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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerating Advanced Light Source Science Through Multi-Facility HPC Workflows

Synchrotron light sources support a wide array of techniques to investigate materials, often producing complex, high-volume data that challenge traditional workflows. At the Advanced Light Source (ALS), we developed infrastructure to move microtomography data over ESnet to ALCF and NERSC, where CPU- and GPU-based algorithms generate 3D reconstructed volumes of experimental samples. We employ two data movement and reconstruction models: real-time processing as data streams directly to NERSC compute nodes, and automated file transfer to NERSC and ALCF file systems. The streaming pipeline provides users with feedback in under ten seconds, while the file-based workflow produces high-quality reconstructions suitable for deeper analysis in 20-30 minutes. This infrastructure enables users to utilize HPC resources without direct access to backend systems. We plan to extend this architecture to more endstations, supporting our beamline scientists and users.

Abramov, David↗
DOE OSTI2025

Position-Enhanced Gradient Attack (PEGA) on Medical Language Models

Federated Learning (FL) enables collaborative training of language models on sensitive clinical notes without sharing the data. However, this paradigm is vulnerable to gradient inversion attacks that can reconstruct private data from shared gradients. We find that state-of-the-art attacks are less effective in the medical domain, failing to overcome the unique challenges posed by its specialized vocabulary and unstructured format. To address this, we introduce the Position-Enhanced Gradient Attack (PEGA), a novel attack that makes gradients position-aware by optimizing token and position embeddings simultaneously. PEGA employs two key innovations: a periodic sorting of positional embeddings to resolve token order ambiguity and a late-stage embedding replacement strategy to correct hard-to-recover critical tokens. To evaluate the leakage of sensitive data more directly, we also propose the Unified PHI-Recall (UPHI), a new metric measuring the recovery of Protected Health Information. Experiments on the MIMIC-III dataset show that PEGA significantly outperforms leading attacks like TAG and LAMP, particularly in its ability to reconstruct identifiable patient information, exposing a more severe and nuanced privacy risk in federated medical NLP.

Xu, Nuo [University of Minnesota]↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2023

Scaling comb-driven resonator-based DWDM silicon photonic links to multi-Tb/s in the multi-FSR regime

The use of chip-based micro-resonator Kerr frequency combs in conjunction with dense wavelength-division multiplexing (DWDM) enables massively parallel intensity-modulated direct-detection data transmission with low energy consumption. Resonator-based modulators and filters used in such systems can limit the number of usable wavelength channels due to practical constraints on the maximum achievable free spectral range (FSR). In this work, we introduce the design of multi-Tb/s comb-driven resonator-based silicon photonic links by leveraging the multi-FSR regime. We demonstrate the viability of the link architecture with yield estimates that are supported by extensive wafer-scale measurements of 704 micro-resonators fabricated in a commercial complementary metal–oxide–semiconductor foundry. We show that a 2.80 Tb/s link is realizable with a ≥6 σ yield (∼99.999%), and that aggregate bandwidths of 3.76 Tb/s and 4.72 Tb/s are possible if yield targets are relaxed (3 σ and 1 σ , respectively). All designs represent a 1.94−3.28× boost to aggregate link bandwidth while maintaining BER≤10 −10 performance, with a theoretical bandwidth of 10.51 Tb/s being possible for sufficiently robust resonators. We use high-speed BER measurements to inform co-optimization of data rate and aggressor spacing ( λ ag ), limiting any additional loss-based power penalties to off-resonance insertion loss (IL) and routing loss. This work demonstrates that, through the multi-FSR regime, there is a clear path toward Kerr comb-driven ultra-broadband, high bandwidth silicon photonic links that can support next-generation data centers and high-performance computers.

James, Aneek (ORCID:0000000262527807)↗
DOE OSTI2021

Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility

Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Final Scientific/Technical Report-FREE-piston Zero Emissions Refrigerator (FREEZER)

