Kinetic theory and Vlasov simulation of nonlinear ion acoustic waves in multi-ion species plasmas
Abstract not provided
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
SEARCH · Search NASA
Results for “ACOUSTIC SIMULATION”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The Derivation of Multiple-Input-Multiple-Output (MIMO) Acoustic Test Specifications to Simulate a Missile Flight.
Abstract not provided.
Cap, Jerome S.↗
The Derivation of Multiple-Input-Multiple Output (MIMO) Acoustic Test Specifications to Simulate a Missile Flight.
Abstract not provided.
Cap, Jerome S.↗
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↗
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↗
Demonstrating the saturation of stimulated Brillouin scattering by ion acoustic decay using fully kinetic simulations
The saturation of stimulated Brillouin scattering (SBS) by the decay to turbulence of the ion acoustic wave (IAW) that participates in the three-wave SBS interaction is demonstrated using a quasi-noiseless one-dimensional numerical solution to the Vlasov-Maxwell system of equations. This simulation technique permits careful examination of the decay process and its role in the complex evolution of SBS. Here, the IAW decay process is shown to be an effective SBS saturation mechanism. In our example, the instantaneous plasma reflectivity saturates at ~30% and drops to ~0% as a direct consequence of IAW decay. A contrasting example where the reflectivity is controlled by dephasing due to the nonlinear frequency of the IAW is also discussed.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
SPECTROPOLARIMETRICALLY ACCURATE MAGNETOHYDROSTATIC SUNSPOT MODEL FOR FORWARD MODELING IN HELIOSEISMOLOGY
We present a technique to construct a spectropolarimetrically accurate magnetohydrostatic model of a large-scale solar magnetic field concentration, mimicking a sunspot. Using the constructed model we perform a simulation of acoustic wave propagation, conversion, and absorption in the solar interior and photosphere with the sunspot embedded into it. With the 6173 Å magnetically sensitive photospheric absorption line of neutral iron, we calculate observable quantities such as continuum intensities, Doppler velocities, as well as the full Stokes vector for the simulation at various positions at the solar disk, and analyze the influence of non-locality of radiative transport in the solar photosphere on helioseismic measurements. Bisector shapes were used to perform multi-height observations. The differences in acoustic power at different heights within the line formation region at different positions at the solar disk were simulated and characterized. An increase in acoustic power in the simulated observations of the sunspot umbra away from the solar disk center was confirmed as the slow magnetoacoustic wave.
79 ASTRONOMY AND ASTROPHYSICS↗
Phonon spectrum and phonon focusing in coarse-grained atomistic simulations
This work investigates the accuracy, efficiency, and applicability of coarse-grained (CG) atomistic methods in simulation of phonon dynamics. First, we compute and compare phonon dispersion relations in CG models with those in atomically resolved models, using the concurrent atomistic-continuum (CAC). The CG atomistic models using the CAC method are shown to reproduce long-wavelength phonons with great accuracy, while capturing the dynamics of some short-wavelength phonons that are usually inaccessible to CG methods. We then present CG simulation results of the propagation of heat pulses in Si with the interaction between atoms being modelled with the Stillinger-Weber potential; the experimentally observed phonon-focusing patterns in the (1 0 0) and (1 1 1) planes of Si crystals are reproduced. The accuracy and efficiency of the CAC method in CG simulation of acoustic and optical phonon branches are quantified with respect to atomically-resolved molecular dynamics simulations. The applicability and limitations of concurrent multiscale methods in the simulation of phonon transport across atomistic-continuum interface are investigated. Possible ways to overcome the limitations are discussed.
