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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 NTRS1997

Analysis of the Role of Update Rate and System Latency in Interactive Virtual Acoustic Environments

The ultimate goal of virtual acoustics is to simulate the complex acoustic field experienced by a listener freely moving around within an environment. This paper discusses some of the engineering constraints that may be faced during implementation and the perceptual consequences of these constraints. In particular, the perceptual impact of parameters like the update rate and overall system latency of interactive spatial audio systems is addressed.

Wenzel, Elizabeth M.↗
NASA NTRS1997

Perceptual Fidelity Versus Engineering Compromises in Virtual Acoustic Displays

Immersive, three-dimensional displays are increasingly becoming a goal of advanced human-machine interfaces. While the technology for achieving truly useful multisensory environments is still being developed, techniques for generating three-dimensional sound are now both sophisticated and practical enough to be applied to acoustic displays. The ultimate goal of virtual acoustics is to simulate the complex acoustic field experienced by a listener freely moving around within an environment. Of course, such complexity, freedom of movement and interactivity is not always possible in a 'true' virtual environment, much less in lower-fidelity multimedia systems. However, many of the perceptual and engineering constraints (and frustrations) that researchers, engineers and listeners have experienced in virtual audio are relevant to multimedia. In fact, some of the problems that have been studied will be even more of an issue for lower fidelity systems that are attempting to address the requirements of a huge, diverse and ultimately unknown audience. Examples include individual differences in head-related transfer functions, A lack of real interactively (head-tracking) in many multimedia displays, and perceptual degradation due to low sampling rates and/or low-bit compression. This paper discusses some of the engineering constraints faced during implementation of virtual acoustic environments and the perceptual consequences of these constraints. Specific examples are given for NASA applications such as telerobotic control, aeronautical displays, and shuttle launch communications. An attempt will also be made to relate these issues to low-fidelity implementations such as the internet.

Wenzel, Elizabeth M.↗
NASA NTRS1997

Perceptual Fidelity vs. Engineering Compromises In Virtual Acoustic Displays

Immersive, three-dimensional displays are increasingly becoming a goal of advanced human-machine interfaces. While the technology for achieving truly useful multisensory environments is still being developed, techniques for generating three-dimensional sound are now both sophisticated and practical enough to be applied to acoustic displays. The ultimate goal of virtual acoustics is to simulate the complex acoustic field experienced by a listener freely moving around within an environment. Of course, such complexity, freedom of movement and interactively is not always possible in a "true" virtual environment, much less in lower-fidelity multimedia systems. However, many of the perceptual and engineering constraints (and frustrations) that researchers, engineers and listeners have experienced in virtual audio are relevant to multimedia. In fact, some of the problems that have been studied will be even more of an issue for lower fidelity systems that are attempting to address the requirements of a huge, diverse and ultimately unknown audience. Examples include individual differences in head-related transfer functions, a lack of real interactively (head-tracking) in many multimedia displays, and perceptual degradation due to low sampling rates and/or low-bit compression. This paper discusses some of the engineering Constraints faced during implementation of virtual acoustic environments and the perceptual consequences of these constraints. Specific examples are given for NASA applications such as telerobotic control, aeronautical displays, and shuttle launch communications. An attempt will also be made to relate these issues to low-fidelity implementations such as the internet.

Wenzel, Elizabeth M.↗
NASA NTRS2008

Vibroacoustic Response of the NASA ACTS Spacecraft Antenna to Launch Acoustic Excitation

The Advanced Communications Technology Satellite was an experimental NASA satellite launched from the Space Shuttle Discovery. As part of the ground test program, the satellite s large, parabolic reflector antennas were exposed to a reverberant acoustic loading to simulate the launch acoustics in the Shuttle payload bay. This paper describes the modelling and analysis of the dynamic response of these large, composite spacecraft antenna structure subjected to a diffuse acoustic field excitation. Due to the broad frequency range of the excitation, different models were created to make predictions in the various frequency regimes of interest: a statistical energy analysis (SEA) model to capture the high frequency response and a hybrid finite element-statistical energy (hybrid FE-SEA) model for the low to mid-frequency responses. The strengths and limitations of each of the analytical techniques are discussed. The predictions are then compared to the measured acoustic test data and to a boundary element (BEM) model to evaluate the performance of the hybrid techniques.

