Reduced Temperature Effects on Aerospace Materials Undergoing Dynamic Impact Events
Explore the source record for details and available documents.
SEARCH · Engineering Papers
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.
Explore the source record for details and available documents.
Here we present a highly scalable, parallel implementation of first-principles electron dynamics coupled with molecular dynamics (MD). By using optimized kernels, network topology aware communication, and by fully distributing all terms in the time-dependent Kohn–Sham equation, we demonstrate unprecedented time to solution for disordered aluminum systems of 2000 atoms (22,000 electrons) and 5400 atoms (59,400 electrons), with wall clock time as low as 7.5 s per MD time step. Despite a significant amount of non-local communication required in every iteration, we achieved excellent strong scaling and sustained performance on the Sequoia Blue Gene/Q supercomputer at LLNL. We obtained up to 59% of the theoretical sustained peak performance on 16,384 nodes and performance of 8.75 Petaflop/s (43% of theoretical peak) on the full 98,304 node machine (1,572,864 cores). Lastly, scalable explicit electron dynamics allows for the study of phenomena beyond the reach of standard first-principles MD, in particular, materials subject to strong or rapid perturbations, such as pulsed electromagnetic radiation, particle irradiation, or strong electric currents.
Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.
Many important processes in materials systems are intrinsically atomistic in nature but involve time scales that span many orders of magnitude, thus exceeding what can be directly simulated using molecular dynamics. This is especially true for materials in nuclear energy applications, in which defects created by collision cascades on the femtosecond-picosecond time scale cause microstructural changes that continue to evolve for years, in many cases leading to failure of the material. In this chapter, we review atomistic methods for reaching long time scales in systems like these. These accelerated molecular dynamics and adaptive kinetic Monte Carlo methods exploit the infrequent-event nature of the diffusive events that comprise this long-time evolution. In favorable cases, these methods can predict state-to-state evolution that approximates what would result from an extremely long molecular dynamics simulation, and the most accurate of the methods can do this to arbitrary accuracy. We present some examples of applications of these methods to problems relevant to nuclear energy materials, the subject of this volume. We then discuss situations that are difficult for the methods, causing them to be less efficient, and we conclude with a short list of the most pressing issues in the further development of these approaches to make them as powerful and predictive as possible for realistic problems.
This grant supported work on the response of crystals to external stress. Our primary work described how disordered structural materials break in two (statistical models of fracture in disordered materials), studied models of deformation bursts (avalanches) that mediate deformation on the microscale, and developed continuum dislocation dynamics models for plastic deformation (as when scooping ice cream bends a spoon, Fig. 9). Glass is brittle -- it breaks with almost atomically smooth fracture surfaces. Many metals are ductile -- when they break, the fracture surface is locally sheared and stretched, and it is this damage that makes them hard to break. Bone and seashells are made of brittle material, but they are strong because they are disordered -- lots of little cracks form as they are sheared and near the fracture surface, diluting the external force. We have studied materials like bone and seashells using simulations, mathematical tools, and statistical mechanics models from physics. In particular, we studied the extreme values of fracture strengths (how likely will a beam in a bridge break far below its design strength), and found that the traditional engineering tools could be improved greatly. We also studied fascinating crackling-noise precursors -- systems which formed microcracks of a broad range of sizes before they broke. Ductile metals under stress undergo irreversible plastic deformation -- the planes of atoms must slide across one another (through the motion of dislocations) to change the overall shape in response to the external force. Microscopically, the dislocations in crystals move in bursts of a broad range of sizes (termed 'avalanches' in the statistical mechanics community, whose motion is deemed 'crackling noise'). In this grant period, we resolved a longstanding mystery about the average shape of avalanches of fixed duration (using tools related to an emergent scale invariance), we developed the fundamental theory describing the shapes of avalanches and how they are affected by the edges of the microscope viewing window, we found that slow creep of dislocations can trigger an oscillating response explaining recent experiments, we explained avalanches under external voltage, and we have studied how avalanches in experiments on the microscale relate to deformation of large samples. Inside the crystals forming the metal, the dislocations arrange into mysterious cellular structures, usually ignored in theories of plasticity. Writing a natural continuum theory for dislocation dynamics, we found that it spontaneously formed walls -- much like models of traffic jams and sonic booms. These walls formed rather realistic cellular structures, which we examined in great detail -- our walls formed fractal structures with fascinating scaling properties, related to those found in turbulent fluids. We found, however, that the numerical and mathematical tools available to solve our equations were not flexible enough to incorporate materials-specific information, and our models did not show the dislocation avalanches seen experimentally. In the last year of this grant, we wrote an invited review article, explaining how plastic flow in metals shares features with other stressed materials, and how tools of statistical physics used in these other systems might be crucial for understanding plasticity.
