Atomic and microstructural origins of stored energy release in neutron-irradiated silicon carbide
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Enterprise KV stores are often not well suited for HPC applications, and thus cumbersome end-to-end KV design customization is required to meet the needs of modern HPC applications. To this end, in this article we present bespoKV, an adaptive, extensible, and scale-out KV store framework. bespoKV decouples the KV store design into the control plane for distributed management and the data plane for local data store. For the control plane, bespoKVprovides pre-built modules, called controlets, supporting common distributed functionalities (e.g., replication, consistency, and topology) and their various combinations. This decoupling allows bespoKV to take a user-provided single-server KV store, called a datalet, and transparently enables a scalable and fault-tolerant distributed KV store service. The resulting distributed stores are also adaptive to consistency or topology requirement changes and can be easily extended for new types of services. Such specializations enable innovative uses of KV stores in HPC applications, especially for emerging applications that utilize KV-friendly workloads. We evaluate bespoKV in a local testbed as well as in a public cloud settings. Experiments show that bespoKV-enabled distributed KV stores scale horizontally to a large number of nodes, and performs comparably and sometimes 1.2× to 2.6× better than the state-of-the-art systems.
Grocery stores consumed approximately 3% of total electricity used by commercial buildings in the U.S. in 2018 (EIA 2018), representing a unique end-use load profile characterized by the critical use of refrigerated display cases. Exploring demand response (DR) scenarios in grocery stores presents an opportunity to enhance the efficiency and sustainability of surrounding communities. In addition, recent studies demonstrate that implementing control algorithms considering demand flexibility strategies can lead to load and peak reductions in standalone refrigerated display cases. Because small business grocery stores operate on thin margins, the energy bill cost savings DR might provide could make a positive difference toward continued operations. Still, uncertainty remains about the extent of demand flexibility potential controls could provide when coupling refrigeration with whole building operation. To enhance economic viability and grid stability, it is essential to quantify the load flexibility capability of grocery stores. Advanced controls can optimize energy consumption by responding to load shedding, shifting, and DR events, as well as daily Time-of-Use (TOU) rates without compromising food safety. Using both quantitative data and interviews with community-based organizations, we developed a full-size store model and two small store models with controlled refrigerated cases, HVAC, and lighting systems based on actual grocery store properties. Through simulations, we have assessed load flexibility strategies with varied DR events. The results highlight potential for energy and peak reduction with advanced or basic controls. However, interviews and data indicate that more support is needed to make DR strategies consistently accessible to small grocery stores.
Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.
Background: Timber harvesting and industrial wood processing laterally transfer the carbon stored in forest sectors to wood products creating a wood products carbon pool. The carbon stored in wood products is allocated to end-use wood products (e.g., paper, furniture), landfill, and charcoal. Wood products can store substantial amounts of carbon and contribute to the mitigation of greenhouse effects. Therefore, accurate accounts for the size of wood products carbon pools for different regions are essential to estimating the land-atmosphere carbon exchange by using the bottom-up approach of carbon stock change. Results: To quantify the carbon stored in wood products, we developed a state-of-the-art estimator (Wood Products Carbon Storage Estimator, WPsCS Estimator) that includes the wood products disposal, recycling, and waste wood decomposition processes. The wood products carbon pool in this estimator has three subpools: (1) end-use wood products, (2) landfill, and (3) charcoal carbon. In addition, it has a user-friendly interface, which can be used to easily parameterize and calibrate an estimation. To evaluate its performance, we applied this estimator to account for the carbon stored in wood products made from the timber harvested in Maine, USA, and the carbon storage of wood products consumed in the United States. Conclusion: The WPsCS Estimator can efficiently and easily quantify the carbon stored in harvested wood products for a given region over a specific period, which was demonstrated with two illustrative examples. In addition, WPsCS Estimator has a user-friendly interface, and all parameters can be easily modified.
