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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measurements of dynamic surface changes by digital holography for in situ plasma erosion applications

There are currently few viable diagnostic techniques for in situ measurement of plasma facing component erosion. Digital holography is intended to fill this gap. Progress on the development of single and dual CO 2 laser digital holography diagnostics for in situ plasma facing component erosion is discussed. The dual laser mode’s synthetic wavelength allows the measurable range to be expanded by a factor of ~400 compared to single laser digital holography. This allows the diagnostic to measure surface height changes of up to 4.5 μm in single laser mode and up to 2 mm in dual laser mode. Results include ex situ measurements of plasma eroded targets and also dynamic measurements of nm and μm scale motion of a target mounted on a precision translation stage. Dynamic measurements have successfully been made with the system operating in both single and dual laser modes, from ~50 nm to ~4 μm in single laser mode and up to ~400 μm in dual laser mode (limited only by the stage speed and camera acquisition duration). These results demonstrate the feasibility of using digital holography to characterize plasma facing component erosion dynamically, i.e., during plasma exposure. Results of proof-of-principle in situ digital holographic measurements of targets exposed to an electrothermal arc plasma source are presented.

Smith, Cary↗
DOE OSTI2024

LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry

Since their advent over 2 decades ago, autonomous Argo floats have revolutionized the field of oceanography, and, more recently, the addition of biogeochemical and biological sensors to these floats has greatly improved our understanding of carbon, nutrient, and oxygen cycling in the ocean. While Argo floats offer unprecedented horizontal, vertical, and temporal coverage of the global ocean, uncertainties remain about whether Argo sampling frequency and density capture the true spatiotemporal variability in physical, biogeochemical, and biological properties. As the true distributions of, e.g., temperature or oxygen are unknown, these uncertainties remain difficult to address with Argo floats alone. Numerical models with synthetic observing systems offer one potential avenue to address these uncertainties. Here, we implement synthetic biogeochemical Argo floats into the Energy Exascale Earth System Model version 2 (E3SMv2), which build on the Lagrangian In Situ Global High-Performance Particle Tracking (LIGHT) module in E3SMv2 (E3SMv2-LIGHT-bgcArgo-1.0). Since the synthetic floats sample the model fields at model run time, the end user defines the sampling protocol ahead of any model simulation, including the number and distribution of synthetic floats to be deployed, their sampling frequency, and the prognostic or diagnostic model fields to be sampled. Using a 6-year proof-of-concept simulation, we illustrate the utility of the synthetic floats in different case studies. In particular, we quantify the impact of (i) sampling density on the float-derived detection of deep-ocean change in temperature or oxygen and on float-derived estimates of phytoplankton phenology, (ii) sampling frequency and sea-ice cover on float trajectory lengths and hence float-derived estimates of current velocities, and (iii) short-term variability in ecosystem stressors on estimates of their seasonal variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hierarchical Bayesian Modeling for Cosmology: Can NPE reliably replace MCMC?

Hierarchical neural posterior estimation has its place Hierarchical Bayesian Modeling (HBM) combined with MCMC algorithms has been shown to provide more robust and accurate inference for real-world phenomena in which nature takes a nested form. However, MCMC-based inference can be computationally expensive, and its performance often suffers for complex posterior geometries. These costs are especially pertinent for HBM. Studies have recently demonstrated the potential for a flexible, expressive, and amortized hierarchical neural posterior estimator (HNPE) built on Normalizing Flows. These studies have mostly been performed on simple datasets, or they focus on a single parameter from each level of the hierarchy. A systematic study analyzing how both hierarchical methods compare for more complex and realistic datasets is necessary before applying HNPE for scientific measurements. Here, we re-explore the theory behind HNPE and conduct comparative numerical experiments of HNPE and MCMC-based HBM methods on real and synthetic data, including strong gravitational lensing simulations. In particular, we use a suite of diagnostics to show trade-offs in terms of accuracy, precision, time to train or sample, reproducibility, and the need for expert domain knowledge. Especially for higher dimensional and complex posteriors, HNPE is expected to drastically improve on time for inference, accuracy, and precision with an upfront training time cost.

