Polonsky, Andrew P.; Raghavan, Narendran; Echlin, McLean P.; Kirka, Michael M.; Dehoff, Ryan R.; Pollock, Tresa M.
Three-dimensional (3D) characterization provides opportunities for understanding processing-structure relationships in additively manufactured (AM) materials. Bulk samples of Inconel 718 were fabricated via electron beam melting (EBM) in order to study microstructural development as a function of energy input and beam scan strategy. TriBeam tomography of bulk Inconel 718 microstructures built under steady-state growth conditions reveals the sensitivity of microstructure formation and evolution to machine process parameters. In this study, samples manufactured using a narrow range of energy input per unit build area result in varied grain morphologies and crystallographic textures. Using TRUCHAS, a thermal simulation software, the thermal history of bulk scan strategies was predicted, and combined with a calibrated microstructure-processing map to accurately predict bulk grain morphologies. The solidification parameters and the 3D measured nucleation density are used to predict the transition between columnar and equiaxed grain morphologies, providing a process map to guide AM parameter choices to locally control as-printed microstructure. A two-dimensional metric for characterizing bulk grain morphology was also found to agree well with predictions from the process map calibrated by 3D data. Combined with 3D tomography and thermal modelling, the physics of structure development were understood at a new level of detail with respect to the competing processes of grain nucleation and epitaxial growth.
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.
Stoichiometric spark-ignition engines suffer efficiency penalties due to throttling losses at low loads, a low specific-heat ratio of the stoichiometric working fluid, and limits on compression ratio due to end-gas autoignition leading to undesirable knocking. Mixed-Mode Combustion (MMC) mitigates these shortcomings by using a lean working fluid where a spark-initiated pilot-stabilized deflagrative flame front is followed by controlled end-gas autoignition. This MMC study investigates the effects of initial conditions (intake air temperature, intake pressure, equivalence ratio, and intake oxygen fraction) on autoignition tendency of four gasoline-range fuels with varying properties and composition. The use of fuels with varying octane sensitivity (S) allowed exploring the importance of low-temperature heat release in triggering autoignition. Fuels with high S were less reactive for conditions that promote low-temperature chemistry (operation at high intake air pressure or without N2 dilution). Conversely, an Alkylate fuel with low S showed a greater autoignition resistance at operating conditions that were unfavorable for low-temperature chemistry. Next, the effect of residual gas composition on autoignition tendency of fuels was examined with a chemical-kinetics model. Among the various molecules in the residual gas, nitric oxide (NO) enhanced the low-temperature chemistry and increased the autoignition tendency most significantly. The fuels’ autoignition response to increasing NO amount corroborates the experimental observations. Next, the sequential autoignition of the end-gas was assessed to be less impacted by thermal stratification because of lean mixtures showing relatively less low-temperature chemistry, when compared to stoichiometric mixtures. Next, the effect of changing equivalence ratio on the autoignition was found to be similar for all fuels, regardless of their S. With changing intake air temperature, the response of fuels’ autoignition tendency depended on the dilution level used. At high dilution (i.e. low intake [O2]), fuels’ reactivity increased with increasing intake air temperature. In contrast, for operation without dilution, the autoignition tendency of the low-S Alkylate fuel decreased with increasing intake air temperature, while that of high-S High Cycloalkane fuel still increased with increasing intake air temperature. In conclusion, conventional octane metrics (RON and MON) have utility in assessing the autoignition tendency under lean MMC operation. Moreover, the fuel requirements for MMC align with that of stoichiometric operation: i.e., high RON and high S fuels are desirable for stable non-knocking operation.
Sandia National Laboratories has interest in mounting enclosures to gather scientific data aboard NASA scientific balloons. This report documents the structural integrity of three separate payloads considered ‘piggybacks.’ To date, there are no design criteria for piggybacks, therefore each piggyback shall follow the same design requirements set by NASA per the Gondola Structural Design Requirements. This analysis report shall describe how each payload meets the design requirements laid out in the Gondola Design Requirements.
