An approach to numerically modeling relativistic magnetrons, in which the electrons are represented with a relativistic fluid, is described. A principal effect in the operation of a magnetron is space-charge-limited (SCL) emission of electrons from the cathode. We have developed an approximate SCL emission boundary condition for the fluid electron model. This boundary condition prescribes the flux of electrons as a function of the normal component of the electric field on the boundary. We show the results of a benchmarking activity that applies the fluid SCL boundary condition to the one-dimensional Child-Langmuir diode problem and a canonical two-dimensional diode problem. Simulation results for a two-dimensional A6 magnetron are then presented. Computed bunching of the electron cloud occurs and coincides with significant microwave power generation. Numerical convergence of the solution is considered. Sharp gradients in the solution quantities at the diocotron resonance, spanning an interval of three to four grid cells in the most well-resolved case, are present and likely affect convergence.
Analytical relations for the mechanical response of single polymer chains are valuable for modeling purposes, on both the molecular and the continuum scale. These relations can be obtained using statistical thermodynamics and an idealized single-chain model, such as the freely jointed chain model. To include bond stretching, the rigid links in the freely jointed chain model can be made extensible, but this almost always renders the model analytically intractable. Here, an asymptotically correct statistical thermodynamic theory is used to develop analytic approximations for the single-chain mechanical response of this model. The accuracy of these approximations is demonstrated using several link potential energy functions. This approach can be applied to other single-chain models, and to molecular stretching in general.
Power systems are subjected to a wide range of disturbances during daily operations. Severe disturbances, such as a loss of a large generator, a three-phase bolted fault on a generator bus, or a loss of a transmission line, can lead to the loss of synchronism of a generator or group of generators. The ability of a power system to maintain synchronism during the few seconds after being subjected to a severe disturbance is known as transient stability. Most of the modern methods of controlling transient stability involve special protection schemes or remedial action schemes. These special protection schemes sense predetermined system conditions and take corrective actions, such as generator tripping or generation re-dispatch, in real time to maintain transient stability. Another method is the use of a real-time feedback control system to modulate the output of an actuator in response to a signal. This paper provides a fundamental evaluation of the use of feedback control strategies to improve transient stability in a power system. An optimal feedback control strategy that modulates the real power injected and absorbed by distributed energy-storage devices is proposed. Its performance is evaluated on a four-machine power system and on a 34-machine reduced-order model of the Western North American Power System. The result shows that the feedback control strategy can increase the critical fault clearing time by 60%, thereby improving the transient stability of the power system.
PV module operating temperature is the second-most important factor influencing PV system yield–after irradiance–and a substantial contributor to uncertainty in energy system yield predictions. Models commonly used to predict operating temperature in system simulations are based on a simplified energy balance that lumps together different heat loss mechanisms–including radiation–and assumes an overall linear behavior. Radiative heat loss to the sky is usually substantial, but modeling it accurately requires additional information about down-welling long-wave radiation or sky temperature and increases the complexity of temperature model equations. In this work we show how radiative losses to the sky can be separated into two parts to improve the accuracy of modeling without additional complexity. We also predict and demonstrate the variation of these losses at different tilt angles and show that the effective view factor is reduced by the non- isotropic distribution of down-welling long-wave radiation. Finally, we demonstrate substantial reduction in bias (MBE) and scatter (RMSE) when the new radiative loss term is added to the Faiman model using one year of measurements at Sandia National Labs.
For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance-based global sensitivity analysis (GSA) using a fully coupled, ultra-low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher-resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.
Jang, Taejin; Mishra, Lubhani; Roberts, Scott A.; Planden, Brady; Subramaniam, Akshay; Uppaluri, Maitri; Linder, David; Gururajan, Mogadalai P.; Zhang, Ji G.; Subramanian, Venkat R.
Electrochemical models at different scales and varying levels of complexity have been used in the literature to study the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale (phase-field approaches), and multiscale models. Thermodynamics-based equations have been used to study phase changes in lithium batteries using phase-field approaches. However, grid convergence studies and the effect of additional parameters needed to simulate these models are not well-documented in the literature. In this paper, using a motivating example of a moving boundary model in one- and two-dimensions, we show how one can formulate phase-field models, implement algorithms for the same and analyze the results. An open-access code with no restrictions is provided as well. The article concludes with some thoughts on the computational efficiency of phase-field models for simulating dendritic growth.
