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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SSAPy - Space Situational Awareness for Python

SSAPy is a fast and flexible orbit modeling and analysis tool for orbits spanning from low-Earth into the cislunar regime. Orbits can be flexibly specified from common input formats such as Keplerian elements or two-line element (TLE) data files. SSAPy allows users to model satellites and specify parameters such as satellite area, mass, and drag coefficients. SSAPy includes a customizable force-propagation with a range of Earth, Lunar, radiation, atmospheric, and maneuvering models. SSAPy makes use of various community integration methods and can calculate time-evolved orbital quantities, including satellite magnitudes and state vectors. Users can specify various space- and ground-based observation models with support for multiple coordinate and reference frames. SSAPy also supports orbit analysis and propagation methods such as multiple hypothesis tracking and has built-in uncertainty quantification. The majority of SSAPy’s methods are vectorized and parallelizable, allowing for effective use of high-performance computer (HPC) systems. Finally, SSAPy has plotting functionality, allowing users to visualize orbits and trajectories. Examples are shown in Figure 1 and Figure 2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Electron Beam Irradiation for Water Treatment of Per- and Polyfluoroalkyl Substances (PFAS)

Per- and polyfluoroalkyl substances (PFAS) are widely used but are now considered a water contamination risk. Fermi National Accelerator Laboratory’s IARC group has demonstrated that passing water through electron beam radiation can destroy PFAS. This project aims to develop a process to scale the system for bulk water treatment. The key is efficient radiation usage, ensuring that all water receives only the minimum dose. Software was developed for this purpose, consisting of a computational fluid dynamics model (CFD) in COMSOL, which calculates particle trajectories through the radiation area. A MATLAB script integrates the radiation dose of these particles, and statistical analysis is performed to evaluate the radiation utilization efficiency. These models are validated with a flow test where colored dye is injected and optically tracked. Radiation dose is measured by testing under an e-beam to measure the degradation of a PFAS analog.

Mueller, Scott [Northern Illinois U.]↗
DOE OSTI2026

Ultrafast Proton-Coupled Electron Transfer Reactions at Unit Quantum Yield

Proton-coupled electron transfer (PCET) reactions mediate essential biological energy-conversion processes, yet dynamical experiments that distinguish concerted electron/proton motion from sequential electron-transfer/proton-transfer pathways are unprecedented on ultrafast time scales. Here we report a tightly coupled donor–spacer–acceptor assembly, NDI-Sp-PhOH/BI, that enables direct interrogation of light-triggered and ultrafast PCET dynamics. Photoexcitation of the naphthalene diimide chromophore drives charge separation forming NDI radical anion and benzimidazolium phenoxy radical (PhO●/BIH+) products with a 440 fs time constant, followed by thermal charge recombination on a 5 ps time scale. Transient visible/near-infrared and mid-infrared spectroscopies independently track electron and proton motion and reveal coincident formation and decay of the charge-separated PCET products, excluding detectable phenol radical cation or phenoxide intermediates expected for sequential electron transfer-proton transfer (ET-PT) pathways. Surface-hopping quasiclassical trajectory simulations demonstrate rapid proton relocation coupled to evolution from locally excited to charge-transfer states, congruent with a concerted mechanism. These combined spectroscopic and computational results establish NDI-Sp-PhOH/BI as an archetype for ultrafast, concerted PCET occurring with unit quantum yield; further, because this platform provides distinct electronic and vibrational spectroscopic handles in its ground, electronically excited, and charge-separated states, it provides new opportunities to characterize mechanistic crossover in biomimetic PCET systems.

10 SYNTHETIC FUELS↗
DOE OSTI2026

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗
DOE OSTI2025

Descriptor: Infrastructure Perception and Control: Multi-Sensor Object Tracking Dataset (IPC-MSOT)

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. Traffic accidents often occur at traffic intersections, including a large proportion of traffic fatalities and about one-half of all traffic injuries in the United States. Object detection data were collected in 2024 across three intersections in Colorado Springs, CO, USA, over the course of multiple days and various times to induce a heterogeneous mix of traffic conditions and behaviors. The purpose of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high-resolution, object-level data that can be used for research and development (e.g., to develop multisensor data fusion algorithms). The Infrastructure Perception and Control:Multi-Sensor Object tracking (IPC-MSOT) dataset was collected as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the city of Colorado Springs, Colorado, and the National Renewable Energy Laboratory collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different intersections. This dataset allows for testing of late-stage sensor fusion algorithms and their ability to ingest multimodal sensor data, and it can be utilized by traffic engineers to design and evaluate trajectory-based signal control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

