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NASA NTRS2015

Development of Long-Lifetime Pulsed Gas Valves for Pulsed Electric Thrusters

It is advantageous for gas-fed pulsed electric thrusters to employ pulsed valves so propellant is only flowing to the device during operation. The propellant utilization of the thruster will be maximized when all the gas injected into the thruster is acted upon by the fields produced by the electrical pulse. Gas that is injected too early will diffuse away from the thruster before the electrical pulse can act to accelerate the propellant. Gas that is injected too late will miss being accelerated by the already-completed electrical pulse. As a consequence, the valve must open quickly and close equally quickly, only remaining open for a short duration. In addition, the valve must have only a small amount of volume between the sealing body and the thruster so the front and back ends of the pulse are as coincident as possible with the valve cycling, with very little latent propellant remaining in the feed lines after the valve is closed. For a real mission of interest, a pulsed thruster can be expected to pulse at least 10(exp 10) - 10(exp 11) times, setting the range for the number of times a valve must open and close. The valves described in this paper have been fabricated and tested for operation in an inductive pulsed plasma thruster (IPPT) for in-space propulsion. In general, an IPPT is an electrodeless space propulsion device where a capacitor is charged to an initial voltage and then discharged, producing a high-current pulse through a coil. The field produced by this pulse ionizes propellant, inductively driving current in a plasma located near the face of the coil. Once the plasma is formed, it can be accelerated and expelled at a high exhaust velocity by the electromagnetic Lorentz body force arising from the interaction of the induced plasma current and the magnetic field produced by the current in the coil. The valve characteristics needed for the IPPT application require a fast-acting valve capable of a minimum of 10(exp 10) valve actuation cycles. Since even 10(exp 9) cycles is well above anything demonstrated, this lower value was selected as the design point for the present work. The valve seal must remain leak-tight throughout operation, and the body must maintain a low internal leakage at relatively high operating temperatures. The full set of design requirements used for this program are summarized in Table 1. In this work, we describe two pulsed gas valves that have been fabricated to have long lifetime and demonstrate the characteristics listed above. The first is a miniaturized, conventional electromagnet-based valve while the second is a piezoelectric-based valve design. The conventional valve, shown in Fig. 1, is opened by use of a solenoid electromagnetic actuator. When current is applied to the solenoid coil, magnetic forces pull the plunger away from the valve seat, allowing fluid to flow through the valve. Removal of electrical current permits the spring and fluid pressure to seat the plunger, halting the flow of fluid. The valve body is fabricated from 304L corrosion resistant steel (CRES) and while the parts that form the magnetic circuit are fabricated from 430 CRES. This material does not have optimum magnetic properties, but its corrosion resistance permits incorporation into a design without requiring an additional plating process. A viton O-ring compound (Parker V0884-75), selected for its mechanical strength at elevated temperatures, was used for the valve seat seal. The design was based solely on the use of analytical sizing calculations, as opposed to a more rigorous finite element analysis. While this valve is small and relatively lightweight, it does not represent a design that is optimized for mass and/or a given volume envelope. The piezoelectric valve is a "puller" valve design. Applying a voltage to the piezo crystal causes it to elongate and pull a pintle off the seat, opening the valve. The valve seal consists of the pintle with an external, spherically-formed tip fabricated from Inconel 625 and the seat fabricated from Inconel 718 and possessing a spherical surface that permits the pintle to align with the valve body centerline after each cycle, maintaining the circumferential metal-on-metal seal. The contact stress on the valve pintle is expected in the worst case to be 96.5 MPa (14 ksi), which is well below the work hardening value of 413.7 MPa (60 ksi) for Inconel 615. Results presented in this work will compare the performance of each valve, both to each other and to the valve design requirements given in Table 1. In general, both valves meet all the performance envelope requirements, but the piezoelectric valve consumes considerably less power and has a faster response time over the conventional solenoid-driven valves.

Burkhardt, Wendel M.↗

DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗

NASA NTRS2017

Computational Assessment of a 3-Stage Axial Compressor Which Provides Airflow to the NASA 11- by 11-Foot Transonic Wind Tunnel, Including Design Changes for Increased Performance