Lithium-sulfur is a redox flow battery with high energy density for applications in safe, reliable, and lasting scaling of energy. However, lithium-based batteries often encounter platting as a problem thanks to poor Li-ions deposition after cycling. Aiming to reduce this impact, a uniform and continuous flow of ions is needed. On this work, novel bio-inspired flow fields in the electrochemical cell were tested to improve ions flowability and lithium platting control, ultimately enhancing battery performance and life. To secure Li-S efficient, low-cost, and secure energy storage capabilities, we chose a configuration with decamethylferrocene and cobaltocene acting as redox mediators, Li metal as anode and sulfur kept in a separate catholyte reservoir. Flow test and battery results insinuated a beneficial influence of bio-inspired designs in flowing electrolyte uniformly with less pressure and pump power in comparison to other conventional designs used in the industry, with an encouraging ability to approach a cheap, safe, and reliable Li-S grid energy storage.
Villa, Daniel V.; Schostek, Tyler; Bianchi, Carlo; Macmillan, Madeline; Carvallo, Juan P.
The Multi-scenario extreme weather simulator (MEWS) is a stochastic weather generation tool. The MEWS algorithm uses 50 or more years of National Oceanic and Atmospheric Association (NOAA) daily summaries [1] for maximum and minimum temperature and NOAA climate norms [2] to calculate historical heat wave and cold snap statistics. The algorithm takes these statistics and shifts them according to multiplication factors provided in the Intergovernmental Panel on Climate Change (IPCC) physical basis technical summary [3] for heat waves.
The Material Protection, Accounting, and Control Technologies (MPACT) program utilizes modeling and simulation to assess Material Control and Accountability (MC&A) concerns for a variety of nuclear facilities. Single analyst tools allow for rapid design and evaluation of advanced approaches for new and existing nuclear facilities. A low enriched uranium (LEU) fuel conversion and fabrication facility simulator has been developed to assist with MC&A for existing LEU fuel fabrication for light water reactors. Simulated measurement blocks were added to the model (consistent with current best practices). Material balance calculations and statistical tests have also been added to the model.
The D-value or dangerous quantity system was designed by the International Commission for Radiological Protection for the determination of source protection categories that can be used to reduce the likelihood of accidents, the consequences of which could result in harm to individuals or costly or expensive cleanup. The process includes multiple scenarios for exposure and two different approaches to the evaluation of detriment. This document provides an example calculation using 137Cs to walk through the complex process of determining its D-value in the hopes of making the process easily understandable.
Write-optimized data structures (WODS), offer the potential to keep up with cyberstream event rates and give sub-second query response for key items like IP addresses. These data structures organize logs as the events are observed. To work in a real-world environment and not fill up the disk, WODS must efficiently expire older events. As the basis for our research into organizing security monitoring data, we implemented a tool, called Diventi, to index IP addresses in connection logs using RocksDB (a write-optimized LSM tree). We extended Diventi to automatically expire data as part of the data structures’ normal operations. We guarantee that Diventi always tracks the N most recent events and tracks no more than N+ k events for a parameter k< N, while ensuring the index is opportunistically pruned. To test Diventi at scale in a controlled environment, we used anonymized traces of IP communications collected at SuperComputing 2019. We synthetically extended the 2.4 billion connection events to 100 billion events. We tested Diventi vs. Elasticsearch, a common log indexing tool. In our test environment, Elasticsearch saw an ingestion rate of at best 37,000 events/s while Diventi sustained ingestion rates greater than 171,000 events/s. Our query response times were as much as 100 times faster, typically answering queries in under 80 ms. Furthermore, we saw no noticeable degradation in Diventi from expiration. We have deployed Diventi for many months where it has performed well and supported new security analysis capabilities.
