This acceptable knowledge (AK) Summary Report has been prepared for the Central Characterization Program (CCP) for remote-handled (RH) transuranic (TRU) waste generated and managed by Sandia National Laboratories/New Mexico (SNL/NM) in Albuquerque, New Mexico. The waste described in this report was predominately generated in the SNL/NM Hot Cell Facility (HCF) during the removal and packaging of experimental material and decontamination operations in Building 6580 at Technical Area (TA)-V. The waste stream also includes a very small amount of waste (estimated at less than 1 gram of fuel similar to that used for research at the HCF) originating from classified research at TA-I. In addition, the waste also includes secondary waste termed by SNL/NM as process generated waste (PGW), created during repackaging operations for this waste at the Auxiliary Hot Cell Facility (AHCF). The waste has been stored at the Sandia Pulsed Reactor (SPR) Dense Pack Storage Facility within TA-V or the Manzano Bunkers located at Manzano Base within Kirtland Air Force Base. All of the waste is being repackaged at the AHCF. This report was prepared in accordance with CCP-TP-005, CCP Acceptable Knowledge Documentation (Reference 1), to implement the AK requirements of DOE/WIPP-02-3214, Remote-Handled TRU Waste Characterization Program Implementation Plan (WCPIP) (Reference 2); Waste Isolation Pilot Plant Hazardous Waste Facility Permit, Waste Analysis Plan (WIPP-WAP) (Reference 3); and DOE/WIPP-02-3122, Transuranic Waste Acceptance Criteria for the Waste Isolation Pilot Plant (WIPP-WAC) (Reference 4).
Improvised explosive devices (IEDs) have injured and killed numerous soldiers and civilians as a consequence of military operations in the Middle East. With gaps in existing technologies, the US military required devices for quickly addressing deadly IEDs without harming military personnel or inflicting severe damage to the environment. In response to this need, Sandia developed Stingray, a clear, plastic handheld device to quickly and safely disable threatening IEDs. Stingray is designed to be used in two configurations: a coherent water blade for cutting operations and as a water slug for general device disruption. Prior to disabling an IED, Explosive Ordnance Disposal (EOD) technicians will x-ray the target using tools such as Sandia's X-ray Toolkit (XTK) to determine which function operators should use to dismantle the IED. For example, if the operator knows exactly which wires to cut, they can use the precision water blade. If the operator wants to create a general disruption, they can use the water slug function.
The purpose of this scoping study is to develop an approach for establishing emergency planning requirements for advanced nuclear power reactors and other new reactor technologies. The approach considers existing emergency planning requirements and guidance. More specifically the study focuses on establishing criteria and process to determine the size of the plume and ingestion exposure pathway emergency planning zone. The review of emergency planning in place for existing licensed nuclear facilities provides insight and informs the suggested process for establishing this Emergency Planning Zone (EPZ) process.
This paper presents a techno-economic analysis of behind-the-meter (BTM) solar photovoltaic (PV) and battery energy storage systems (BESS) applied to an Electric Vehicle (EV) fast-charging station. The goal is to estimate the maximum return on investment (ROI) that can be obtained for optimum BESS and PV size and their operation. Fast charging is a technology that will speed up mass adoption of EVs, which currently requires several hours to achieve full recharge in level 1 or 2 chargers. Fast chargers demand from tens to hundreds of kilowatts from the distribution grid, potentially leading to system congestion and overload. The problem is formulated as a linear program that obtains the size of PV, power and energy ratings of BESS as well as charging and discharging scheduling of the storage system to maximize ROI under operational constraints of BESS and PV. The revenue are cost-savings of demand and time-of-use charges, with a penalty for BESS degradation. We have considered Los Angeles Department of Water and Power tariff A-2 and fast charger data derived from the EV Project. The results show that a 46.5 kW/28.3 kWh BESS can obtain a ROI of about $22.4k over 10 years for a small 4-port fast-charging station.
Increased deployment of rooftop solar in California has resulted in the "duck curve", where there is a decrease in the midday net load followed by a very fast increase in late afternoon caused by declining solar output and a simultaneous increase in load. The large observed and even larger predicted ramp rates present a reliability concern. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) hour-ahead scheduling process (HASP) real-time market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes was analyzed to identify trends and opportunities for the deployment of solar plus storage. In addition, a comparison of the opportunities in the day ahead and HASP real-time energy markets is presented.