We show, through the use of the Landauer-Büttiker (LB) formalism and a tight-binding (TB) model, that the transport gap of twinned graphene can be tuned through the application of a uniaxial strain in the direction normal to the twin band. Remarkably, we find that the transport gap Egap bears a square-root dependence on the control parameter ϵx−ϵc, where ϵx is the applied uniaxial strain and ϵc∼19% is a critical strain. We interpret this dependence as evidence of criticality underlying a continuous phase transition, with ϵx−ϵc playing the role of control parameter and the transport gap Egap playing the role of order parameter. For ϵx<ϵc, the transport gap is non-zero and the material is semiconductor, whereas for ϵx>ϵc the transport gap closes to zero and the material becomes conductor, which evinces a semiconductor-to-conductor phase transition. The computed critical exponent of 1/2 places the transition in the meanfield universality class, which enables far-reaching analogies with other systems in the same class.
With the anticipated growth in hydrogen generation and use as part of a broad shift in energy use away from fossil fuels, concerns have been raised regarding the impact of increased H2 emissions on global warming. Atmospheric scientists have long recognized that H2 emissions into the atmosphere do have an indirect impact on global warming, largely because a portion of emitted H2 is consumed by the hydroxyl radical (OH), which is the primary reactant that removes the potent greenhouse gas methane from the atmosphere. Therefore, increases in H2 emissions will result in decreases in the average OH concentrations in the atmosphere and an increase in the atmospheric lifetime of methane. Various assessments of the impact of H2 emissions on global warming have been performed over the past couple of decades. These assessments have yielded significant variability and recognized uncertainty in the magnitude of the warming effect of a given quantity of emitted H2, and an even greater uncertainty in the magnitude of H2 leakage and releases that can be anticipated with an expanded H2 infrastructure. Consequently, definitive estimates of the magnitude of the warming effect of additional emitted H2 are lacking. However, given the current understanding of the warming potential of emitted H2 and given reasonable expectations of the emission rate of an expanded H2 infrastructure, it is anticipated that warming effects from emitted H2 will offset no more than 5% of the reduction in warming associated with avoided CO2 emissions from using clean H2. Further, it is highly unlikely that the warming effects from emitted H2 will offset more than 10% of the benefit from avoided CO2 emissions, at least as considered over a typical 100-year accounting period. Because of the short atmospheric lifetimes of H2 and methane, however, the warming effect of emitted H2 is enhanced over the first few years following increases in H2 emission.
We propose developing numerical methods informed by novel experimental diagnostics that transition from solid-to-fluid, while accurately predicting the stress and deformation regardless of phase.
This report summarizes the international collaborations conducted by Sandia funded by the US Department of Energy Office (DOE) of Nuclear Energy Spent Fuel and Waste Science & Technology (SFWST) as part of the Sandia National Laboratories Salt R&D and Salt International work packages. This report satisfies the level-three milestone M3SF-22SN010303063. Several stand-alone sections make up this summary report, each completed by the participants. The sections discuss international collaborations on geomechanical benchmarking exercises (WEIMOS), granular salt reconsolidation (KOMPASS), engineered barriers (RANGERS), numerical model comparison (DECOVALEX) and an NEA Salt Club working group on the development of scenarios as part of the performance assessment development process. Finally, we summarize events related to the US/German Workshop on Repository Research, Design and Operations. The work summarized in this annual update has occurred during the COVID-19 pandemic, and little international or domestic travel has occurred. Most of the collaborations have been conducted via email or as virtual meetings, but a slow return to travel and in-person meetings has begun.
This report documents the updated seismic shake table test plan. The report describes the shake table inputs (ground motions), test hardware, shake table facility, friction experiment, and proposed instrumentation.
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.
This SAND Report Guide offers support to authors, technical writers, principal investigators, and others involved in the process of creating, formatting, or refining a SAND Report. It details what you need to know before you begin compiling a SAND Report, directs you to the SAND Report templates, outlines the order of elements in a SAND Report, and explains what to do when your report is completed and ready for Review and Approval and subsequent distribution. Supporting information is provided in the appendix, such as where to get technical assistance, trademarks, Microsoft Word, and equations.
