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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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NASA NTRS2006

Oscillator Strengths and Predissociation Widths for Rydberg Transitions in Carbon Monoxide

CO is used as a probe of astronomical environments ranging from planetary atmospheres and comets to interstellar clouds and the envelopes surrounding stars near the end of their lives. One of the processes controlling the CO abundance and the ratio of its isotopomers is photodissociation. Accurate oscillator strengths for Rydberg transitions are needed for modeling this process. Absorption bands were analyzed by synthesizing the profiles with codes developed independently in Meudon and Toledo. Each synthetic spectrum was adjusted to match the experimental one in a non-linear least-squares fitting procedure with the band oscillator strength, the line width (instrumental and predissociation.

Federman, Steven R.↗

DOE OSTI2022

Development of a synthetic phase contrast imaging diagnostic for turbulence studies at Wendelstein 7-X

Abstract We present a synthetic phase contrast imaging (PCI) diagnostic for studying turbulence at the Wendelstein 7-X (W7-X) stellarator. We first describe the implemented instrument response model, which captures diffraction effects, detector noise, and the long-wavelength cutoff due to the phase plate of the PCI system. To verify the instrument response model, we show that it is capable of reproducing the PCI signal generated by the sound wave speaker used for calibration at W7-X. Next, we discuss the calculation of synthetic PCI signals based on the global, nonlinear gyrokinetic codes GENE-3D and EUTERPE, including results from some of the first stellarator simulations of this type with kinetic electrons (KEs) in GENE-3D. While the simulations used in this work lack a neoclassical radial electric field, which is crucial for reproducing experimental PCI signals, they do indicate that the dominant rotation direction and velocities of the turbulent fluctuations can be inferred from the wave number-frequency spectra of the PCI signals, as expected. The synthetic PCI wave number spectra are further shown to be similar to those of the line-integrated fluctuating electron density, with distinct differences between adiabatic and KE simulations, explainable by previously published turbulence models. For example, the wave number spectra of all adiabatic electron simulations analyzed here follow a power law with an exponent close to −5 for sufficiently large wave numbers. This indicates that universal features of electron density turbulence at W7-X may be studied using the PCI system.

EUTERPE↗

DOE OSTI2022

Diagnosing the DARHT Electron Beam-Target Interaction and Hydrodynamic Expansion [Dissertation]

Quantitative electron beam-target interaction studies on the Dual-Axis Radiographic Hydrodynamic Test (DARHT) electron linear induction accelerators have only been performed recently. This includes characterization of the temperature, density, pressure, extent, and expansion velocity of the plasma plume. The results presented in this dissertation present a detailed and unified overview of the accelerator systems, target heating physics, beam transport, and diagnostic tools. Additional information includes calibration sources, radiation hydrodynamics, spectroscopic-quality radiation transport modeling, experimental measurements, and analyses of electron beam driven aluminum experiments. The first set of spatially and temporally resolved spectroscopic measurements of electron beam driven aluminum are presented. Contamination quantification analyses are used to understand the origin of the strong Na-I 3p-3s lines that are observed in absorption within the aluminum plasma continuum. These results inform the creation of the first spectroscopic-quality radiation transport model that links several atomic physics codes to interpret the conditions from which the Na-I lines originate. A good agreement is found between the surface analysis results and the model which confirms the concentration of the sodium present within the aluminum alloy foil material. It also demonstrates, for the first time on electron beam-driven target experiments, the ability to interpret plasma conditions from measured absorption lines. In a second experimental campaign which focuses on pure aluminum, the Al-I 3p-4s and 3p3d doublets are both measured in emission. A detailed analysis of the Al-I 3p-4s doublet reveals that the lines undergo moderate self-absorption. A simple model of the self-absorption effect is successfully used to match measured spectra at various temperature/density/plasma scale length combinations. These measurements led to the realization of the minimum density that can be resolved by the spectrometers for the Al-I 3p-4s lines due to the large slit width required to observe a signal on aluminum. These measurements also demonstrate the sensitivity of visible and long wave UV spectroscopy to minor changes in both temperature and density. The simple self-absorption model will be useful for analysis of other beam-target interaction experiments with spectra exhibiting either self-absorption or full self-reversal. Substantial headway has been made on the modeling front by linking together several codes needed to model both the energy deposition, hydrodynamic motion, and atomic kinetics to produce synthetic spectral calculations that are compared with experimental measurements. There exists ample space for improvement, especially with benchmarking the hydrodynamics codes and equation-of-state tables with experimental measurements. The X-ray diagnostics required to make these new measurements along with the simulation capabilities required to interpret the results are under development and will be the subject of future studies.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Prediction of electric and magnetic fields from spectral data using machine learning algorithms for Doppler-free saturation spectroscopy diagnostics

The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.

