Applications in Energy and Combustion Science BLASTNet: A Call for Community-Involved Big Data in Combustion Machine Learning
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Journal of Nuclear Materials
Comprehensive molecular dynamics tensile test simulations have been performed to study the delamination processes of seven different grain boundaries / cleavage planes (Σ1{111}, Σ3{111}, Σ5{100}, Σ7{111}, Σ9{411}, Σ11{311}, and R{100}/{411}) containing a helium bubble. Combinations of a variety of conditions are explored including different strain rates, system dimensions, bubble density, bubble radius, bubble pressure, and temperature. We found that in general, grain boundaries absorb less energies with decreasing strain rate but increasing bubble areal density, bubble pressure, bubble radius, and temperature. The propensity of grain boundary delamination is sensitive to grain boundary type: The random grain boundary R{100}/{411} is one of the most brittle boundaries whereas the Σ1{111} cleavage plane and the Σ3{111} twin boundary are two of the toughest boundaries. The sorted list of grain boundary fracture vulnerability obtained from our dynamic tensile test simulations differs from the one obtained from our decohesion energy calculations, confirming the important role of plastic deformation during fracture. Detailed mechanistic analyses are performed to interpret the simulated results.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
INFORMS Journal on Computing
We consider a bilevel attacker–defender problem to find the worst-case attack on the relays that control transmission grid components. The attacker infiltrates some number of relays and renders all of the components connected to them inoperable with the goal of maximizing load shed. The defender responds by minimizing the resulting load shed, redispatching using a DC optimal power flow (DCOPF) problem on the remaining network. Though worst-case interdiction problems on the transmission grid have been studied for years, there remains a need for exact and scalable methods. Methods based on using duality on the inner problem rely on the bounds of the dual variables of the defender problem in order to reformulate the bilevel problem as a mixed integer linear problem. Valid dual bounds tend to be large, resulting in weak linear programming relaxations and, hence, making the problem more difficult to solve at scale. Often smaller heuristic bounds are used, resulting in a lower bound. In this work, we also consider a lower bound, but instead of bounding the dual variables, we drop the constraints corresponding to Ohm’s law, relaxing DCOPF to capacitated network flow. We present theoretical results showing that, for uncongested networks, approximating DCOPF with network flow yields the same set of injections and, thus, the same load shed, which suggests that this restriction likely gives a high-quality lower bound in the uncongested case. Furthermore, we show that, in the network flow relaxation of the defender problem, the duals are bounded by one, so we can solve our restriction exactly. Finally, because the big-M values in the linearization are equal to one and network flow has a well-known structure, we see empirically that this formulation scales well computationally with increased network size. Through empirical experiments on 16 networks with up to 6,468 buses, we find that this bound is almost always as tight as we can get from guessing the dual bounds even for congested networks in which the theoretical results do not hold. In addition, calculating the bound is approximately 150 times faster than achieving the same bound with the reformulation guessing the dual bounds.
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As the U.S. electrifies the transportation sector, cyberattacks targeting vehicle charging could impact several critical infrastructure sectors including power systems, manufacturing, medical services, and agriculture. This is a growing area of concern as charging stations increase power delivery capabilities and must communicate to authorize charging, sequence the charging process, and manage load (grid operators, vehicles, OEM vendors, charging network operators, etc.). The research challenges are numerous and complicated because there are many end users, stakeholders, and software and equipment vendors interests involved. Poorly implemented electric vehicle supply equipment (EVSE), electric vehicle (EV), or grid operator communication systems could be a significant risk to EV adoption because the political, social, and financial impact of cyberattacks — or public perception of such — would ripple across the industry and produce lasting effects. Unfortunately, there is currently no comprehensive EVSE cybersecurity approach and limited best practices have been adopted by the EV/EVSE industry. There is an incomplete industry understanding of the attack surface, interconnected assets, and unsecured inter faces. Comprehensive cybersecurity recommendations founded on sound research are necessary to secure EV charging infrastructure. This project provided the power, security, and automotive industry with a strong technical basis for securing this infrastructure by developing threat models, determining technology gaps, and identifying or developing effective countermeasures. Specifically, the team created a cybersecurity threat model and performed a technical risk assessment of EVSE assets across multiple manufacturers and vendors, so that automotive, charging, and utility stakeholders could better protect customers, vehicles, and power systems in the face of new cyber threats.
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Incipient melting is a phenomenon that can occur in aluminum alloys where solute rich areas, such as grain boundaries, can melt before the rest of the material; incipient melting can degrade mechanical and corrosion properties and is irreversible, resulting in material scrapping. After detecting indications of incipient melting as the cause of failure in 7075 aluminum alloy parts (AA7075), a study was launched to determine threshold temperature for incipient melting. Samples of AA7075 were solution annealed using temperatures ranging from 870-1090F. A hardness profile was developed to demonstrate the loss of mechanical properties through the progression of incipient melting. Additionally, Zeiss software Zen Core Intellesis was utilized to more accurately quantify the changes in microstructural properties as AA7075 surpassed the onset of incipient melting. The results from this study were compared with previous AA7075 material that demonstrated incipient melting.