3D radiation, density, and MHD structures following neon shattered pellet injection into stable DIII-D Super H-mode discharges

Six nominally repeat neon shattered pellet injection (SPI) shutdowns of stable DIII-D Super H-modes are studied to understand the 3D properties of the radiation and impurity transport. The radiation efficiency and radiation peaking determine whether first wall melting is expected following disruption mitigation in ITER. Previous studies make use of axisymmetric approximations to infer radiation efficiencies, but validating the high efficiency required by ITER necessitates improved accuracy, and this work contributes by exploring the 3D radiation and density structures that will inform forward modeling. When the neon shatter plume produced by the SPI reaches the plasma edge, m/n = 3/1 and 2/1 island O-points are observed to align with the injection trajectory in five out of six cases, suggesting that the injected material seeds the island O-points. Field aligned neon structures emitting Ne-I line radiation drift at 1 km/s in the ion diamagnetic drift direction during the pre-TQ, tracking the motion of the m/n = 2/1 island O-point. Neon fragments penetrate to the q = 2 surface by the time of the TQ. Techniques to constrain the 3D emissivity are explored, and one method constrains a 3D flux tube that is consistent with the radiation data, and when mapped to the interferometers, intersects the lasers that measure the highest density. The resulting structure derived from the radiation measurements exists near the 2/1 island X-point. In five repeatable discharges, the peak of the radiation in the toroidal direction exists in a 120° toroidal sector where the injection occurs, in contrast with the outlier discharge where the toroidal peak exists in the complementary 240° toroidal sector far from the injector, and where a 50% lower density rise is observed. In conclusion, the n = 1 phase behavior is markedly different in the outlier discharge, suggesting a possible dependence of the radiation structure and the assimilation efficiency on MHD.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation.

02 PETROLEUM↗
DOE OSTI2022

Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks

Identifying the molecular systems and proteins that modify the progression of Alzheimer’s disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Impact of confined geometries on hopping and trapping of motile bacteria in porous media

We use a random walk particle-tracking (RWPT) approach to elucidate the impact of porous media confinement and cell-cell interactions on bacterial transport. The model employs stochastic alternating motility states consisting of hopping movement and trapping reorientation. The stochastic motility patterns are defined based on direct visualization of individual trajectory data. We validate our model against experimental data, at single-cell resolution, of bacterial E. coli motion in three-dimensional confined porous media. Results show that the model is able to efficiently simulate the spreading dynamics of motile bacteria as it captures the impact of cell-cell interaction and pore confinement, which marks the transition to a late-time subdiffusive regime. Furthermore, the model is able to qualitatively reproduce the observed directional persistence. Lastly, our RWPT model constitutes a meshless simple method which is easy to implement and does not invoke ad hoc assumptions but represents the basis for a multiscale approach to the study of bacterial dispersal in porous systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

SPRUCE FT-ICR MS, Bulk Chemistry, and Mass Loss from Litter Decomposition Study in Experimental Plots, Marcell Experimental Forest, Minnesota, 2015-2017

This dataset contains molecular, bulk chemical, and mass loss measurements from a litter decomposition study at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. This site is in a Sphagnum spp. ombrotrophic bog forest. Litterbags were deployed into the peat in September 2015 across three warming levels (+0, +4.5, and +9°C) under ambient and elevated carbon dioxide (CO₂ - +500 ppm) and retrieved after roughly 0.5, 1, and 2 years of field incubation (2015-09-23 to 2017-08-02). Litterbags containing six peatland litter types: black spruce needles (Picea mariana - SPL), spruce fine roots (SPR), Sphagnum angustifolium (ANG), Sphagnum magellanicum (MAG), Labrador tea leaves (Rhododendron groenlandicum - LTL), and Labrador tea roots (LTR). Molecular composition of water-soluble organic matter extracts was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) at 9.4 Tesla, operated in negative ion mode with electrospray ionization, providing molecular formula assignments and compound-class distributions across the decomposition time series. Bulk chemical characterization included elemental analysis (percent carbon, nitrogen, and phosphorus) and Fourier Transform Infrared Spectroscopy (FTIR) to quantify functional group composition. Litter mass loss was tracked gravimetrically at each retrieval interval, expressed as percent mass remaining relative to initial dry mass for each litter type and treatment combination. These data are valuable for understanding how vegetation shifts driven by increased atmospheric CO2 and temperature in peatlands alter litter inputs and organic matter stabilization trajectories, with implications for projecting and modeling peatland carbon cycling. This dataset contains two data files in comma-separated value (.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

