observations for process understanding, by allowing different factors that contribute to the aerosol budget, such as surface wind speed, precipitation, surface gas exchange, etc. to be separated, and 3) studying Aitken-mode aerosol and its variability with a focus over the Southern Ocean and Antarctica, using data from Atmospheric Radiation Measurement (ARM) sites and other available observations. Initial computer studies in more idealized conditions found that elevated concentrations of Aitken-mode aerosols above the clouds could help prevent the breakup of those clouds by acting as cloud-condensation nuclei after they were entrained into the cloudy boundary layer. Simulations of a day during the ACE-ENA field campaign showed that Aitken-mode aerosols could also prevent cloud breakup under more realistic conditions. A new method that extracts information about Aitken-mode aerosols from measurements of aerosols onto which cloud droplets can form finds that Aitken-mode aerosols do vary seasonally over the Southern Ocean, with a peak in summertime, as described above. Other work during this project has focused on understanding how patterns of water vapor, clouds and precipitation are coupled within low-lying clouds over the oceans, and also on how cloud droplets cluster within clouds and how the distribution of cloud droplet sizes change as dry air is mixed into clouds, with the latter studies also using observations from ACE-ENA.
The primary aim of this investigation is to elucidate the mechanisms by which a group of metals pertinent to battery technology exhibit unstable growth morphology during electrodeposition, which are crucial for the operational efficacy of batteries. The metals under consideration include alkali metals alongside zinc, all of which are characterized by high atomic mobility and reactivity with the electrolytes employed in battery systems. These materials represent promising electrode candidates for next-generation rechargeable batteries that are anticipated to deliver significantly enhanced energy density at a reduced cost. Nevertheless, the widespread occurrence of distinctly non-uniform morphologies, exemplified by the emergence of filamentous and moss-like structures, constitutes a significant impediment to their effective utilization, as it leads to accelerated capacity degradation and potential internal short circuits. This study addresses a critical question that has thus far garnered limited attention: Does the residual stress induced by the plating process play an instrumental role in inducing the morphological instability? Utilizing zinc as the primary model system, this project will be executed through three interconnected tasks: (1) the quantification of the relationship between plating stress and electroplating morphologies, (2) the three-dimensional imaging of the microstructure of the mossy structure at the nanoscale, and (3) the development of a mesoscale electroplating model to interpret empirical findings and generate additional predictive insights.
Accumulation of soils and other particulate matter on the front cover glass of solar photovoltaic (PV) modules results in transmission losses that detrimentally affect the power output of PV installations. The conventional solution to this problem is manual abrasive scrubbing of modules, which entails significant water usage and manpower. To further combat this issue, there is a concerted effort within the PV industry to develop anti-soiling (AS) coatings to mitigate power losses due to soiling of PV modules, which can exceed 35% in some cases. However, relatively little is known about the fundamental physical and chemical mechanisms that enable anti-soiling behavior. In this project, a combination of advanced X-ray analysis tools and first principles computational modeling has employed to understand the physical and chemical interactions between environmental soils and PV cover glass materials. The specific objectives were: 1) investigate soiling interactions and cementation behavior of relevant PV materials, including bare glass, conventional antireflective (AR) coatings, and a commercial AS coating, using a library of representative particulate soils, and 2) utilize first principles computational spectroscopy modeling to interpret the experimental X-ray spectroscopy measurements and determine the detailed surface chemistry changes that occur on these surfaces in the presence of soiling and cementation.