Lead–acid batteries are important to modern society because of their wide usage and low cost. The primary source for production of new lead–acid batteries is from recycling spent lead–acid batteries. In spent lead–acid batteries, lead is primarily present as lead pastes. In lead pastes, the dominant component is lead sulfate (PbSO4, mineral name anglesite) and lead oxide sulfate (PbO•PbSO4, mineral name lanarkite), which accounts for more than 60% of lead pastes. In the recycling process for lead–acid batteries, the desulphurization of lead sulfate is the key part to the overall process. In this work, the thermodynamic constraints for desulphurization via the hydrometallurgical route for recycling lead pastes are presented. The thermodynamic constraints are established according to the thermodynamic model that is applicable and important to recycling of lead pastes via hydrometallurgical routes in high ionic strength solutions that are expected to be in industrial processes. The thermodynamic database is based on the Pitzer equations for calculations of activity coefficients of aqueous species. The desulphurization of lead sulfates represented by PbSO4 can be achieved through the following routes. (1) conversion to lead oxalate in oxalate-bearing solutions; (2) conversion to lead monoxide in alkaline solutions; and (3) conversion to lead carbonate in carbonate solutions. Among the above three routes, the conversion to lead oxalate is environmentally friendly and has a strong thermodynamic driving force. Oxalate-bearing solutions such as oxalic acid and potassium oxalate solutions will provide high activities of oxalate that are many orders of magnitude higher than those required for conversion of anglesite or lanarkite to lead oxalate, in accordance with the thermodynamic model established for the oxalate system. An additional advantage of the oxalate conversion route is that no additional reductant is needed to reduce lead dioxide to lead oxide or lead sulfate, as there is a strong thermodynamic force to convert lead dioxide directly to lead oxalate. As lanarkite is an important sulfate-bearing phase in lead pastes, this study evaluates the solubility constant for lanarkite regarding the following reaction, based on the solubility data, PbO•PbSO4 + 2H+ ⇌ 2Pb2+ + SO42− + H2O(l).
Advances in sensors and robotics have dramatically improved the diversity of experimental approaches available to the materials community. Autonomous data collection platforms, either custom-made or commercially available, provide researchers with novel tools with which to probe materials behavior and perform advanced materials characterization. The application of novel control algorithms and active learning approaches can create much more robust experimental data, or can be used to improve the performance of existing characterization tools. Five papers within this special topic focus on experimental and computational methodologies for use in automatic data collection routines for materials characterization. From novel platforms for materials discovery to new statistical frameworks for assessing the autonomous experimentation process, these five papers highlight the diverse range of applications of automation for advancing materials science.
Many engineering design problems can be formulated as decisions between two possible options. This is the case, for example, when a quantity of interest must be maintained below or above some threshold. The threshold thereby determines which input parameters lead to which option, and creates a boundary between the two options known as the decision boundary. This report details a machine learning approach for estimating decision boundaries, based on support vector machines (SVMs), that is amenable to large scale computational simulations. Because it is computationally expensive to evaluate each training sample, the approach iteratively estimates the decision boundary in a manner that requires relatively few training samples to glean useful estimates. The approach is then demonstrated on three example problems from structural mechanics and heat transport.
The Advanced Reactor Safeguards (ARS) program was established in 2020 as part of appropriations for the Advanced Reactor Demonstration Program (ARDP) through the Office of Nuclear Energy in the Department of Energy. The goal of this program is to help address near term challenges that advanced nuclear reactor vendors face in meeting domestic Material Control and Accountancy (MC&A) and Physical Protection System (PPS) requirements for U.S. construction. The technical work in the program is meant to (1) support nuclear reactor vendors with advanced MC&A and PPS designs for next generation reactors, (2) provide technical bases for the regulator, and (3) promote the integration of Safeguards and Security by Design early in the design process. Existing domestic regulations for safeguards and security, as outlined in the Code of Federal Regulations, were written for large light water reactors, and rule-making efforts are underway to develop regulations more suited to different reactor designs. The ARS program seeks to remove roadblocks in the deployment of new and advanced reactors by solving regulatory challenges, reducing safeguards and security costs, and utilizing the latest technologies and approaches for robust plant monitoring and protection. This roadmap discusses the goals of the ARS program, current research, and program plan for the next five years.
We examine the DC and radio frequency (RF) response of superconducting transmission line resonators comprised of very thin NbTiN films, < 12 nm in thickness, in the high-temperature limit, where the photon energy is less than the thermal energy. The resonant frequencies of these superconducting resonators show a significant nonlinear response as a function of RF input power, which can approach a frequency shift of Δ f = - 0.15 % in a - 20 dB span in the thinnest film. The strong nonlinear response allows these very thin film resonators to serve as high kinetic inductance parametric amplifiers.
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.