Understanding the stored energy release behavior of SiC is crucial for the design of SiC-based fuel cladding for light water reactors because stored energy release affects response during accident conditions. Differential scanning calorimetry was used to evaluate the stored energy of monolithic SiC and SiC fiber–reinforced SiC matrix composite variants following neutron irradiation under light water reactor–relevant dose and temperature conditions. The tests were performed at heating rates of 0.5, 3, and 20 K/min up to 1,273 K. The onset temperature of the energy release roughly corresponded to the irradiation temperature, and energy release was delayed as the heating rate increased. The monolithic and composite specimens exhibited similar stored energy release behavior up to ~1,000 K, and above that temperature, the energy release depended more on material type. The energy release measured in this study is below the material-specific heat capacity at temperatures below ~1,000 K but exceeds it under certain material and annealing conditions at temperatures above ~1,000 K. The amount of irradiation-induced volumetric swelling of the specimens was an indication of the total stored energy. Based on this observation, the energy release behavior was described by a swelling recovery model.
This project leverages work completed under funding from previous U.S. Department of Energy (DOE) projects and other sources of funding around the Cryogenic Carbon Capture Process™ (CCC). The purpose of this project is to further develop a perturbation of the CCC process that stores energy called the Cryogenic Carbon Capture™ Energy Storing (CCC-ES) process. The successful completion of the tasks in this project has prepared the CCC-ES process for scaling and developed initial plans and a feasibility analysis for an engineering-scale or small-commercial scale system that can store at least 10 MWh of energy. The specific areas of work in this project are: (1) Develop a conceptual, site-specific integrated process flow diagram: Sustainable Energy Solutions (SES) developed a conceptual, site-specific plan including a process flow diagram for integrating CCC-ES at a specific location. The proposed system was based on a typical engineering-scale CCC system designed to capture 30-50 metric tonnes of CO 2 per day (TPD), with modifications and additional changes and equipment put in place to add the energy storage element to the system. The 30-50 TPD scale corresponds to the required 10 MWh of stored energy. This task includes updating simulation software for transient analysis. SES also coordinated with the Jim Bridger Power Plant in Point of Rocks, Wyoming to get site-specific data. Using these data, SES completed site-specific preliminary process flow diagrams, estimated capital and operating costs for the CCC-ES system, and used the upgraded software package to estimate the primary figures of merit. (2) Complete a technoeconomic analysis (TEA): SES completed a full-scale TEA of the CCC-ES system to meet specific success criteria. This TEA used process information from the host site selected above. (3) Technology and data gap assessment for the CCC-ES process: As part of the technology gap assessment, SES performed an assessment of the best alternative energy storage solutions including figures of merit and operational strengths and weaknesses. SES then compared the proposed system with a discussion of how it addresses the weaknesses of alternative systems, showing for example a cost of stored energy at less than $50/MWh and with a roundtrip efficiency of greater than 95%. This assessment includes technical and other risk analyses and technology gaps with research and development needs to commercialize the process by 2030. Finally, work includes a commercialization roadmap/development pathway outlining the major milestones to move to a commercially operating CCC-ES system. (4) Phase II pre-FEED project plan: SES completed a project plan for the Phase II pre-FEED analysis using the same host site as in the Phase I conceptual study.
The US Department of Energy supports the development of commercial building energy codes and standards. To support commercial building energy research activities and the development of commercial building energy codes and standards, continuous efforts have been made to convert 30 prototype building models (20 different building types), which cover 80% of US commercial building floor space, to OpenStudio prototype buildings. Additionally, the suite of prototype building models was expanded to include the addition of new building prototype models (e.g., courthouse, college building). This report documents the building and system characteristics of the prototype convenience store building model. Multiple sources, including databases, documented projects, and personal communications, were used to define the prototype convenience store building characteristics, and a one-story, 3,000 ft 2 building was considered as the prototype convenience store to represent an average-sized convenience store in the United States. The Oak Ridge National Laboratory team defined the operational hours of the convenience store as 17 hours per day. The types of exterior walls, roof, and floor were defined as brick wall, metal surfacing roof, and slab-on-grade floor, respectively. Double glazing was selected, and the window-towall ratio was set as 15% on the front side. For the system part, packaged system and centralized water heater were selected for the space heating and cooling system and water heater system, respectively. In terms of the refrigerator system, the number of refrigerators and freezers were determined as eight closed cases and two walk-in units.