Doppler-free saturation spectroscopy↗

DOE OSTI2022

Fluid-Kinetic Coupling: Advanced Discretizations for Simulations on Emerging Heterogeneous Architectures (LDRD FY20-0643)

Plasma physics simulations are vital for a host of Sandia mission concerns, for fundamental science, and for clean energy in the form of fusion power. Sandia's most mature plasma physics simulation capabilities come in the form of particle-in-cell (PIC) models and magnetohydrodynamics (MHD) models. MHD models for a plasma work well in denser plasma regimes when there is enough material that the plasma approximates a fluid. PIC models, on the other hand, work well in lower-density regimes, in which there is not too much to simulate; error in PIC scales as the square root of the number of particles, making high-accuracy simulations expensive. Real-world applications, however, almost always involve a transition region between the high-density regimes where MHD is appropriate, and the low-density regimes for PIC. In such a transition region, a direct discretization of Vlasov is appropriate. Such discretizations come with their own computational costs, however; the phase-space mesh for Vlasov can involve up to six dimensions (seven if time is included), and to apply appropriate homogeneous boundary conditions in velocity space requires meshing a substantial padding region to ensure that the distribution remains sufficiently close to zero at the velocity boundaries. Moreover, for collisional plasmas, the right-hand side of the Vlasov equation is a collision operator, which is non-local in velocity space, and which may dominate the cost of the Vlasov solver. The present LDRD project endeavors to develop modern, foundational tools for the development of continuum-kinetic Vlasov solvers, using the discontinuous Petrov-Galerkin (DPG) methodology, for discretization of Vlasov, and machine-learning (ML) models to enable efficient evaluation of collision operators. DPG affords several key advantages. First, it has a built-in, robust error indicator, allowing us to adapt the mesh in a very natural way, enabling a coarse velocity-space mesh near the homogeneous boundaries, and a fine mesh where the solution has fine features. Second, it is an inherently high-order, high-intensity method, requiring extra local computations to determine so-called optimal test functions, which makes it particularly suited to modern hardware in which floating-point throughput is increasing at a faster rate than memory bandwidth. Finally, DPG is a residual-minimizing method, which enables high-accuracy computation: in typical cases, the method delivers something very close to the $L^2$ projection of the exact solution. Meanwhile, the ML-based collision model we adopt affords a cost structure that scales as the square root of a standard direct evaluation. Moreover, we design our model to conserve mass, momentum, and energy by construction, and our approach to training is highly flexible, in that it can incorporate not only synthetic data from direct-simulation Monte Carlo (DSMC) codes, but also experimental data. We have developed two DPG formulations for Vlasov-Poisson: a time-marching, backward-Euler discretization and a space-time discretization. We have conducted a number of numerical experiments to verify the approach in a 1D1V setting. In this report, we detail these formulations and experiments. We also summarize some new theoretical results developed as part of this project (published as papers previously): some new analysis of DPG for the convection-reaction problem (of which the Vlasov equation is an instance), a new exponential integrator for DPG, and some numerical exploration of various DPG-based time-marching approaches to the heat equation. As part of this work, we have contributed extensively to the Camellia open-source library; we also describe the new capabilities and their usage. We have also developed a well-documented methodology for single-species collision operators, which we applied to argon and demonstrated with numerical experiments. We summarize those results here, as well as describing at a high level a design extending the methodology to multi-species operators. We have released a new open-source library, MLC, under a BSD license; we include a summary of its capabilities as well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Wilkins: HPC in situ workflows made easy

In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is designed for ease-of-use while providing scalable and efficient execution of workflow tasks. Wilkins provides a flexible workflow description interface, employs a high-performance data transport layer based on HDF5, and supports tasks with disparate data rates by providing a flow control mechanism. Wilkins seamlessly couples scientific tasks that already use HDF5, without requiring task code modifications. We demonstrate the above features using both synthetic benchmarks and two science use cases in materials science and cosmology.

