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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 OSTI2025

A novel transformation of the ice sheet Stokes equations and some of its properties and applications

We introduce a novel transformation of the Stokes equations into a form closely resembling the shallow Blatter–Pattyn equations. The two forms differ by only a few additional terms, while their variational formulations differ only by a single term in each horizontal direction. Specifically, the variational formulation of the Blatter–Pattyn model drops the vertical velocity in the second invariant of the strain rate tensor. Here we make use of the new transformation in two ways. First, we consider incorporating the transformed equations into a code that can be very easily converted from a Stokes to a Blatter–Pattyn model, and vice versa, by switching these terms on or off. This may be generalized so that the Stokes model is switched on adaptively only where the Blatter–Pattyn model loses accuracy. Second, the key role played by the vertical velocity in the Blatter–Pattyn approximation motivates new approximations. Two examples are presented. These require a mesh that enables the discrete continuity equation to be invertible for the vertical velocity in terms of the horizontal velocity components. Examples of such meshes, such as the first-order P1–E0 mesh and the second-order P2–E1 mesh, are given in both 2D and 3D. However, the transformed Stokes model has the same type of gravity forcing as the Blatter–Pattyn model, determined by the ice surface slope, thereby forgoing some of the mesh generality of the traditional formulation of the Stokes model.

58 GEOSCIENCES↗
DOE OSTI2024

Performance of high-order Godunov-type methods in simulations of astrophysical low Mach number flows

High-order Godunov methods for gas dynamics have become a standard tool for simulating different classes of astrophysical flows. Their accuracy is mostly determined by the spatial interpolant used to reconstruct the pair of Riemann states at cell interfaces and by the Riemann solver that computes the interface fluxes. In most Godunov-type methods, these two steps can be treated independently, so that many different schemes can in principle be built from the same numerical framework. Because astrophysical simulations often test out the limits of what is feasible with the computational resources available, it is essential to find the scheme that produces the numerical solution with the desired accuracy at the lowest computational cost. However, establishing the best combination of numerical options in a Godunov-type method to be used for simulating a complex hydrodynamic problem is a nontrivial task. In fact, formally more accurate schemes do not always outperform simpler and more diffusive methods, especially if sharp gradients are present in the flow. For this work, we used our fully compressible Seven-League Hydro (SLH) code to test the accuracy of six reconstruction methods and three approximate Riemann solvers on two- and three-dimensional (2D and 3D) problems involving subsonic flows only. We considered Mach numbers in the range from 10 −3 to 10 −1 , which are characteristic of many stellar and geophysical flows. In particular, we considered a well-posed, 2D, Kelvin–Helmholtz instability problem and a 3D turbulent convection zone that excites internal gravity waves in an overlying stable layer. Although the different combinations of numerical methods converge to the same solution with increasing grid resolution for most of the quantities analyzed here, we find that (i) there is a spread of almost four orders of magnitude in computational cost per fixed accuracy between the methods tested in this study, with the most performant method being a combination of a low-dissipation Riemann solver and a sextic reconstruction scheme; (ii) the low-dissipation solver always outperforms conventional Riemann solvers on a fixed grid when the reconstruction scheme is kept the same; (iii) in simulations of turbulent flows, increasing the order of spatial reconstruction reduces the characteristic dissipation length scale achieved on a given grid even if the overall scheme is only second order accurate; (iv) reconstruction methods based on slope-limiting techniques tend to generate artificial, high-frequency acoustic waves during the evolution of the flow; and (v) unlimited reconstruction methods introduce oscillations in the thermal stratification near the convective boundary, where the entropy gradient is steep.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Slide Conveying of Granular Materials-Thinking Out of the Glovebox

The vibratory conveyor, routinely employed for normal-gravity transport of granular materials, usually consists of a continuous open trough vibrated sinusoidally to induce axial movement of a granular material. Motivated in part by a hypothetical application in zero gravity, we propose a novel modification of the vibratory conveyor based on a closed 2d trough operating in a "slide-conveying" mode, with the granular mass remaining permanently in contact with the trough walls. We present a detailed analysis of the mechanics of transport, based on a rigid-slab model for the granular mass with frictional (Coulomb) slip at the upper and lower walls. The form of the vibration cycle plays a crucial role, and the optimal conveying cycle is not the commonly assumed rectilinear sinusoidal motion. The conveying efficiency for the novel slide conveyor will be presented for several simple vibration cycles, including one believed to represent the theoretical optimum.