The FREE-piston Zero Emissions Refrigerator (FREEZER™) is a natural refrigerant Stirling-cycle cooler developed by AMSC in partnership with Heatcraft, targeted as a replacement refrigeration system for walk-in coolers or freezers found commonly in restaurants, convenience stores, supermarkets, etc. The FREEZER™, combined with a closed-loop CO2 heat-transfer system, had the goal to provides an efficient (Target COP = 1.4), zero ODP (Ozone Depletion Potential) and near zero GWP (Global Warming Potential) refrigeration solution with no synthetic, flammable, or toxic refrigerants. Other near zero GWP refrigeration systems do not exist at the scale and performance level of FREEZER. ARPA-E Continuation Funding enabled the design and fabrication of one Advanced Development Unit (ADU) cooler and two Field Test Unit (FTU) coolers, and a new revised Field Test Unit (new FTU). Heatcraft was to extensively test the various coolers at their facilities, which closely simulate real-world conditions. The original plan was to install and operate FREEZER FTUs alongside a traditional cooling solution at one or more customer locations to obtain data on direct performance comparisons with existing commercial refrigeration systems. Success in this FREEZER refinement and field-test project will establish the basis for a new class of environmentally friendly refrigeration systems that will revolutionize the industry. The hardware developed under the program is shown below in Figure 1. Five Stirling-cycle machines were built within the program, with a sixth cooler still under construction. The original DU and the ADU coolers met the performance target with a coefficient of performance (COP) of 1.2. The FTU was underperforming during the initial tuning of the free-piston Stirling cycle dynamics when there was a catastrophic failure of the fasteners on pressure boundary. This caused a pause to the program while the failure mechanism was investigated. The root cause was an inadequate flange design that allowed an excessive cyclic loading on the fasteners, leading to a fatigue failure. It was determined that existing FTU design could not be reasonably salvaged in a safe manner, and that fact, combined with the lower-than-expected performance of the new experimental heat exchanger modules lead to the design of the “new FTU” with a more conventional shell-and-tube heat exchanger design which should get close to the design target COP of 1.4, but have a higher overall machine cost. The program ran out of time before the fabrication of the new FTU was completed, but AMSC is still working on the cooler, with the majority of the parts fabricated, and hopes to have the unit under test sometime in early 2022, after which it will be shipped to Heatcraft for additional testing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

LLNL Macroscopic Anisotropic Explosives Research at INL National Security Test Range - Test Results

A select team of 23 engineers, scientists, and explosives specialists from LLNL, LANL, INL, and Marine Raiders from Marine Special Operations Command (MARSOC) and U.S. Special Operations Command (SOCOM) assembled during the second week of November at the INL National Security Test Range near Idaho Falls to investigate and demonstrate fundamental principles of explosives anisotropy. Today's explosives are isotropic in their detonation performance. That is, no matter what direction a detonation runs through bulk explosive, the performance is the same; whereas, anisotropic explosives exhibit different performance, depending on which direction the detonation wave moves through the explosive. The ANISO Team worked in subfreezing temperatures on the Snake River Plain, carrying out 55 experimental explosives shots in four days that lead to a clear understanding of the performance and behavior of an assembly of small, linerless, C4 shaped charges. These shots clearly demonstrated, for the first time, on a macroscopic scale, the principle of anisotropy in measured progression of the detonation through the explosive assembly. The outputs of nine piezo timing pins in the explosive assembly clearly showed detonation progressing through the assembly faster than nominal detonation velocity and moving slower than nominal detonation velocity in the opposite direction. Basic data from these experiments will be used to design and construct explosives assemblies that will be shot in the LLNL High Explosives Applications Facility's (HEAF). These experimental tests will provide refined basic data that will then be used by modelers to develop high explosives models. Computer simulations using these models will then be run to predict performance and design inhomogeneous, anisotropic bulk explosive charges that will be tested at LLNL.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Optimizing Alabama’s CO 2 Storage in Shelby County (Project OASIS): Task 4.0 Deliverable – Geologic Analysis Report

Project OASIS (Optimizing Alabama’s CO 2 Storage in Shelby County) is a geologic and reservoir characterization study designed to evaluate deep saline formations for potential long-term carbon dioxide (CO 2 ) storage in central Alabama near the National Carbon Capture Center (NCCC) and Alabama Power’s Plant Gaston. The project centers on the Cambro-Ordovician Knox Group and underlying strata such as the Conasauga and Rome Formations, which were investigated through the drilling of two stratigraphic test wells to obtain electronic well logs, core, and sidewall core plugs. These data provide direct measurements of porosity, permeability, and lithologic variability critical for reservoir characterization. Complementing the well program, a limited 2D seismic survey was acquired to help select the site for Westover #2, and a more regional Seismic Exchange (SEI) seismic survey was licensed and interpreted to define structural and stratigraphic frameworks in a new Static Earth Model (SEM), map reservoir continuity, and identify potential sealing intervals. Integrated with geologic and reservoir modeling, these datasets form the basis for evaluating storage capacity, injectivity, and containment.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Optimizing Alabama’s CO 2 Storage in Shelby County (Project OASIS): Task 6.0 Deliverable – CarbonSAFE Phase III Readiness