The microstructure, trapped transmutation gases, stored energy, and mechanical behavior of samples from an irradiated Inconel 718 proton beam window were characterized using transmission electron microcopy, thermal desorption spectrometry (TDS), differential scanning calorimetry (DSC), and tensile testing. In the as-irradiated condition the microstructure contained a high number density of 1–3 nm gas-filled nanocavities. Emissions of trapped gases, H and He, during TDS correlated with peaks of the energy release curves from DSC examinations, which suggest these gases were likely stored in highly stable defect traps. The stored energy from radiation damage saturated at doses of a few dpa and did not increase with increasing radiation dose, but the amount of stored H and He increased with increasing dose. Effects of post-irradiation annealing were studied as well. After exposure to 700 °C, the nanocavities grew only slightly to 2–4 nm in diameter, but after exposure to 900 °C, the cavities grew to 10–20 nm in diameter and electron energy-loss spectroscopy showed these cavities contained a core of He surrounded by a shell of H. Further, this study demonstrated that the irradiation defect structures containing H and He were remarkably stable during irradiation and after exposure up to 700 °C. The effect of the irradiation temperature, defect mobility, and interaction of H, He, and irradiation defects on mechanical behavior provides insight into the processes responsible for the unusual recovery in ductility with increasing radiation dose observed in Inconel 718 after high energy proton and spallation neutron irradiation.
The City of Los Angeles Harbor Department (Harbor Department, Port of Los Angeles) partnered with Equilon Enterprises LLC (d/b/a Shell Oil Products US) (Shell), Toyota Motor North America (Toyota), and Kenworth Truck Company (Kenworth) partnered with the Port of Hueneme, United Parcel Service, Total Transportation Services Inc., Southern Counties Express, Toyota Logistics Services, Air Liquide, National Renewable Energy Laboratory, Coalition For A Safe Environment, and the South Coast Air Quality Management District to introduce hydrogen fuel into the Southern California drayage truck market by demonstrating near-commercial heavy-duty hydrogen fuel cell electric trucks at and between freight facilities throughout the region, while continuing to lay the groundwork for battery-electric operations. The Shore to Store project built on project team experience to help realize our vision of zero-emission freight operations in the future. Ten Kenworth zero-emission Class 8 fuel cell electric trucks, integrated with Toyota’s fuel cell drive technology, were operated by United Parcel Service, Total Transportation Services Inc., Southern Counties Express, and Toyota Logistics Services in commercial service. The demonstration fleet fueled at the Shore to Store hydrogen fueling stations that were built in Ontario, California, and Wilmington, California. An additional station at the Port of Long Beach (Portal Station) was available for fueling the fleet. Portal Station was supported by grants from the California Energy Commission and South Coast Air Quality Management District and used as match funding for the Shore to Store project. The Port of Hueneme demonstrated two battery-electric yard tractors, and Toyota Logistics Services demonstrated two zero-emission forklifts at their warehouse facility, showcasing elements of the entire supply chain operating with zero emissions. This project showcased a snapshot of the zero-emission supply chain of the future, providing a model by which freight facilities can support zero-emission operations. The Shore to Store project: Demonstrated the technical feasibility of zero-emission hydrogen fueled Class 8 heavy-duty trucks and electric cargo handling equipment in rigorous goods movement operation throughout the Southern California region. Cumulatively completed 59,212 miles of zero-emission operation, with 21,650 miles driven in-service with the fleets, using hydrogen fuel cell electric Class 8 heavy-duty trucks. Operated zero-emission yard tractors for a total of 2,749.6 hours. Created direct localized emission reductions in designated disadvantaged communities, including those in zip codes 90220, 90247, 90248, 90731, 90744, 90802, and 91761. For the 59,212 miles of zero-emission operation, reduced emissions by an estimated total of 15.5 kg NOX (0.163 g/km), 1.01 kg SOX (0.0106 g/km), and 304.8 metric tonnes of CO2e (3.2 kg/km) compared to diesel baseline vehicles including both tailpipe emissions and the emissions from producing the fuel sources.