Goddard, J. D.↗
DOE OSTI2024

Digital bead modeling for wire-arc directed energy deposition

Prediction of 2D cross-section and full 3D geometry for stacked weld beads is critical for the outcome of wire-arc directed energy deposition (DED) parts; however, most additive path planning software packages model beads as extrusions of a rectangle. Weld beads are not rectangular, and the resulting shape is dependent upon physics effects at the moment of deposition. Physics phenomena such as the geometry of the underlying surface, the heat input of the welding mode, and the direction of gravity contribute to bead shape. Here, this paper presents a novel implicit modeling method that discretizes a 2D area or 3D volume of space into pixels or voxels and constructs fields based on these physics phenomena. The fields are combined using a weighting scheme trained on 3D scan measurements of welds and wire-arc DED prints. Pixels or voxels are added until the known amount of deposited volume has been achieved. Thereby, a strong conservation of mass principle is applied to the process. Utilizing machine learning techniques, the present model can be trained on a database of scans allowing for the representation of a wide variety of prints. Results show that this method can produce predictions with realistic bead morphology and sub-millimeter form error.

Bead geometry modeling↗
NASA NTRS1993

Ejecting basaltic achondrites from Vesta: Hydrodynamical impact models

Vesta is a large asteroid whose crust is mostly basaltic. Spectral heterogeneity suggests a sizable olivine feature which may be explained as impact excavation. The spectral data probably show a localized feature approximately 200 km in diameter or a diffuse feature approximately 400 km in diameter. Lightcurve irregularities suggest heterogeneity on a similar scale. This heterogeneity may represent the crater bowl, the extent of its ejecta deposit, or indeed something unrelated to cratering. In any case, drawing direct inferences about the state of Vesta's surface on the basis of these observations involves substantial speculation. We are still far from understanding the boundary between cratering and catastrophic disruption, particularly on targets for which strength and self-gravity both matter. But we are now able to model the underlying physical process -- dynamic fragmentation -- accurately with fragmentation hydrocodes such as SALE 2D and SPH3D. We present several impact scenarios for Vesta; our study is similar to a previous impact model for the formation of Stickney crater on Phobos. We illustrate the effects of impactors of different sizes and velocities, and the effect of gravity and structural inhomogeneity.

Asphaug, E.↗
NASA NTRS2001

Quasi-Decadal Oscillations Generated by the QBO

Quasi-decadal oscillations (QDO) have been observed in the stratosphere and have been linked to the equatorial Quasi-Biennial Oscillation (QBO) and to the 11-year solar activity cycle. With the use of a 2D version of our Numerical Spectral Model (NSM) that incorporates Hines' Doppler Spread Parameterization (DSP) for gravity waves (GW), we demonstrate that beat periods between 9 and 11 years can be generated by the QBO as it interacts through GW filtering with the Annual Oscillation (AO) and Semi-annual Oscillation (SAO). Results are discussed from computations covering up to 50 years, and our analyses leads to the following conclusions. The QDO as a stand-alone signature is largely confined to the upper mesosphere. Its largest signature appears in the form of amplitude modulations of the QBO, AO and SAO, and these extend into the lower stratosphere. The downward control that characterizes the QBO apparently comes into play, and the longer time constants for diffusion and radiative loss at lower altitudes facilitate the QDO response. Although excited by the QBO, which is confined to low latitudes, the QDO is shown to extend to high latitudes. The effect is particularly large for the QBO with period around 33.5 month (near the upper limit of observations), which interacts with the SAO to produce a hemispherically symmetric QDO. Our analysis indicates that the QDO is transferred to high latitudes by the meridional circulation, which prominently exhibits this periodicity particularly in the amplitude modulation of the AO.

Mayr, H. G.↗
NASA NTRS2001

Quasi-Decadal Oscillations Generated by the QBO

Quasi-decadal oscillations (QDO) have been observed in the stratosphere and have been linked to the equatorial Quasi-Biennial Oscillation (QBO) and to the 11-year solar activity cycle. With the use of a 2D version of our Numerical Spectral Model (NSM) that incorporates Hines'Doppler Spread Parameterization (DSP) for gravity waves (GW), we demonstrate that beat periods between 9 and I I years can be generated by the QBO as it interacts through GW filtering with the Annual Oscillation (AO) and Semi-annual Oscillation (SAO). Results are discussed from computations covering up to 50 years, and our analyses leads to the following conclusions. The QDO as a stand-alone signature is largely confined to the upper mesosphere. Its largest signature appears in the form of amplitude modulations of the QBO, AO and SAO, and these extend into the lower stratosphere. The downward control that characterizes the QBO apparently comes into play, and the longer time constants for diffusion and radiative loss at lower altitudes facilitate the QDO response. Although excited by the QBO, which is confined to low latitudes, the QDO is shown to extend to high latitudes. The effect is particularly large for the QBO with period around 33.5 month (near the upper limit of observations), which interacts with the SAO to produce a hemispherically symmetric QDO. Our analysis indicates that the QDO is transferred to high latitudes by the meridional circulation, which prominently exhibits this periodicity particularly in the amplitude modulation of the AO. The propensity for beat periods around 10 years may help to increase significantly the efficiency for generating solar cycle signatures in the atmosphere, and we discuss numerical experiments related to that.