The goals for the work were to create and probe matter under extreme conditions using ultrafast optical and x-ray techniques. We measured electronic structure and plasma properties of warm and hot dense matter, and validate modeling and simulation codes, with a goal of understanding the static and dynamic properties of such materials.
X-ray photon correlation spectroscopy (XPCS) and dynamic light scattering (DLS) reveal materials dynamics using coherent scattering, with XPCS permitting the investigation of dynamics in a more diverse array of materials than DLS. Heterogeneous dynamics occur in many material systems. The authors' recent work has shown how classic tools employed in the DLS analysis of heterogeneous dynamics can be extended to XPCS, revealing additional information that conventional Kohlrausch exponential fitting obscures. The present work describes the software implementation of inverse transform analysis of XPCS data. This software, called CONTIN XPCS , is an extension of traditional CONTIN analysis and accommodates the various dynamics encountered in equilibrium XPCS measurements.
We describe an optical pump/probe system for sensitive measurements of time-resolved optical measurements of material dynamics. The instrument design is optimized for time-resolved magneto-optic Kerr effect (TR-MOKE) measurements of dynamics in magnetic materials. The system also allows for time-domain thermoreflectance (TDTR) measurements of thermal transport properties and picosecond acoustic measurements of film thickness and/or elastic constants. The system has several advantages over the conventional designs for TRMOKE and/or TDTR systems. Measurements of pump-induced changes to the probe beam intensity are shot-noise limited. The system’s design allows for MOKE and/or thermoreflectance measurements of both sides of a sample. Pumping and probing the sample on opposite sides allows nanoscale flash diffusivity measurements of transport properties. The wavelengths of the pump and probe beams are straightforward to tune between 350–525 nm and 690–1050 nm. A tunable wavelength allows for optical resonances in a wide array of materials to be excited and/or probed. Finally, the setup is calibrated to allow for the real and imaginary components of Kerr signals to be separately quantified.
Researchers at Sandia National Laboratories have recently conducted a series of experiments on novel cold spray deposited materials to understand dynamic material properties at high strain rates.
In January 2020, staff members from the Engagement and Performance Operations Center (EPOC) and the Lonestar Education And Research Network (LEARN) met with researchers and staff at Baylor University for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Profiled scientific use cases included: - Experimental High Energy Physics (HEP) - Proton Computed Tomography (pCT) - Nutrition and Relation to Digestive Microbiome - Baylor University Core Research Facilities - Molecular Quantum-dot Cellular Automata (QCA), and Material Science of Quantum Computing - Modeling and Simulation of Low-Dimensional and Nano-Structured Materials - Computational Fluid Dynamics Material for this event included the written documentation from each of the research areas at Baylor University, documentation about the current state of technology support, and a write-up of the discussion that took place in person. The Case Studies highlighted the ongoing challenges that Baylor University has in supporting a cross-section of established and emerging research use cases. Each Case Study mentioned unique challenges which were summarized into common needs. These included: - Tradeoffs for network/software security, and usability of the resulting infrastructure. Better communication to set expectations and understand realities is required. - Computation use on campus is widespread and healthy. While no major problems were uncovered, upgrades to maintain current usage patterns and encourage growth will be required. - Storage is a critical need for enterprise use cases and research. In particular, a campus wide ‘storage architecture’ to support research use cases (e.g. instruments, data sharing) is required in the 2-5 year time window. - Instrumentation on campus is healthy and expanding. Technology must scale with this in the form of computation and storage. - Working with LEARN to upgrade network capacity (in multiples of 10G, or upgrades to 100G) will be required in the 1-3 year time frame. - Network monitoring and visibility will help to establish external science use cases. - Data sharing via portal systems is not currently a critical need, but growing in scope. EPOC can assist Baylor with options.
