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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Study of Particle Loss in Synchrotron Phase Space Injection for ESR Using Weak-Strong Beam-Beam Simulation with Nonlinear Lattice

In this report, we use tracking simulations to investigate synchrotron phase space injection for electron accumulation in the electron storage ring of the Electron Ion Collider. Our simulation model accounts for both beam-beam interactions and lattice nonlinearities. Specifically, we examine how particle loss is influenced by various parameters. Additionally, we conduct a theoretical analysis and derive an analytical formula for the rapid evaluation of particle loss. Our results demon strate the feasibility of synchrotron phase-space injection for the electron storage ring and provide insights to guide parameter selection for the design of the injection line.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the presentation prepared for the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗
DOE OSTI2025

Enabling Next Generation Reaction Injection Molding (RIM) for Lightweight Structures

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this project, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. This optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption. This research has been performed in collaborations with McClarin Composites. The research outcome has been submitted to the Journal of Manufacturing Processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) Injection Transport Beamline (Y-Line Ext.) Lattice Design and Magnet Parameters

The preliminary design of the Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) Normal Conducting Injection Beamline ( Y-Line Ext. ) lattice is discussed. The preliminary design includes the periodic cells, matching sections, state of the art Twiss parameters at the HSR injection, the magnet parameters and first order instrumentation placement. Furthermore, the beamline parameters, element coordinates, and other information are discussed. The names of the HSR Injection Beamline elements used in this technical report ( i.e. HD1, HQ1 ) are preliminary and not the official site wide names.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Active Seismoelectric Monitoring of CO 2 Injection

Conference presentation at Carbon Capture, Utilization, and Storage (CCUS) Conference 2024, Houston, Texas, March 11–13, 2024. We investigated the use of active seismoelectric monitoring (ASE) to determine its effectiveness in detecting CO 2 injected deep underground and as an additional monitoring option for carbon storage projects. The EERC performed an ASE survey in February of 2023 at Pennel oil field near Baker, Montana, as part of the U.S. Department of Energy-sponsored Williston Basin CO 2 Field Laboratory in conjunction with operating partner Denbury Inc. The field site is part of the Cedar Creek Anticline geologic structure, with CO 2 planned for injection into a stacked storage complex (SSC) in the Interlake Formation containing both a residual oil zone and a conventional reservoir. The objective was to test the ASE method’s ability to characterize baseline fluid distribution in both the near surface and the SSC prior to CO 2 injection.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2026

Estimation of radiation fields generated by injected beam losses at the EIC's RCS

This technical note provides a general estimate of radiation fields generated by injection fault events at the electron-ion collider´s (EIC) Rapid Cycling Synchrotron (RCS), calculated with the Monte Carlo particle transport and interaction code FLUKA. Calculations were performed for two major injection loss scenarios that involve iron targets and featured different electron beam energy and current values. The results presented here constitute a first order assessment of several radiological quantities associated with these electromagnetic showers and their potential effect on environmental safety and health (ESH) systems in the vicinity of injection areas.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Dark Energy Survey Year 6 Results: Synthetic-source Injection Across the Full Survey Using Balrog

Synthetic source injection (SSI), the insertion of sources into pixel-level on-sky images, is a powerful method for characterizing object detection and measurement in wide-field, astronomical imaging surveys. Within the Dark Energy Survey (DES), SSI plays a critical role in characterizing all necessary algorithms used in converting images to catalogs, and in deriving quantities needed for the cosmology analysis, such as object detection rates, galaxy redshift estimation, galaxy magnification, star-galaxy classification, and photometric performance. We present here a source injection catalog of 146 million injections spanning the entire 5000 deg 2 DES footprint, generated using the Balrog SSI pipeline. Through this SSI sample, we demonstrate that the DES Year 6 (Y6) image processing pipeline provides accurate estimates of the object properties, for both galaxies and stars, at the percent-level, and we highlight specific regimes where the accuracy is reduced. We then show the consistency between SSI and data catalogs, for all galaxy samples developed within the weak lensing and galaxy clustering analyses of DES Y6. The consistency between the two catalogs also extends to their correlations with survey observing properties (seeing, airmass, depth, extinction, etc.). Lastly, we highlight a number of applications of this catalog to the DES Y6 cosmology analysis, such as estimates of the redshift distribution and lens magnification. This dataset is the largest SSI catalog produced at this fidelity and will serve as a key testing ground for exploring the utility of SSI catalogs in upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Control of Cement Timing, Mineralogy, and Texture on Hydro-chemo-mechanical Coupling from CO2 Injection into Sandstone: A Synthesis