The uncertainty quantification toolkit (UQTk) is a collection of c++ libraries that assess the confidence of numerical models. Surrogate approximations, often polynomial chaos expansions (PCEs), lessen the computational cost of these assessments. I developed a Python interface in UQTk for regression and Bayesian compressive sensing to add to the existing Galerkin projection method. These methods receive an object containing the polynomial basis information and NumPy arrays of sample points, call c++ methods, and return the PCE coefficients in a NumPy array. To demonstrate these methods, I wrote a tutorial in which I use them to construct surrogates for Genz functions and calculate the resulting error.
Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. In this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.
Cartwright, Keith C.; Komrska, Allison M.; Breen, Lorin I.; Loveless, Amanda M.; Garner, Allen L.
Crossed-field diodes (CFDs) are used in multiple high-power applications and are characterized by an applied magnetic field orthogonal to the electric field, induced by the applied voltage across the anode-cathode gap. In vacuum, the Hull cutoff magnetic field (HCMF) represents the maximum applied magnetic field for which an electron from the cathode can reach the anode. This study investigates the effects of non-vacuum conditions on electron trajectories by introducing electron mobility, which represents particle collisions. We used numerical solutions of the electron force law and particle-in-cell simulations (XPDP1) to assess electron motion for various electron mobilities. For magnetic fields above the HCMF in vacuum, reducing the electron mobility increases the time for an electron emitted from the cathode to reach the anode. Reducing mobility below 22 C s/kg eliminates the HCMF for any magnetic field, meaning that an emitted electron will always cross the gap. We derived the magnetic field, mobility, and electron transit time corresponding to this condition by solving for the condition when the electron velocity in the direction across the anode-cathode gap going to zero at the anode. A parametric study of these conditions using theory and XPDP1 is performed under different gap distances, voltages, and magnetic fields.
Imposing a boundary condition on a structure can significantly alter its dynamic properties. However, sometimes the specifics of the new boundary conditions are not known. When the effects of a boundary condition are uncertain or there is not enough information, engineers need to excite the complex structure to obtain these modified properties. In order to experimentally obtain the new properties, engineers need multiple experiments and many outputs for interpolation in order to sufficiently represent the entire structure. The researchers attached a stinger to a cantilever beam, acting as a new transverse restraint of unknown properties. This paper presents a conversion expression that predicts the dynamic behavior of any point in the system with the new boundary condition. This expression relies only on one impact hammer experiment with one output and the model of the stinger-free cantilever beam, referred to as the simple structure. Researchers estimated the Transfer Function (TRF) of the beam and compared it with an experimentally measured TRF to validate the method. The mean absolute error of the estimated TRF compared to the experimental TRF is 1.99 dB. This demonstrates the use of the proposed method for estimating unmeasured TRFs in a system with an uncertain boundary condition using a single input, single output (SISO) test and a model of the simple structure.
Solid state nuclear magnetic resonance (NMR) spectroscopy and small-to wide-angle X-ray scattering (SWAXS) methods were used to characterize the heterogeneous dynamics and polymer domain structure in rubber modified thermoset materials containing the diglycidyl ether of bisphenol A (DGEBA) epoxy resin and a mixture of Jeffamine reactive rubber and 4,4-diaminodicyclohexylmethane (PACM) amine curing agent. The polymer chain dynamics and morphologies as a function of the PACM/Jeffamine ratio were determined. Using dipolar-filtered NMR experiments, the resulting networks are shown to be composed of mobile and rigid regions that are separated on nanometer length scales, along with a dynamically immobilized interface region. Proton NMR spin diffusion experiments measured the dimensions of the mobile phase to range between 9 and 66 nm and varied with the relative PACM concentration. Solid state 13C magic angle spinning NMR experiments show that the highly mobile phase is composed entirely of the dynamically flexible polyether chains of the Jeffamine rubber, the immobilized interface region is a mixture of DGEBA, PACM, and the Jeffamine rubber, with the PACM cross-linked to DGEBA predominantly residing in the rigid phase. The SWAXS results showed compositional nanophase separation spanning the 11–77 nm range. These measurements of the nanoscale compositional and dynamic heterogeneity provide molecular level insight into the very broad and controllable glass transition temperature distributions observed for these highly cross-linked polymer networks.
