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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 OSTI2022

Atomic Mixing of Initially Separated Mixtures

We consider the atomic mixing of two materials, initially separated in space, at fixed temperature– pressure conditions [T, p], and zero mass-mean velocity. Each material is composed of a specified set of atomic species whose density corresponds to the specieswise real Equation of State (EOS) $\tilde{&#961}$ i [T, p]. Because there are no spatial gradients in the mean [T, p] state, the only mechanism for mixing is a specieswise nonequilibrium thermal velocity distribution. The recipe for nonequilibrium species mass fluxes is taken directly from Hirschfelder, Curtiss, and Bird, who express the diffusion velocities in terms of gradients in the molar concentrations and pairwise coefficients of diffusion. EOS data from the SESAME library is used for all species. Data for the specieswise self diffusion coefficients is adopted from the Orbital Free Molecular Dynamics (OFMD) simulations of Ticknor et al.; the so-called Darken approximation is used to express the mutual diffusion coefficients of a mixture in the manner used by White et al. A one-dimensional spatial domain is used; with the left half occupied by one multispecies material and the right half occupied by the other, and with zero–gradient boundary conditions at the ends of the domain. Hence the total mass in the domain is a constant. At time t = 0 the materials are separated. Accordingly, spatial gradients at the separation point are infinite and the species fluxes are also infinite. As t → $\textbf{∞}$ the spatial gradients approach zero and a uniform set of species concentrations is obtained; at this point the system is said to be at “thermodynamic equilibrium.” Analytic solution of the problem is not possible in general due to the nonlinear concentration dependence of the species fluxes. Hence we utilize the numerical solution apparatus in Mathematica for obtaining accurate time-dependent solutions for the mixing of various initial species masses for conditions of interest in laser-driven Inertial Confinement Fusion. Concentration profiles and the speed of propagation of the mixing fronts are discussed in light of the nonlinearity of the problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Methanol mixing−controlled compression ignition enabled via homogeneous charge compression ignition of dimethyl ether through catalytic decomposition of methanol

Methanol is a potentially attractive fuel for marine and off−road engines owing to its availability at bunkering and global distribution locations. Although methanol is well−distributed worldwide, its fuel chemistry and ignition properties make it poorly suited as a direct drop−in replacement for diesel fuel in compression−ignition engines. However, industrial processes are regularly used to convert methanol, via catalytic dehydration, to dimethyl ether (DME) over nonprecious metal catalysts. This chemical conversion can occur at relatively low pressures, temperatures, and catalyst space velocities, highlighting a potential opportunity to generate DME via onboard catalytic dehydration of methanol. DME’s fuel kinetic and ignition properties for compression ignition are much more favorable than those of methanol or even diesel fuel, but DME is more challenging than diesel fuel or methanol to pump, store, and deliver through conventional diesel fueling injection hardware. Thus, a potential opportunity exists to use the ignition and kinetic properties of DME, with the transportation and delivery advantages of methanol, in a methanol−fueled mixing−controlled compression−ignition engine. The present work explores performance, combustion behavior, and emissions reduction opportunities for methanol mixing−controlled combustion, enabled by a HCCI of DME that represents a small fraction of the total fuel energy that can be generated onboard via catalytic dehydration of methanol.

Splitter, Derek [ORNL] (ORCID:0000000174044047)↗
DOE OSTI2027

Hydrotreating pine-derived catalytic fast pyrolysis oil to jet fuel: Process durability and impact of operating conditions

Hydrotreating woody biomass-derived catalytic fast pyrolysis (CFP) oil to jet fuel has the potential to enhance energy security due to the large abundance of forest and woody resources. CFP oil produced from pine over ZSM-5 was hydrotreated over sulfided NiMo/Al 2 O 3 for 410 h to study the impact of hydrotreating conditions (pressure, temperature, and weight hourly space velocity (WHSV)). The degree of deoxygenation remained high across the tested hydrotreating conditions, and the products contained < 0.01 wt% oxygen. Increasing hydrotreating pressure from 84 to 125 bar enhanced hydrogenation of aromatic rings and the formation of cycloalkanes and increasing the temperature from 385 to 400 degrees C enhanced cracking and the formation of jet-range molecules. Decreasing the weight hourly space velocity from 0.2 to 0.1 g/(g cat h) further enhanced cracking and led to higher gasoline- and lower jet fuel-range fractions. The highest jet fuel fraction (49 wt%) was obtained at 400 degrees C, 125 bar, 0.2 g/(g cat h) WHSV. CFP oils produced from pine over phosphorous-modified ZSM-5 were hydrotreated at this condition for 588 h to investigate the process durability. The hydrogenation performance of the catalyst gradually stabilized in approximately 300 h after which the product composition remained constant. Of the final hydrotreated CFP oil, 53 wt% fell in the jet fuel range with most of the tested fuel properties meeting ASTM D4054 and/or ASTM D7566 specifications. The freeze points were < -50 degrees C vs. guideline of < -40 degrees C and the viscosities at -20 degrees C were 3.8-4.1 mm 2 /s vs guideline of 8 mm 2 /s. Increasing the fraction of cycloalkanes could increase the cetane number and heating value, which did not meet the guidelines.

