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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 289 records · Page 16

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DOE OSTI2026

Hofmann Stability Charts Revisited for PIP-II: From Classical Theory to Assumption-Free and ML-Driven Maps

The Hofmann stability chart remains a standard for visualizing parametric resonances in space-charge–dominated linacs, but its use typically relies on non-oscillatory Vlasov dispersion relations with simplifying assumptions (continuous focusing, KV phase space, linear optics, limited transverse–longitudinal coupling). We revisit the chart for the PIP-II linac along three tracks. (1) We reproduce the conventional maps in the (νz/νx, νx/ν0x) plane for relevant εz/εx, providing a validated reference. (2) We remove key assumptions by deriving stability surfaces directly from multi-particle tracking with realistic lattice discreteness, RF defocusing, solenoid/quad optics, and bunched-beam dynamics; local tunes and early-time growth rates are estimated from envelope oscillations and projected to the same coordinates. These assumption-reduced maps recover the canonical stopbands while revealing shifts and broadenings driven by tune modulation, non-KV distributions, and transverse–longitudinal coupling at PIP-II intensities. (3) We train a compact machine-learning surrogate that emulates the growth surface from zero-current optics, tune depression, emittance ratio, bunching factor, and selected lattice descriptors, enabling rapid scans and online working-point selection. We compare the three representations on representative PIP-II sections and discuss implications for commissioning guard bands, resonance avoidance, and routine operations.

Pathak, Abhishek [Fermilab] (ORCID:000000021704208↗

NASA NTRS1983

A case study of gravity waves-convective storms interaction 9 May 1979

An analysis is presented of a series of severe storms which occurred in the north central United States on 9 May 1979 and whose spatial distribution and movement correlate well with observed gravity waves. Two gravity wave trains of 2.1-3 mb amplitude, 2.5-3.3 h period and 240-265 km horizontal wavelength were isolated through power spectra analysis and cross-correlation techniques applied to National Weather Service barograph traces. The wave trains propagated in the 200 deg direction, which coincided with the jet axis, with a phase velocity of 20-30 m/s and within a 300 km wide band. The storms were identified on enhanced infrared GOES satellite pictures with the help of radar summaries. These convective systems initially developed in Nebraska and propagated north-northeast at 25 m/s, revealing wave-like characteristics with a separation of 300-400 km. The convective systems were closely linked to the observed wave trains with cell intensity, height and associated rainfall maximized at the wave ridge. One of the two wave trains developed in regions of weak or no convection and appeared to initiate more intense convective clusters downstream from the point of origin. It is shown that the characteristics of the wave trains are consistent with those of gravity waves generated in a region of strong vertical shear associated with the jet. It is suggested that the wave trains continue to extract energy from the basic state all along their track through critical level interaction.

Stobie, J. G.↗

NASA NTRS1990

Network modelling for the Space Station Training Facility

Simulation models for LANs to be used in the proposed Space Station Training Facility, including models developed for Ethernet, Starlan, token ring, and Fiber Distributed Data Interface LANs, are discussed. Results of model runs for the basic configuration, for burst traffic, and for a growth scenario are presented. The PAWS and Network II.5 modeling tools are compared.

Hajare, Ankur R.↗

NASA NTRS1993

Origin of orbital debris impacts on LDEF's trailing surfaces

A model was developed to determine the origin of orbital impacts measured on the training surfaces of LDEF. The model calculates the expected debris impact crater distribution around LDEF as a function of debris orbital parameters. The results show that only highly elliptical, low inclination orbits could be responsible for these impacts. The most common objects left in this type of orbit are orbital transfer stages used by the U.S. and ESA to place payloads into geosynchronous orbit. Objects in this type of orbit are difficult to catalog by the U.S. Space Command; consequently there are independent reasons to believe that the catalog does not adequately represent this population. This analysis concludes that the relative number of cataloged objects with highly elliptical, low inclination orbits must be increased by a factor of 20 to be consistent with the LDEF data.

Kessler, Donald J.↗

NASA NTRS1960

A Summary of Operating Conditions Experienced by Three Military Helicopters and a Mountain-Based Commercial Helicopter

The results of a survey of the flight conditions experienced by three military helicopters engaged in simulated and actual military missions, and a commercial helicopter operated in the mountainous terrain surrounding Denver, CO, are presented. The data, obtained with NASA helicopter VGHN recorders, represent 813 flights or 359 flying hours, and are compared where applicable to previous survey results. The current survey results show that none of the helicopters exceeded the maximum design airspeed. One military helicopter, used for instrument flight training, never exceeded 70 percent of its maximum design airspeed. The rates of climb and descent utilized by the IFR training helicopter and of the mountain-based helicopter were generally narrowly distributed within all the airspeed ranges. The number of landings per hour for all four of the helicopters ranged from 1.6 to 3.3. The turbine-engine helicopter experienced more frequent normal-acceleration increments above a threshold of +/-0.4g (where g is acceleration due to gravity) than the mountain-based helicopter, but the mountain-based helicopter experienced acceleration increments of greater magnitude. Limited rotor rotational speed time histories showed that all the helicopters were operated at normal rotor speeds during all flight conditions.

Connor, Andrew B.↗

NASA NTRS1996

Solar Dynamic Power System Fault Diagnosis

The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.

