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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Effects of fuel-injector design on ultra-lean combustion performance

Emissions data were obtained for six fuel injector configurations tested with ultra lean combustion. Fuel injectors included three multiple source designs and three configurations using a single air assist injector. Only the multiple source fuel injectors provided acceptable emissions. Values of 16g CO/kg fuel, 1.9g HC/kg fuel, and 19.g NO2/kg fuel were obtained for the combustion temperature range of 1450 to 1700 K for both a high blockage 19 source injector and a low blockage 41 source injector. It was shown that high fuel injector pressure drop may not be required to achieve low emissions performance at high inlet air temperature when the fuel is well dispersed in the airstream.

Anderson, D. N.↗
DOE OSTI2018

Generating a heated fluid using an electromagnetic radiation-absorbing complex

A vessel including a concentrator configured to concentrate electromagnetic (EM) radiation received from an EM radiation source and a complex configured to absorb EM radiation to generate heat. The vessel is configured to receive a cool fluid from the cool fluid source, concentrate the EM radiation using the concentrator, apply the EM radiation to the complex, and transform, using the heat generated by the complex, the cool fluid to the heated fluid. The complex is at least one of consisting of copper nanoparticles, copper oxide nanoparticles, nanoshells, nanorods, carbon moieties, encapsulated nanoshells, encapsulated nanoparticles, and branched nanostructures. Further, the EM radiation is at least one of EM radiation in an ultraviolet region of an electromagnetic spectrum, in a visible region of the electromagnetic spectrum, and in an infrared region of the electromagnetic spectrum.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Improved source design for p-type tunnel field-effect transistors: Towards truly complementary logic

Complementary logic based on tunnel field-effect transistors (TFETs) would drastically reduce power consumption thanks to the TFET's potential to obtain a sub-60 mV/dec subthreshold swing (SS). However, p-type TFETs typically do not meet the performance of n-TFETs for direct bandgap III-V configurations. The p-TFET SS stays well above 60 mV/dec, due to the low density of states in the conduction band. We therefore propose a source configuration in which a highly doped region is maintained only near the tunnel junction. In the remaining part of the source, the hot carriers in the exponential tail of the Fermi-Dirac distribution are blocked by reducing the doping degeneracy, either with a source section with a lower doping concentration or with a heterostructure. We apply this concept to n-p-i-p configurations consisting of In{sub 0.53}Ga{sub 0.47}As and an InP-InAs heterostructure. 15-band quantum mechanical simulations predict that the configurations with our source design can obtain sub-60 mV/dec SS, with an on-current comparable to the conventional source design.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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 OSTI2015

A SOLAR CORONAL JET EVENT TRIGGERS A CORONAL MASS EJECTION

In this paper, we present multi-point, multi-wavelength observations and analysis of a solar coronal jet and coronal mass ejection (CME) event. Employing the GCS model, we obtained the real (three-dimensional) heliocentric distance and direction of the CME and found it to propagate at a high speed of over 1000 km s{sup −1}. The jet erupted before the CME and shared the same source region. The temporal and spacial relationship between these two events lead us to the possibility that the jet triggered the CME and became its core. This scenario hold the promise of enriching our understanding of the triggering mechanism of CMEs and their relations to coronal large-scale jets. On the other hand, the magnetic field configuration of the source region observed by the Solar Dynamics Observatory (SDO)/HMI instrument along with the off-limb inverse Y-shaped configuration observed by SDO/AIA in the 171 Å passband provide the first detailed observation of the three-dimensional reconnection process of a large-scale jet as simulated in Pariat et al. The eruption process of the jet highlights the importance of filament-like material during the eruption of not only small-scale X-ray jets, but likely also of large-scale EUV jets. Based on our observations and analysis, we propose the most probable mechanism for the whole event, with a blob structure overlaying the three-dimensional structure of the jet, to describe the interaction between the jet and the CME.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Development of a new superconducting electron cyclotron resonance ion source for operations up to 18 GHz at LBNL

