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DOE OSTI2024

Phase and Morphology of Water-ice Grains Formed in a Cryogenic Laboratory Plasma

Abstract Grains of ice are formed spontaneously when water vapor is injected into a weakly ionized laboratory plasma in which the background gas has been cooled to cryogenic temperatures comparable to those of deep space. These ice grains are levitated indefinitely within the plasma so that their time evolution can be observed under free-floating conditions. Using microscope imaging, ice grains are shown to have a spindle-like fractal structure and grow over time. Both crystalline and amorphous phases of ice are observed using Fourier transform infrared spectroscopy. A mix of crystalline and amorphous grains coexists under certain thermal conditions, and a linear mixing model is used on the ice absorption band surrounding 3.2 μ m to examine the ice phase composition and its temporal stability. The extinction spectrum is also affected by inelastic scattering as grains grow, and characteristic grain radii are obtained from Mie scattering theory and compared to size measurements from direct imaging. Observations are used to compare possible ice nucleation mechanisms, and it is concluded that nucleation is likely catalyzed by ions, as ice does not nucleate in the absence of plasma and impurities are not detected. Ice grain properties and infrared extinction spectra show similarity to observations of some astrophysical ices observed in protoplanetary disks, implying that the fractal morphology of the ice and observed processes of homogeneous ice nucleation could occur as well in such astrophysical environments with weakly ionized conditions.

Astronomy & Astrophysics↗

DOE OSTI2022

Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods

Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.

Lu, Dan↗

DOE OSTI2020

Ducted fuel injection

Various technologies presented herein relate to heating a fuel and enhancing fuel and charge-gas mixing inside a combustion chamber to enable minimal, or no, generation of soot and/or other undesired emissions during ignition and subsequent combustion of the locally premixed mixtures. To enable sufficient mixing of the fuel and charge-gas, a jet of fuel can be directed to pass through a bore of a duct causing charge-gas to be drawn into the bore creating turbulence to mix the fuel and the drawn charge-gas. The duct can be heated to provision heating of the fuel and charge-gas mixture. The duct can be located proximate to an opening in a tip of a fuel injector. An ignition assist component can be located downstream of the duct to facilitate ignition of the fuel/charge-gas mixture.

42 ENGINEERING↗

DOE OSTI2022

Transport of polymer-coated metal–organic framework nanoparticles in porous media

Abstract Injecting fluids into deep underground geologic structures is a critical component to development of long-term strategies for managing greenhouse gas emissions and facilitating energy extraction operations. Recently, we reported that metal–organic frameworks are low-frequency, absorptive-acoustic metamaterial that may be injected into the subsurface to enhance geophysical monitoring tools used to track fluids and map complex structures. A key requirement for this nanotechnology deployment is transportability through porous geologic media without being retained by mineral-fluid interfaces. We used flow-through column studies to estimate transport and retention properties of five different polymer-coated MIL-101(Cr) nanoparticles (NP) in siliceous porous media. When negatively charged polystyrene sulfonate coated nanoparticles (NP-PSS-70K) were transported in 1 M NaCl, only about 8.4% of nanoparticles were retained in the column. Nanoparticles coated with polyethylenimine (NP-PD1) exhibited significant retention (> 50%), emphasizing the importance of complex nanoparticle-fluid-rock interactions for successful use of nanofluid technologies in the subsurface. Nanoparticle transport experiments revealed that nanoparticle surface characteristics play a critical role in nanoparticle colloidal stability and as well the transport.

58 GEOSCIENCES↗

DOE OSTI2023

Nuclear magnetic resonance and molecular simulation study of H 2 and CH 4 adsorption onto shale and sandstone for hydrogen geological storage