decomposition↗
DOE OSTI2024

Utilizing HYSPLIT for Emergency Response Modeling at SRS

At SRS, emergency responders use a variety of tools to detect, track, and mitigate hazardous material releases into the atmosphere. Two models currently used at SRS are Puff-Plume and the Lagrangian Particle Dispersion Model (LPDM), a Gaussian and Lagrangian model, respectively. A decision has been made to replace LPDM with the more widely-supported Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for evaluating inhalation and ingestion doses following a release. HYSPLIT is designed to compute complex dispersion and deposition simulation To achieve the implementation of HYSPLIT, we have developed a preliminary UI framework that will allow HYSPLIT to be run on ATG computers without the need for active network connections, thus avoiding the loss of capabilities in the event of a network outage during an emergency.

Earley, Ian↗
DOE OSTI2023

Strong-field nonsequential double photoionization using virtual-detector theory with path summation

We present an ab initio study of the nonsequential strong-field ionization dynamics of a model two-electron atom with helium character. Single- and double-ionization events are characterized and displayed using detector signals extracted at different points in the two-electron two-dimensional space. The double photoelectron momentum distribution is calculated via coherent path-summation over virtual-particle trajectories. A comparison is made between the momentum distributions obtained with the virtual-detector method, the Schrödinger wave function, and the time-dependent surface flux method developed by Tao and Scrinzi [New J. Phys. 14, 013021 (2012)]. Insights into different ionization and electron recollision pathways are gained from detailed virtual-particle tracking and energy-time readouts. Furthermore, this study demonstrates the extension of virtual-detector theory to strong-field multielectron quantum dynamics and highlights the importance of the evolving quantum phase in quasiclassical electron propagation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Testing the contribution of dispersal to microbial succession following a wildfire

ABSTRACT Given increased wildfire activity, there is growing interest in understanding the drivers of microbial succession after fire. Dispersal may be especially important to post-fire succession as biotic communities can be more susceptible to invasion following a disturbance. Here, we experimentally manipulated dispersal into disturbed leaf litter communities collected following a wildfire and tracked bacterial and fungal dispersal assemblages over time. We show that the identity and source of microbes immigrating into the soil surface post-fire change across time with seasonal shifts and the reemergence of aboveground vegetation. Further, dispersal significantly contributed to the reassembly of leaf litter microbial communities after the fire, producing an increasingly distinct assembly trajectory. The effect of dispersal on α-diversity and β-diversity was ecosystem dependent but, unexpectedly, influenced bacterial and fungal communities in a similar manner within ecosystems. Collectively, these results demonstrate that dispersal explicitly alters the course of microbial community succession following a wildfire and may impact bacteria and fungi in parallel ways, despite differing in traits expected to alter dispersal patterns. IMPORTANCE Identifying the mechanisms underlying microbial community succession is necessary for predicting how microbial communities, and their functioning, will respond to future environmental change. Dispersal is one mechanism expected to affect microbial succession, yet the difficult nature of manipulating microorganisms in the environment has limited our understanding of its contribution. Using a dispersal exclusion experiment, this study isolates the specific effect of environmental dispersal on bacterial and fungal community assembly over time following a wildfire. The work demonstrates the potential to quantify dispersal impacts on soil microbial communities over time and test how dispersal might further interact with other assembly processes in response to environmental change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Substrate-Mediated Evaporation and Stochastic Evolution of Supported Au Nanoparticles

Here, we use in situ transmission electron microscopy with automated tracking to study supported gold nanoparticles (NPs) during high-temperature vacuum annealing. The average mass loss per NP is governed by a flat, nearly size-independent substrate-mediated evaporation profile. On top of this mean shrinkage, individual NPs show significant fluctuations in apparent growth or shrinkage, and NP volume follows a random-walk-like trajectory. To rationalize both the ensemble-mean behavior and the particle-resolved variability, we develop a self-consistent theory that couples substrate-mediated evaporation to collective 2D Ostwald-type mass exchange through a shared adatom field, described in terms of a renormalized screening length and background concentration. In the experimentally relevant regime, the theory predicts an approximately size-independent mean shrinkage rate and clarifies how net mass loss suppresses classical coarsening. Superimposed on this deterministic drift, we quantify stochastic volume trajectories and capture their fluctuation spectrum with a minimal Langevin description consistent with intermittent adatom attachment and detachment events. In addition, we characterize the lateral diffusive motion of NPs, which is responsible for their coalescence. Altogether, our results highlight that stochasticity is intrinsic at the nanoscale and that predicting the evolution of supported NPs at early and intermediate times requires a unified framework combining substrate-mediated evaporation, collective mass exchange, and stochastic fluctuations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Tracer Particles for Core-collapse Supernova Nucleosynthesis: The Advantages of Moving Backward