09 BIOMASS FUELS↗
DOE OSTI2021

Development of a cosmic ray oriented trigger for the fluorescence telescope on EUSO-SPB2

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2), in preparation, aims to make the first observations of Ultra-High Energy Cosmic Rays (UHECRs) from near space using optical techniques. EUSO-SPB2 will prototype instrumentation for future satellite-based missions, including the Probe of Extreme Multi-Messenger Astrophysics (POEMMA) and K-EUSO. The payload will consist of two telescopes. The first is a Cherenkov telescope (CT) being developed to quantify the background for future below-the-limb very high energy (E 10 PeV) astrophysical neutrino observations, and the second is a fluorescence telescope (FT) being developed for detection of UHECRs. The FT will consist of a Schmidt telescope, and a 6192 pixel ultraviolet camera with an integration time of 1.05 s. The first step in the data acquisition process for the FT is a hardware level trigger in order to decide which data to record. In order to maximize the number of UHECR induced extensive air showers (EASs) which can be detected, a novel trigger algorithm has been developed based on the intricacies and limitations of the detector. Finally, the expected performance of the trigger has been characterized by simulations and, pending hardware verification, shows that EUSO-SPB2 is well positioned to attempt the first near-space observation of UHECRs via optical techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A robust three-parameter reference curve for condensed phase materials

Many isothermal equations of state lead to numerical problems under tension conditions due to negative bulk modulus. We summarize the properties of four existing models and devise a continuous non-piecewise reference curve to address this issue. The final functional form contains three fitting parameters that are directly related to the bulk modulus and its pressure derivative at the reference state and at infinite pressures. We also include bounds that guarantee the monotonicity of its pressure derivative in all volume space. To demonstrate the qualitative features of each isotherm, we conduct calibrations against experimental data for cyclotetramethylene tetranitramine, magnesium oxide, gold, and platinum. Furthermore, pressure residuals and root mean square deviations show that the proposed functional form is capable of adequately fitting the data compared to other three-parameter models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2025

Evaluation of Hanford Supernatant Waste Evaporation

Storage space within Hanford’s double shell tanks is running low, with only an approximate 4.1 Mgal of space remaining. Construction of additional storage space is not a pursuable option, but treatment of the waste via vacuum evaporation may be used to reduce the waste volume and increase the available amount of free space. However, without proper care and consideration, concentrating the waste may lead to harmful effects such as excessive solids generation, growth in liquid density and ionic concentration, and development of aggressive properties in the solution. OLI Studio was used to simulate a subset of DSTs using BBI ionic concentrations to generate recipes of every tank’s supernatant layer. Vacuum evaporation was performed by taking each simulated solution and bringing it to 50°C under a pressure of 60 Torr, after which the vapor phase was removed from the simulation, and the solution was brought back to ambient temperature and pressure. Several million gallons of space were created, and only about 100-200 kgal of solids were precipitated during evaporation. Minimal changes to the solution’s pitting factor occurred, and no solution is expected to develop aggressive characteristics due to evaporation. Although a theoretical result is provided, it is unique to the specific temperature and pressure value chosen for the simulation, and future transfers of waste are expected to cause changes in waste properties, which would cause the OLI recipes to lose accuracy. Though the results are not anticipated to be used as a target value or a guide, as an exercise, they show that the extent of volume gains could be equivalent to the construction of new DSTs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Hydrogenation and C-S bond activation pathways in thiophene and tetrahydrothiophene reactions on sulfur-passivated surfaces of Ru, Pt, and Re nanoparticles