Understanding pure H 2 and H 2 /CH 4 adsorption and diffusion in earth materials is one vital step toward a successful and safe H 2 storage in depleted gas reservoirs. Despite recent research efforts such understanding is far from complete. In this work we first use Nuclear Magnetic Resonance (NMR) experiments to study the NMR response of injected H 2 into Duvernay shale and Berea sandstone samples, representing materials in confining and storage zones. Then we use molecular simulations to investigate H 2 /CH 4 competitive adsorption and diffusion in kerogen, a common component of shale. Our results indicate that in shale there are two H 2 populations, i.e., free H 2 and adsorbed H 2 , that yield very distinct NMR responses. However, only free gas presents in sandstone that yields a H 2 NMR response similar to that of bulk H 2 . About 10 % of injected H 2 can be lost due to adsorption/desorption hysteresis in shale, and no H 2 loss (no hysteresis) is observed in sandstone. Here, our molecular simulation results support our NMR results that there are two H 2 populations in nanoporous materials (kerogen). The simulation results also indicate that CH 4 outcompetes H 2 in adsorption onto kerogen, due to stronger CH 4 -kerogen interactions than H 2 -kerogen interactions. Nevertheless, in a depleted gas reservoir with low CH 4 gas pressure, about ~30 % of residual CH 4 can be desorbed upon H 2 injection. The simulation results also predict that H 2 diffusion in porous kerogen is about one order of magnitude higher than that of CH 4 and CO 2 . This work provides an understanding of H 2 /CH 4 behaviors in deleted gas reservoirs upon H 2 injection and predictions of H 2 loss and CH 4 desorption in H 2 storage.

08 HYDROGEN↗

DOE OSTI2021

Atacama Cosmology Telescope: Modeling the gas thermodynamics in BOSS CMASS galaxies from kinematic and thermal Sunyaev-Zel’dovich measurements

The thermal and kinematic Sunyaev-Zel'dovich effects (tSZ, kSZ) probe the thermodynamic properties of the circumgalactic and intracluster medium (CGM and ICM) of galaxies, groups, and clusters, since they are proportional, respectively, to the integrated electron pressure and momentum along the line-of-sight. We present constraints on the gas thermodynamics of CMASS galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) using new measurements of the kSZ and tSZ signals obtained in a companion paper. Combining kSZ and tSZ measurements, we measure within our model the amplitude of energy injection $\epsilon M_\star c^2$, where $M_\star$ is the stellar mass, to be $\epsilon=(40\pm9)\times10^{-6}$, and the amplitude of the non-thermal pressure profile to be $\alpha_{\rm Nth}<0.2$ (2$\sigma$), indicating that less than 20% of the total pressure within the virial radius is due to a non-thermal component. We estimate the effects of including baryons in the modeling of weak-lensing galaxy cross-correlation measurements using the best fit density profile from the kSZ measurement. Our estimate reduces the difference between the original theoretical model and the weak-lensing galaxy cross-correlation measurements in arXiv:1611.08606 by half, but does not fully reconcile it. Comparing the kSZ and tSZ measurements to cosmological simulations, we find that they under predict the CGM pressure and to a lesser extent the CGM density at larger radii. This suggests that the energy injected via feedback models in the simulations that we compared against does not sufficiently heat the gas at these radii. We do not find significant disagreement at smaller radii. These measurements provide novel tests of current and future simulations. Here, this work demonstrates the power of joint, high signal-to-noise kSZ and tSZ observations, upon which future cross-correlation studies will improve.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Motion of Air Bubbles in a Cement Slurry

The dynamics of air (gas) bubbles in a column of cement slurry is examined numerically. The air injected at the bottom of a laboratory-scale column through a porous distributor plate spatially distributes and migrates as a swarm of bubbles throughout the slurry toward the freeboard. The two-phase system of the cement slurry and the air bubbles is modeled using the conservation equations of mass and linear momentum in the framework of the volume-of-fluid (VOF) approach. The cement slurry is modeled using the Herschel–Bulkley and Bingham fluid models. Results show that the mean Sauter diameter and the mean rise velocity of the bubbles decrease with the gas flow rate. Meanwhile, it is found that the rising of the bubbles is controlled by breakup events, along with relatively weak path instabilities of the bubbles resulting in relatively straight trajectories, independent of the gas flow rate. The extent of the yielded region appears larger for the Herschel–Bulkley model compared to the Bingham fluid model (by approximately 10%).