ERMA is leveraging Sandia’s Microgrid Design Toolkit (MDT) [1] and adding significant new features to it. Development of the MDT was primarily funded by the Department of Energy, Office of Electricity Microgrid Program with some significant support coming from the U.S. Marine Corps. The MDT is a software program that runs on a Microsoft Windows PC. It is an amalgamation of several other software capabilities developed at Sandia and subsequently specialized for the purpose of microgrid design. The software capabilities include the Technology Management Optimization (TMO) application for optimal trade-space exploration, the Microgrid Performance and Reliability Model (PRM) for simulation of microgrid operations, and the Microgrid Sizing Capability (MSC) for preliminary sizing studies of distributed energy resources in a microgrid.
The Spent Fuel & Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 accomplishments by the PFLOTRAN Development group of the SFWST Campaign. The mission of this group is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of generic disposal concepts. In FY 2022, the PFLOTRAN development team made several advancements to our software infrastructure, code performance, and process modeling capabilities.
This document is aimed at providing guidance to the National Nuclear Security Administration’s (NNSA) Office of International Nuclear Security’s (INS) country and regional teams for implementing effective physical protection systems (PPSs) for nuclear power plants (NPPs) to prevent the radiological consequences of sabotage. This recommendation document includes input from the Physical Protection Functional Team (PPFT), the Response Functional Team (RFT), and the Sabotage Functional Team (SFT) under INS. Specifically, this document provides insights into increasing and sustaining physical protection capabilities at INS partner countries’ NPP sites. Nuclear power plants should consider that the intent of this document is to provide a historical context as well as technologies and methodologies that may be applied to improve physical protection capabilities. It also refers to relevant guidance from the International Atomic Energy Agency (IAEA) and the U.S. Nuclear Regulatory Commission (NRC).
Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.
We show how to sample uniformly within the three-sided region bounded by a circle, a radial ray, and a tangent, called a “chock.” By dividing a 2D planar rectangle into a background grid, and subtracting Poisson disks from grid squares, we are able to represent the available region for samples exactly using triangles and chocks. Uniform random samples are generated from chock areas precisely without rejection sampling. This provides the first implemented algorithm for precise maximal Poisson-disk sampling in deterministic linear time. We prove O(n · M(b) log b), where n is the number of samples, b is the bits of numerical precision and M is the cost of multiplication. Prior methods have higher time complexity, take expected time, are non-maximal, and/or are not Poisson-disk distributions in the most precise mathematical sense. We fill this theoretical lacuna.
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.
In June of 2022, dust samples were collected from the surface of an in-service spent nuclear fuel dry storage canister during an inspection at an Independent Spent Fuel Storage Installation. The site is anonymous but is a near-marine or brackish water east coast location referred to here as "Site C". The purpose of the sampling was to assess the composition and abundance of the soluble salts present on the canister surface, information that provides a metric for potential corrosion risks. Following collection, the samples were delivered to Sandia National Laboratories for analysis. At Sandia, the soluble salts were leached from the dust and quantified by ion chromatography. In addition, subsamples of the dust were taken for scanning electron microscopy to determine the particle sizes, morphology, and mineralogy of the dust and salts. The results of those analyses are presented in this report.
In this work we employ an encoder–decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities. The process we model is complex, with a progression from initial void nucleation, to saturation, and ultimately failure. The objective of predicting failure locations presents an extreme case of class imbalance since most of the material in the specimens does not fail. In response to this challenge, we develop and demonstrate the effectiveness of data- and loss-based regularization methods. Since there is considerable sensitivity of the failure location to the particular configuration of voids, we also use variational inference to provide uncertainties for the neural network predictions. We connect the deterministic and Bayesian convolutional neural network formulations to explain how variational inference regularizes the training and predictions. We demonstrate that the resulting predicted variances are effective in ranking the locations that are most likely to fail in any given specimen.