Thiophene-H2 reactions proceed via sulfur removal and hydrogenation routes on dispersed metal nanoparticles that become decorated by refractory S-adlayers during catalysis. The identity and kinetic relevance of the required elementary steps are described here based on rates measured at S-chemical potentials set by H2S/H2 ratios similar to those prevalent during practical catalysis on Re, ReSx, Ru, and Pt catalysts. Free energies for S adatom formation (from H2S decomposition and H2 evolution) are strongly exothermic (< -50 kJ mol-1 on Pt(111) and < -150 kJ mol-1 on Re and Ru(0001)), but strong repulsions between S adatoms cause adsorption free energies to increase significantly with coverage on all three surfaces, preventing complete monolayer formation. These adlayers, composed of unreactive S-atoms (S') that cover 1/3–2/3 ML leave residual interstitial spaces (*) that bind S-atoms (S*), intermediates, and transition states reversibly, as required for catalytic turnovers. The number and binding properties of these interstices depend on the identity and chemical state of the nanoparticle bulk phase, which influences S'-binding and coverages and cause large differences in direct desulfurization and hydrogenation turnover rates (per exposed metal atom) on dispersed Re, ReSx, Ru, and Pt. The identity and kinetic relevance of elementary steps for desulfurization (to C4¬ hydrocarbons) and hydrogenation (to tetrahydrothiophene; THT) are similar among these catalysts; they involve the kinetically-relevant formation of a thiophene-derived intermediate (monohydrothiophene on Re and ReSx; dihydrothiophene on Ru and Pt) that either cleaves its C-S bond or “over-hydrogenates” to THT in one surface sojourn. THT then undergoes C-S bond cleavage in secondary reactions that correct such over-hydrogenation to form the more unsaturated species that cleave C-S bonds. THT/C4 product ratios are insensitive to H2S/H2 ratios and thiophene pressure, even though active interstitial spaces are covered by kinetically-detectable coverages of S* and thiophene; therefore, primary and secondary reactions must involve the same active surfaces. The observed increase in THT/C4 ratios with H2 pressure shows that THT formation transition states involve a larger number of H-atoms than for C-S cleavage. The requirement for bound species with intermediate unsaturation (between THT and thiophene) for C-S bond cleavage is reminiscent of the H-shuttling required in C-C and C-O hydrogenolysis, reactions that involve the partial dehydrogenation of alkanes and alkanols, respectively, to weaken such bonds and to increase the formation entropy of the relevant transition states via the evolution of H2(g). These mechanistic details challenge prevalent paradigms about different site requirements for hydrogenation and desulfurization pathways and about how metal-sulfur bond energies act as descriptors of reactivity; in fact, such binding energies merely act to define the refractory S-adlayers that enable the formation of weakly-binding interstices that reversibly bind intermediates and transition states, thus allowing catalytic turnovers.

Yik, Edwin↗
DOE OSTI2022

Parametric studies of stream instability-induced higher harmonics in plasma ionization breakdown near an emissive dielectric surface

Here, in this work, we comprehensively investigate the generation of higher harmonic (HH) electric fields normal to the applied rf electric field in multipactor-coexisting plasma breakdown by fully kinetic particle-in-cell (PIC) simulations and a theoretical model. Firstly, a base case at driving frequency 1 GHz, transverse rf electric field amplitude 3 MV m –1 , and background gas pressure 0.2 Torr, is studied in detail. The enhanced harmonic frequency observed is around ten times the fundamental rf frequency, significantly lower than the Langmuir frequency. A theoretical model reveals that the fundamental mechanism of HHs generation is stream–plasma instability, which originates from stream-like secondary electron emission interacting with plasma. The resulting HH frequency and the growth rate of its oscillating amplitude from the theoretical model, agree well with the PIC simulations. With increasing pressure, the HH oscillations are found to be significantly reduced. This is because at higher pressure the gas ionization rate is higher, which causes a more rapidly increasing plasma density, leaving less time for the growth of instability. Furthermore, the parameter space in terms of background gas pressure and rf field amplitude within which the HHs appear is revealed. Finally, the effect of the driving rf frequency on HHs is also investigated, and it is found that the instability-induced oscillating HHs field remains when the driving frequency is increased to 2 GHz, however, it is highly reduced at higher driving frequency of 5 GHz, as oscillations at the fundamental frequency start playing a more important role.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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 OSTI2024