36 MATERIALS SCIENCE↗

DOE OSTI2023

A unified non-equilibrium phase change model for injection flow modeling

The homogenous relaxation model (HRM) is one of the most widely used models to describe the liquid- gas phase transition. However, in its original formulation, it is unable to handle multispecies vapor-liquid equilibrium (VLE), which limits its applicability to single-component fluids. In this work, a unified non-equilibrium phase change model that considers the VLE of multicomponent mixtures is proposed building upon the HRM's structure. A time factor is introduced to mimic the effect of different phase change timescales due to different mechanisms, e.g., cavitation, flash-boiling, and evaporation. Here to assess the model's performance, computational fluid dynamics simulations of the internal and near-nozzle injection flow of the Engine Combustion Network's Spray G injector were performed using the nine-component PACE-20 fuel with both the unified model and the original HRM. The predicted fuel density in the near-nozzle region matched well with X-ray tomography measurements. The simulation results indicated that, whereas the HRM failed to capture the vaporization due to convective mixing between the fuel and ambient gas, the unified model performed well in predicting the mixing-driven vaporization and the corresponding evaporative cooling. Further comparisons using the nine-component fuel formula and a single-component fuel surrogate demonstrated the unified model's ability to predict preferential vaporization, which affects the predictions of local mixture composition and rate of vaporization. Finally, it is shown that the unified model is capable of representing multiple phase change mechanisms, and the relaxation time factor plays an important role in determining the degree of phase change due to the different mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Investigating Liquid Water Transport in Different Pore Structure of Gas Diffusion Layers for PEMFC Using Lattice Boltzmann Method

Proton exchange membrane fuel cells (PEMFC) require a gas diffusion layer (GDL) to aid in the transport of liquid fuel to the catalyst layer. In this work, direct modeling using the Lattice Boltzmann Method (LBM) was applied to X-ray CT scans of four different carbon gas diffusion layers to understand the mass transport properties through the samples. Three injection orientations were used to study local saturation levels, water evolution through the sample, and mass transport behavior at breakthrough conditions. The LBM, combined with computational fluid dynamic modeling techniques, can accurately predict liquid saturation at the macro and micro scale, which provides more insight into the mass transport phenomena through the GDL. The change of pore structure and orientation in both the in-plane and through-plane determines the path that liquid water must take, which could aid or impact PEMFC performance. The outcomes from this work will also benefit any research that needs knowledge of internal mass transport qualities of gas diffusion media.

Sepe, M.↗

DOE OSTI2024

Geochemical and Microbial Dynamics of Hydrogen in a Methane Storage Reservoir

Hydrogen has been identified as a flexible energy carrier with zero or negative emission across multiple energy systems, and existing natural gas infrastructure could be leveraged if hydrogen gas (H2) was blended with methane (CH4). For example, subsurface methane storage reservoirs could be slightly modified to also store hydrogen if a methane/hydrogen blend were injected. However, the compatibility of methane storage reservoirs to include H2 injection has not been fully demonstrated, and this could lead to geochemical and microbiological reactions that alter the reservoir and stored gas content. It is essential that we understand the impact of H2 gas on the biogeochemistry of subsurface storage reservoirs before deploying large-scale H2-CH4 storage, We collected produced fluid from two separate methane storage reservoirs in the Southwestern US. First, we completed a baseline analysis of the biogeochemistry through qPCR, 16S rRNA sequencing, metagenomic sequencing, and geochemical analysis. Each reservoir was found to have unique geochemical conditions and a unique microbial community structure, with Site 1 having a higher TDS and an abundance of Shewanella and Site 2 having a lower TDS and high abundance of Eubacterium and Acetobacterium. Next, we ran a series of high pressure, high temperature reactors under hydrogen storage conditions with the biological sample from one of the storage reservoirs and a 20% H2-80% CH4 gas blend for up to 7 days. Our results show a decrease of hydrogen by 5% in reactors as early as 1-3 days. Previous hydrogen storage work has linked subsurface microorganisms with methanogenesis hydrogen sulfide production, acid production, and microbial corrosion. Our results show minimal change in the fluid chemistry, with the exception of a decrease in dissolved sulfate concentrations. Taxonomic sequencing demonstrated the presence of microorganisms capable of iron redox, acid generation, and hydrogen sulfide production throughout the reactors, suggesting microbial hydrogen consumption may occur through various metabolic pathways. This work demonstrates that site-specific geochemistry and microbiology may impact the efficiency of hydrogen storage in methane storage reservoirs.