Understanding the dynamics of polymers of different topologies is an important field of study and one of the most important unanswered questions in this field is "how do circular polymers move," particularly in the melt state. New information suggests that molecular topology, specifically circular rings, impacts the response of materials in nanometer- scale confinement. The work performed here tackles these subjects through a collaboration in which specialists in polymer synthesis, polymer rheology and scattering methods, and in the physics of nanoconfinement work together to investigate the thermophysical behavior of circular macromolecules. The project goals are to use a novel synthesis method of "green" oxidative polymerization to produce high purity macrocycles and to investigate their linear viscoelastic behavior in order to fully characterize the dynamics of circular macromolecules to sizes that correspond to linear molecule entanglement densities higher than previously obtainable with synthetic polymers. Chemical chain end characterization and critical edge fractionation coupled with direct visualization of the prepared polymers through the use of polymer amplification methods are proposed in order to quantify the numbers of circular and linear molecules in each sample. The study of the linear viscoelastic response of blends of linear and circular molecules is also to be undertaken in the project. Nonlinear viscoelastic measurements, including extensional viscosity, are to be made. Strong differences in behavior between linear chains and circular ones also provides an important opportunity for new technology to break the "magic triangle" for tires: it is believed that improving one of the three key attributes - traction, wear, rolling resistance - necessarily sacrifices at least one of the others. Rubber made from cyclic polymers has the potential to break out of this triangle by reducing the so-called Payne effect in reinforced rubbers because circular macromolecules are less sensitive to nanoconfinement than their linear counterparts. The work has as one objective to characterize the impact of nanoconfinement on the behavior of large circular macromolecules. In addition, the dynamics of carbon black filled circular macromolecules are to be studied to test the hypothesis that the resulting rubbery materials will exhibit a reduced Payne effect.
Here, we developed an iterative forward analysis (IFA) technique with the ability to use hydrocode simulations as a fitting function for analysis of dynamic compression experiments. The IFA method optimizes over parameterized quantities in the hydrocode simulations, breaking the degeneracy of contributions to the measured material response. Velocity profiles from synthetic data generated using a hydrocode simulation are analyzed as a first-order validation of the technique. We also analyze multiple magnetically driven ramp compression experiments on copper and compare with more conventional techniques. Excellent agreement is obtained in both cases.
Flash radiography with 800 MeV kinetic energy protons at Los Alamos National Laboratory is an important experimental tool for investigations of dynamic material behavior driven by high explosives or pulsed power. The extraction of quantitative information about density fields in a dynamic experiment from proton generated images requires a high fidelity model of the proton imaging process. It is shown that accurate calculations of the transmission through the magnetic lens system require terms beyond second order for protons far from the tune energy. The approach used integrates the correlated multiple Coulomb scattering distribution simultaneously over the collimator and the image plane. Comparison with a series of static calibration images demonstrates the model’s accurate reproduction of both the transmission and blur over a wide range of tune energies in an inverse identity lens that consists of four quadrupole electromagnets.