HPC↗

NASA NTRS1978

Properties of the clouds of Venus, as inferred from airborne observations of its near-infrared reflectivity spectrum

The shape and absolute value of Venus' reflectivity spectrum is measured in the 1.2- to 4.0 micrometer spectral region with a circular variable filter wheel spectrometer having a spectral resolution of 1.5%. Comparing these spectra with synthetic spectra generated with a multiple-scattering computer code, a number of properties of the Venus clouds are inferred. Evidence is obtained indicating that the clouds are made of a water solution of sulfuric acid in their top unit optical depth, and that the clouds are made of this material down to an optical depth of at least 25. In addition, the acid concentration is 84 plus or minus 2% H2SO4 by weight in the top unit optical depth, the total optical depth of the clouds is 37.5 plus or minus 12.5, and the cross-sectional weighted mean particle radius lies between 0.5 and 1.4 micrometers in the top unit optical depth of the clouds. It is found that the average volume mixing ratio of H2SO4 and H2O contained in the cloud material both equal approximately 2 x 10 to the -6. Employing vapor pressure arguments, the acid concentration is shown to equal 84 plus or minus 6% at the cloud bottom and the water vapor mixing ratio beneath the clouds lies between 6 x 10 to the -4 and 10 to the -2.

Pollack, J. B.↗

DOE OSTI2026

Resource-Adaptive Federated Text Generation with Differential Privacy

In cross-silo federated learning (FL), sensitive text datasets remain confined to local organizations due to privacy regulations, making repeated training for each downstream task both communication-intensive and privacy-demanding. A promising alternative is to generate differentially private (DP) synthetic datasets that approximate the global distribution and can be reused across tasks. However, pretrained large language models (LLMs) often fail under domain shift, and federated finetuning is hindered by computational heterogeneity: only resource-rich clients can update the model, while weaker clients are excluded, amplifying data skew and the adverse effects of DP noise. We propose a flexible participation framework that adapts to client capacities. Strong clients perform DP federated finetuning, while weak clients contribute through a lightweight DP voting mechanism that refines synthetic text. To ensure the synthetic data mirrors the global dataset, we apply control codes (e.g., labels, topics, metadata) that represent each client’s data proportions and constrain voting to semantically coherent subsets. This two-phase approach requires only a single round of communication for weak clients and integrates contributions from all participants. Experiments show that our framework improves distribution alignment and downstream robustness under DP and heterogeneity.

Wang, Jiayi [ORNL]↗

NASA NTRS1999

Synthetic Scene Generation of Stennis Target Range for the Calibration of Remote Sensing Systems

The validation and verification (V&V) target range developed at Stennis Space Center is a useful tool for the calibration of remote sensing systems. In this paper, we present an algorithm for generating synthetic scenes or digital models of this target range. The atmospheric radiative transfer code Modtran 4 is used in simulating at-sensor radiance for a given sensor. Software is developed to generate the scene with different spatial and spectral resolutions using the at-sensor radiance values. Synthetic scenes provide a cost-effective way for in-flight validation for the spatial and radiometric accuracy of the data. Other applications include mission planning, sensor simulation, and trade-off analysis in sensor design. The radiometric and spatial accuracy of the simulation is evaluated by comparing simulated scenes with AVIRIS acquired values.

Cao, Chang-Yong↗

DOE OSTI2022

Predicted performance of a tangential viewing hard x-ray camera for the DIII-D high field side lower hybrid current drive experiment

High field side launch of lower hybrid current drive (LHCD) has improved accessibility and penetration over low field side launch on DIII-D. Simulations predict single pass absorption under a wide range of plasma conditions. Hard x-ray (HXR) measurement of LHCD generated fast electron bremsstrahlung (50–250 keV) will validate wave propagation and absorption. Emissivity profiles are recovered from one-dimensional inversion of HXR brightness to determine LH damping location, fast electron slowing down time, and some indication of the fast electron energy. The camera will be implemented by populating 32 tangential sightlines of the existing Gamma Ray Imager with Kromek SPEARTM Cadmium Zinc Telluride (CZT) detectors sensitive to 10–1000 keV photons with 10 keV energy resolution. Expected count rates allow for <0.5 ms time resolution. Pulses are processed using 50 ns shaping time Cremat CR-200 Gaussian shaping modules and are digitized by 25 MHz D-TACQ ACQ216 digitizers. The performance of the HXR camera is evaluated by comparing predicted fast electron density profiles and inverted synthetic brightnesses obtained from the ray-tracing/Fokker–Planck codes GENRAY/CQL3D. Inversions closely matched predicted fast electron profiles for a range of experimental parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