Thermographic phosphors can be employed for optical sensing of surface, gas phase, and bulk material temperatures through different strategies including the time-decay method, time-integrated method, and frequency-domain method. We focus on the time-integrated method, also known as the ratio method, as it can be more practical in many situations. This work advances the ratio method using two machine vision cameras with CMOS detectors for full-field temperature measurements of a solid surface. A phosphor calibration coupon is fabricated using aerosol deposition and employed for in situ determination of the temperature-versus-intensity ratio relationship. Algorithms from digital image correlation are employed to determine the stereoscopic imaging system intrinsic and extrinsic parameters, and accurately register material points on the sample to subpixel locations in each image with 0.07 px or better accuracy. Detector nonlinearity is carefully characterized and corrected. Temperature-dependent, spatial non-uniformity of the full-field intensity ratio-posited to be caused by a blue-shift effect of the bandpass filter for non-collimated light and/or a wavelength-dependent transmission efficiency of the lens-is assessed and treated for cases where a standard flat-field correction fails to correct the non-uniformity. In sum, pixel-wise calibration curves relating the computed intensity ratio to temperature in the range of T = 300-430 K are generated, with an embedded error of less than 3 K. This work offers a full calibration methodology and several improvements on two-color phosphor thermography, opening the door for full-field temperature measurements in dynamic tests with deforming test articles.
This report is a companion document to a series of six white papers, prepared jointly by the Proliferation Resistance and Physical Protection Working Group (PRPPWG) and the six System Steering Committees (SSCs) and provisional System Steering Committees (pSSCs). This publication is an update to a similar series published in 2011 presenting crosscutting Proliferation Resistance & Physical Protection (PR&PP) characteristics for the six systems selected by the Generation IV International Forum (GIF) for further research and development, namely: the Lead-cooled Fast Reactor (LFR), the Sodium-cooled fast Reactor (SFR), the Very high temperature reactor (VHTR), the gas-cooled fast reactor (GFR), the Molten salt reactor (MSR) and the Supercritical water–cooled reactor (SCWR).
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.
We present a polynomial preconditioner for solving large systems of linear equations. The polynomial is derived from the minimum residual polynomial (the GMRES polynomial) and is more straightforward to compute and implement than many previous polynomial preconditioners. Our current implementation of this polynomial using its roots is naturally more stable than previous methods of computing the same polynomial. We implement further stability control using added roots, and this allows for high degree polynomials. We discuss the effectiveness and challenges of root-adding and give an additional check for stability. In this article, we study the polynomial preconditioner applied to GMRES; however it could be used with any Krylov solver. This polynomial preconditioning algorithm can dramatically improve convergence for some problems, especially for difficult problems, and can reduce dot products by an even greater margin.
The U.S. Department of Energy has a broad mission to ensure the nation’s security by addressing ongoing environmental challenges, developing novel energy production technologies, and mitigating nuclear security concerns. These efforts benefit from developing more effective semiconductor materials and devices. The research discussed in this report aims to further the development of these new electronics by improving implementations of vertical device structures, which offer theoretically better performance compared to lateral structures. Specifically, it reviews the background and development of a new and easy-to-use program for data processing and analysis from experiments run on vertical gallium nitride power devices. Current vertical structures fall short in critical performance metrics, such as On-Resistance and Breakdown Voltage, due to poor management of the device’s electric field. Junction termination extensions, or JTEs, are a field management technique that increases a device’s resilience to expected failure modes by controlling its surface electric fields. Each JTE design requires significant experimental validation, consisting of hundreds of tested devices, each outputting an enormous set of data points. The new software includes multiple methods of filtering large sets of device testing data to identify and remove flawed devices, automate device analyses, and provide statistical breakdowns for four metrics used to define the effectiveness of the JTE and the current carrying capacity of the device. These results can then be related back to fabrication techniques and device design, and in the future, be combined with processing simulations to further improve our understanding of the JTE mechanisms.
We present an elementary and self-contained proof that there are exactly four symmetry classes of the elasticity tensor in two dimensions: oblique, rectangular, square, and isotropic. In two dimensions, orthogonal transformations are either reflections or rotations. The proof is based on identification of constraints imposed by reflections and rotations on the elasticity tensor, and it simply employs elementary tools from trigonometry, making the proof accessible to a broad audience. For completeness, we identify the sets of transformations (rotations and reflections) for each symmetry class and report the corresponding equations of motions in classical linear elasticity.
Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical, mechanical, chemical, and optical properties. These properties directly depend upon the thin-film's microstructural features, which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication. As such, understanding processing-microstructure relationships is essential to designing thin-films with optimized properties, and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures. Here, a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale. Phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies.
Qu, Haozheng J.; Srinivasan, Jayendran; Zhao, Yangyang; Mao, Keyou S.; Taylor, Jason M.; Marino, Gabriella; Montoya, Timothy M.; Johnson, Kyle; Locke, Jenifer S.; Schaller, Rebecca S.; Schindelholz, Eric; Wharry, Janelle P.