Mayr, Hans G.↗
NASA NTRS1989

Analysis of the low gravity tolerance of Bridgman-Stockbarger crystal growth. I - Steady and impulse accelerations

The effects of steady and impulse-type residual accelerations on dopant distributions during directional solidification in 2D and 3D 'generic' models of the Bridgman-Stockbarger technique are investigated using numerical methods. The calculations are based on the thermophysical properties of molten germanium doped with a low concentration of gallium. A Chebyshev collocation pseudospectral method is used for the solution of the governing momentum-, mass-, species-, and heat-transfer equations. Only convection caused by temperature gradients is considered. It is found that lateral nonuniformity in composition is very sensitive to the orientation of the steady component of the residual gravity vector and to the particular operating conditions under consideration. It is also found that laterally or radially averaged composition profiles are alone insufficient to describe the extent of residual convection in a spacecraft environment. The effects of impulse-type disturbances can be severe and can extend for times on the order of 1000 sec after the termination of the impulse.

Alexander, J. Iwan D.↗
NASA NTRS1993

Numerical simulation of thermocapillary flow under zero and low gravity conditions

This paper discusses the numerical solution methods and results of steady and unsteady thermocapillary (surface-tension) and buoyancy driven flows in 2D cavities and liquid columns. The 2D cavity was assumed to be square with one free surface with a zero Capillary number (i.e., the free surface was constrained to be flat). A pseudospectral method was used to solve steady and unsteady surface tension-driven and mixed buoyancy-surface tension flows in a square cavity. For the liquid column a finite-difference scheme based on a Picard iteration was used to solve for the flow, temperature and free surface shape. The surface of the liquid column was allowed to deform and, as for the 2D cavity, the surface tension was assumed to depend on temperature.

Alexander, J. I. D.↗
NASA NTRS2000

Downward Link of Solar Activity Variations Through Wave Driven Equatorial Oscillations (QBO and SAO)

Signatures of the 11-year solar activity/irradiance cycle are observed in the Quasi Biennial Oscillation (QBO) of the lower stratosphere. At these altitudes, the QBO is understood to be the result of "downward control" exerted by the wave mean flow interactions that drive the phenomenon. It is reasonable then to speculate that the QBO is a natural conduit to lower altitudes of solar activity variations in radiance (SAV). To test this hypothesis, we conducted experiments with a 2D version of our Numerical Spectral Model that incorporates Hines' Doppler Spread Parameterization for small-scale gravity waves (GW). To account for the SAV, we change the solar heating rate on a logarithmic scale from 0.1% at the surface to 1% at 50 kin to 10% at 100 km. With the same GW flux, we then conduct numerical experiments to evaluate the magnitude of the solar activity irradiance effect (SAE) on the zonal circulation at low latitudes. The numerical results obtained show that, under certain conditions, the SAE is significant in the zonal circulation and does extend to lower altitudes where the SAV is small. The differences in the wind velocities can be as large as 5 m/s at 20 kin. We carried out two numerical experiments with integrations over more than 20 years: 1) With the QBO period "tuned" to be 30 months, of academic interest but instructive, the seasonal cycle in the solar forcing [through the Semi-annual Oscillation (SAO)] acts as a strong pacemaker to produce a firm lock on the period and phase of the QBO. The SAE then shows up primarily as a distinct but relatively weak amplitude modulation. 2) With the QBO period between 30 and 34 (or less than 30, presumably) months, the seasonal phase lock is weak compared with (1). The SAV in the seasonal cycle then causes variations in the QBO period and phase, and this amplifies the SAE to produce relatively large variations in the wind field. We conclude that, under realistic conditions as in (2), the solar seasonal forcing, with activity variations in radiance, causes the phase and period of the QBO to change to produce a relatively large solar activity effect in the zonal circulation at low latitudes and low altitudes.