The Catalogue for Astrophysical Turbulence Simulations (CATS)

Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbulence and related processes such as star formation and cosmic ray propagation. However, data from numerical simulations of astrophysical turbulence are often not made public. We introduce a new simulation-oriented database for the astronomical community: the Catalogue for Astrophysical Turbulence Simulations (CATS), located at www.mhdturbulence.com. CATS includes magnetohydrodynamic (MHD) turbulent box simulation data products generated by the public codes athena++, arepo, enzo, and flash. CATS also includes several synthetic observational data sets, such as turbulent HI data cubes. We also include measured power spectra and three-point correlation functions from some of these data. We discuss the importance of open-source statistical and visualization tools for the analysis of turbulence simulations such as those found in CATS.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1995

Hubble Space Telescope high resolution spectroscoy of the exposed white dwarf in the dwarf nova VW Hydri in quiescence: A rapidly rotating white dwarf

We obtained a far-ultraviolet spectrum of the dwarf nova VW Hyi in quiescence, with the Hubble Space Telescope Goddard High Resolution Spectrograph covering the region of the Si iv lambda(lambda)1393, 1402 resonance doublet. The broad, shallow Si iv doublet feature is fully resolved, has a total equivalent width of 2.8 A, and is the first metal absorption feature to be clearly detected in the exposed white dwarf. Our synthetic spectral analysis, using a model grid constructed with the code TLUSTY, resulted in a reasonable fit to a white dwarf photosphere with T(sub eff) = 22,000 +/- 2000 K, log g = 8.0 +/- 0.3, an approximately solar Si/H abundance, and a rotational velocity, v sin i approximately equal to 600 km/s. This rotation rate, while not definitive because it is based upon just one line transition, is 20% of the Keplerian (breakup) velocity of the white dwarf and hence does not account for the unexpectedly low boundary-layer luminosity inferred from the soft-X-ray/extreme ultra-violet bands where most of the boundary-layer luminosity should be radiated. The predicted boundary-layer luminosity for a 0.6 solar mass white dwarf accreting at the rate 10(exp -10) solar mass/yr and rotating at 600 km/s, corresponding to VW Hyi in quiescence, is 2 x 10(exp 32) ergs/s when proper account is taken of the rotational kinetic energy going into spinning up the white dwarf. If the boundary-layer area is equal to that of the white dwarf, then T(sub bl) = 24,000 K. This is essentially identical to the photspheric luminosity and temperature determined in far-ultraviolet photospheric analyses. If the boundary-layer area is 10(exp -3) of the white dwarf surface area, then T(sub bl) = 136,000 K.

Sion, Edward M.↗

NASA NTRS2010

Creating Synthetic Coronal Observational Data From MHD Models: The Forward Technique

We present a generalized forward code for creating simulated corona) observables off the limb from numerical and analytical MHD models. This generalized forward model is capable of creating emission maps in various wavelengths for instruments such as SXT, EIT, EIS, and coronagraphs, as well as spectropolari metric images and line profiles. The inputs to our code can be analytic models (of which four come with the code) or 2.5D and 3D numerical datacubes. We present some examples of the observable data created with our code as well as its functional capabilities. This code is currently available for beta-testing (contact authors), with the ultimate goal of release as a SolarSoft package

Rachmeler, Laurel A.↗

DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗

DOE OSTI2023

Density wavenumber spectrum measurements, synthetic diagnostic development, and tests of quasilinear turbulence modeling in the core of electron-heated DIII-D H-mode plasmas