36 MATERIALS SCIENCE↗
An O(N logN) multi-layer boundary element method for direct computation of sound propagation in shallow water environments
Direct three-dimensional (3D) numerical simulations of acoustic fields in range-dependent shallow water environments remains a challenge due to environmental complexities and large computational cost.We develop an efficient 3D boundary element method (BEM) for shallow water acoustic propagation which utilizes a Pre-corrected Fast Fourier Transform (PFFT) approach to reduce the computational effort from O ( N 2~3 ) to O ( N log N ) where N is the total number of boundary unknowns. To account for inhomogeneous media, the method allows for arbitrary number of coupled multi-layer BEM sub-domains. With O ( N log N ) efficiency and the use of massively parallel high-performance computing platforms, we are able to conduct multi-layer 3D direct simulations of low-mid frequency acoustics over kilometer ranges. Furthermore we perform extensive validations of the method and provide two shallow water waveguide examples benchmarked against theoretical solutions. To illustrate the efficacy and usefulness of the PFFT-BEM method, we perform 3D large-scale direct simulations to assess the performance of two established canonical models: axisymmetric coupled mode model for 3D seamount; and Kirchhoff approximation and perturbation theory for 3D rough surface scattering.
42 ENGINEERING↗
Ultrafast Nanoimaging of Spin-Mediated Shear Waves in an Acoustic Cavity
Strong spin–lattice coupling in van der Waals (vdW) magnets shows potential for innovative magneto-mechanical applications. Here, nanoscale and picosecond imaging by ultrafast electron microscopy reveal heterogeneous spin-mediated coherent acoustic phonon dynamics in a thin-film cavity of the vdW antiferromagnet FePS3. The harmonics of the interlayer shear acoustic modes are observed, in which the even and odd harmonics exhibit distinct nanoscopic dynamics. Corroborated by acoustic wave simulation, the role of defects in forming even harmonics is elucidated. Above the Néel temperature (T N ), the interlayer shear acoustic harmonics are suppressed, while the in-plane traveling wave is predominantly excited. The dominant acoustic dynamics shifts from the out-of-plane shear to the in-plane traveling wave across T N , demonstrating that magnetic properties can influence phonon scattering pathways. In conclusion, the spatiotemporally resolved structural characterization provides valuable nanoscopic insights for interlayer-shear-mode-based acoustic cavities, opening up possibilities for magneto-mechanical applications of vdW magnets.
36 MATERIALS SCIENCE↗
Fully kinetic simulation of dust-ion acoustic waves: Landau damping and dust concentration effect
A fully kinetic (Vlasov) numerical scheme for dusty plasma dynamics is presented. The parametric dependence of Landau damping of dust-ion acoustic waves on the electron-to-ion temperature ratio and dust concentration is discussed.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Simulations of ion heating due to ion-acoustic instabilities in presheaths
Particle-in-cell, direct simulation Monte Carlo simulations reveal that ion-acoustic instabilities excited in presheaths can cause significant ion heating. Ion-acoustic instabilities are excited by the ion flow toward a sheath when the neutral gas pressure is small enough and the electron temperature is large enough. A series of 1D simulations were conducted in which neutral plasma (electrons and ions) was uniformly sourced with an ion temperature of 0.026 eV and different electron temperatures (0.1 eV–50 eV). Ion heating was observed when the electron-to-ion temperature ratio exceeded the minimum value predicted by linear response theory to excite ion-acoustic instabilities at the sheath edge (T e /T i ≈ 28). When this threshold was exceeded, the temperature equilibration rate between ions and electrons rapidly increased near the sheath so that the local temperature ratio did not significantly exceed the threshold for instability. This resulted in significant ion heating near the sheath edge, which also extended back into the bulk plasma; presumably due to wave reflection from the sheath. Furthermore, this ion-acoustic wave heating mechanism was found to decrease for higher neutral pressures, where ion-neutral collisions damp the ion-acoustic waves and ion heating is instead dominated by inelastic collisions in the presheath.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
An efficient mixed-precision, hybrid CPU-GPU implementation of a nonlinearly implicit one-dimensional particle-in-cell algorithm