Larko, Jeffrey M.↗
NASA NTRS2011

Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions

This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.

Strutzenberg, Louise L.↗
DOE OSTI2015

Hybrid (Vlasov-Fluid) simulation of ion-acoustic solitons chain formation including trapped electrons

Disintegration of a Gaussian profile into ion-acoustic solitons in the presence of trapped electrons [H. Hakimi Pajouh and H. Abbasi, Phys. Plasmas 15, 082105 (2008)] is revisited. Through a hybrid (Vlasov-Fluid) model, the restrictions associated with the simple modified Korteweg de-Vries (mKdV) model are studied. For instance, the lack of vital information in the phase space associated with the evolution of electron velocity distribution, the perturbative nature of mKdV model which limits it to the weak nonlinear cases, and the special spatio-temporal scaling based on which the mKdV is derived. Remarkable differences between the results of the two models lead us to conclude that the mKdV model can only monitor the general aspects of the dynamics, and the precise picture including the correct spatio-temporal scales and the properties of solitons should be studied within the framework of hybrid model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Numerical simulation of scattering of acoustic waves by inelastic bodies using hypersingular boundary integral equation

A numerical method for solving a problem of diffraction of acoustic waves by system of solid and thin objects based on the reduction the problem to a boundary integral equation in which the integral is understood in the sense of finite Hadamard value is proposed. To solve this equation we applied piecewise constant approximations and collocation methods numerical scheme. The difference between the constructed scheme and earlier known is in obtaining approximate analytical expressions to appearing system of linear equations coefficients by separating the main part of the kernel integral operator. The proposed numerical scheme is tested on the solution of the model problem of diffraction of an acoustic wave by inelastic sphere.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1980

VCE early acoustic test results of General Electric's high-radius ratio coannular plug nozzle

Results of variable cycle engine (VCE) early acoustic engine and model scale tests are presented. A summary of an extensive series of far field acoustic, advanced acoustic, and exhaust plume velocity measurements with a laser velocimeter of inverted velocity and temperature profile, high radius ratio coannular plug nozzles on a YJ101 VCE static engine test vehicle are reviewed. Select model scale simulated flight acoustic measurements for an unsuppressed and a mechanical suppressed coannular plug nozzle are also discussed. The engine acoustic nozzle tests verify previous model scale noise reduction measurements. The engine measurements show 4 to 6 PNdB aft quadrant jet noise reduction and up to 7 PNdB forward quadrant shock noise reduction relative to a fully mixed conical nozzle at the same specific thrust and mixed pressure ratio. The influences of outer nozzle radius ratio, inner stream velocity ratio, and area ratio are discussed. Also, laser velocimeter measurements of mean velocity and turbulent velocity of the YJ101 engine are illustrated. Select model scale static and simulated flight acoustic measurements are shown which corroborate that coannular suppression is maintained in forward speed.

Knott, P. R.↗
NASA NTRS1996

Periodic Time-Domain Nonlocal Nonreflecting Boundary Conditions for Duct Acoustics

Periodic time-domain boundary conditions are formulated for direct numerical simulation of acoustic waves in ducts without flow. Well-developed frequency-domain boundary conditions are transformed into the time domain. The formulation is presented here in one space dimension and time; however, this formulation has an advantage in that its extension to variable-area, higher dimensional, and acoustically treated ducts is rigorous and straightforward. The boundary condition simulates a nonreflecting wave field in an infinite uniform duct and is implemented by impulse-response operators that are applied at the boundary of the computational domain. These operators are generated by convolution integrals of the corresponding frequency-domain operators. The acoustic solution is obtained by advancing the Euler equations to a periodic state with the MacCormack scheme. The MacCormack scheme utilizes the boundary condition to limit the computational space and preserve the radiation boundary condition. The success of the boundary condition is attributed to the fact that it is nonreflecting to periodic acoustic waves. In addition, transient waves can pass rapidly out of the solution domain. The boundary condition is tested for a pure tone and a multitone source in a linear setting. The effects of various initial conditions are assessed. Computational solutions with the boundary condition are consistent with the known solutions for nonreflecting wave fields in an infinite uniform duct.