Abstract Measurements of the turbulent density wavenumber spectrum, δ n ˆ e ( k ⊥ ) , using the Doppler Back-Scattering (DBS) diagnostic are reported from DIII-D H-mode plasmas with electron cyclotron heating as the only auxiliary heating method. These electron-heated plasmas have low collisionality, ν e ∗ < 1 , T e / T i > 1 , and zero injected torque—a regime expected to be relevant for future fusion devices. We probe density fluctuations in the core ( ρ ≈ 0.7) over a broad wavenumber range, 0.5 ⩽ k ⊥ ⩽ 16 cm −1 ( 0.1 ⩽ k ⊥ ρ s ⩽ 5 ), to characterize plasma instabilities and compare with theoretical predictions. We present a novel synthetic DBS diagnostic to relate the back-scattered power spectrum, P s ( k ⊥ ) —which is directly measured by DBS—to the underlying electron density fluctuation spectrum, δ n ˆ e ( k ⊥ ) . The synthetic DBS P s ( k ⊥ ) spectrum is calculated by combining the SCOTTY beam-tracing code with a model δ n ˆ e ( k ⊥ ) predicted either analytically or numerically. In this work we use the quasi-linear code Trapped Gyro-Landau Fluid (TGLF) to approximate the δ n ˆ e ( k ⊥ ) spectrum. We find that TGLF, using the experimental profiles, is capable of closely reproducing the DBS measurements. Both the DBS measurements and the TGLF-DBS synthetic diagnostic show a wavenumber spectrum with variable decay. The measurements show weak decay ( k −0.6 ) for k < 3.5 cm −1 , with k −2.6 at intermediate- k ( 3.5 ⩽ k ⩽ 8.5 cm −1 ), and rapid decay ( k −9.4 ) for k > 8.5 cm −1 . Scans of physics parameters using TGLF suggest that the normalized ∇ T e scale-length, R / L T e , is an important factor for distinguishing microturbulence regimes in these plasmas. A combination of DBS observations and TGLF simulations indicate that fluctuations remain peaked at ITG-scales (low k ) while R / L T e -driven TEM/ETG-type modes (intermediate/high k ) are marginally sub-dominant.

synthetic diagnostics↗

DOE OSTI2024

Developing a pipeline to expand the genetic code of diverse bacteria for microbial engineering

Microbial biotechnologies are key to addressing grand challenges to promote human health, reverse carbon emissions, recycle mixed plastic waste, remediate contaminated soils, and achieve sustainable economies. Synthetic biology has enabled design of diverse microbes and their proteins for useful purposes, but the narrowness of the natural genetic code limits functional diversity (e.g., biosynthesis) of engineered microbes. The natural genetic code defines the fundamental rules of translating genetic information into proteins comprised of 22 ‘canonical’ amino acids. However, using a technique called genetic code expansion (GCE), the chemical properties and therefore functions of proteins can be transformed by incorporation of one or more of ~200 chemically diverse ‘non-canonical’ amino acids. The effective application of genetic code expansion in diverse microbes has the potential to revolutionize biotechnology. However, despite over 50 years of research and its transformative potential, the application of genetic code expansion has been limited to a handful of bacterial species. In this project, we will perform three tasks to both overcome the barriers that prevent wide spread adoption of GCE as molecular tool and demonstrate its potential for biotechnological applications. Specifically, we will (1) develop a genetic engineering methodology that will enable use of GCE in a broad range of bacterial hosts, (2) use high-throughput functional genomics methods to identify physiological responses to both genetic code expansion and exposure to non-canonical amino acids in three different bacteria, and (3) demonstrate an application of GCE by selectively incorporate non-canonical amino acids into surface displayed peptides such as those used for biomining.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1991

Performance effects of irregular communications patterns on massively parallel multiprocessors

A detailed study of the performance effects of irregular communications patterns on the CM-2 was conducted. The communications capabilities of the CM-2 were characterized under a variety of controlled conditions. In the process of carrying out the performance evaluation, extensive use was made of a parameterized synthetic mesh. In addition, timings with unstructured meshes generated for aerodynamic codes and a set of sparse matrices with banded patterns on non-zeroes were performed. This benchmarking suite stresses the communications capabilities of the CM-2 in a range of different ways. Benchmark results demonstrate that it is possible to make effective use of much of the massive concurrency available in the communications network.

Saltz, Joel↗

NASA NTRS1991

Performance effects of irregular communications patterns on massively parallel multiprocessors

Saltz, Joel↗