A new superconducting Electron Cyclotron Resonance Ion Source (ECRIS) is under development at LBNL to harness the winding techniques of a closed-loop sextupole coil for the next generation ECRIS and to enhance the capability of the 88-in. cyclotron facility. The proposed ECRIS will use a superconducting closed-loop sextupole coil to produce the radial field and a substantial portion of the axial field. The field strengths of the injection, central and extraction regions are adjusted by a three solenoids outside the closed-loop sextupole coil. In addition to maintaining the typical ECRIS magnetic field configuration, this new source will also be able to produce a dustpan-like minimum-B field to explore possible ECRIS performance enhancement. The dustpan-like minimum-B field configuration has about the same strengths for the maximum axial field at the injection region and the maximum radial pole fields at the plasma chamber walls but it can be substantially lower at the extraction region. The dustpan-like minimum-B will have a field maximum B{sub max} ≥ 2.6 T for operations up to 18 GHz with a ratio of B{sub max}/B{sub res} ≥ 4 and higher ratios for lower frequencies. The field maxima of this new source can reach over 3 T both at the injection and the plasma chamber walls which could also support operation at 28 GHz. The source will be built of cryogen-free with the magnets directly cooled by cryo-coolers to simplify the cryostat structure. The source design features will be presented and discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Secure Policy-Based Configuration Framework (PBCONF)

In 2013, EPRI and a project team comprised of the University of Illinois at Urbana-Champaign, Ameren, and Schweitzer Engineering Laboratories began a multi-year effort under an award from the U.S. Department of Energy Office of Electricity Delivery and Energy Reliability Cyber Security or Energy Delivery Systems (CEDS) to develop a framework for secure remote configuration of modern and legacy devices. A goal of this Secure Policy-Based Configuration Framework (PBCONF) is to develop an extensible, open source, policy-based configuration framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Intersensory coordination

The dependence of body orientation and body configuration perception on multiple sources of afferent and efferent information about the spatial configuration of the body and its relationship to the support and force characteristics of the surroundings is discussed.

Lackner, James R.↗
NASA NTRS2009

Chemical Source Inversion using Assimilated Constituent Observations in an Idealized Two-dimensional System

We present a source inversion technique for chemical constituents that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier-Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green's function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral spectral model, but differs by an unbiased Gaussian model error, and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out by either direct use of synthetically generated observations with added noise, or by first assimilating the observations and using the analyses to extract observations. We have conducted 20 identical twin experiments for each set of source and observation configurations, and find that in the limiting cases of a very few localized observations, or an extremely large observation network there is little advantage to carrying out assimilation first. However, in intermediate observation densities, there decreases in source inversion error standard deviation using the Kalman filter algorithm followed by Green's function inversion by 50% to 95%.

Tangborn, Andrew↗
DOE OSTI2012

Fast-neutron coded-aperture imaging of special nuclear material configurations

In the past year, a prototype fast-neutron coded-aperture imager has been developed that has sufficient efficiency and resolution to make the counting of warheads for possible future treaty confirmation scenarios via their fission-neutron emissions practical. The imager is constructed from custom-built pixelated liquid scintillator detectors. The liquid scintillator detectors enable neutron-gamma discrimination via pulse shape, and the pixelated construction enables a sufficient number of pixels for imaging in a compact detector with a manageable number of channels of readout electronics. The imager has been used to image neutron sources at ORNL, special nuclear material (SNM) sources at the Idaho National Laboratory (INL) Zero Power Physics Reactor (ZPPR) facility, and neutron source and shielding configurations at Sandia National Laboratories. This paper reports on the design and construction of the imager, characterization measurements with neutron sources at ORNL, and measurements with SNM at the INL ZPPR facility.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Fast neutron coded-aperture imaging of special nuclear material configurations

In the past year, a prototype fast-neutron coded-aperture imager has been developed that has sufficient efficiency and resolution to make the counting of warheads for possible future treaty confirmation scenarios via their fission-neutron emissions practical. The imager is constructed from custom-built pixelated liquid scintillator detectors. The liquid scintillator detectors enable neutron-gamma discrimination via pulse shape, and the pixelated construction enables a sufficient number of pixels for imaging in a compact detector with a manageable number of channels of readout electronics. The imager has been used to image neutron sources at ORNL, special nuclear material (SNM) sources at the Idaho National Laboratory (INL) Zero Power Physics Reactor (ZPPR) facility, and neutron source and shielding configurations at Sandia National Laboratories. This paper reports on the design and construction of the imager, characterization measurements with neutron sources at ORNL, and measurements with SNM at the INL ZPPR facility.