The chloride-induced stress corrosion cracking (CISCC) mechanism of cold spray (CS) coating on a galvanically similar substrate is investigated. Arc welded 304L stainless steel (SS) specimens are loaded into four-point bend fixtures, cold sprayed with 304L SS, then immersed in boiling MgCl2. Interconnected porosity forms through crevice corrosion along CS splat boundaries, allowing corrosive species to penetrate through the CS layer. Nevertheless, the substrate is resistant to CISCC likely because of compressive stress introduced by peening during CS particle impacts. These findings underscore the importance of residual stress in the environmental degradation of CS coatings or repairs of engineering structures.
Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
Kinsey, Geoffrey S.; Riedel-Lyngskaer, Nicholas C.; Miguel, Alonso A.; Boyd, Matthew; Braga, Marilia; Shou, Chunhui; Cordero, Raul R.; Duck, Benjamin C.; Fell, Christopher J.; Feron, Sarah; Georghiou, George E.; Habryl, Nicholas; John, Jim J.; Ketjoy, Nipon; Lopez, Gabriel; Louwen, Atse; Maweza, Elijah L.; Mittal, Ankit; Molto, Cecile; Garrido, Gustavo N.; Norton, Matthew; Paudyal, Basant R.; Pereira, Enio B.; Poissant, Yves; Pratt, Lawrence; Shen, Qu; Reindl, Thomas; Rennhofer, Marcus; Rodriguez-Gallegos, Carlos D.; Ruther, Ricardo; Van Sark, Wilfried; Sevillano-Bendezu, Miguel A.; Seigneur, Hubert; Tejero, Jorge A.; Theristis, Marios; Tofflinger, Jan A.; Vilela, Waldeir A.; Xia, Xiangao; Yamasoe, Marcia A.
In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada.
Metal-organic frameworks (MOFs) have recently been shown to exhibit unique mechanisms of luminescence based on charge transfer between structural units in the framework. These MOFs have the potential to be structural tuned for targeted emission with little or no metal participation. A computationally led, material design and synthesis methodology is presented here that elucidates the mechanisms of light emission in interpenetrated structures comprised of metal centers (M = In, Ga, InGa, InEu) and BTB (1,3,5-Tris(4-carboxyphenyl)benzene) linkers, forming unique luminescent M-BTB MOF frameworks. Gas phase and periodic electronic structure calculations indicate that the intensity of the emission and the wavelength are overwhelmingly controlled by a combination of the number of interacting stacked linkers and their interatomic spacings, respectively. In the MOF, the ionic radii of the metal centers primarily control the expansion or shrinkage of the linker stacking distances. Experimentally, multiple M-BTB-based MOFs are synthesized and their photoluminescence was tested. Experiments validated the modeling by confirming that shifts in the crystal structure result in variations in light emission. Through this material design method, the mechanisms of tuning luminescence properties in interpenetrated M-BTB MOFs have been identified and applied to the design of MOFs with specific wavelength emission based on their structure.
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.
Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this article, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-To-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.
The surfaces of most metals immersed in aqueous electrolytes have a several-nanometer-thick oxide/hydroxide surface layer. This gives rise to the existence of both metal∣oxide and oxide∣liquid electrotlyte interfaces, and makes it challenging to correlate atomic length-scale structures with electrochemical properties such the potential-of-zero-charge (PZC). The PZC has been shown to be correlated the onset potential for pitting corrosion. In this work, we conduct large-scale Density Functional Theory and ab initio molecular dynamics to calculate the PZC of a Al(111)∣γ-Al2O3(110)∣ water double-interface model within the context of aluminum corrosion. By partitioning the multiple interfaces involved into binary components with additive contributions to the overall work function and voltage, we predict the PZC to be −1.53 V vs SHE for this model. We also calculate the orbital energy levels of defects like oxygen vacancies in the oxide, which are critical parameters in theories associated with pitting corrosion. We predict that the Fermi level at the PZC lies above the impurity defect levels of the oxygen vacancies, which are therefore uncharged at the PZC. From the PZC estimate, we predict the voltage needed to create oxygen vacancies with net postive charges within a flatband approximation.
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.