Recently, a fully implicit, energy- and charge-conserving particle-in-cell method has been developed for multi-scale, full-f kinetic simulations [G. Chen, et al., J. Comput. Phys. 230, 18 (2011)]. The method employs a Jacobian-free Newton-Krylov (JFNK) solver and is capable of using very large timesteps without loss of numerical stability or accuracy. A fundamental feature of the method is the segregation of particle orbit integrations from the field solver, while remaining fully self-consistent. This provides great flexibility, and dramatically improves the solver efficiency by reducing the degrees of freedom of the associated nonlinear system. However, it requires a particle push per nonlinear residual evaluation, which makes the particle push the most time-consuming operation in the algorithm. This paper describes a very efficient mixed-precision, hybrid CPU-GPU implementation of the implicit PIC algorithm. The JFNK solver is kept on the CPU (in double precision), while the inherent data parallelism of the particle mover is exploited by implementing it in single-precision on a graphics processing unit (GPU) using CUDA. Performance-oriented optimizations, with the aid of an analytical performance model, the roofline model, are employed. Despite being highly dynamic, the adaptive, charge-conserving particle mover algorithm achieves up to 300 400 GOp/s (including single-precision floating-point, integer, and logic operations) on a Nvidia GeForce GTX580, corresponding to 20 25% absolute GPU efficiency (against the peak theoretical performance) and 50-70% intrinsic efficiency (against the algorithm s maximum operational throughput, which neglects all latencies). This is about 200-300 times faster than an equivalent serial CPU implementation. When the single-precision GPU particle mover is combined with a double-precision CPU JFNK field solver, overall performance gains 100 vs. the double-precision CPU-only serial version are obtained, with no apparent loss of robustness or accuracy when applied to a challenging long-time scale ion acoustic wave simulation.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
The quantum mechanics derived atomistic mechanism underlying the acceleration of catalytic CO oxidation on Pt(110) by surface acoustic waves
Quantum mechanics simulations unveil the mechanism of surface acoustic waves (SAW) enhancement of CO oxidation on Pt(110) via shock spikes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Global gyrokinetic simulation of microturbulence with kinetic electrons in the presence of magnetic island in tokamak
An electrostatic model has been formulated and implemented in the gyrokinetic toroidal code to study the nonlinear ion temperature gradient (ITG) turbulence in the presence of an n = 1, m = 2 magnetic island. The ions are described by the gyrokinetic equation while the electrons are treated with the drift-kinetic equation. In our simulation, an n = 1, m = 2 electrostatic mode is formed with the same vortex structure of the magnetic island. When the magnetic island flattening effect is turned on, the island vortex mode is well preserved and couples to the n = 0, m = 0 geodesic acoustic mode. Simulation shows that the magnetic island can suppress the ITG turbulence at the island O-point and strengthen it near the X-point. We show that the vortex mode can generate a substantial helical shear flow around the island. Finally, we also find that the turbulence and transport are suppressed inside the island and enhanced at the island X-point
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
SU-E-CAMPUS-T-02: Exploring Radiation Acoustics CT Dosimeter Design Aspects for Proton Therapy
Purpose: Investigate the design aspects and imaging dose capabilities of the Radiation Acoustics Computed Tomography (RA CT) dosimeter for Proton induced acoustics, with the objective to characterize a pulsed pencil proton beam. The focus includes scanner geometry, transducer array, and transducer bandwidth on image quality. Methods: The geometry of the dosimeter is a cylindrical water phantom (length 40cm, radius 15cm) with 71 ultrasound transducers placed along the length and end of the cylinder to achieve a weighted set of projections with spherical sampling. A 3D filtered backprojection algorithm was used to reconstruct the dosimetric images and compared to MC dose distribution. First, 3D Monte Carlo (MC) Dose distributions for proton beam energies (range of 12cm, 16cm, 20cm, and 27cm) were used to simulate the acoustic pressure signal within this scanner for a pulsed proton beam of 1.8x107 protons, with a pulse width of 1 microsecond and a rise time of 0.1 microseconds. Dose comparison within the Bragg peak and distal edge were compared to MC analysis, where the integrated Gaussian was used to locate the 50% dose of the distal edge. To evaluate spatial fidelity, a set of point sources within the scanner field of view (15×15×15cm3) were simulated implementing a low-pass bandwidth response function (0 to 1MHz) equivalent to a multiple frequency transducer array, and the FWHM of the point-spread-function determined. Results: From the reconstructed images, RACT and MC range values are within 0.5mm, and the average variation of the dose within the Bragg peak are within 2%. The spatial resolution tracked with transducer bandwidth and projection angle sampling, and can be kept at 1.5mm. Conclusion: This design is ready for fabrication to start acquiring measurements. The 15 cm FOV is an optimum size for imaging dosimetry. Currently, simulations comparing transducer sensitivity, bandwidth, and proton beam parameters are being evaluated to assess signal-to-noise.