Hausladen, Paul↗
DOE OSTI2018

Quantum random number generators

Random number generators include a thermal optical source and detector configured to produce random numbers based on quantum-optical intensity fluctuations. An optical flux is detected, and signals proportional to optical intensity and a delayed optical intensity are combined. The combined signals can be electrical signals or optical signals, and the optical source is selected so as to have low coherence over a predetermined range of delay times. Balanced optical detectors can be used to reduce common mode noise, and in some examples, the optical flux is directed to only one of a pair of balanced detectors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantum random number generators

Random number generators include a thermal optical source and detector configured to produce random numbers based on quantum-optical intensity fluctuations. An optical flux is detected, and signals proportional to optical intensity and a delayed optical intensity are combined. The combined signals can be electrical signals or optical signals, and the optical source is selected so as to have low coherence over a predetermined range of delay times. Balanced optical detectors can be used to reduce common mode noise, and in some examples, the optical flux is directed to only one of a pair of balanced detectors.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Issues concerning centralized versus decentralized power deployment

The results of a study of proposed lunar base architectures to identify issues concerning centralized and decentralized power system deployment options are presented. The power system consists of the energy producing system (power plant), the power conditioning components used to convert the generated power into the form desired for transmission, the transmission lines that conduct this power from the power sources to the loads, and the primary power conditioning hardware located at the user end. Three power system architectures, centralized, hybrid, and decentralized, were evaluated during the course of this study. Candidate power sources were characterized with respect to mass and radiator area. Two electrical models were created for each architecture to identify the preferred method of power transmission, dc or ac. Each model allowed the transmission voltage level to be varied at assess the impact on power system mass. The ac power system models also permitted the transmission line configurations and placements to determine the best conductor construction and installation location. Key parameters used to evaluate each configuration were power source and power conditioning component efficiencies, masses, and radiator areas; transmission line masses and operating temperatures; and total system mass.

Metcalf, Kenneth J.↗
DOE OSTI2019

Retrieval, Disposal, and Disposition of Legacy 252 Cf Sealed Sources

In this article, we describe work performed on behalf of the US Department of Energy (DOE) to disposition 467 government-owned, sealed californium-252 ( 252 Cf) sources located at 52 academic and government research sites. These sources are the legacy of the DOE 252 Cf Loan/Lease Program, which operated from the late 1960s until 2008, when the loan program closed abruptly. Although the DOE took decades to fabricate and distribute the sources, best safety and nuclear accountability practices demanded retrieval or disposition of all loaned sources as soon as achievable after the loan program was closed. An adaptable disposition approach was developed, and disposition of the sources was pursued. The approach involved a combination of source retrievals, disposal actions, and source ownership transfers. A new shielded shipping container was designed and deployed to aid in source retrievals. In a couple cases, incomplete source fabrication and shipping records prevented shipment, and those sources were left indefinitely with the customer in a safe configuration. Lastly, all the sources in the inventory were safely dispositioned in some way, and lessons learned from this effort are provided.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Mixing and trapping of dissolved CO 2 in deep geologic formations with shale layers

For dissolution trapping, the spatial variability of the geologic properties of naturally complex storage formations can significantly impact flow patterns and storage mechanisms of dissolved CO 2 . The significance of diffusive mixing that occurs in low permeability layers embedded between relatively higher permeability materials was highlighted by Agartan et al. (2015) using a highly controlled laboratory experimental study on trapping of dissolved CO 2 in multilayered systems. In this paper, we present a numerical modeling study on the impacts of low permeability layers on flow and storage of dissolved CO 2 in realistic field-scale settings. The simulator of variable-density flow used in this study was first verified using the experimental data in Agartan et al. (2015) to capture the observed processes. The simulator was then applied to a synthetic, field-scale multilayered system, with 19 sensitivity cases having variable permeability and thickness of the shale layers as well as the source strength and geometry of the source zone of dissolved CO 2 . Simulation results showed that the presence of continuous shale layers in the storage system disrupts the convective mixing by enhancing lateral spreading of dissolved CO 2 in sandstone layers and retarding the vertical mixing of dissolved CO 2 . The effectiveness of trapping of dissolved CO 2 depends on the physical properties of the shale layers and configurations of the source zone. The comparison to homogeneous cases with effective vertical permeability shows that it is important to capture these continuous thin shale layers in a storage formation and include them in the models to enhance dissolution trapping.

58 GEOSCIENCES↗