It has been demonstrated that grid cells in the brain are encoding physical locations using hexagonally spaced, periodic phase-space representations. We explore how such a representation may be computationally advantageous for related engineering applications. Theories of how the brain decodes from a phase-space representation have been developed based on neuroscience data. However, theories of how sensory information is encoded into this phase space are less certain. Here we show a method for how a navigation-relevant input space such as elevation trajectories may be mapped into a phase-space coordinate system that can be decoded using previously developed theories. We also consider how such an algorithm may then also be mapped onto neuromrophic systems. Just as animals can tell where they are in a local region based on where they have been, our encoding algorithm enables the localization to a position in space by integrating measurements from a trajectory over a map. In this paper, we walk through our approach with simulations using a digital elevation model.
It has been demonstrated that grid cells in the brain are encoding physical locations using hexagonally spaced, periodic phase-space representations. We explore how such a representation may be computationally advantageous for related engineering applications. Theories of how the brain decodes from a phase-space representation have been developed based on neuroscience data. However, theories of how sensory information is encoded into this phase space are less certain. Here we show a method for how a navigation-relevant input space such as elevation trajectories may be mapped into a phase-space coordinate system that can be decoded using previously developed theories. We also consider how such an algorithm may then also be mapped onto neuromrophic systems. Just as animals can tell where they are in a local region based on where they have been, our encoding algorithm enables the localization to a position in space by integrating measurements from a trajectory over a map. In this paper, we walk through our approach with simulations using a digital elevation model.
Dirac semimetals have attracted a great deal of current interests due to their potential applications in topological quantum computing, low-energy electronic devices, and single photon detection in the microwave frequency range. Herein are results from analyzing the low magnetic (B) field weak-antilocalization behaviors in a Dirac semimetal Cd3As2 thin flake device. At high temperatures, the phase coherence length lΦ first increases with decreasing temperature (T) and follows a power law dependence of lΦ ∝ T–0.4. Below ~3 K, lΦ tends to saturate to a value of ~180 nm. Another fitting parameter α, which is associated with independent transport channels, displays a logarithmic temperature dependence for T > 3 K, but also tends to saturate below ~3 K. The saturation value, ~1.45, is very close to 1.5, indicating three independent electron transport channels, which we interpret as due to decoupling of both the top and bottom surfaces as well as the bulk. This result, to our knowledge, provides first evidence that the surfaces and bulk states can become decoupled in electronic transport in Dirac semimetal Cd3As2.
The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager-Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the "ground-truth" molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ38from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications.
We are currently witnessing the dawn of hydrogen (H2) economy, where H2 will soon become a primary fuel for heating, transportation, and longdistance and long-term energy storage. Among diverse possibilities, H2 can be stored as a pressurized gas, a cryogenic liquid, or a solid fuel via adsorption onto porous materials. Metal–organic frameworks (MOFs) have emerged as adsorbent materials with the highest theoretical H2 storage densities on both a volumetric and gravimetric basis. However, a critical bottleneck for the use of H2 as a transportation fuel has been the lack of densification methods capable of shaping MOFs into practical formulations while maintaining their adsorptive performance. Here, we report a high-throughput screening and deep analysis of a database of MOFs to find optimal materials, followed by the synthesis, characterization, and performance evaluation of an optimal monolithic MOF (monoMOF) for H2 storage. After densification, this monoMOF stores 46 g L–1 H2 at 50 bar and 77 K and delivers 41 and 42 g L–1 H2 at operating pressures of 25 and 50 bar, respectively, when deployed in a combined temperature– pressure (25–50 bar/77 K → 5 bar/160 K) swing gas delivery system. This performance represents up to an 80% reduction in the operating pressure requirements for delivering H2 gas when compared with benchmark materials and an 83% reduction compared to compressed H2 gas. Our findings represent a substantial step forward in the application of high-density materials for volumetric H2 storage applications.
Nanothermite NiO-Al is a promising material system for low gas emission heat sources; yet, its reactive properties are highly dependent on material processing conditions. In the current study, sputter deposition is used to fabricate highly controlled nanolaminates comprised of alternating NiO and Al layers. Films having an overall stoichiometry of 2Al to 3NiO were produced with different bilayer thicknesses to investigate how ignition and self-sustained, high temperature reactions vary with changes to nanometer-scale periodicity and preheat conditions. Ignition studies were carried out with both hot plate and laser irradiation and compared to slow heating studies in hot-stage x-ray diffraction. Ignition behavior has bilayer thickness and heating rate dependencies. The 2Al/3NiO with λ ≤ 300 nm ignited via solid/solid diffusion mixing (activation energy, Ea = 49 ± 3 kJ/mole). Multilayers having λ≥ 500 nm required a more favorable mixing kinetics of solid/liquid dissolution into molten Al (Ea = 30 ± 4 kJ/mole). This solid/liquid dissolution Ea is a factor of 5 lower than that of the previously reported powder compacts due to the elimination of a passivating Al oxide layer present on the powder. The reactant mixing mechanism between 300 and 500 nm bilayer thicknesses was dependent on the ignition source's heating rate. The self-propagating reaction velocities of 2Al/3NiO multilayers varied from 0.4 to 2.5 m/s. Pre-heating nanolaminates to temperatures below the onset reaction temperatures associated with forming intermediate nickel aluminides at multilayer interfaces led to increased propagation velocities, whereas pre-heating samples above the onset temperatures inhibited subsequent attempts at laser ignition.