Evaluating progress toward a built environment that is best equipped to serve communities during a regional power outage will require metrics that capture the energy resilience of the unique buildings and businesses most crucial to the well-being of those nearby. We focused on grocery stores as key buildings for which access, and thus energy resilience, is critical during a disaster when power is unavailable. We evaluated the energy resilience of these buildings by offering and testing building-scale metrics that assess business continuity potential during a power outage. Metrics proposed in this study are calculated based on the unique power loads characteristic to grocery stores, primarily refrigeration and maintaining safe indoor environmental conditions. Building simulations based on varying levels of backup power were carried out against occupant safety and comfort parameters to apply these metrics, with additional criteria imposed on grocery stores to capture the inventory and sales loss from food spoilage resulting from a lack of refrigeration power. Findings from this study demonstrate the feasibility of our proposed metrics and methodology to serve as a low-data burden means for stakeholders to evaluate the energy resilience of grocery stores, with greater implications in helping to understand the impact on community-scale energy resilience.
Abstract We present a phase-field (PF) model to simulate the microstructure evolution occurring in polycrystalline materials with a variation in the intra-granular dislocation density. The model accounts for two mechanisms that lead to the grain boundary migration: the driving force due to capillarity and that due to the stored energy arising from a spatially varying dislocation density. In addition to the order parameters that distinguish regions occupied by different grains, we introduce dislocation density fields that describe spatial variation of the dislocation density. We assume that the dislocation density decays as a function of the distance the grain boundary has migrated. To demonstrate and parameterize the model, we simulate microstructure evolution in two dimensions, for which the initial microstructure is based on real-time experimental data. Additionally, we applied the model to study the effect of a cyclic heat treatment (CHT) on the microstructure evolution. Specifically, we simulated stored-energy-driven grain growth during three thermal cycles, as well as grain growth without stored energy that serves as a baseline for comparison. We showed that the microstructure evolution proceeded much faster when the stored energy was considered. A non-self-similar evolution was observed in this case, while a nearly self-similar evolution was found when the microstructure evolution is driven solely by capillarity. These results suggest a possible mechanism for the initiation of abnormal grain growth during CHT. Finally, we demonstrate an integrated experimental-computational workflow that utilizes the experimental measurements to inform the PF model and its parameterization, which provides a foundation for the development of future simulation tools capable of quantitative prediction of microstructure evolution during non-isothermal heat treatment.
This paper describes a new non-charge-based data storing technique in NAND flash memory called watermark that encodes read-only data in the form of physical properties of flash memory cells. Unlike traditional charge-based data storing method in flash memory, the proposed technique is resistant to total ionizing dose (TID) effects. To evaluate its resistance to irradiation effects, we analyze data stored in several commercial single-level-cell (SLC) flash memory chips from different vendors and technology nodes. These chips are irradiated using a Co-60 gamma-ray source array for up to 100 krad(Si) at Sandia National Laboratories. Experimental evaluation performed on a flash chip from Samsung shows that the intrinsic bit error rate (BER) of watermark increases from 0.8% for TID = 0 krad(Si) to 1% for TID = 100 krad(Si). Conversely, the BER of charge-based data stored on the same chip increases from 0% at TID = 0 krad(Si) to 1.5% at TID = 100 krad(Si). Overall, the results imply that the proposed technique may potentially offer significant improvements in data integrity relative to traditional charge-based data storage for very high radiation (TID > 100 krad(Si)) environments. These gains in data integrity relative to the charge-based data storage are useful in radiation-prone environments, but they come at the cost of increased write times and higher BERs before irradiation.
BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.
In nuclear industry, damages caused to in-core structural materials by high-energy neutrons have been studied for years. Recently, the possibility of storing energy through the creation of defects in crystalline materials via heavy particle bombardment was investigated. To evaluate the potential of the Wigner effect as energy-storing process, a graphite-moderated Molten Salt Reactor was adopted as a test case. Neutronics simulations performed with OpenMC Monte Carlo code allowed estimating the amount of energy released during fission events, the fraction that is deposited in graphite, and the fraction that has the potential to cause radiation damages. Additionally, molecular dynamics simulations were then performed to study the evolution of cascades triggered by high-energy neutrons. As a major outcome, a more accurate estimate of the radiation damage with respect to the traditional approaches was obtained, and the fraction of the deposited neutron energy that can be stored through long-lived defects was quantified.