07 ISOTOPE AND RADIATION SOURCES↗
Anisotropic Thermally Conductive Composites Enabled by Acoustophoresis and Stereolithography
Opportunities to improve thermal management in electronic devices are currently hindered by processing constraints that limit thermal conductivity in polymer-matrix composites. Active patterning of filler particles is a promising route to improve conductivity while retaining processability by improving particle contact density and directing heat along optimized pathways. Here, we employ acoustic patterning to align and compact filler particles into stripes during stereolithographic 3D printing. This approach produces polymer-based composite materials with highly efficient embedded heat transport pathways which reach 95 vol% particle utilization (relative to the parallel conduction upper limit). These composites exhibit anisotropic thermal conductivity up to 300% higher than unpatterned composites, with in-plane anisotropy ratios of up to 350%. Combining this high conductivity with 3D printing enables materials with engineered heat networks that optimize transport from hot spots to heatsinks while maintaining low viscosity for fast particle patterning and for infiltration around electronic components. Finally, numerical simulations of acoustic assembly of particles with varied geometry, when compared to experimentally characterized particle packing, illuminate pathways for further improving conductivity by optimizing particle geometry for alignment and stacking of particles with maximum contact surface area.
36 MATERIALS SCIENCE↗
Earthquake detection in a simulated lunar regolith using distributed acoustic sensing
Current models of inner lunar geology have largely been inferred from the seismic experiments and observations performed during the Apollo missions that comprised a relatively small number of seismic instruments. Refining constraints on fundamental lunar relationships such as crust-mantle and mantle-core boundaries in the future will require seismic arrays spanning larger epicentral distances. A promising technology for installing dense seismic arrays with minimal human effort is distributed acoustic sensing (DAS), an approach that allows a single length of fiber optic cable to act as hundreds or thousands of sensors when coupled with a DAS interrogator. While terrestrial uses of DAS technology for seismic monitoring rely on burying the cable to maximize fidelity of seismic signal transmission to the fiber, digging meters of trench to bury optical fiber on lunar or planetary surfaces is logistically infeasible. To evaluate DAS signal attenuation due to surface deployment of cable in lunar regolith, we completed earthquake detection analyses that evaluated the sensitivity of an optic-fiber DAS system to seismic signals at different burial depths. We deployed a single-mode fiber in a 10-m open-bottom wooden box filled with a lunar regolith simulant (LRS) with fiber buried at different depths within the LRS and recorded signals for four regional and local earthquakes. The results were used to identify and evaluate signal attenuation in surface-deployed fiber compared to buried fiber in the LRS. Burial depth responses to active-source signals were also evaluated similar to previous studies characterizing DAS sensitivity of surface-deployed fiber. Atmospheric noise was minimal as the cable was deployed in an indoor environment; however, where observed, atmospheric and anthropogenic noise was filtered out using the same bandpass filtering used to identify earthquake events. We found that signal attenuation of the surface-deployed fiber compared to buried fiber was relatively high in active-source experiments but was not consistently observed in earthquake signals. That burial depth is not highly correlated to attenuation of the observed earthquake signals indicates that in a noise-limited environment, placing DAS-interrogated fiber directly at the regolith surface may be a promising deployment strategy to consider for sensing remote seismic signals during lunar exploration.
58 GEOSCIENCES↗