Carbon capture, utilization, and storage (CCUS) has been widely applied to enhance oil recovery (CO2-EOR). A thorough investigation of the impact of injecting CO2 into a heterogeneous reservoir is critical to understanding the overall reservoir robustness and storage performance. We conducted fifteen flow-through tests on Morrow B sandstone that allowed for chemical reactions between a CO2-rich brackish solution and the sandstones, and four creep/flow-through tests that simultaneously allowed for chemical reactions and stress monitoring. From fluid chemistry and X-ray computed tomography, we found that the dissolution of disseminated cements and the precipitation of iron-rich clays did not significantly affect the permeability and geomechanical properties. Minor changes in mechanical properties from Brazilian and creep tests indicated that the matrix structure was well-supported by early diagenetic quartz overgrowth cement and the reservoir’s compaction history at deep burial depths. However, one sample experienced a dissolution of poikilotopic calcite, leading to a permeability increase and significant tensile strength degradation due to pore opening, which overcame the effect of the early diagenetic cements. We concluded that the Morrow B sandstone reservoir is robust for CO2 injection. Most importantly, cement timing, the abundance and texture of reactive minerals, and the reservoir’s burial history are critical in predicting reservoir robustness and storage capacity for CO2 injection.

Wu, Zhidi (ORCID:0000000338869492)↗
DOE OSTI2018

CO2 Injection Effect on Geomechanical and Flow Properties of Calcite-Rich Reservoirs

Geologic carbon storage is considered as a requisite to effectively mitigate climate change, so large amounts of carbon dioxide (CO2) are expected to be injected in sedimentary saline formations. CO2 injection leads to the creation of acidic solution when it dissolves into the resident brine, which can react with reservoir rock, especially carbonates. We numerically investigated the behavior of reservoir-caprock system where CO2 injection-induced changes in the hydraulic and geomechanical properties of Apulian limestone were measured in the laboratory. We found that porosity of the limestone slightly decreases after CO2 treatment, which lead to a permeability reduction by a factor of two. In the treated specimens, calcite dissolution was observed at the inlet, but carbonate precipitation occurred at the outlet, which was closed during the reaction time of three days. Additionally, the relative permeability curves were modified after CO2–rock interaction, especially the one for water, which evolved from a quadratic to a quasi-linear function of the water saturation degree. Geomechanically, the limestone became softer and it was weakened after being altered by CO2. Simulation results showed that the property changes occurring within the CO2 plume caused a stress redistribution because CO2 treated limestone became softer and tended to deform more in response to pressure buildup than the pristine rock. The reduction in strength induced by geochemical reactions may eventually cause shear failure within the CO2 plume affected rock. This combination of laboratory experiments with numerical simulations leads to a better understanding of the implications of coupled chemo-mechanical interactions in geologic carbon storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Comparative Evaluation of U.S. Brand and Generic Intravenous Sodium Ferric Gluconate Complex in Sucrose Injection: Physicochemical Characterization

The objective of this study was to evaluate physicochemical equivalence between brand (i.e., Ferrlecit) and generic sodium ferric gluconate (SFG) in sucrose injection by conducting a series of comparative in vitro characterizations using advanced analytical techniques. The elemental iron and carbon content, thermal properties, viscosity, particle size, zeta potential, sedimentation coefficient, and molecular weight were determined. There was no noticeable difference between brand and generic SFG in sucrose injection for the above physical parameters evaluated, except for the sedimentation coefficient determined by sedimentation velocity analytical ultracentrifugation (SV-AUC) and molecular weight by asymmetric field flow fractionation-multi-angle light scattering (AFFF-MALS). In addition, brand and generic SFG complex products showed comparable molecular weight distributions when determined by gel permeation chromatography (GPC). The observed minor differences between brand and generic SFG, such as sedimentation coefficient, do not impact their biological activities in separate studies of in vitro cellular uptake and rat biodistribution. Coupled with the ongoing clinical study comparing the labile iron level in healthy volunteers, the FDA-funded post-market studies intended to illustrate comprehensive surveillance efforts ensuring safety and efficacy profiles of generic SFG complex in sucrose injection, and also to shed new light on the approval standards on generic parenteral iron colloidal products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2026

The Role of Three-dimensional Effects on Ion Injection and Acceleration in Perpendicular Shocks

Understanding the conditions that enable particle acceleration at nonrelativistic collisionless shocks is essential to unveil the origin of cosmic rays. We employ 2D and 3D hybrid simulations (with kinetic ions and fluid electrons) to explore particle acceleration and magnetic field amplification in nonrelativistic perpendicular shocks, focusing on the role of shock drift acceleration and its dependence on the shock Mach number. We perform an analysis of the ion injection process and demonstrate why efficient acceleration is only observed in 3D. In particular, we show that ion injection critically depends on the “porosity” of the magnetic turbulence in the downstream region near the shock, a property describing how easily the postshock region allows particles to traverse it and return upstream without being trapped. This effect can only be properly captured in 3D. Additionally, we explore the impact of numerical resolution on ion energization, highlighting how resolving small-scale turbulence—on scales below the thermal ion gyroradius—is essential for accurately modeling particle injection. Overall, our results emphasize the necessity of high-resolution 3D simulations to capture the fundamental microphysics driving particle acceleration at perpendicular shocks.

