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Accelerated aging in 4H-SiC as a betavoltaic semiconductors using an electron beam system
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Defect-Mediated Charge-Carrier Trapping and Nonradiative Recombination in WSe 2 Monolayers
Nonradiative charge-carrier recombination in transition-metal dichalcogenide (TMD) monolayers severely limits their use in solar energy conversion technologies. Because defects serve as recombination sites, developing a quantitative description of charge-carrier dynamics in defective TMD monolayers can shed light on recombination mechanisms. Herein we report a first-principles investigation of charge-carrier dynamics in pristine and defective WSe 2 monolayers with three of the most probable defects, namely, Se vacancies, W vacancies, and SeW antisites. We predict that Se vacancies slow down recombination by nearly an order of magnitude relative to defect-free samples by breaking the monolayer’s symmetry and thereby reducing the spectral intensity of the A 1g phonon mode that promotes recombination in the pristine monolayer. By contrast, we find W vacancies accelerate recombination by more than an order of magnitude, with half of the recombination events bypassing charge traps. The subsequent dynamics feature both charge trapping and charge-trap-assisted recombination. Although SeW antisites also slightly accelerate recombination, the predicted mechanism is different from the W vacancy case. First, a shallow energy level traps a photoexcited electron. Then, both shallow- and deep-trap-assisted recombination can occur simultaneously. Accelerated recombination arises for W vacancies and SeW antisites because they introduce new phonon modes that strongly couple to electron and hole dynamics. This work thus provides a detailed understanding of the mechanisms behind charge-carrier recombination in WSe 2 monolayers with distinct defects. Furthermore, materials engineering, particularly to avoid W vacancies, could advance this technology. The insights derived are important for future design of high-performance photoactive devices based on WSe 2 monolayers.
EXAGRAPH: Graph and combinatorial methods for enabling exascale applications
Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.
TidGen Power System Commercialization Project
ORPC Maine, LLC, a wholly-owned subsidiary of Ocean Renewable Power Company, LLC (collectively ORPC), submits this Final Technical Report for the TidGen® Power System Commercialization Project (Project), partially funded by the U.S. Department of Energy (DE-EE0003647). The Project was built and operated in compliance with the Federal Energy Regulatory Commission (FERC) pilot project license (P-12711) and other permits and approvals needed for the Project. This report documents the methodologies, activities and results of the various phases of the Project, including design, engineering, procurement, assembly, installation, operation, licensing, environmental monitoring, retrieval, maintenance and repair. The Project represents a significant achievement for the renewable energy portfolio of the U.S. in general, and for the U.S. marine hydrokinetic (MHK) industry in particular. The stated Project goal was to advance, demonstrate and accelerate deployment and commercialization of ORPC’s tidal-current based hydrokinetic power generation system, including the energy extraction and conversion technology, associated power electronics, and interconnection equipment capable of reliably delivering electricity to the domestic power grid. ORPC achieved this goal by designing, building and operating the TidGen® Power System in 2012 and becoming the first federally licensed hydrokinetic tidal energy project to deliver electricity to a power grid under a power purchase agreement in North America. Located in Cobscook Bay between Eastport and Lubec, Maine, the TidGen® Power System was connected to the Bangor Hydro Electric utility grid at an on-shore station in North Lubec on September 13, 2012. ORPC obtained a FERC pilot project license for the Project on February 12, 2012 and the first Maine Department of Environmental Protection General Permit issued for a tidal energy project on January 31, 2012. In addition, ORPC entered into a 20-year agreement with Bangor Hydro Electric Company on January 1, 2013 for up to 5 megawatts at a price of $215/MWh, escalating at 2.0% per year.
The ASD Scorpius Accelerator at the NNSS. Recruitment Slides for the University of Nevada, Reno [Slides]
The Scorpius Accelerator: Supports the maintenance of the US nuclear stockpile, helps the US not return to nuclear testing, is one of four diagnostics being installed 963 feet below ground, probes subcritical implosions of nuclear material, and supports the validation of LANL and LLNL computer codes. We are searching engineers and scientists with experienced in: Mechanical Engineering, Electrical Engineering, Pulsed Power, Plasma Physics, Controls, Diagnostics, and Fluids and pressure systems.
Aviation Fuel Characterization at Operationally Relevant Conditions
To accelerate approval and potentially expand the allowable property range for aviation fuels, we are using high performance computing simulations to reveal fuel property effects on aviation combustor performance. These simulations are supported by fuel property measurements over temperatures and pressures that the fuel experiences in an aircraft engine and by validated chemical kinetics models for SAF combustion. Here we report density, viscosity, and surface tension results for conventional jet fuel and multiple synthetic fuels from -30 degrees Celsius to 200 degrees Celsius (-40 degrees Celsius for viscosity) and 1 atm to 70 atm including an assessment of method repeatability. Properties of surrogate mixtures are also investigated. Distillation, ICN, LHV, flashpoint, and Cp are also reported, and data are being used to develop models to predict fuel properties from composition (GCxGC).