The selective amorphization of SiGe in Si/SiGe nanostructures via a 1 MeV Si+ implant was investigated, resulting in single-crystal Si nanowires (NWs) and quantum dots (QDs) encapsulated in amorphous SiGe fins and pillars, respectively. The Si NWs and QDs are formed during high-temperature dry oxidation of single-crystal Si/SiGe heterostructure fins and pillars, during which Ge diffuses along the nanostructure sidewalls and encapsulates the Si layers. The fins and pillars were then subjected to a 3 × 1015 ions/cm2 1 MeV Si+ implant, resulting in the amorphization of SiGe, while leaving the encapsulated Si crystalline for larger, 65-nm wide NWs and QDs. Interestingly, the 26-nm diameter Si QDs amorphize, while the 28-nm wide NWs remain crystalline during the same high energy ion implant. This result suggests that the Si/SiGe pillars have a lower threshold for Si-induced amorphization compared to their Si/SiGe fin counterparts. However, Monte Carlo simulations of ion implantation into the Si/SiGe nanostructures reveal similar predicted levels of displacements per cm3. Molecular dynamics simulations suggest that the total stress magnitude in Si QDs encapsulated in crystalline SiGe is higher than the total stress magnitude in Si NWs, which may lead to greater crystalline instability in the QDs during ion implant. The potential lower amorphization threshold of QDs compared to NWs is of special importance to applications that require robust QD devices in a variety of radiation environments.
The structural and dynamical properties of nanoconfined solutions can differ dramatically from those of the corresponding bulk systems. Understanding the changes induced by confinement is central to controlling the behavior of synthetic nanostructured materials and predicting the characteristics of biological and geochemical systems. A key outstanding issue is how the molecular-level behavior of nanoconfined electrolyte solutions is reflected in different experimental, particularly spectroscopic, measurements. This is addressed here through molecular dynamics simulations of the OH stretching infrared (IR) spectroscopy of NaCl, NaBr, and NaI solutions in isotopically dilute HOD/D2O confined in hydroxylated amorphous silica slit pores of width 1-6 nm and pH ∼2. In addition, the water reorientation dynamics and spectral diffusion, accessible by pump-probe anisotropy and two-dimensional IR measurements, are investigated. The aim is to elucidate the effect of salt identity, confinement, and salt concentration on the vibrational spectra. It is found that the IR spectra of the electrolyte solutions are only modestly blue-shifted upon confinement in amorphous silica slit pores, with both the size of the shift and linewidth increasing with the halide size, but these effects are suppressed as the salt concentration is increased. This indicates the limitations of linear IR spectroscopy as a probe of confined water. However, the OH reorientational and spectral diffusion dynamics are significantly slowed by confinement even at the lowest concentrations. The retardation of the dynamics eases with increasing salt concentration and pore width, but it exhibits a more complex behavior as a function of halide.
A study was conducted to investigate the outgassing characteristics of four thermal filler materials. The purpose of this screening was to identify any outgassing products that might be considered reactive, specifically compounds that could result in corrosion in the systems where these materials are used. A range of compounds was observed in the sample headspaces, though most do not stand out as being known reactive species of concern. However, several halogenated compounds and sulfurous compounds- classes compounds known to facilitate corrosion reactions under certain conditions- were observed in low concentrations. The TFLEX 760 exhibited the highest total outgassing, while the GR130 had the lowest. Therm-a-Gap75 and the Si thermal grease exhibited very similar outgassing profiles. It is difficult to predict the extent to which any given compound observed in an analysis of this type might pose a risk in an actual system; factors such as temperature, system geometry, concentration, and gas conductance all play a role in the kinetics governing chemical reactions. It is recommended that the results of these analyses are shared with pertinent materials SMEs familiar with the system(s) in question to evaluate potential risks.