Orusa, Luca [Princeton Univ., NJ (United States); ↗
DOE OSTI2018

Large-eddy Simulations of Spray Variability Effects on Flow Variability in a Direct-injection Spark-ignition Engine Under Non-combusting Operating Conditions

Large-eddy Simulations (LES) have been carried out to investigate spray variability and its effect on cycle-to-cycle flow variability in a direct-injection spark-ignition (DISI) engine under non-reacting conditions. Initial simulations were performed of an injector in a constant volume spray chamber to validate the simulation spray set-up. Comparisons showed good agreement in global spray measures such as the penetration. Local mixing data and shot-to-shot variability were also compared using Rayleigh-scattering images and probability contours. The simulations were found to reasonably match the local mixing data and shot-to-shot variability using a random-seed perturbation methodology. After validation, the same spray set-up with only minor changes was used to simulate the same injector in an optically accessible DISI engine. Particle Image Velocimetry (PIV) measurements were used to quantify the flow velocity in a horizontal plane intersecting the spark plug gap. The engine was operated in a skip-fired operating mode and comparisons focused on cycles that included fuel injection, but no spark event and therefore no combustion. 105 total LES engine cycles were simulated using a parallel cycle simulation approach and 3 different perturbation methods in an attempt to isolate the effects of shot-to-shot spray variability and the initial turbulent flow field as well as their interaction effects on overall engine CCVs. The experimental mean and standard deviations were reasonably well matched by the simulations, though quantitative comparisons near the injection event during the intake stroke were difficult due to the high uncertainty in the PIV measurements at these crank angles. The 3 simulation perturbation methods resulted in very similar results, though further analysis found the current parallel cycle approach may be limiting the ability of the simulations to isolate the spray and flow effects.

Cycle-to-cycle Variability↗
DOE OSTI2023

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗
NASA NTRS1972

Gas-injection-start and shutdown characteristics of a 2-kilowatt to 15-kilowatt Brayton power system

Two methods of starting the Brayton power system have been considered: (1) using the alternator as a motor to spin the Brayton rotating unit (BRU), and (2) spinning the BRU by forced gas injection. The first method requires the use of an auxiliary electrical power source. An alternating voltage is applied to the terminals of the alternator to drive it as an induction motor. Only gas-injection starts are discussed in this report. The gas-injection starting method requires high-pressure gas storage and valves to route the gas flow to provide correct BRU rotation. An analog computer simulation was used to size hardware and to determine safe start and shutdown procedures. The simulation was also used to define the range of conditions for successful startups. Experimental data were also obtained under various test conditions. These data verify the validity of the start and shutdown procedures.

Cantoni, D. A.↗
NASA NTRS1973

Effect of primary-zone water injection on pollutants from a combustor burning liquid ASTM A-1 and vaporized propane fuels

A combustor segment 0.457 meter (18 in.) long with a maximum cross section of 0.153 by 0.305 meter (6 by 12 in.) was operated at inlet-air temperatures of 590 and 700 K, inlet-air pressures of 4 and 10 atmospheres, and fuel-air ratios of 0.014 and 0.018 to determine the effect of primary-zone water injection on pollutants from burning either propane or ASTM A-1 fuel. At a simulated takeoff condition of 10 atmospheres and 700 K, multiple-orifice nozzles used to inject water at 1 percent of the airflow rate reduced nitrogen oxides 75 percent with propane and 65 percent with ASTM A-1 fuel. Although carbon monoxide and unburned hydrocarbons increased with water injection, they remained relatively low; and smoke numbers were well below the visibility limit.

Ingebo, R. D.↗
NASA NTRS1972

Multiple-orifice liquid injection into hypersonic air streams.

Review of oblique water and fluorocarbon injection test results obtained in experimental studies of the effects of multiple-orifice liquid injection into hypersonic air streams. The results include the finding that maximum lateral penetration from such injections increases linearly with the square root of the jet-to-freestream dynamic-pressure ratio and is proportional to an equivalent orifice diameter.

Weaver, W. L.↗