Abstract After decades, the theoretical study of core-collapse supernova explosions is moving from parameterized, spherically symmetric models to increasingly realistic multidimensional simulations. However, obtaining nucleosynthesis yields based on such multidimensional core-collapse supernova simulations is not straightforward. Frequently, tracer particles are employed. Tracer particles may be tracked in situ during the simulation, but often they are reconstructed in a post-processing step based on the information saved during the hydrodynamic simulation. Reconstruction can be done in a number of ways, and here we compare the approaches of backward and forward integration of the equations of motion to the results based on inline particle trajectories. We find that both methods agree reasonably well with the inline results for isotopes for which a large number of particles contribute. However, for rarer isotopes that are produced only by a small number of particle trajectories, deviations can be large. For our setup, we find that backward integration leads to better agreement with the inline particles by more accurately reproducing the conditions following freeze-out from nuclear statistical equilibrium, because the establishment of nuclear statistical equilibrium erases the need for detailed trajectories at earlier times. Based on our results, if inline tracers are unavailable, we recommend backward reconstruction to the point when nuclear statistical equilibrium was last applied, with an interval between simulation snapshots of at most 1 ms for nucleosynthesis post-processing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Extracting forces from noisy dynamics in dusty plasmas

Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning (ML) represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised ML to extract the electrostatic, dissipative, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma (dusty plasma). By tracking the sub-pixel motion of particles in subsequent images, we successfully estimated these forces from their random motion. The experiments contained important sources of non-Gaussian noise, such as drift and pixel-locking, representing a data mismatch from methods used to analyze simulated data with purely Gaussian noise. Our model was trained on simulated particle trajectories that included all of these artifacts, and used more than 100 dynamical and statistical features, resulting in a prediction with 50\% better accuracy than conventional methods. Lastly, in systems with two interacting particles, the model provided non-contact measurements of the particle charge and Debye length in the plasma environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The CLAS12 drift chamber system

The CEBAF Large Acceptance Spectrometer at 12~GeV (CLAS12) is located in Hall~B, one of the experimental halls at Jefferson Lab. The forward part of CLAS12 is built around a superconducting toroidal magnet. The six coils of the toroid divide the detector azimuthally into six sectors. Each sector contains three multi-layer drift chambers for reconstructing the trajectories of charged particles originating from a fixed target. Each of the 18 planar chambers has two ``superlayers'' of six layers each, with the wires in the two adjacent superlayers oriented at ± 6° stereo angles. Each layer has 112 hexagonal cells spanning a range from about 5° to 40° in polar angle. The six-layer structure provides redundancy in track segment finding and good tracking efficiency even in the presence of some individual wire inefficiency. The design, construction, operation, and calibration methods are described, and estimates of the efficiency and resolution are presented from in-beam measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Distributed Solar 2020 Data Update [Slides]

Berkeley Lab’s Tracking the Sun report summarizes installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. This report is now being published on a biannual cycle. In 2020, Berkeley Lab has released a more limited Distributed Solar 2020 Data Update, which consists of the same data otherwise published in Tracking the Sun report. The update includes data on more than 1.9 million systems installed through 2019, covering 82% of all distributed PV systems installed nationally through that timeframe.As in prior years, the data update focuses to a large degree on installed prices reported for distributed PV projects, describing both historical trends and variability in pricing across projects.With respect to the historical price trajectory, national median installed prices fell, from 2018 to 2019, by roughly 1% for residential systems, remained essentially flat for small non-residential systems, and fell by 4% for large non-residential systems. Across all three customer segments, these are the slowest annual percentage declines since 2006-2008.Pricing continues to vary widely across individual projects, reflecting, among other things, differences in system sizing and design, installer-level pricing strategies, and local market conditions. For example, among residential systems installed in 2019, the lowest 20% were priced below $3.1/W, while the highest 20% were above $4.5/W. The distributions for non-residential systems exhibit similarly wide spreads.In addition to data on installed prices, the data update also covers a broad range of trends related to distributed PV system design, including: system sizing, module efficiency, module-level power electronics, inverter-loading ratios, solar+storage installations, mounting configuration, panel orientation, third-party ownership, and customer segmentation.

14 SOLAR ENERGY↗