Hur, Rachel [Chicago U.] (ORCID:000900089890445X)↗
DOE OSTI2024

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗
DOE OSTI2022

Characterization and Geological Implications of Precambrian Calcite-Hosted Phosphate

Constraints on marine phosphate availability and cycling directly inform our understanding of long-term biological evolution. However, early Earth phosphate records are sparse, biased toward siliciclastic samples, and susceptible to post-depositional modification. Well-preserved shallow marine inorganic carbonate precipitates provide a complementary yet understudied record of phosphate cycling. We combined micro-X ray fluorescence mapping, X-ray absorption, and Nuclear Magnetic Resonance spectroscopy on samples of Precambrian syndepositional herringbone calcite (HBC) and microspar to characterize phosphorus speciation and distribution in these carbonate fabrics. Phosphorus spectroscopy from synthetic calcite, HBC, and microspar, is qualitatively consistent with a disordered distribution of phosphate. These characteristics are diagnostic of calcite-hosted phosphate, which is pervasive at low concentrations in HBC and microspar. This study provides evidence that ancient, well-preserved carbonate fabrics retain phosphate sourced from seawater and highlights the potential for an unaltered archive of marine phosphate concentration over geologic time.

58 GEOSCIENCES↗
DOE OSTI2022

Tracking blobs in the turbulent edge plasma of a tokamak fusion device

Abstract The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. By making a dataset and benchmark publicly available, we aim to lower the entry barrier to tokamak plasma research, thereby greatly broadening the community of scientists and engineers who might apply their talents to this endeavor.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Fusion product measurements by nuclear diagnostics in the Joint European Torus deuterium–tritium 2 campaign (invited)

We report a new deuterium–tritium experimental, DTE2, campaign has been conducted at the Joint European Torus (JET) between August 2021 and late December 2021. Motivated by significant enhancements in the past decade at JET, such as the ITER-like wall and enhanced auxiliary heating power, the campaign achieved a new fusion energy world record and performed a broad range of fundamental experiments to inform ITER physics scenarios and operations. New capabilities in the area of fusion product measurements by nuclear diagnostics were available as a result of a decade long enhancement program. These have been tested for the first time in DTE2 and a concise overview is provided here. Confined alpha particle measurements by gamma-ray spectroscopy were successfully demonstrated, albeit with limitations at neutron rates higher than some 10 17 n/s. High resolution neutron spectroscopy measurements with the magnetic proton recoil instrument were complemented by novel data from a set of synthetic diamond detectors, which enabled studies of the supra-thermal contributions to the neutron emission. In the area of escaping fast ion diagnostics, a lost fast ion detector and a set of Faraday cups made it possible to determine information on the velocity space and poloidal distribution of the lost alpha particles for the first time. This extensive set of data provides unique information for fundamental physics studies and validation of the numerical models, which are key to inform the physics and scenarios of ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

On the local measurement of electric currents and magnetic fields using Thomson scattering in Weibel-unstable plasmas

Here, we demonstrate the capability of the Thomson Scattering (TS) diagnostic to measure locally the microscopic electron and ion currents in counter-streaming plasmas unstable to the Weibel or current-filamentation instability. Synthetic TS spectra are calculated with particle distribution functions obtained from particle-in-cell simulations and used to accurately reproduce the simulated currents. We show that this technique allows accurate local measurements of the magnetic field, thus opening the way for the complete experimental characterization of the growth rate, saturation, and nonlinear dynamics of electromagnetic instabilities in plasmas. We illustrate the application of this diagnostic to experimental TS data, which yields local measurements of the magnetic field in Weibel-unstable plasmas and indicates that the magnetic energy density reaches ~1% of the kinetic energy density of the flows, in agreement with previous numerical studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Modeling hydrodynamics, magnetic fields, and synthetic radiographs for high-energy-density plasma flows in shock-shear targets

Three-dimensional FLASH radiation-magnetohydrodynamics (radiation-MHD) modeling is carried out to study the hydrodynamics and magnetic fields in the shock-shear derived platform. Here, simulations indicate that fields of tens of Tesla can be generated via the Biermann battery effect due to vortices and mix in the counterpropagating shock-induced shear layer. Synthetic proton radiography simulations using MPRAD and synthetic X-ray image simulations using SPECT3D are carried out to predict the observable features in the diagnostics. Quantifying the effects of magnetic fields in inertial confinement fusion and high-energy-density plasmas represents frontier research that has far-reaching implications in basic and applied sciences.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Investigating the impact of intermediate-mode perturbations on diagnosing plasma conditions in DT cryogenic implosions via synthetic x-ray Thomson scattering