environmental microbiology↗

DOE OSTI2025

Laboratory evaluation of cyclic underground hydrogen storage in the Temblor sandstone of the San Joaquin Basin, California

Underground Hydrogen Storage (UHS) in depleted oil and gas reservoirs could provide a cost-effective solution to balance seasonal fluctuations in renewable energy generation. However, data and knowledge on UHS at subsurface conditions are limited so it is difficult to estimate how effective this type of storage could be. In this study, we perform high pressure experiment to measure the effectiveness of cyclic hydrogen (H 2 ) storage in a specimen of Temblor sandstone retrieved from the San Joaquin Basin of California. Our experiment mimics reservoir pressure conditions to measure H 2 -brine relative permeability and fluid-rock interactions over the course of ten charging and discharging cycles. Initial gas breakthrough occurred at 15 % to 25 % H2 saturation in the specimen with 3 % NaCl brine as the resident fluid. Continuing injecting to 4 pore volumes (PV) of H 2 yielded an asymptotic H 2 saturation of 38 % to 41 %, a level often referred to as the irreducible gas saturation based on two-phase flow. The boundary condition in this study mimics the near wellbore region, which experiences bi-directional H 2 flow. This bi-directional flow led to evaporative drying of the specimen resulting in 94 % H 2 saturation at the end of 10th cycle. This indicates that cyclic flow and evaporative drying can lead to more efficient reservoir storage where a larger fraction of the reservoir porosity is usable to store H 2 . The produced gas stream consisted of H 2 mixed with 8 % to 22 % H 2 O, indicating formation dry-out by evaporation. Meanwhile, produced water chemistry indicated calcite and silicate dissolution, with calcite sourced from fossil fragments. This led to a loss of cementation and weakened the rock sample. Combined, our results indicate dry-out, compaction, increased H 2 saturation, rock weakening, and permeability loss during cyclic UHS. Overall, we anticipate that the combined effects should lead to higher than anticipated UHS storage efficiency per volume of sandstone reservoir rock.

08 HYDROGEN↗

DOE OSTI2025

Testing strong-field QED with the avalanche precursor

A two-beam high-power laser facility is essential for the study of one of the most captivating phenomena predicted by strong-field quantum electrodynamics (QED) and yet unobserved experimentally: the avalanche-type cascade. In such a cascade, the energy of intense laser light can be efficiently transformed into high-energy radiation and electron–positron pairs. The future 50-petawatt-scale laser facility NSF OPAL will provide unique opportunities for studying such strong-field QED effects, as it is designed to deliver two ultra-intense, tightly focused laser pulses onto the interaction point. In this work, we investigate the potential of such a facility for studying elementary particle and plasma dynamics deeply in the quantum radiation-dominated (RD) regime, and the generation of QED avalanches. With three-dimensional particle-in-cell simulations, we demonstrate that QED avalanche precursors can be reliably triggered under realistic laser parameters and layout (namely, focusing f/2, tilted optical axes, and non-ideal co-pointing) with the anticipated capabilities of NSF OPAL. We demonstrate that seed electrons can be efficiently injected into the laser focus by using targets of three types: a gas of heavy atoms, an overcritical plasma, and a thin foil. A strong positron and high-energy photon signal is generated in all cases. The cascade properties can be identified from the final particle distributions, which have a clear directional pattern. At increasing laser field intensity, such distributions provide signatures of the transition, first, to the RD interaction regime, and then to a QED avalanche. Our findings can also be used for designing related future experiments.