Engineered $\mathrm{Ru}$ on $\mathrm{HY}$ zeolite catalyst for continuous and selective hydrodeoxygenation of lignin phenolics to cycloalkanes under moderate conditions

Here we report a continuous selective hydrodeoxygenation (HDO) process of guaiacol conversion to cycloalkanes under 180 °C/1 MPa, which results in improved HDO chemistry for lignin-based jet fuel production. The incipient wetness impregnation method was modified to prepare an HY zeolite-supported Ru catalyst with better metal dispersion and acid site uniformity, which overcomes the low conversion and selectivity of previous literature. The modified catalyst (Ru-HY-60-MI) was tested in a continuous fixed bed reactor, resulting in increased HDO conversion of guaiacol to cycloalkanes compared to the unmodified catalyst. Pressure, temperature, and weight hourly space velocity-dependent tests validate guaiacol HDO over Ru-HY-60-MI catalyzed ring hydrogenation of guaiacol to 2-methoxycyclohexanol, acid-catalyzed demethoxylation and dehydration to cyclohexene, and further hydrogenation of cyclohexene to cyclohexane. These experiments enable exploring a continuous HDO process, demonstrate effectiveness for other ß-ß and a-O-4 lignin representatives and real lignin bio-oil, and pave the way towards commercialization of lignin-based jet fuel

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Ultrafast olivine-ringwoodite transformation during shock compression

Meteorites from interplanetary space often include high-pressure polymorphs of their constituent minerals, which provide records of past hypervelocity collisions. These collisions were expected to occur between kilometre-sized asteroids, generating transient high-pressure states lasting for several seconds to facilitate mineral transformations across the relevant phase boundaries. However, their mechanisms in such a short timescale were never experimentally evaluated and remained speculative. Here, we show a nanosecond transformation mechanism yielding ringwoodite, which is the most typical high-pressure mineral in meteorites. An olivine crystal was shock-compressed by a focused high-power laser pulse, and the transformation was time-resolved by femtosecond diffractometry using an X-ray free electron laser. Our results show the formation of ringwoodite through a faster, diffusionless process, suggesting that ringwoodite can form from collisions between much smaller bodies, such as metre to submetre-sized asteroids, at common relative velocities. Even nominally unshocked meteorites could therefore contain signatures of high-pressure states from past collisions.

42 ENGINEERING↗
DOE OSTI2026

A wide-range thermodynamically consistent constitutive model for dynamic loading of partially saturated porous media

SUMMARY This paper presents a novel thermodynamically consistent constitutive model for partially saturated porous rocks across a wide range of conditions. The material states generated behind the shock wave from an explosive source can vary significantly, ranging from crushed and melted rock near the source to a poroelastic medium in the far field. In the model, rock strength is determined by the effective pressure, which is calculated using two independent equations of state: one for the solid rock and another for the pore fluid. The model accounts for shock-induced liquefaction resulting from fluid pressure buildup in the pore spaces near the explosive source. Simultaneously, it describes the increase in wave propagation speed due to elastic pore contraction in both dry and partially saturated rocks. This model is applied to investigate how fluid saturation affects the amplitude and shape of the generated waves, as well as the residual stress surrounding the cavity formed by spherical explosions.

Vorobiev, Oleg [Lawrence Livermore National Labora↗
DOE OSTI2022

Passive spiral galaxies deeply captured by Subaru Hyper Suprime-Cam

Abstract This paper presents a thousand passive spiral galaxy samples at z = 0.01–0.3 based on a combined analysis of the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) and the GALEX–SDSS–WISE Legacy Catalog (GSWLC-2). Among 54871 gri galaxy cutouts taken from the HSC-SSP PDR3 over 1072 deg2, we conducted a search with deep-learning morphological classification for candidates of passive spirals below the star-forming main sequence derived by ultraviolet to mid-infrared spectral energy distribution fitting in the GSWLC-2. We then classified the candidates into 1100 passive spirals and 1141 secondary samples based on visual inspections. Most of the latter cases are considered to be passive ringed S0 or pseudo-ringed galaxies. The remaining secondary samples have ambiguous morphologies, including two peculiar objects with diamond-shaped stellar wings. The selected passive spirals have a similar distribution to the general quiescent galaxies on the EWHδ–Dn4000 diagram and concentration indices. Moreover, we detected an enhanced passive fraction of spiral galaxies in X-ray clusters. Passive spirals in galaxy clusters are preferentially located in the midterm or late infall phase on the phase–space diagram, supporting the ram pressure scenario, which has been widely advocated in previous studies. The source catalog and gri-composite images are available on the HSC-SSP PDR3 website 〈https://hsc.mtk.nao.ac.jp/ssp/data-release/〉. Future updates, including integration with a citizen science project dedicated to the HSC data, will achieve more effective and comprehensive classifications.