The OASIS CarbonSAFE Phase II Project OASIS (Optimizing Alabama’s CO 2 Storage in Shelby County) is a geologic and reservoir characterization study designed to evaluate deep saline formations for potential long-term carbon dioxide (CO 2 ) storage in central Alabama near the National Carbon Capture Center (NCCC) and Alabama Power’s Plant Gaston. The project centers on understanding the potential of the Cambro-Ordovician Knox Group and underlying strata such as the Conasauga and Rome Formations for geologic storage of CO 2 . These formations were investigated as part of SECARB-USA (DE-FE0031830) and Project OASIS through the drilling of two stratigraphic test wells to obtain electronic well logs, core, and sidewall core plugs. These data provide direct measurements of porosity, permeability, and lithologic variability critical for reservoir characterization. Complementing the well program, a limited 2D seismic survey was conducted to help select the site for Westover #2 as part of SECARB-USA (DE-FE0031830), and a more regional Seismic Exchange (SEI) seismic survey was licensed and interpreted to define structural and stratigraphic frameworks in a new Static Earth Model (SEM), map reservoir continuity, and to identify potential sealing intervals. Integrated with geologic and reservoir modeling, these datasets form the basis for evaluating storage capacity, injectivity, and containment. While this document endeavors to provide readers with a high-level overview of Project OASIS activities and its suitability for subsequent CarbonSAFE Phases, such as a Phase III effort. Other project deliverables and milestones will provide more details on individual subjects.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Data From: Influence of Hydrological Perturbations and Riverbed Sediment Characteristics on Hyporheic Zone Respiration of CO2 and N-2, Journal of Geophysical Research-Biogeosciences

This data package contains pumping data (.txt), parameter matrices, and R code (.R, .RData) to perform bootstrapping for parameter selection for the bioclogging model development. The pumping data were collected from the Russian River Riverbank Filtration site located in Sonoma County, California from 2010-2017 from three riverbank collection wells located alongside the study site. The pumping data is directly correlated with water table oscillations, so the code performs these correlations and simulates stochastic versions of water table oscillations. See Metadata Description.pdf for full details on dataset production. This dataset must be used with the R programming language. This dataset and R code is associated with the publication "Influence of Hydrological Perturbations and Riverbed Sediment Characteristics on Hyporheic Zone Respiration of CO2 and N-2"This research was supported by the Jane Lewis Fellowship from the University of California, Berkeley, the Sonoma County Water Agency (SCWA), the Roy G. Post Foundation Scholarship, the U.S. Department of Energy, Office of Science Graduate Student Research (SCGSR) Program, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award DE-AC02-05CH11231, and the UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

EdgeAI: Machine learning via direct attached accelerator for streaming data processing at high shot rate x-ray free-electron lasers

We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Strongly Heated Turbulent Flow in a Channel with Pin Fins

Large-eddy simulations (LES) were performed to study the turbulent flow in a channel of height H with a staggered array of pin fins with diameter D = H/2 as a function of heating loads that are relevant to the cooling of turbine blades and vanes. The following three heating loads were investigated—wall-to-coolant temperatures of Tw/Tc = 1.01, 2.0, and 4.0 - where the Reynolds number at the channel inlet was 10,000 and the back pressure at the channel outlet was 1 bar. For the LES, two different subgrid-scale models—the dynamic kinetic energy model (DKEM) and the wall-adapting local eddy-viscosity model (WALE)—were examined and compared. This study was validated by comparing with data from direct numerical simulation and experimental measurements. The results obtained show high heating loads to create wall jets next to all heated surfaces that significantly alter the structure of the turbulent flow. Results generated on effects of heat loads on the mean and fluctuating components of velocity and temperature, turbulent kinetic energy, the anisotropy of the Reynolds stresses, and velocity-temperature correlations can be used to improve existing RANS models.