The pursuit of inertial confinement fusion ignition target designs requires precise experimental validation of the conditions within imploding capsules, in particular the density and temperature of the compressed shell. Previous work has identified x-ray Thomson scattering (XRTS) as a viable diagnostic tool for inferring the in-flight compressed deuterium-tritium shell conditions during capsule implosions (Poole et al 2022 Phys. Plasmas 29 072703). However, this study focused on one-dimensional simulations, which do not account for the growth of hydrodynamic instabilities. In this work, two-dimensional DRACO simulations incorporating intermediate-mode perturbations up to Legendre mode l = 50 were used to generate synthetic XRTS spectra with the SPECT3D code. The analysis employed Markov-Chain Monte Carlo techniques to infer plasma conditions from these spectra. The results demonstrate that the XRTS diagnostic platform can effectively discern the in-flight compressed shell conditions for targets with varying adiabats, even in the presence of intermediate-mode perturbations. This work underscores the potential of XRTS for realistic inertial confinement fusion experiments, providing a robust method for probing the complex dynamics of fusion implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Inferring performance metrics for laser direct drive experiments on OMEGA

Quantifying performance improvements on the OMEGA laser facility requires robust inference of established no-alpha performance metrics, which requires, at minimum, a model to infer the shocked fuel mass and pressure of the confined fusion plasma. In this work, we describe the methodology used to infer performance metrics on OMEGA and present the current state-of-the art model used to infer these metrics from OMEGA experiments. In particular, since neutron images of cryogenic implosions are not available on OMEGA at present, we present how x-ray sizes are determined on OMEGA using a Gaussian Process regression model and how the neutron production region's size is inferred from them. As a result, we end by benchmarking the model using synthetic data and 1-D LILAC simulations and test its experimental self-consistency across available x-ray diagnostic channels.

Gopalaswamy, V. [Laboratory for Laser Energetics, ↗
DOE OSTI2021

Analysis of the Innovation Trend in Cell-Free Synthetic Biology

Cell-free synthetic biology is a maturing field that aims to assemble biomolecular reactions outside cells for compelling applications in drug discovery, metabolic engineering, biomanufacturing, diagnostics, and education. Cell-free systems have several key features. They circumvent mechanisms that have evolved to facilitate species survival, bypass limitations on molecular transport across the cell wall, enable high-yielding and rapid synthesis of proteins without creating recombinant cells, and provide high tolerance towards toxic substrates or products. Here, we analyze ~750 published patents and ~2000 peer-reviewed manuscripts in the field of cell-free systems. Three hallmarks emerged. First, we found that both patent filings and manuscript publications per year are significantly increasing (five-fold and 1.5-fold over the last decade, respectively). Second, we observed that the innovation landscape has changed. Patent applications were dominated by Japan in the early 2000s before shifting to China and the USA in recent years. Finally, we discovered an increasing prevalence of biotechnology companies using cell-free systems. Our analysis has broad implications on the future development of cell-free synthetic biology for commercial and industrial applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Development of light-responsive protein binding in the monobody non-immunoglobulin scaffold

Abstract Monobodies are synthetic non-immunoglobulin customizable protein binders invaluable to basic and applied research, and of considerable potential as future therapeutics and diagnostic tools. The ability to reversibly control their binding activity to their targets on demand would significantly expand their applications in biotechnology, medicine, and research. Here we present, as proof-of-principle, the development of a light-controlled monobody (OptoMB) that works in vitro and in cells and whose affinity for its SH2-domain target exhibits a 330-fold shift in binding affinity upon illumination. We demonstrate that our αSH2-OptoMB can be used to purify SH2-tagged proteins directly from crude E. coli extract, achieving 99.8% purity and over 40% yield in a single purification step. By virtue of their ability to be designed to bind any protein of interest, OptoMBs have the potential to find new powerful applications as light-switchable binders of untagged proteins with the temporal and spatial precision afforded by light.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Lyman- α forest from LBGs: First 3D correlation measurement with DESI and prospects for cosmology