A 24 foot diameter 3-stage axial compressor powered by variable-speed induction motors provides the airflow in the closed-return 11- by 11-Foot Transonic Wind Tunnel (11-Foot TWT) Facility at NASA Ames Research Center at Moffett Field, California. The facility is part of the Unitary Plan Wind Tunnel, which was completed in 1955. Since then, upgrades made to the 11-Foot TWT such as flow conditioning devices and instrumentation have increased blockage and pressure loss in the tunnel, somewhat reducing the peak Mach number capability of the test section. Due to erosion effects on the existing aluminum alloy rotor blades, fabrication of new steel rotor blades is planned. This presents an opportunity to increase the Mach number capability of the tunnel by redesigning the compressor for increased pressure ratio. Challenging design constraints exist for any proposed design, demanding the use of the existing driveline, rotor disks, stator vanes, and hub and casing flow paths, so as to minimize cost and installation time. The current effort was undertaken to characterize the performance of the existing compressor design using available design tools and computational fluid dynamics (CFD) codes and subsequently recommend a new compressor design to achieve higher pressure ratio, which directly correlates with increased test section Mach number. The constant cross-sectional area of the compressor leads to highly diffusion factors, which presents a challenge in simulating the existing design. The CFD code APNASA was used to simulate the aerodynamic performance of the existing compressor. The simulations were compared to performance predictions from the HT0300 turbomachinery design and analysis code, and to compressor performance data taken during a 1997 facility test. It was found that the CFD simulations were sensitive to endwall leakages associated with stator buttons, and to a lesser degree, under-stator-platform flow recirculation at the hub. When stator button leakages were modeled, pumping capability increased by over 20 of pressure rise at design point due to a large reduction in aerodynamic blockage at the hub. Incorporating the stator button leakages was crucial to matching test data. Under-stator-platform flow recirculation was thought to be large due to a lack of seals. The effect of this recirculation was assessed with APNASA simulations recirculating 0.5, 1, and 2 of inlet flow about stators 1 and 2, modeled as axisymmetric mass flux boundary conditions on the hub before and after the vanes. The injection of flow ahead of the stators tended to re-energize the boundary layer and reduce hub separations, resulting in about 3 increased stall margin per 1 of inlet flow recirculated. In order to assess the value of the flow recirculation, a mixing plane simulation of the compressor which gridded the under-stator cavities was generated using the ADPAC CFD code. This simulation indicated that about 0.65 of the inlet flow is recirculated around each shrouded stator. This collective information was applied during the redesign of the compressor. A potential design was identified using HT0300 which improved overall pressure ratio by removing pre-swirl into rotor 1, replacing existing NASA 65 series rotors with double circular arc sections, and re-staggering rotors and the existing stators. The performance of the new design predicted by APNASA and HT0300 is compared to the existing design.

Turbomachinery↗

NASA NTRS2014

5.8kV SiC PiN Diode for Switching of High-Efficiency Inductive Pulsed Plasma Thruster Circuits

Inductive Pulsed Plasma Thruster (IPPT) pulse circuits, such as those needed to operate the Pulsed Inductive Thruster (PIT), are required to quickly switch capacitor banks operating at a period of μs while conducting current at levels on the order of at least 10 kA. [1,2] For all iterations of the PIT to date, spark gaps have been used to discharge the capacitor bank through an inductive coil. Recent availability of fast, high‐power solid state switching devices makes it possible to consider the use of semiconductor switches in modern IPPTs. In addition, novel pre‐ionization schemes have led to a reduction in discharge energy per pulse for electric thrusters of this type, relaxing the switching requirements for these thrusters. [3,4] Solid state switches offer the advantage of greater controllability and reliability, as well as decreased drive circuit dimensions and mass relative to spark gap switches. The use of solid state devices such as Integrated Gate Bipolar Transistors (IGBTs), Gate Turn‐off Thyristors (GTOs) and Silicon‐Controlled Rectifiers (SCRs) often involves the use of power diodes. These semiconductor devices may be connected antiparallel to the switch for protection from reverse current, or used to reduce power loss in a circuit by clamping off current ringing. In each case, higher circuit efficiency may be achieved by using a diode that is able to transition, or 'switch,' from the forward conducting state ('on' state) to the reverse blocking state ('off' state) in the shortest amount of time, thereby minimizing current ringing and switching losses. Silicon Carbide (SiC) PiN diodes offer significant advantages to conventional fast‐switching Silicon (Si) diodes for high power and fast switching applications. A wider band gap results in a breakdown voltage 10 times that of Si, so that a SiC device may have a thinner drift region for a given blocking voltage. [5] This leads to smaller, lighter devices for high voltage applications, as well as reduced forward conduction losses, faster reverse recovery time (faster turn‐off), and lower‐magnitude reverse recovery current. In addition, SiC devices have lower leakage current as compared to their Si counterparts, and a high thermal conductivity, potentially allowing the former to operate at higher temperatures with a smaller, lighter heatsink (or no heatsink at all).

Toftul, Alexandra↗

DOE OSTI2020

Charge collection and electrical characterization of neutron irradiated silicon pad detectors for the CMS High Granularity Calorimeter

The replacement of the existing endcap calorimeter in the Compact Muon Solenoid (CMS) detector for the high-luminosity LHC (HL-LHC), scheduled for 2027, will be a high granularity calorimeter. It will provide detailed position, energy, and timing information on electromagnetic and hadronic showers in the immense pileup of the HL-LHC. The High Granularity Calorimeter (HGCAL) will use 120-, 200-, and 300-$\mu\textrm{m}$ thick silicon (Si) pad sensors as the main active material and will sustain 1-MeV neutron equivalent fluences up to about $10^{16}~\textrm{n}_\textrm{eq}\textrm{cm}^{-2}$. In order to address the performance degradation of the Si detectors caused by the intense radiation environment, irradiation campaigns of test diode samples from 8-inch and 6-inch wafers were performed in two reactors. Characterization of the electrical and charge collection properties after irradiation involved both bulk polarities for the three sensor thicknesses. Since the Si sensors will be operated at -30 $^\circ$C to reduce increasing bulk leakage current with fluence, the charge collection investigation of 30 irradiated samples was carried out with the infrared-TCT setup at -30 $^\circ$C. TCAD simulation results at the lower fluences are in close agreement with the experimental results and provide predictions of sensor performance for the lower fluence regions not covered by the experimental study. All investigated sensors display 60$\%$ or higher charge collection efficiency at their respective highest lifetime fluences when operated at 800 V, and display above 90$\%$ at the lowest fluence, at 600 V. The collected charge close to the fluence of $10^{16}~\textrm{n}_\textrm{eq}\textrm{cm}^{-2}$ exceeds 1 fC at voltages beyond 800 V.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE 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↗