42 ENGINEERING↗
DOE OSTI2023

Coupled Hydromechanical Modeling and Assessment of Induced Seismicity at FWU: Utilizing Time-Lapse VSP and Microseismic Data

The objective of this work is to utilize integrated geomechanics, field vertical seismic profile (VSP) and microseismic data to characterize the complex subsurface stress conditions at the Farnsworth Unit (FWU). The model is based on a five-spot sector model extracted from a primary geomechanical model. The five-spot well injection pattern is characterized by extensive reservoir characterization data, such well logs, extracted cores and borehole geophone data, to facilitate the detailed examination of stress changes and microseismic event occurrences. The study utilizes field vertical seismic volumes acquired from the injection well 13-10A. The seismic volumes successfully provided snapshots of the behavior of the reservoir at distinct times. The use of VSP and microseismic data provided direct and indirect estimates of the dynamic stress changes occurring in the overburden, reservoir and underburden rock formations. In order to illuminate the stress regions and identify rocks that have undergone inelastic failure, microseismic event occurrences were utilized. Microseismic activity has been detected at the FWU; further study of its locations, timing, and magnitude was needed to deduce the nature of the changing stress state. The results of the study revealed that microseismic events were successfully modeled within the Morrow B formation. Moment magnitudes of seismic events were within the same magnitudes for events in the reservoir, suggesting the suitability of the model. The results of the study showed that the computed moment magnitudes for seismic events were insignificant to warrant safety concerns. The study findings showed the usefulness of coupled hydromechanical models in predicting the subsurface stress changes associated with CO 2 injection. The knowledge gained from this study will serve as a guideline for industries planning to undertake underground CO 2 storage, and characterize the subsurface stress changes.

02 PETROLEUM↗
DOE OSTI2020

Estimating the Ultraviolet Emission of M Dwarfs with Exoplanets from Ca ii and Hα

M dwarf stars are excellent candidates around which to search for exoplanets, including temperate, Earth-sized planets. To evaluate the photochemistry of the planetary atmosphere, it is essential to characterize the UV spectral energy distribution of the planet’s host star. This wavelength regime is important because molecules in the planetary atmosphere such as oxygen and ozone have highly wavelength-dependent absorption cross sections that peak in the UV (900–3200 Å). We seek to provide a broadly applicable method of estimating the UV emission of an M dwarf, without direct UV data, by identifying a relationship between noncontemporaneous optical and UV observations. Our work uses the largest sample of M dwarf star far- and near-UV observations yet assembled. We evaluate three commonly observed optical chromospheric activity indices—Hα equivalent widths and log{sub 10} L Hα /L bol , and the Mount Wilson Ca ii H&K S and R HK ' indices—using optical spectra from the HARPS, UVES, and HIRES archives and new HIRES spectra. Archival and new Hubble Space Telescope COS and STIS spectra are used to measure line fluxes for the brightest chromospheric and transition region emission lines between 1200 and 2800 Å. Our results show a correlation between UV emission-line luminosity normalized to the stellar bolometric luminosity and Ca ii R HK ' with standard deviations of 0.31–0.61 dex (factors of ~2–4) about the best-fit lines. We also find correlations between normalized UV line luminosity and Hα log 10 L Hα /L bol and the S index. These relationships allow one to estimate the average UV emission from M0 to M9 dwarfs when UV data are not available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
DOE OSTI2026

Statistical imprints of wave-like dark matter on multiply-imaged galaxies in strong cluster lenses from JWST

Wave-like dark matter ($ψ$DM) is an elusive dark matter (DM) candidate. The model, often also called fuzzy or ultralight DM, proposes that DM is an extremely light ($m\sim10^{-22}$ eV) boson and thereby has a kpc-scale de Broglie wavelength. Hence, interference of DM gives rise to sub-galactic density fluctuations that can be studied with strong gravitational lensing. In this paper, we use the residual power spectrum, $\mathrm{P}_δ(k)$, as a probe of $ψ$DM, which quantifies deviations from smooth lensing predictions, measured from multiply-imaged galaxies in strong cluster lenses. The key idea is that imprinted in these deviations are lensing distortions from DM substructure, which can be harnessed statistically to distinguish among DM theories. We simulate JWST-quality mock observations of strong gravitational lensing in galaxy clusters, modeling line-of-sight DM substructure within $ψ$DM and the standard cold dark matter (CDM) paradigms. Using mock deep observations ($\sim$ 20 hours), we find that $\mathrm{P}_δ(k)$ is sensitive to both $ψ$DM particle mass and fluctuation amplitude, and can distinguish $ψ$DM fluctuations from CDM subhalos. We demonstrate that $\mathrm{P}_δ(k)$ can be measured directly from data by modeling the smooth lensing with a local Curved Arc Basis formalism. With realistic modeling systematics, we find a statistically significant separation between $ψ$DM and CDM across $1 \lesssim k \lesssim 11\,\mathrm{kpc}^{-1}$ -- offering an independent probe of the wave-like nature of DM complementary to existing constraints.

Cosmology and Nongalactic Astrophysics (astro-ph.C↗