Bulletin of the American Physical Society
22nd Biennial Conference of the APS Topical Group on Shock Compression of Condensed Matter
Volume 67, Number 8
Monday–Friday, July 11–15, 2022; Anaheim, California
Session O03: Data-Driven ExperimentsRecordings Available
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Chair: Trevor Willey, Lawrence Livermore Natl Laboratory Room: Anaheim Marriott Platinum 1 |
Wednesday, July 13, 2022 9:15AM - 9:30AM |
O03.00001: Simultaneous Bayesian calibration of strength, kinetics, and phase boundaries William Schill, Ryan Austin, Jonathan L Belof, Kathleen Schmidt, Justin L Brown, Nathan R Barton Ramp-driven compression-release experiments offer possibilities to explore material response under conditions distinct from those accessed by shock-driven loading conditions. For a material undergoing phase transformation, the problem of material model identification from experimental measurement is made substantially more complex by the need to untangle not only elasticity and plasticity, but also features introduced by the phase transformation. Tin exhibits a complex phase diagram within a relatively accessible range of temperature and pressures and the characterization of its phases is considered an open problem with significant scientific merit. Moreover, under extreme loading conditions, equilibrium phase transition modeling appears insufficient, suggesting the presence of important kinetic processes. In this study, we construct a full forward model of the experiment and simulation results are compared to recent observations of Sn response in ramp-driven compression-release experiments. We employ Bayesian statistical techniques to explore the interactions between inelasticity and phase transition kinetics in Sn. The degree to which these different kinetic processes can be distinguished given velocimetry data is discussed. |
Wednesday, July 13, 2022 9:30AM - 9:45AM |
O03.00002: A machine-guided approach to connect porosity characteristics to corner turning performance in PBX 9502 Dylan O Walters, Levi Lystrom, Oishik Sen, Lee Perry Machine Learning (ML) methods have been used to connect microstructural details in energetic materials (EMs) to macroscale performance parameters. One such EM, PBX 9502, has historically shown a wide range of corner turning behavior, likely due to microstructural manufacturing variations. The 10 nm – 10 µm void size distribution (VSD) influences EM behavior; here we investigate 'corner turning'. In this work, our ML algorithm learns the correlation between VSD details and the corresponding corner-turning performance. Training data is generated by simulating experiments using a physically-informed reactive flow model running on HPC resources. The study observes the robustness of the approach and, as there is limited experimental data, we heuristically evaluate the predictions. We conclude that our algorithm did robustly learn the correlations, finding that porosity characteristics in the 100-200 nm range have the strongest effect. This agrees with our qualitative knowledge and we discuss the validity of that prediction. The ML algorithm provides a powerful tool to assess and predict the effects of manufacturing variability on the corner turning behavior of PBX 9502, through which it can guide manufacturing and formulation. The method can be extended to other EMs. |
Wednesday, July 13, 2022 9:45AM - 10:00AM |
O03.00003: Experimentally Constraining the High-Pressure Behavior of Beryllium Kazem Alidoost, Damian C Swift, Amy L Coleman, Raymond F Smith, Amy E Jenei, James M McNaney, Jon H Eggert Beryllium is a commonly used material in high-pressure diffraction targets, but its high-pressure behavior has not been experimentally characterized. Its equation of state calculations have long predicted an on-Hugoniot HCP to BCC transition, but current experimental results have shown no evidence of this transition from the HCP phase. Laser-driven shock and shock-ramp compression experiments on a nanosecond time scale were performed on Beryllium samples using the Omega-EP Laser System at the University of Rochester. These experiments have allowed us to characterize the behavior of Beryllium at higher pressures and temperatures than have previously been explored, both on and off the Hugoniot. The results of the experiments, including VISAR and X-ray diffraction measurements will be presented, along with radiation hydrodynamics simulations, which were used to design the experiments and validate the results. The pulse shapes, which were designed using the ideal shape of ramp compression waves to avoid shock formation [DOI:10.1103/PhysRevE.78.066115], are also presented. Simulations which were performed using a recently developed dislocation-based strength model for high energy density conditions [arXiv:2110.06345] are presented as well. |
Wednesday, July 13, 2022 10:00AM - 10:15AM |
O03.00004: Simulated X-ray Diffraction and Machine Learning for Interpretation of Dynamic Compression Experiments David O Montes de Oca Zapiain, Dane V Morgan, Bryce A Thurston, Tommy Ao, Mark A Rodriguez, Marcus Knudson, J Matthew D Lane X-ray diffraction data collected under dynamic compression conditions are extremely challenging to interpret due to non-ideal sources and geometries, in addition to an extreme paucity of data. We resolve these issues with a robust simulated X-ray diffraction tool based within the LAMMPS molecular dynamics code that enables the generation of accurate and realistic diffraction patterns in a computationally efficient manner. Subsequently, we leverage this enhanced simulation tool to generate a diverse dataset on which we train machine learning models that are capable of removing noise/background and extracting lattice symmetry and orientation from experimental diffraction experiments. Specifically, we have developed two ML-based tools. The first tool is a convolutional neural network (CNN) capable of identifying the lattice and orientations of the underlying structure from multicomponent experimental diffraction patterns. The second tool identifies and separates X-ray diffraction contributions from pulsed power specific background, including reflection and detector noise using a CNN-based Auto-Encoder/Decoder architecture. |
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