Watson, Willie R.↗
NASA NTRS2017

Validating a Monotonically-Integrated Large Eddy Simulation Code for Subsonic Jet Acoustics

The results of subsonic jet validation cases for the Naval Research Lab's Jet Engine Noise REduction (JENRE) code are reported. Two set points from the Tanna matrix, set point 3 (Ma = 0.5, unheated) and set point 7 (Ma = 0.9, unheated) are attempted on three different meshes. After a brief discussion of the JENRE code and the meshes constructed for this work, the turbulent statistics for the axial velocity are presented and compared to experimental data, with favorable results. Preliminary simulations for set point 23 (Ma = 0.5, Tj=T1 = 1.764) on one of the meshes are also described. Finally, the proposed configuration for the farfield noise prediction with JENRE's Ffowcs-Williams Hawking solver are detailed.

subsonic jet↗
NASA NTRS1998

Tutorial on the Psychophysics and Technology of Virtual Acoustic Displays

Virtual acoustics, also known as 3-D sound and auralization, is the simulation of the complex acoustic field experienced by a listener within an environment. Going beyond the simple intensity panning of normal stereo techniques, the goal is to process sounds so that they appear to come from particular locations in three-dimensional space. Although loudspeaker systems are being developed, most of the recent work focuses on using headphones for playback and is the outgrowth of earlier analog techniques. For example, in binaural recording, the sound of an orchestra playing classical music is recorded through small mics in the two "ear canals" of an anthropomorphic artificial or "dummy" head placed in the audience of a concert hall. When the recorded piece is played back over headphones, the listener passively experiences the illusion of hearing the violins on the left and the cellos on the right, along with all the associated echoes, resonances, and ambience of the original environment. Current techniques use digital signal processing to synthesize the acoustical properties that people use to localize a sound source in space. Thus, they provide the flexibility of a kind of digital dummy head, allowing a more active experience in which a listener can both design and move around or interact with a simulated acoustic environment in real time. Such simulations are being developed for a variety of application areas including architectural acoustics, advanced human-computer interfaces, telepresence and virtual reality, navigation aids for the visually-impaired, and as a test bed for psychoacoustical investigations of complex spatial cues. The tutorial will review the basic psychoacoustical cues that determine human sound localization and the techniques used to measure these cues as Head-Related Transfer Functions (HRTFs) for the purpose of synthesizing virtual acoustic environments. The only conclusive test of the adequacy of such simulations is an operational one in which the localization of real and synthesized stimuli are directly compared in psychophysical studies. To this end, the results of psychophysical experiments examining the perceptual validity of the synthesis technique will be reviewed and factors that can enhance perceptual accuracy and realism will be discussed. Of particular interest is the relationship between individual differences in HRTFs and in behavior, the role of reverberant cues in reducing the perceptual errors observed with virtual sound sources, and the importance of developing perceptually valid methods of simplifying the synthesis technique. Recent attempts to implement the synthesis technique in real time systems will also be discussed and an attempt made to interpret their quoted system specifications in terms of perceptual performance. Finally, some critical research and technology development issues for the future will be outlined.

Wenzel, Elizabeth M.↗
DOE OSTI2021

Integration of Simulated and Real Distributed Acoustic Sensor Measurements to Develop AI Enhanced Intelligent Sensing Operations

Distributed acoustic sensors (DAS) have shown remarkable success in monitoring critical civil, energy and transportation assets over long distances and under harsh environmental conditions. Integration of artificial intelligence (AI) technologies with DAS can extend their operational capabilities from conventional tasks (e.g., vibration frequency detection) to more advanced intelligent tasks like anomaly detection. However, these AI technologies often require large labeled/ annotated DAS measurements datasets of the assets in both normal and anomalous operating conditions. This data acquisition task can be both time consuming and costly. This work develops a generative adversarial network based unsupervised domain adaptation framework to build intelligent DAS operational capabilities.

Venketeswaran, Abhishek↗
NASA NTRS1995

A Variational Formulation for the Finite Element Analysis of Sound Wave Propagation in a Spherical Shell

Development of design tools to furnish optimal acoustic environments for lightweight aircraft demands the ability to simulate the acoustic system on a workstation. In order to form an effective mathematical model of the phenomena at hand, we have begun by studying the propagation of acoustic waves inside closed spherical shells. Using a fully-coupled fluid-structure interaction model based upon variational principles, we have written a finite element analysis program and are in the process of examining several test cases. Future investigations are planned to increase model accuracy by incorporating non-linear and viscous effects.

Lebiedzik, Catherine↗
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↗