Instrumentation and Data Analysis Supporting the Transient Rod Pneumatic System Design Study at the Annular Core Research Reactor
The Sandia National Laboratories Annular Core Research Reactor (ACRR) has been experiencing intermittent failures of its pneumatically operated transient rod system. The most frequent failures have been to the dashpot rod where it connects to the upper aluminum connecting rod. Within the three systems, Transient Rod (TR) A has the most frequent failures. In order to conduct the pulse operations, the Transient Rods are pneumatically driven to quickly remove "poison" from the reactor core. During these pulse operations increased forces and vibrations are placed on the TR system. In an effort to fix these problems, Sandia's TA-V Nuclear Reactor and Systems Engineering Department (Engineering Department) has started an extensive Transient Rod Pneumatic System Design Study. The goal of this study is to determine the causes of the elevated failure rates in TR A. This necessitates an increased understanding of the dynamics in the TR systems. This report will cover the approach taken and reasoning behind the choices of instrumentation, calibration, data collection and data analysis used to diagnose these failures. In order to determine the types and specifications for the sensors that will be installed, estimates of the TR system performance needed to be determined. Using known and assumed parameters, mechanical and thermodynamic models were developed, and calculations performed to estimate ranges of displacement, speed, and acceleration experienced by the transient rod system. After some preliminary data collection, the thermodynamic models were refined for better accuracy. After installation of the full suite of sensors, data can be collected and analyzed in order to determine the cause of the increased failures.
A microgrid deployment framework to support drayage electrification
The electrification of heavy-duty commercial vehicles (HDCVs) is key to enhancing air quality and reducing urban air pollution, but it also imposes significant demands on an electric grid not designed for such high loads. Without complementary infrastructure, electrification may yield limited air quality improvements. This article explores the critical role of microgrids—integrating solar photovoltaics and battery storage—in supporting HDCV electrification. We present an integrated framework to identify viable microgrid sites in a given region, estimate deployment costs, and optimize power use to reduce dependence on the grid. As a case study, we apply the framework to the region around the Port of Savannah, GA, USA, demonstrating how targeted microgrid deployment can enhance grid capacity, improve energy resiliency, and support electrified freight transport.
Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City
In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.
An intrinsic velocity–curvature–acceleration relationship for weakly unstable gaseous detonations
The shock motion of calculated weakly unstable cellular detonation was analyzed using concepts from velocity-curvature theory to develop new insight into the underlying physical mechanisms driving the cellular instability. The cellular cycle was shown to follow a surface in a three-dimensional coordinate system composed of the local shock velocity, curvature, and acceleration. Wavelets of the detonation shock were found to follow a velocity-curvature trajectory that was characteristic of an initially reactive wave that decays in time to a decoupled blast wave. Near the Mach stem, these trajectories were only modulated by the strength of the Mach stem at the time the wavelet was generated, indicating that the small reaction zone present in this region is the dominant factor driving the flow. Away from the Mach stem, all wavelet trajectories collapsed to a common curve in velocity-curvature space that was consistent with motion of a decaying blast wave. For the mixture studied, the apparent adherence of shock motion to a unique surface in velocity-curvature-acceleration space indicates the possible existence of an intrinsic mixture-specific relationship for cellular gaseous detonation. In conclusion, this relationship and analysis methodology also provides a mechanism for quantification of the shock velocity and shape fluctuations present in cellular detonation, which may provide utility for modeling detonation engineering applications such as rotating detonation engine design.
High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions
Abstract Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12// ω B97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol −1 ) relative to those calculated at ω B97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients $${k}_{\infty }(T)$$ k ∞ ( T ) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.
Progress toward picosecond on-chip magnetic memory
Here, we offer a perspective on the prospects of ultrafast spintronics and opto-magnetism as a pathway to high-performance, energy-efficient, and non-volatile embedded memory in digital integrated circuit applications. Conventional spintronic devices, such as spin-transfer-torque magnetic-resistive random-access memory (STT-MRAM) and spin–orbit torque MRAM, are promising due to their non-volatility, energy-efficiency, and high endurance. STT-MRAMs are now entering into the commercial market; however, they are limited in write speed to the nanosecond timescale. Improvement in the write speed of spintronic devices can significantly increase their usefulness as viable alternatives to the existing CMOS-based devices. In this article, we discuss recent studies that advance the field of ultrafast spintronics and opto-magnetism. An optimized ferromagnet–ferrimagnet exchange-coupled magnetic stack, which can serve as the free layer of a magnetic tunnel junction (MTJ), can be optically switched in as fast as ~3 ps. Integration of ultrafast magnetic switching of a similar stack into an MTJ device has enabled electrical readout of the switched state using a relatively larger tunneling magnetoresistance ratio. Purely electronic ultrafast spin–orbit torque induced switching of a ferromagnet has been demonstrated using ~6 ps long charge current pulses. We conclude our Perspective by discussing some of the challenges that remain to be addressed to accelerate ultrafast spintronics technologies toward practical implementation in high-performance digital information processing systems.