Lasers↗

DOE OSTI2023

How do supernova remnants cool? – I. Morphology, optical emission lines, and shocks

ABSTRACT Supernovae (SNe) inject ∼1051 erg in the interstellar medium, thereby shocking and heating the gas. A substantial fraction of this energy is later lost via radiative cooling. We present a post-processing module for the flash code to calculate the cooling radiation from shock-heated gas using collisional excitation data from mappings v. When applying this tool to a simulated SN remnant (SNR), we find that most energy is emitted in the EUV. However, optical emission lines ([O iii], [N ii], [S ii], H α, H β) are usually best observable. Our shock detection scheme shows that [S ii] and [N ii] emissions arise from the thin shell surrounding the SNR, while [O iii], H $\rm \alpha$, and H $\rm \beta$ originate from the volume-filling hot gas inside the SNR bubble. We find that the optical emission lines are affected by the SNR’s complex structure and its projection on to the plane of the sky because the escaping line luminosity can be reduced by 10–80 per cent due to absorption along the line of sight. Additionally, the subtraction of contaminating background radiation is required for the correct classification of an SNR on the oxygen or sulphur BPT diagrams. The electron temperature and density obtained from our synthetic observations match well with the simulation but are very sensitive to the assumed metallicity.

Astronomy & Astrophysics↗

DOE OSTI2021

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Geochemical Impact of Acid Spearhead and Slickwater Stimulation on Wolfcamp Shale from the Hydraulic Fracturing Test Site

The Hydraulic Fracturing Test Site 1 (HFTS-1) was a field study performed in the Wolfcamp Formation in the West Texas Permian (Midland) Basin, USA, with a focus on improving the efficiency of hydraulic fracturing. Investigating site-specific rock-fluid geochemical interactions during hydraulic fracturing is an important step to understanding the impact on formation shale porosity, permeability, and long-term shale gas production. During field operations in this region, hydraulic fracturing fluid (HFF) injection usually starts with a concentrated acid spearhead for rapid rock dissolution, followed by the injection of near-neutral pH slickwater containing chemicals and proppants. A multistep sequential injection approach was used to investigate different stages of rock-fluid interactions. The carbonate content in the host rock is important when acid spearhead is considered, as carbonate mineral dissolution is rapid and can result in porosity and permeability changes in the shale matrix. Here, in this study, we designed flow-through experiments using fractured carbonate-rich and clay-rich Wolfcamp shale cores with (1) a short-time acid soaking step and (2) a long-term slickwater flow-through step to simulate the injection method used at HFTS-1. The fluid chemistry was analyzed. A thorough mineralogical progression [e.g., Calcium (Ca) dissolution and iron (Fe) redox progression] in the cores during HFF injection was also characterized and imaged by synchrotron microprobe. Reactive transport modeling was performed based on the experimental setup. The results showed that the acid spearhead is a crucial step in creating a reaction front by mineral dissolution, especially in carbonate-rich shales. A slight layer of ferrihydrite precipitated during the slickwater flow-through period. This study provides insights into potential geochemical impact due to hydraulic fracturing operations in the Permian Basin.

clastic rock↗

DOE OSTI2020

Summary for Z-Petawatt Laser Activities (CY2019)

The high-energy upgrade of the Z-Petawatt (ZPW) laser system involved a shot hiatus starting in October 2018 to allow the installation of full-aperture optics in the chain, including in the main amplifier section as well as in the branches associated with the ZPW compressor and co-injection (the narrowband nanosecond operating mode that is co-bored with Z-Beamlet). Laser shots resumed May 1 st , with most shots since being in the ns mode to reduce complexity (while conditioning optics and calibrating diagnostics) as well as to support experimental requests. To facilitate experimental needs while a final full-sized co-injection optic was prepared, a sub-apertured beam has been used for experimental applications and a full-apertured beam has been used to validate the system. In the latter case, the beam is directed to a large calorimeter placed before branching occurs to the ZPW compressor or co-injection. As of September 2019, full-aperture co-injection shots had ramped up to 960J in 2.3ns, with many cross-calibrations being used to map out a relative humidity dependency in the leaky diagnostic mirror. Several such cross-calibration shots have been done using a chirped pulse seed as well, verifying that the diagnostic calibrations are valid in both broadband and narrow-band modes. Summarizing, since May 1 st of 2019 when shots resumed, 93 full system shots were performed with the ZPW laser. This included 2 co-injection shots with ZBL into Z (in support of MagLIF), 26 Pecos gas cell shots (in support of MagLIF), 11 Conchas gas cell shots (in support of UXI applications development), 6 Jemez shots (in support of backlighter development), and 48 calorimeter shots (in support of validating the system performance). Of these 93 shots, 45 shots were in conjunction with ZBL.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. This work highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

Kumar, Abhash↗