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Advanced Materials
Targeted doping of grain boundaries is widely pursued as a pathway for combating thermal instabilities in nanocrystalline metals. However, certain dopants predicted to produce grain-boundary-segregated nanocrystalline configurations instead form small nanoprecipitates at elevated temperatures that act to kinetically inhibit grain growth. Here, thermodynamic modeling is implemented to select the Mo–Au system for exploring the interplay between thermodynamic and kinetic contributions to nanostructure stability. Using nanoscale multilayers and in situ transmission electron microscopy thermal aging, evolving segregation states and the corresponding phase transitions are mapped with temperature. The microstructure is shown to evolve through a transformation at lower homologous temperatures (<600 °C) where solute atoms cluster and segregate to the grain boundaries, consistent with predictions from thermodynamic models. An increase in temperature to 800 °C is accompanied by coarsening of the grain structure via grain boundary migration but with multiple pinning events uncovered between migrating segments of the grain boundary and local solute clustering. Direct comparison between the thermodynamic predictions and experimental observations of microstructure evolution thus demonstrates a transition from thermodynamically preferred to kinetically inhibited nanocrystalline stability and provides a general framework for decoupling contributions to complex stability transitions while simultaneously targeting a dominant thermal stability regime.
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This SAND Report documents the development of organic binder system for PZT 95/5 consisting of Kollidon 90F, PEG300, and glycerin. Binders are a critical component in the manufacture of ceramic components. Low performing binders can result in poor yield and lower performance of ceramic components. This study sought to develop a well performing binder system for PZT 95/5 ceramics to support Ferroelectric Neutron Generator manufacturing. The study explored the glass transition temperature, powder compaction properties, green strengths, and thermal decomposition over a wide composition range. An optimal composition was found consisting of 1.3 wt.% Kollidon 90F, 0.5 wt.% PEG300, and 0.2 wt.% glycerin, and incorporating humidity conditioning . The results showed that the optimized binder exhibited improved compaction performance and enhanced green strength compared to a baseline binder and at lower loading. Further, sintered PZT 95/5 parts prepared using the optimized binder exhibited ~5% improved polarization performance without alteration to the microstructure.
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Combustion and Flame
Time-resolved soot and PAH formation from gasoline and diesel spray pyrolysis are visualized and quantified using diffuse back illumination (DBI) and laser induced fluorescence (LIF) at 355 nm, respectively, in a constant-volume vessel at 60 bar from 1400 to 1700 K for up to 30 ms. The delay, maximum formation rate, and yield of soot and PAHs are compared across fuels and temperatures and correlated with the yield sooting indices on either the mass or mole basis. The delays generally decrease with increasing temperature, and the formation rates of both PAHs and soot generally increase with temperature. The apparent PAH-LIF yield may decrease with temperature due to PAH growth and conversion into larger species, signal trapping, and thermal quneching. Soot yield generally increases with temperature. The mass-based YSI correlates reasonably well with soot delay, but YSI does not correlate well with soot yield. The mass-based YSI is a more appropriate predictor of sooting propensity than the mole-based YSI.
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Chaos
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Energies
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A need for a standardized Division 10000 onboarding program for virtual hires was identified by management to formalize the way employees and interns are onboarded and trained into Division 10000. This white paper provides effective short and long-term suggestions in the efforts of improving virtual onboarding. Data suggests that remote work is going to become the forefront of many industry practices, which indicates the need of a standardized virtual onboarding practices. With our research, gap assessments, benchmarking, and conducting interviews both internally and externally, we found that clarity, culture, and connection proved to be the strongest solutions in order to maintain Sandia’s competitive edge and sustain workers both remote and in-person.
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Estimation of two-phase fluid flow properties is important to understand and predict water and gas movement through the vadose zone for agricultural, hydrogeological, and engineering applications, such as containment transport and/or containment of gases in the subsurface. To estimate rock fluid flow properties and subsequently predict physically realistic processes such as patterns and timing of water, gas, and energy (e.g., heat) movement in the subsurface, laboratory spontaneous water imbibition with simultaneous temperature measurement and numerical modeling methods are presented in the FY22 progress report. A multiple-overlapping-continua conceptual model is used to explain and predict observed complex multi-phenomenological laboratory test behavior during spontaneous imbibition experiments. This report primarily addresses two complexities that arise during the experiments: 1) capturing the late-time behavior of spontaneous imbibition tests with dual porosity; and 2) understanding the thermal perturbation observed at or ahead of the imbibing wetting front, which are associated with adsorption of water in initially dry samples. We use numerical approaches to explore some of these issues, but also lay out a plan for further laboratory experimentation and modeling to best understand and leverage these unique observations.
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