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 T03: Data-Driven SimulationRecordings Available
|
Hide Abstracts |
Chair: Avinash Dongare, University of Connecticut Room: Anaheim Marriott Platinum 1 |
Thursday, July 14, 2022 9:15AM - 9:30AM |
T03.00001: Numerical simulations of the Los Alamos gapstick experiment Matthew A Price The gapstick is a relatively new high explosive (HE) experiment that was inspired by the traditional gap test. The gapstick consists of a series of HE and inert pellets, which are increasingly longer, in a rate stick configuration. As the shock attenuates in the inert pellets it becomes more difficult to initiate successive HE pellets. Simulations of these experiments present numerous challenges. To start with, the material model for Polyvinylidene Fluoride (PVDF), used for the inert pellets, is not well characterized but is quite important. Properly modeling the initiation and detonation of the HE is also important and requires a reactive burn model with sufficient mesh resolution to capture the reaction scales. For this work, we employ the Scaled Uniform Reactive Burn (SURF) and Arrhenius Wescott-Stewart-Davis (AWSD) reactive burn models. These burn models are sensitive to mesh resolution and other code settings which are investigated (e.g. artificial viscosity in the Lagrangian-based staggered-grid hydrodynamics solver). Finally, running the full gapstick simulation at a reasonable mesh resolution is very computationally demanding and mesh strategies such as adaptive mesh refinement (AMR) will be explored. Despite these challenges, the gapstick is a very unique experiment that can be used for validation and potential calibration of reactive burn models. |
Thursday, July 14, 2022 9:30AM - 9:45AM |
T03.00002: A physics-informed machine learning model for go/no-go criteria on reactive metamaterials. Seungjoon Lee, Kibaek Lee, Alberto M Hernández, Donald S Stewart We present a physics-informed machine learning framework for predicting Go/No-Go criteria on reactive metamaterials. The effectiveness of this framework was demonstrated by analyzing shock propagation through a one-dimensional laminate structure. The laminate material is composed of an HMX bed with equally distributed 2mm thick copper pillars. The Wide-Ranging equation of state (EOS) was used to model HMX while the Romenski EOS is used for an elastic regime of copper and perfect plasticity is assumed. A gauge was placed at the entry of the first copper pillar and at the exit of the last pillar and an Aluminum impactor was used to initiate the shock. A modified machine learning model was then developed to predict the criteria for the laminate structure.The proposed model uses only short time measurements for predicting this behavior expecting in large reductions of computational cost in higher dimension analysis. This framework can suggest a data-driven guideline for the design of optimal laminate structures (e.g number of copper pilers, thickness and distribution). |
Thursday, July 14, 2022 9:45AM - 10:00AM |
T03.00003: Multiscale Development of Predictive Constitutive Models to Resolve the Shock to Detonation Transition Michael Sakano, Judith A Brown, Mitchell A Wood Predicting the initiation of energetic materials under a variety of mechanical stimuli and thermodynamic conditions will result in much needed insight into the connection between fundamental chemical and microstructural properties and the detonation performance. Current experimental characterization of new/novel energetic materials can be both costly and time-consuming, thus resulting in a lack of materials property data necessary to deploy accurate constitutive models into existing simulation methods. These poorly constrained simulations can thus only provide a qualitative understanding of shock-to-detonation. We demonstrate a multiscale framework that leverages different computational methods to study materials behavior across a variety of shock conditions and length- and time-scales. Parameterization of continuum-scale strength models have been parameterized from high fidelity simulation codes to efficiently study hotspot dynamics. This talk will detail our computational approach for generating a massive number of candidate models, from which a few are screened and analyzed in terms of their pore collapse behavior. We will demonstrate the viability for this multiscale approach to provide rapid progress toward accurate strength models beyond what is experimentally capable. |
Thursday, July 14, 2022 10:00AM - 10:15AM |
T03.00004: Hydrocode validation for short pulse laser driven shocks in aluminum Sophie E Parsons, Michael R Armstrong, Ross E Turner, Christian M Childs, Paulius Grivickas, Harry B Radousky, Javier E Garay, Farhat N Beg A joint theoretical and experimental campaign is being conducted focusing specifically on modeling shock waves in aluminum induced by a short pulse laser in the 100ps time scale regime. The goal of this study is to increase the fundamental understanding of the reaction of aluminum under laser induced compression both experimentally and through simulations. |
Thursday, July 14, 2022 10:15AM - 10:30AM |
T03.00005: Examining the shock response of iron using molecular-spin dynamics Svetoslav Nikolov, Julien Tranchida, Kushal Ramakrishna, Attila Cangi, Mitchell A Wood For magnetic materials like iron, where structural deformations are coupled to magnetic properties, resolving the underlying magnetization dynamics within the spin subsystem is critical for capturing the correct material response. Herein, utilizing a large dataset of high temperature/pressure ab-initio calculations, we introduce a novel data-driven framework for building molecular-spin dynamics machine learned potentials that can capture both phononic and magnonic excitations. We show that our molecular-spin model for iron can accurately reproduce, elastic properties, bcc-hcp (~15GPa) / bcc-fcc (~26 GPa) transition pressures, and experimental ramp compression curve data. We further test our framework by carrying out large scale molecular-spin dynamics calculations that examine the shock response of iron for piston speeds in the range of 0.1-3.5 kmps. Doing this we characterize both the phase transformation and shock-induced melting pressures. Additionally, we examine how the shock-hugoniot curves vary for different preheat temperatures. The impact of longitudinal spin fluctuations on the shock-induced phase transformation pressures is also examined. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700