We develop a microscopic scattering formalism to describe Yu-Shiba-Rusinov (YSR) states due to a single Cr adatom on the Bi-terminated surface of beta Bi2Pd, by combining ab initio Wannier functions with a real-space Green's function approach in the Bogoliubov-de Gennes formalism[1]. Our framework reproduces key scanning tunneling spectroscopy features, including a single particle-hole asymmetric YSR peak and isotropic dIdV maps around the impurity. Decomposing the YSR states reveals contributions from four nearly degenerate C4v representations, with energy broadening masking their individual signatures. Spin-orbit coupling induces partial spin polarization, while the spatial asymmetry between particle and hole components arises from Cr d-Bi p hybridization. These results highlight the importance of realistic band structures and microscopic modeling for interpreting STM data for magnetic in-gap states on superconductors. Further advances examining layered 2D material surfaces, such as NbSe2, will be described[2]. For this system the superconducting properties are obtained from a full anisotropic Eliashberg calculation of the superconducting order parameter along with the charge density wave gap. Additional features associated with proposals to measure the dynamics of these individual YSR states will be presented. [1] arXiv:2507.08740 [2] arXiv:2507.11856
Quantitative black box model for interpreting data on binaural masking level difference at high frequencies
Model for interpreting data, describing binaural masked threshold dependence on proportion of noise in masking signals
Three dimensional thermospheric model for interpreting tides, planetary waves, and magnetic disturbances
Investigation of problems related to control of a mobile planetary vehicle according to a systematic plan for the exploration of Mars has been undertaken. Problem areas receiving attention include: (1) overall systems analysis; (2) vehicle configuration and dynamics; (3) toroidal wheel design and evaluation; (4) on-board navigation systems; (5) satellite-vehicle navigation systems; (6) obstacle detection systems; (7) terrain sensing, interpretation and modeling; (8) computer simulation of terrain sensor-path selection systems; and (9) chromatographic systems design concept studies. The specific tasks which have been undertaken are defined and the progress which has been achieved during the period July 1, 1971 to December 31, 1971 is summarized.
Continuous measurement of electron temperature and electron concentration during the great magnetic storm of Mar. 8, 1970, when the F2 peak rose as high as 800 km. Continuous measurements of the vertical component of E x B drift were made from 1130 to 1730 LT. A synthesis of the observed electron concentrations was made using a model that solved the time-dependent electron-ion continuity equation for O(+) and the molecular ions; the latter chemistry was included to improve the solutions at lower heights. It was found, using the observed values of vertical drift, that most of the observed features of the F2-region ionosphere were synthesized by the model. An interpretation of the synthesis that emphasizes the importance of horizontal diffusion at the equator is given. Observations are presented for Mar. 7, 8, and 9, 1970, including the Huancayo magnetograms, which display a close correlation with the drift measurements.
Traditional approaches to microdosimetry, the fundamental physics of energy deposition, the importance of statistical processes, an illustration of possible radiobiological interpretation, and modeling based on microdosimetric concepts are discussed. Emphasis is on the inadequacies in linear energy transfer (LET) theory. For many reasons, concepts based on averaging may not be applicable to ionizing radiation absorption by and damage to small biological targets.
We report the concentrations of Th, K, Fe, Mg, and Ti in 28 geographic regions overflown by the Apollo 15 and 16 spacecraft, as determined by gamma-ray spectrometry. The observed chemical compositions are consistent with ground truth, and the two missions give reasonable agreement in a region observed by both. The chemical compositions observed require a more complex model for interpretation than the previously reported radioactivity maps. Both highlands and maria show significant variations in composition. The van de Graaff region is unique; it may possibly be the source region for 'granitic' materials such as rock 12013.
The application of airborne infrared technology to the requirements for energy conservation in buildings was studied. Quantitative airborne data of the City of Ypsilanti, Michigan, were collected and processed to identify roof temperatures. A thermal scanner was flown at an altitude of 1,200 feet with two thermal bands 8.2-9.3 micrometers and 10.4-12.5 micrometers recorded by an analog system. Calibration was achieved by standard hot and cold plates. Using a thermal model to interpret ceiling insulation status, environmental factors were found to influence the relation between roof temperature and insulation. These include interior and sky temperatures, roofing materials, and the pitch and orientation of the roof. A follow-up mail survey established the ability to identify insulated and uninsulated houses from the airborne infrared data.
The transition from small simple craters to large complex or modified craters is characterized by eleven changes in the shape of fresh lunar craters which occur within a diameter range of 10 to 30 km. In the present paper, seven ratio-level variations - those of rim-crest diameter with depth, rim height, flank width, rimwall slope, floor diameter, circularity, and rim-crest evenness - are defined for fresh-appearing craters from the new Apollo data and are expressed mathematically where practicable. These relations constitute a shape model for interpreting fresh craters on the moon. The size dependent changes reflect the occurrence of central peaks, rimwall terraces, and a flat floor within craters measuring 10 to 20 km in diameter.
With an improved model, accelerated high-field tests can be used to predict gate-oxide breakdown in metal-oxide-semi-conductor (MOS) structures. Principal mechanism in MOS breakdown is mobile-ion emission from metal/oxide interface, which occurs during application of positive gate-bias field. Breakdown is related to clustering of emitted ions at localized defect sites in oxide/silicon interface. Using new model to interpret data, tests that normally take several weeks at low fields to accumulate sufficient statistics can be completed in only a few hours at high fields.