Shimakawa, Rhythm (ORCID:0000000344422750)↗
DOE OSTI2022

High-Temperature Ceramic-Carbonate Dual-Phase Membrane Reactor for Pre-combustion Carbon Dioxide Capture (Final Scientific/Technical Report)

Arizona State University, in collaboration with University of South Carolina, worked on a project aimed at development of a new high temperature, high pressure CO 2 perm-selective membrane reactor for water-gas-shift reaction (WGS) with simulated gasifier syngas to produce a high concentration H 2 stream with CO 2 capture. The membrane reactor is made of a CO 2 semi-permeable ceramic-carbonate dual-phase (CCDP) membrane with high CO 2 perm-selectivity/permeance and thermal/mechanical stability for application in WGS reaction. The objectives of this project were to (1) synthesize the chemically/thermally stable tubular CCDP membranes with CO 2 permeance and selectivity (with respect to H 2 , CO or H 2 O) larger than 6.5×10-7 mol/m2·s·Pa and 500, respectively; (2) establish CCDP membrane reactor setup and study high pressure CO 2 permeation and WGS reaction with CO 2 capture using the setup; and (3) identify conditions for WGS in the CCDP membrane reactor that produce CO 2 and H 2 streams with purity of >99% and >90% respectively at CO conversion >95% and overall carbon capture >90%. The work in this project included both membrane development and membrane reactor process study. The membrane development efforts were focused on investigating a H 2 S resistant and highly oxygen-ionic conducting metal oxide material and membrane for CO 2 separation, fabrication of tubular samaria-doped-ceria/molten-carbonate CCDP membrane with high mechanical strength, and experimental and modeling study of high-pressure CO 2 permeation of the CCDP membranes. Mathematical models were developed to describe WGS in the CCDP membrane reactor without a catalyst or packed with a commercial high temperature WGS catalyst. Experiments on WGS in the CCDP membrane reactor with the commercial WGS catalyst, guided by the model analysis, were performed to identify optimum conditions for achieving the CO conversion, carbon capture, and the purity of the H 2 and CO 2 streams mentioned above. At 30 atm feed pressure, 750°C operation temperature, space velocity of 250 h-1, and with steam sweep, a single-stage CCDP membrane reactor with average CO 2 permeation flux of 0.5 cm3(STP)/min.cm2 can achieve CO 2 conversion of 95% and overall carbon capture of 94%, and produce CO 2 and H 2 streams with dry-based purity of >99% and 92% respectively. The project also included process design and techno-economic analysis (TEA) for a CCDP membrane reactor process for WGS reaction with CO 2 capture for a 550 MW coal-fired IGCC power plant, and its comparison with the conventional fixed-bed reactor system for WGS with follow-up CO 2 capture by an amine absorption process. The target performance for the reactor for WGS with CO 2 capture includes CO conversion >95%, hydrogen stream purity >90%, CO 2 stream purity >95%, and total carbon capture >90%. The CCDP membrane developed in this project can achieve the performance target, without subsequent CO 2 capture process at the optimum conditions identified in this project. The outcome of the process design and TEA analysis shows that the membrane reactor for WGS with in-situ CO 2 capture has an operating cost about 40% lower than that for the conventional fixed-bed reactor with a separate amine absorption process for CO 2 capture. However, the capital cost of the membrane reactor process is about twice that of the conventional process because of the higher cost of the CCDP membrane. Modeling analysis shows that a membrane reactor using a CCDP membrane with higher CO 2 permeance (about three times the current value) can deliver the targeted performance for WGS reaction with CO 2 capture at a much higher space velocity and lower membrane surface area to catalyst volume ratio, leading to a smaller catalyst amount and/or membrane area and hence significantly reduced membrane reactor capital costs.

20 FOSSIL-FUELED POWER PLANTS↗