Mengel, J. G.↗
NASA NTRS2002

Are Solar Activity Variations Amplified by the QBO: A Modeling Study

Solar cycle activity effects (SCAE) in the lower and middle atmosphere, reported in several studies, are difficult to explain on the basis of the small changes in solar radiation that accompany the 11-year cycle. It is therefore natural to speculate that dynamical processes may come into play to produce a leverage. Such a leverage may be provided by the Quasi-Biennial Oscillation (QBO) in the zonal circulation of the stratosphere, which has been linked to solar activity variations. Driven primarily by wave mean flow interaction, the QBO period and its amplitude are variable but are also strongly influenced by the seasonal cycle in the solar radiation. This influence extends to low altitudes and is referred to as 'downward control'. Small changes in the solar radiative forcing may produce small changes in the period and phase of the QBO, but these in turn may produce measurable differences in the wind field. Thus, the QBO may be an amplifier of solar activity variations and a natural conduit of these variations to lower altitudes. To test this hypothesis, we conducted experiments with a 2D version of our Numerical Spectral Model that incorporates Hines' Doppler Spread Parameterization for small-scale gravity waves (GW). Solar cycle radiance variations (SCRV) are accounted for by changing the radiative heating rate on a logarithmic scale from 0.1% at the surface to 1% at 50 km to 10% at 100 km. With and without SCRV, but with the same GW flux, we then conduct numerical experiments to evaluate the magnitude of the SCAE in the zonal circulation. The numerical results indicate that, under certain conditions, the SCAE is significant and can extend to lower altitudes where the SCRV is small. For a modeled QBO period of 30 months, we find that the seasonal cycle in the solar forcing acts as a strong pacemaker to lock up the phase and period of the QBO. The SCAE then shows up primarily as a distinct but relatively weak amplitude modulation. But with a different QBO period between 30 and 34 (or less than 30, presumably) months, the seasonal phase lock is weak. Solar flux variations in the seasonal cycle then cause variations in the QBO period and phase. These amplify the SCAE to produce relatively large variations in the wind field. The SCAE in this case extends to mid-latitudes.

Mengel, J. G.↗
DOE OSTI2025

ARM SGP PBLH and MLH datasets from Raman lidar and Doppler lidar

The planetary boundary layer (PBL) plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including the daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary for studying PBL characterization and processes. Here, new approaches are developed to provide reliable PBLH and MLH estimates to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) User Facility. Compared to widely used lidar aerosol measurements for PBLH, WVMR is a better tracer for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to reconstruct 2D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm-season RL and ML measurements at the SGP site for five years to study warm-season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in a warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable data set of PBLH and MLH values will be valuable for studying PBL processes, model evolution, and PBL parameterization improvements. The MLH dataset includes the MLH in values of km above ground level. The PBLH dataset includes the PBLH in values of km above ground level, along with a flag ("situation_PBLH") to determine the state of the PBL (1 = Cloudy Condition, 2 = Stable Layer, 3 = Multi-layer WVMR structure, 4 = Well-Mixed PBL, 5 = A de-coupled layer, 6 = Other).

mixing layer height↗
DOE OSTI2022

$T\bar{T}$ in JT Gravity and BF Gauge Theory

JT gravity has a first-order formulation as a two-dimensional BF theory, which can be viewed as the dimensional reduction of the Chern-Simons description of 3d 3 d gravity. We consider {T\overbar{T}} T T ¯ -type deformations of the (0+1) ( 0 + 1 ) -dimensional dual to this 2d 2 d BF theory and interpret the deformation as a modification of the BF theory boundary conditions. The fundamental observables in this deformed BF theory, and in its 3d 3 d Chern-Simons lift, are Wilson lines and loops. In the 3d 3 d Chern-Simons setting, we study modifications to correlators involving boundary-anchored Wilson lines which are induced by a {T\overbar{T}} T T ¯ deformation on the 2d 2 d boundary; results are presented at both the classical level (using modified boundary conditions) and the quantum-mechanical level (using conformal perturbation theory). Finally, we calculate the analogous deformed Wilson line correlators in 2d 2 d BF theory below the Hagedorn temperature where the principal series dominates over the discrete series.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Chemical Mixing Induced by Internal Gravity Waves in Intermediate-mass Stars