Average or mean is understood by most; standard deviation (SD) is understood by few. Certainty is easy to model; uncertainty is not. The flowsheet simulation, described in this paper, placed uncertainties in the G2 model of the Hanford Tank Waste Treatment and Immobilization Plant's (WTP) Direct Feed Low-Activity Waste (DFLAW) process for performance and sensitivity evaluations. G2 is a dynamic material balance model that starts and proceeds with operating logic until all the feed is processed. Dynamic material balance is achieved by tracking the flows, storage, and change of all materials within the plant as time increments. Processing equipment and limitations of the flowsheet are modeled (e.g., equipment types, piping, volumes, flowrates, efficiencies, and physical and chemical environments that impact separations, phase equilibriums, and chemical reactions). Operating logic represents the rules and strategies of a running plant. The G2 model simulated both certainty and uncertainty scenarios for comparison purposes. For the uncertainty scenarios, process decisions were made using presumed values to simulate various operational measurement errors and process variabilities. Presumed means that the known G2 values are adjusted to replicate measurement errors and variabilities. These include: 1) volume measurement, 2) sample analysis, 3) melter decontamination factor (DF), 4) glass former weighing, and 5) glass former composition variations. The errors were randomly generated in accordance with SD values of measurement devices, laboratory methods, and glass former composition ranges. The G2 model runs give insight on the DFLAW process' controllability and the expected range of glass properties. (authors)
To realize the promise of electric vehicles, personal electronics, and renewable energy sources, improvements in energy storage technology are crucial. However, avenues for improvement such as designing superior electrode materials or modifying the battery electrolyte and separator are difficult without sufficient understanding of the materials and processes used to produce them. One such process is electrospinning, which uses high voltage to produce polymer nanofibers cheaply and continuously. To examine the effect of electrospinning on material morphology, the placement of nanoparticles within lamella-forming polymer blends and block copolymers is simulated using molecular dynamics under planar elongational flow. Then, extensional flow is combined with cylindrical confinement to simulate a block copolymer-filled cylinder with a constantly shrinking diameter, which is a better model for electrospinning. The appearance of ordered or disordered morphologies is found to be connected to a Weissenberg number relating the deformation and self-assembly time scales. A second approach towards understanding energy storage systems is to simulate the battery’s electrochemical performance. Of particular interest is the lithium-sulfur (Li-S) battery, which aims to replace lithium-ion batteries due to its higher specific capacity and cheaper material costs. However, well-known challenges such as polysulfide crossover, large volume changes during cycling, and deposition of insulating lithium sulfide limit the capacity and capacity retention of Li-S batteries. Using a continuum-level numerical simulation which includes reaction, mass transport, adsorption, and nucleation, these challenges are investigated in order to explain the mechanisms underlying Li-S discharge behavior. The model is used to explain the end-of-discharge failure behavior of liquid and gel electrolyte systems, finding that insufficient mass transport of polysulfides is the cause. In addition, the effects of adsorptive materials, cathode passivation with insulating lithium sulfide, and spherical carbon cathode particle geometry is investigated. It is found that adsorptive materials can greatly improve battery performance when the location of lithium sulfide deposition is considered in the model. Finally, the model suggests that different cathode processing techniques in experiment can lead to different end-of-discharge failure mechanisms.
Topics addressed include the prediction of helicopter component loads using neural networks, spacecraft on-orbit coupled loads analysis, hypersonic flutter of a curved shallow panel with aerodynamic heating, thermal-acoustic fatigue of ceramic matrix composite materials, transition elements based on transfinite interpolation, damage progression in stiffened composite panels, a direct treatment of min-max dynamic response optimization problems, and sources of helicopter rotor hub inplane shears. Also discussed are dynamics of a layered elastic system, confidence bounds on structural reliability, mixed triangular space-time finite elements, advanced transparency development for USAF aircraft, a low-velocity impact on a graphite/PEEK, an automated mode-tracking strategy, transonic flutter suppression by a passive flap, a nonlinear response of composite panels to random excitation, an optimal placement of elastic supports on a simply supported plate, a probabilistic assessment of composite structures, a model for mode I failure of laminated composites, a residual flexibility approach to multibody dynamics,and multilayer piezoelectric actuators.
Using a high-speed camera and an intensified charge-coupled device (ICCD), a simultaneous X-ray imaging and diffraction technique has been developed for studying dynamic material behaviors during high-rate tensile loading. A Kolsky tension bar has been used to pull samples at 1000 s -1 and 5000 s -1 strain-rates for super-elastic equiatomic NiTi and 1100-O series aluminium, respectively. By altering the ICCD gating time, temporal resolutions of 100 ps and 3.37 µs have been achieved in capturing the diffraction patterns of interest, thus equating to single-pulse and 22-pulse X-ray exposure. Furthermore, the sample through-thickness deformation process has been simultaneously imaged via phase-contrast imaging. It is also shown that adequate signal-to-noise ratios are achieved for the detected white-beam diffraction patterns, thereby allowing sufficient information to perform quantitative data analysis diffraction via in-house software ( WBXRD_GUI ). Of current interest is the ability to evaluate crystal d -spacing, texture evolution and material phase transitions, all of which will be established from experiments performed at the aforementioned elevated strain-rates.
Topics addressed include: composite plates; buckling predictions; missile launch tube modeling; structural/control systems design; optimization of nonlinear R/C frames; error analysis for semi-analytic displacement; crack acoustic emission; and structural dynamics.