Experimental and theoretical comparison of ion properties from nanosecond laser-produced plasmas of metal targets
The ion emission properties of laser-produced plasmas as a function of laser intensities between 4–50 GW cm –2 and varying angles with respect to the target normal were investigated. The plasmas were produced by focusing 1064 nm, 6 ns pulses from an Nd:YAG laser on various metal targets. The targets used for this study include Ti, Mo, and Gd (Z = 22, 42, 64). It is noted that all ion profiles are composed of multiple peaks—a prompt emission peak trailed by three ion peaks (ultrafast, fast, and thermal). Experimentally, it is shown that each of these ion peaks follows a unique trend as a function of laser intensity, angle, and distance away from the target. Theoretically, it is shown that simple analytical models can be used to explain the properties of the ions. The variations in the ion velocity and density as a function of laser intensity are found to be in good agreement with theoretical models of sheath acceleration, isothermal self-similar expansion, and ablative plasma flow for various ion peaks.
High Level Synthesis of RDF Queries for Graph Analytics
In this paper we present a set of techniques that enable the synthesis of efficient custom accelerators for memory intensive, irregular applications. To address irregular applications challenges (large memory footprints, unpredictable fine- grained data accesses, and high synchronization intensity), and exploit their opportunities (thread level parallelism, memory level parallelism), we propose a novel accelerator design which take advantage of an adaptive and Distributed Controller (DC) architecture, and a Memory Interface (MI) that supports parallel memory subsystems. Among the multitude of algorithms that may benefit from our solution, we focus on the acceleration of graph analytics applications, and in particular, on the syn- thesis of SPARQL queries on Resource Description Framework (RDF) databases. We achieve this objective by incorporating the synthesis techniques into Bambu, an Open Source high- level synthesis tools, and interfacing the system with GEMS, the Graph database Engine for Multithreaded Systems. The front- end of GEMS generates optimized C implementations of the input queries, modeled as pattern matching routines, which are then automatically synthesized by Bambu. We validate our approach synthesizing several SPARQL queries from the Lehigh University Benchmark (LUBM).
Modeling a Bulb-Style Kaplan Unit Hydrogovernor and Turbine in Mathworks-Simulink and RTDS-RSCAD
Some run-of-river (ROR) hydropower plants use a horizontal bulb-style Kaplan turbine. This turbine and the corresponding governor are not represented well in standard high-fidelity power-system modeling platforms. This paper presents the characterization, development, and modeling of bulb-style Kaplan turbine and associated hydrogovernor equipment in two platforms: Simulink from the MathWorks, Inc., and RSCAD from RTDS Technologies, Inc. The developed models enable accurate assessment of the response of a bulb-style Kaplan hydropower unit to changes in electrical loading and water conditions. The resulting models enable accurate digital real-time simulation with hardware-in-the-loop testing of this class of turbine, which was not previously possible. This, in turn, improves the ability to innovate these technologies and hybridize them in a lab setting, accelerating the potential for innovation in this type of ROR hydropower plant.
Approach for Selection of Rayleigh Damping Parameters Used for Time History Analysis
Nonlinearities, whether geometric or material, need to be addressed in seismic analysis. One good analysis method that can address these nonlinearities is direct time integration with Rayleigh damping. Modal damping is the damping typically specified in seismic analysis Codes and Standards. Modal damping is constant for all frequencies where Rayleigh damping varies with frequency. An approach is proposed here for selection of Rayleigh damping coefficients to be used in seismic analyses that is consistent with given Modal damping. The approach uses the difference between the modal damping response and the Rayleigh damping response along with effective mass properties of the model being evaluated to match overall system response levels. This paper provides a simple example problem to demonstrate the approach. It also provides results for a finite element model representing an existing piping system. Displacement, acceleration, and stress results are compared from model runs using modal damping and model runs using Rayleigh damping with coefficients selected using the proposed method.
Nitrogen mass transfer models for plasma-based low-energy ion implantation
The nitrogen mass transfer process in plasma-based low-energy ion implantation (PBLEII) is theoretically and experimentally studied in order to explore the process mechanism of PBLEII and therefore to optimize the apparatus design and the process conditions. An electron cyclotron resonance (ECR) microwave discharge generates the nitrogen plasma with a high density of 10{sup 11}–10{sup 12} ions/cm{sup 3}, which diffuses downstream to the process chamber along the divergent magnetic field. The nitrogen ions in the plasma implant into the surface and transport to the matrix of an austenitic stainless steel under the low negative pulsed bias of −2 kV at a process temperature of 400 °C. A global plasma model is used to simulate the ECR microwave plasma discharge for a range of working pressures and microwave powers. The fluid models are adopted to calculate the plasma downstream diffusion, the sheath expansion and the low-energy ion implantation on the surface. A nonlinear kinetic discrete model is established to describe the nitrogen transport in the austenitic stainless steel and the results are compared with the experimental measurements. Under an average implantation current density of 0.3–0.6 mA/cm{sup 2}, the surface nitrogen concentration in the range from 18.5 to 29 at. % is a critical factor for the nitrogen transport in the AISI 304 austenitic stainless steel by PBLEII, which accelerates the implanted nitrogen diffusion inward up to 6–12 μm during a nitriding time of 4 h.