Deep level defects in wide bandgap semiconductors, whose response times are in the range of power converter switching times, can have a significant effect on converter efficiency. We use deep level transient spectroscopy (DLTS) to evaluate such defect levels in the n-drift layer of vertical gallium nitride (v-GaN) power diodes with VBD ∼1500 V. DLTS reveals three energy levels that are at ∼0.6 eV (highest density), ∼0.27 eV (lowest density), and ∼45 meV (a dopant level) from the conduction band. Dopant extraction from capacitance-voltage measurement tests (C-V) at multiple temperatures enables trap density evaluation, and the ∼0.6 eV trap has a density of 1.2 × 1015 cm-3. The 0.6 eV energy level and its density are similar to a defect that is known to cause current collapse in GaN based surface conducting devices (like high electron mobility transistors). Analysis of reverse bias currents over temperature in the v-GaN diodes indicates a predominant role of the same defect in determining reverse leakage current at high temperatures, reducing switching efficiency.
A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (LC2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined LC2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.
The Multi-Fidelity Toolkit (MFTK) is a simulation tool being developed at Sandia National Laboratories for aerodynamic predictions of compressible flows over a range of physics fidelities and computational speeds. These models include the Reynolds-Averaged-Navier-Stokes (RANS) equations, the Euler equations, and modified Newtonian aerodynamics (MNA) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal code- and solution-verification activities as well as model validation with uncertainty considerations. Code verification is performed on the MNA model by comparing with an analytical solution for flat-plate and inclined-plate geometries. Solution-verification activities include grid-refinement studies of HIFiRE-1 wind tunnel measurements, which are used for validation, for all model fidelities. A thorough treatment of the validation comparison with prediction error and validation uncertainty is also presented.
Social Infrastructure Service Burden (abbr. Social Burden) is defined as the burden to a population for attaining services needed from infrastructure. Infrastructure services represent opportunities to acquire things that people need, such as food, water, healthcare, financial services, etc. Accessing services requires effort, disruption to schedules, expenditure of money, etc. Social Burden represents the relative hardship people experience in the process of acquiring needed services. Social Burden is comprised of several components. One component is the effort associated with travel to a facility that provides a needed service. Another component of burden is the financial impact of acquiring resources once at the providing location. We are applying Social Burden as a resilience metric by quantifying it following a major disruption to infrastructure. Specifically, we are most interested in quantifying this metric for events in which energy systems are a major component of the disruption. We do not believe this is the only such use of the Social Burden metric, and therefore we will also be exploring its use to describe blue-sky conditions of a society in the future. Furthermore, while the construct can be applied to a dynamically changing situation, we are applying it statically, directly following a disruption. This notably ignores recovery dynamics that are a key capability of resilient systems. This too will be explored in future research.
In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.
Levelized costs of electricity (LCOE) approaching the U.S. Department of Energy Solar Energy Technologies Office 2030 goal of 0.05 $/kWh may be achievable using Brayton power cycles that use supercritical CO2 as the working fluid and flowing solid particles with temperatures >700° C as the heat transfer media. The handling and conveyance of bulk solid particles at these temperatures in an insulated environment is a critical technical challenge that must be solved for this approach to be used. A design study was conducted at the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories in Albuquerque, NM, with the objective of identifying the technical readiness level, performance limits, capital and O&M costs, and expected thermal losses of particle handling and conveyance components in a particle-based CSP plant. Key findings indicated that chutes can be a low-cost option for particle handling but uncertainties in tower costs make it difficult to know whether they can be cost effective in areas above the receiver if tower heights must then be increased. Skips and high temperature particle conveyance technology are available for moving particles up to 640° C. This limits the use of mechanical conveyance above the heat exchanger and suggests vertical integration of the hot storage bin and heat exchanger to facilitate direct gravity fed handling of particles.
Progress and status reviews allow teams to provide updates and targeted information designed to inform the customer of progress and to help the customer understand current risks and challenges. Both presenters and the customer should have well-calibrated expectations for the level of content and information. However, what needs to be covered in systems-level management reviews can too often be poorly defined. These unclear expectations can lead teams to overpreparing or attempting to guess what information the customer considers as most critical. This aspect of the review process is stressful, disruptive, and bad for morale – and time spent overpreparing reports is time spent not focusing on the technical work necessary to stay on schedule. To define and address these issues, this report was designed to observe various aspects of development program coordination and review activities for NNSA and Navy customers, and then to conduct unbiased, independent Human Factors observation and analysis from an outside perspective. The report concludes with suggestions and recommendations for improving the efficiency of information flow related to reviews, with the goals of increasing productivity and benefitting both Sandia and the customer.