Internal gravity waves can cause mixing in the radiative interiors of stars. We study this mixing by introducing tracer particles into 2D hydrodynamic simulations. Following the work of Rogers & McElwaine, we extend our study to different masses (3, 7, and 20 M ⊙ ) and ages (ZAMS, midMS, and TAMS). The diffusion profiles of these models are influenced by various parameters such as the Brunt–Väisälä frequency, density, thermal damping, the geometric effect, and the frequencies of waves contributing to these mixing profiles. We find that the mixing profile changes dramatically across age. In younger stars, we noted that the diffusion coefficient increases toward the surface, whereas in older stars the initial increase in the diffusion profile is followed by a decreasing trend. We also find that mixing is stronger in more massive stars. Hence, future stellar evolution models should include this variation. In order to aid the inclusion of this mixing in 1D stellar evolution models, we determine the dominant waves contributing to these mixing profiles and present a prescription that can be included in 1D models.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

Photon bubbles - Overstability in a magnetized atmosphere

The formation of 'photon bubbles' in a convectively stable scattering atmosphere supported against gravity entirely by radiation pressure is studied by means of linear stability theory. A simple model is developed for the 2D structure of a plasma mound formed by laminar accretion onto the magnetic poles of a neutron star, in which upward photon diffusion balances downward photon advection with the plasma. It is shown that the vertical pressure and density structure is the same as in an isothermal atmosphere. Application of the stability theory to this model suggests photon bubbles would form in a polar accretion mound under the conditions expected in accretion-powered pulsars within a few tenths of a millisecond. Because long-wavelength modes have the largest rise speeds, eventual dominance by a few large bubbles is suggested, and possible connections between bubble formation and short-time variability in accretion-powered pulsars is discussed, as well as a possible connection of the photon bubble phenomenon to the rapid time variability observed in the Rapid Burster and in quasi-period oscillator sources.

Arons, Jonathan↗
NASA NTRS1992

Seasonal and latitudinal variations of gravity wave-driven fluctuations in OH nightglow

A model which incorporates extended OH-layer emission and gravity-wave dynamics with eddy diffusivities is set forth to analyze seasonal and latitudinal variations in the OH nightglow. The 2D analysis is aimed at determining the nature of the relationship between the oscillation in intensity about a mean intensity and the oscillation in temperature of the emission region about a mean temperature. Competing eddy diffusivities modify the local values and altitudes of maximum wave amplitude, so long-period characterization of the relationship is imprecise. Seasonal trends in the relationship are noted for periods of several hrs or less that are primarily related to seasonal changes in the mesopause undisturbed temperature. Latitudinal trends are shown to be less pronounced, and trends that exist at intermediate periods are difficult to isolate due to interference effects.

Hickey, M. P.↗
NASA NTRS2001

Solar Cycle Variations and Equatorial Oscillations: Modeling Study

Solar cycle activity effects (SCAE) in the lower and middle atmosphere, reported in several studies, are difficult to explain on the basis of the small changes in solar radiation that accompany the 11-year cycle, It is therefore natural to speculate that dynamical processes may come into play to produce a leverage. Such a leverage may be provided by the Quasi-Biennial Oscillation (QBO) in the zonal circulation of the stratosphere, which has been linked to solar activity variations. Driven primarily by wave mean flow interaction, the QBO period and its amplitude are variable but are also strongly influenced by the seasonal cycle in the solar radiation. This influence extends to low altitudes referred to as "downward control". Relatively small changes in solar radiative forcing can produce small changes in the period and phase of the QBO, but this in turn can produce measurable differences in the wind field. Thus, the QBO may be an amplifier of solar activity variations and a natural conduit of these variations to lower altitudes. To test this hypothesis, we conducted experiments with a 2D (two-dimensional) version of our Numerical Spectral Model that incorporates Hines' Doppler Spread Parameterization for small-scale gravity waves (GW). Solar cycle radiance variations (SCRV) are accounted for by changing the radiative heating rate on a logarithmic scale from 0.1 % at the surface to 1 % at 50 km to 10% at 100 km. With and without SCRV, but with the same GW flux, we then conduct numerical experiments to evaluate the magnitude of the SCAE in the zonal circulation. The numerical results indicate that, under certain conditions, the SCAE is significant and can extend to lower altitudes where the SCRV is inconsequential. At 20-km the differences in the modeled wind velocities are as large as 5 m/s. For a modeled QBO period of 30 months, we find that the seasonal cycle in the solar forcing (through the Semi-annual Oscillation (SAO)) acts as a strong pacemaker to lockup the phase and period of the QBO. The SCAE then shows up primarily as a distinct but relatively weak amplitude modulation. But with the QBO period between 30 and 34 (or less than 30, presumably) months, the seasonal phase lock is weak. Solar flux radiance variations in the seasonal cycle then cause variations in the QBO period and phase that amplify the SCAE to produce relatively large variations in the wind field. These variations also extend to mid latitudes.

Mayr, H. G.↗