The Lyman-α (Lyα) forest is a key tracer of large-scale structure at redshifts z > 2, traditionally studied using the spectra of luminous but relatively rare quasars. In this work, we explore the viability of using the fainter yet significantly more abundant Lyman Break Galaxies (LBGs) as alternative background sources for Lyα forest studies. We analyze 4,151 Lyα forest skewers extracted from LBG spectra obtained in the DESI pilot surveys conducted in the COSMOS and XMM-LSS fields. From this dataset, we present the first measurement of the Lyα forest auto-correlation function derived exclusively from LBG spectra, probing comoving separations up to 48 h -1 Mpc at an effective redshift of z eff = 2.70. The measured LBG Lyα forest auto-correlation is consistent with that derived from DESI DR2 quasar Lyα forest spectra at a comparable redshift, validating the use of LBGs as reliable background sources for Lyα forest analyses. In addition, we measure the cross-correlation between the LBG Lyα forest and the positions of 13,362 galaxies, demonstrating that this observable serves as a sensitive diagnostic for assessing the precision and accuracy of galaxy redshift estimates, and for identifying and correcting systematic offsets. Finally, using both synthetic LBG spectra and Fisher matrix forecasts, we show that a future wide-area survey covering ∼5,000 deg 2 , targeting 1,000 LBGs per square degree at signal-to-noise levels comparable to our sample, could enable LBG-based Lyα forest baryon acoustic oscillation (BAO) measurements with expected uncertainties of σ α ISO = 0.4% (isotropic) and σ α AP = 1.3% (Alcock-Paczynski). This performance is further enhanced when combining the BAO analysis with a Lyα forest Full Shape (FS) approach, yielding a predicted uncertainty of σ α ISO FS = 0.6%. These results open a new avenue for precision cosmology at high redshift using the Lyα forest in dense LBG samples.

Lyman alpha forest↗
DOE OSTI2025

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗
DOE OSTI2025

Analysis of streaked images of x-ray self-emission in laser-driven spherical implosions

Imaging of x-ray self-emission provides a powerful in situ measurement of the spatial and temporal evolution of high-energy-density plasmas. However, interpretation of these measurements requires detailed understanding of the data-generating process. This work presents a case study in the interpretation of x-ray self-emission data for the specific application of streaked one-dimensional slit imaging of spherical laser-driven implosions. A comprehensive generative model of the streaked slit-imaging diagnostic is developed including detailed treatments of the radiation transfer, photometrics, and photostatistics associated with the measurement. The model is used to generate realistic synthetic streaked images and to analyze experimental streaked images to extract important physical quantities of interest. An example analysis of streaked images from implosion experiments on the OMEGA laser is presented, where the model developed in this work is used to constrain the trajectory and peak velocity of the implosion using Bayesian inference.

Bayesian inference↗
DOE OSTI2022

Development of a Freeze-Dried CRISPR-Cas12 Sensor for Detecting Wolbachia in the Secondary Science Classroom

Training the future synthetic biology workforce requires the opportunity for students to be exposed to biotechnology concepts and activities in secondary education. Detecting Wolbachia bacteria in arthropods using polymerase chain reaction (PCR) has become a common way for secondary students to investigate and apply recombinant DNA technology in the science classroom. Despite this important activity, cutting-edge biotechnologies such as clustered regularly interspaced short palindromic repeat (CRISPR)-based diagnostics have yet to be widely implemented in the classroom. To address this gap, we present a freeze-dried CRISPR-Cas12 sensing reaction to complement traditional recombinant DNA technology education and teach synthetic biology concepts. The reactions accurately detect Wolbachia from arthropod-derived PCR samples in under 2 h and can be stored at room temperature for over a month without appreciable degradation. Here, the reactions are easy-to-use and cost less than $40 to implement for a classroom of 22 students including the cost of reusable equipment. We see these freeze-dried CRISPR-Cas12 reactions as an accessible way to incorporate synthetic biology education into the existing biology curriculum, which will expand biology educational opportunities in science, technology, engineering, and mathematics.

59 BASIC BIOLOGICAL SCIENCES↗