Abstract As fumigants face increasing regulatory restrictions, resistance, and consumer pushback, it is vital to expand the integrated pest management (IPM) chemical toolkit for stored products. The production of biomass derived insecticides (e.g., bio-oil fraction) from byproducts of biofuel production may be a promising alternative source of chemistries for controlling stored product insects. These potential insecticidal bio-oils were fractionated based on boiling points (ranging from 115 to 230°C in one series and 245–250°C in another). Fractions were analyzed using GC-MS, and were found to be unique in composition. The lethality of these fractions was tested on Tribolium castaneum, Tribolium confusum, and Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae). Fractions were tested at concentrations ranging from 5–260 mg/ml to screen for efficacy against adults for durations of 2–8 hr sprayed on concrete arenas. In addition, a separate assay evaluated adult emergence of larvae after 6 wk with supplemental food in arenas, while repellency was evaluated against four stored product insect species in a laminar wind tunnel. A greenhouse gas (GHG) emissions life cycle assessment was also performed, which found the use of the bio-oil fraction could reduce GHG emissions associated with the insecticide supply chain by 25–61% relative to a fossil-fuel based insecticide or pyrethroid. While adults were largely unaffected, we found that larval emergence was significantly suppressed compared to controls by roughly half or more. We also determined that there was minimal repellency to most fractions by most species. We conclude that the use of bio-oil fractions is a climate-friendly choice that may support IPM programs.
This paper describes efficient algorithms for computing rank-revealing factorizations of matrices that are too large to fit in main memory (RAM), and must instead be stored on slow external memory devices such as disks (out-of-core or out-of-memory). Traditional algorithms for computing rank-revealing factorizations (such as the column pivoted QR factorization and the singular value decomposition) are very communication intensive as they require many vector-vector and matrix-vector operations, which become prohibitively expensive when data is not in RAM. Randomization allows to reformulate new methods so that large contiguous blocks of the matrix are processed in bulk. The paper describes two distinct methods. The first is a blocked version of column pivoted Householder QR, organized as a “left-looking” method to minimize the number of the expensive write operations. The second method results employs a UTV factorization. It is organized as an algorithm-by-blocks to overlap computations and I/O operations. As it incorporates power iterations, it is much better at revealing the numerical rank. Numerical experiments on several computers demonstrate that the new algorithms are almost as fast when processing data stored on slow memory devices as traditional algorithms are for data stored in RAM.
High field science relies on ultrashort pulse lasers with multi-joule pulse energies for studying light–matter interactions under extreme conditions and for driving particle accelerators and secondary radiation sources of x rays, gamma rays, neutrons, positrons, muons, and protons. Next-generation laser drivers will require a 10 3 -10 4 times increase in pulse repetition rates, producing multi-joule energies at multi-kilowatt average powers to enable practical applications in nuclear engineering, advanced materials, medicine, biology, homeland security, and high-energy physics. Spatially coherently combined femtosecond fiber lasers are recognized as a pathway to these next-generation drivers, with significant practical advantages including high efficiency and the possibility of compact integration. However, chirped pulse amplification in fibers is capable of extracting only a small fraction (usually ~1%) of the maximum stored energy. Here we demonstrate near-complete maximum stored energy extraction with low accumulated nonlinearity from a large-core fiber amplifier using coherent pulse stacking amplification. We have amplified a 81-pulse stacking burst in a 85 µm core chirally coupled core Yb-doped fiber, extracting up to 9.5 mJ (~90% of stored energy) with < 4.5 radians of accumulated nonlinear phase, temporally combined this burst into a single pulse, and achieved 4.2 mJ pulses of 313 fs bandwidth-limited duration after compression. This represents, to our knowledge, the highest energy extracted and compressed into a femtosecond pulse from a single fiber amplifier, enabling approximately two orders of magnitude size reduction of future high-energy coherently spatially combined fiber laser arrays.