Momoh, James A.↗

DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗

NASA NTRS2023

A Global Land Cover Training Dataset From 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Radost Stanimirova↗

NASA NTRSDate not supplied

Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach

This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.

Zhe Wang↗

NASA NTRSDate not supplied

Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach

Zhe Wang↗

DOE OSTI2025

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Superstructure Optimization of Waste Plastic Pyrolysis, Integrating Thermal, Catalytic, and Plasma Technologies with Machine Learning

Global plastic waste generation exceeds 430 million tonnes per year, yet fewer than 9% are recycled in the United States. Pyrolysis offers a chemical recycling route at scale, but existing techno-economic and life cycle assessments fix product yields to single pure polymers, producing economic and environmental outputs that break down when the feed composition changes. Here, we present a superstructure optimization framework that addresses this by embedding a composition-aware random forest yield predictor, trained on 566 pyrolysis experiments, within a full-scale process simulation. Product distributions update automatically as feed allocation shifts across four reactor chemistries: conventional thermal, catalytic (HZSM-5), thermal oxo-degradation, and nonequilibrium CO2 plasma. The optimal superstructure achieves minimum selling prices of −0.56 to −0.76/kg feed and global warming potentials of −0.276 to −0.322 kg CO2-eq/kg feed across four commodity price scenarios, confirming profitable, carbon-negative operation without tipping fees. Carbon abatement costs of $\$$0.46 to $\$$1.25/kg CO2-eq are competitive with direct air capture. Sensitivity analysis shows that the catalytic-plasma split fraction is the single largest driver of both economic and climate performance, while hydrocracking allocation in the wax upgrading stage is emission-neutral across the full variable range. Mixed plastic waste streams, evaluated as composition-variable feedstocks rather than pure resins, are profitable and carbon-negative across realistic market conditions. These results give a quantitative basis for reactor selection, circular economy investment, and policy design targeting chemical recycling on a large scale.

Life cycle assessment↗

DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

MENT-Flow: maximum-entropy phase space tomography using normalizing flows

Generative models can be trained to reproduce low-dimensional projections of high-dimensional phase space distributions. Normalizing flows are generative models that parameterize invertible transformations, allowing exact probability density evaluation and sampling. Consequently, flows are unbiased entropy estimators and could be used to solve the high-dimensional maximum-entropy tomography (MENT) problem. In this work, we evaluate a flow-based MENT solver (MENT-Flow) against exact maximum-entropy solutions and Minerbo's iterative MENT algorithm in two dimensions.

Hoover, Austin↗

DOE OSTI2025

PUFFIn Software Modeling for Quality Management

PUFFIn (PENELOPE User Friendly Fast Interface) was designed as a fast and simple Monte Carlo simulation tool for the transport of photons and electrons, with a primary purpose as a learning and education tool for a broad range of static configurations in the radiation processing industry. Development of the PUFFIn software is funded by the Office of Radiological Security (ORS) within the United States National Nuclear Security Administration (NNSA). PUFFIn helps fill the education and knowledge gaps in the industry, as identified in reports by Fermilab (2017) and the IAEA (2020). PUFFIn uses the PENELOPE (NEA-2023) physics engine to perform simulations on static configurations. PUFFIn has support for multiple geometry types from simple, single material simulations to full 3D configurations created from CAD input files or images from X-Ray Tomography scans. PUFFin was designed to be easy for the novice user, it will generate the input and geometry files required by PENLOPE and will display the output plots within the PUFFIn interface. PUFFin is distributed for free but requires a free workshop so users can be adequately trained in its use. Workshops have been presented in the past at Texas A&M university, the Aerial-CRT facility in Strasbourg France and Jakarta Indonesia. PUFFIn simulations have been validated by 10 MeV ebeam experiments done at Aerial-CRT in France (Radiation Physics and Chemistry 222 (2024) 111774). Further user experimental comparisons were made at the medical product hands on workshop at Texas A&M in October 2024.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2024

Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.

remote sensing↗

DOE OSTI2024

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

Pandey, Vikas (ORCID:0000000154771095)↗

DOE OSTI2026

D$e^+e^-$ffusion: Capturing the Beam-Beam Physics of $e^+e^-$ Collisions with Diffusion Models

Beam-induced backgrounds at high-luminosity $e^+e^-$ colliders, such as the FCC-ee, are dominated by incoherent pair creation (IPC), and require computationally expensive simulations with dedicated Monte Carlo (MC) event generators. Reliable detector and machine-detector interface studies necessitate event samples that are several orders of magnitude larger than what is practically attainable with existing MC. To alleviate this bottleneck, we present D$e^+e^-$ffusion, a denoising diffusion probabilistic model that operates as a permutation-equivariant, set-valued surrogate for fast IPC simulation. Trained on a small GuineaPig++ sample, D$e^+e^-$ffusion faithfully reproduces the marginal and joint kinematic, angular, and positional distributions of all three IPC production processes. In addition, we assess the fidelity at the detector level by propagating both Geant4 and D$e^+e^-$ffusion events through a Geant4 simulation of the CLD vertex detector and by training a transformer-based two-sample classifier; the classifier achieves an area under the ROC curve of $0.553 \pm 0.016$. The trained model generates events nearly four orders of magnitude faster than Geant4, paving the way for a fast-simulation surrogate for FCC-ee design studies.

Chahine, Antonio [Imperial Coll., London]↗