The U.S. Nuclear Regulatory Commission (NRC) with Sandia National Laboratories (Sandia) have completed three uncertainty analyses (UAs) as part of the State-of-the-Art Reactor Consequence Analyses (SOARCA) program. The SOARCA UAs included an integrated evaluation of uncertainty in accident progression, radiological release, and offsite health consequence projections. The UA for Peach Bottom, a boiling-water reactor (BWR) with a Mark I containment located in the State of Pennsylvania, analyzed the unmitigated long-term station blackout SOARCA scenario. The UA for Sequoyah, a 4-loop Westinghouse pressurized-water reactor (PWR) located in the State of Tennessee, analyzed the unmitigated short-term station blackout SOARCA scenario, with a focus on issues unique to the ice condenser containment and the potential for early containment failure due to hydrogen deflagration. The UA for Surry, a 3-loop Westinghouse PWR with a sub-atmospheric large dry containment located in the State of Virginia, analyzed the unmitigated short-term station blackout SOARCA scenario including the potential for thermally-induced steam-generator tube rupture. These three UAs are currently documented in three NUREG/CR reports. This report provides input to planned NRC documentation on the insights and findings from the SOARCA UA program. The purpose of the summary report is to provide a useful reference for regulatory applications that require the evaluation of offsite consequence risk from beyond design basis event severe accidents. This report focuses on the accident progression and source term insights developed from the MELCOR analyses. MELCOR is the NRC's best-estimate, severe accident computer code used in the SOARCA UAs. In anticipation of the SOARCA UA insights work, NRC and Sandia benchmarked the response of the Peach Bottom model to selected reference calculations from the Peach Bottom SOARCA UA. Peach Bottom was the first SOARCA UA performed and was completed in 2015 using the MELCOR 1.8.6 code. The PWR SOARCA UAs evolved the original methodology and utilized the updated MELCOR 2.2 computer code. The Peach Bottom model has been systematically updated for other NRC research efforts and has been updated to MELCOR 2.2. computer code. The findings from the new reference calculations using the updated model with the MELCOR 2.2 code are also integrated into the report. A second objective is an assessment of the applicability of the results to the other nuclear reactors in the U.S. As the key findings are reviewed, judgments are presented on the applicability of the results to other U.S. nuclear power plants. An important objective of the SOARCA program relied on high- fidelity plant-specific modeling. However, the nature of the insights and conclusions allowed judgements to be made on the applicability of the various insights to the same general classification of plant (i.e., BWR or PWR) or the entire fleet of plants. Finally, the results from the SOARCA UA accident progression calculations contain a wealth of information not previously documented in the NUREG/CRs. This report includes new but related information that can be used to benchmark past or support future regulatory decisions related to severe accidents. The new work includes a benchmark of the NUREG-1465 licensing source term definitions, the variability of key accident progression events and timing to radionuclide release, and an improved understanding of the timing and source terms from consequential steam generator tube ruptures. iii ACKNOWLEDGEMENTS The Sandia authors gratefully acknowledge the significant technical and programmatic contributions from the NRC SOARCA team which are reflected throughout the report. Dr. Tina Ghosh has been involved throughout the SOARCA UAs, providing the primary managerial and technical oversight. The long lists of NRC and Sandia contributors from the SOARCA UAs are cited in the three NUREG/CRs and are also gratefully acknowledged by the small team of authors compiling the results of their efforts. Significant technical contributions, advice, and reviews were provided by Dr. Hossein Esmaili, Dr. Alfred Hathaway, and Dr. Edward Fuller (retired) of the NRC. Dr. Randal Gauntt (retired), Mr. Patrick Mattie, Mr. Joseph Jones (retired), and Dr. Doug Osborn from Sandia are recognized as the SOARCA UA managers guiding the past efforts. There is a comparable list of project managers at the NRC including Ms. Patricia Santiago, Dr. Salman Haq, and Mr. Jon Barr. Sadly, we have lost Mr. Charlie Tinkler and Mr. Robert Prato, who were important contributors to the original SOARCA project. Finally, Mr. Kyle Ross and Mr. Mark Leonard have also retired but were significant technical contributors. Mr. Kyle Ross was the technical lead on all three SOARCA UAs and the original pressurized water reactor SOARCA study. Mr. Leonard was the technical lead on the original boiling water reactor SOARCA study and a key contributor to the first Peach Bottom SOARCA UA. iv
Fast-frequency control strategies have been proposed in the literature to maintain inertial response of electric generation and help with the frequency regulation of the system. However, it is challenging to deploy such strategies when the inertia constant of the system is unknown and time-varying. In this paper, we present a data-driven system identification approach for an energy storage system (ESS) operator to identify the inertial response of the system (and consequently the inertia constant). The method is first tested and validated with a simulated genset model using small changes in the system load as the excitation signal and measuring the corresponding change in frequency. The validated method is then used to experimentally identify the inertia constant of a genset. The inertia constant of the simulated genset model was estimated with an error of less than 5% which provides a reasonable estimate for the ESS operator to properly tune the parameters of a fast-frequency controller.
Gamma Detector Response and Analysis Software (GADRAS) is used by the radiation detection and emergency response community to perform modeling and spectral analysis for gamma detector systems. Built into GADRAS is the ability to define a detector, geometry, background characteristics and source composition to generate synthetic spectra for drills and exercises (injects). Consequence Management is currently in development of a sample result data simulator tool in which a deposition model is probed for source conditions at moments in time and locations in space. These values are used to generate realistic sample results for use in drills and exercises. In addition to sample results, there is a need to simulate the actual spectra that would be observed in the field by downlooking HPGe instruments given a deposition activity. This way, the FRMAC Gamma Spectroscopist can practice their process of generating quantified results from spectra on realistic data as well. Recognizing the decades of work done in GADRAS to accurately generate synthetic spectra, this team decided to build a link between the new simulator and GADRAS to generate these spectra quickly and easily. The simulator tool will generate a file that specifies the name of the spectra, its location, date/time of measurement, duration of measurement, height off the ground, and the deposition activity and age for every radionuclide in the simulation. Then, a new tool within the Inject Tab of GADRAS was developed to read in this file given a detector selection and generate In-Situ spectra for each row in the file in any file format the user chooses. This way, simulation cell staff can take these files and then upload them to the appropriate data system (RAMS or RadResponder) for use during drills and exercises. An advanced feature of this tool allows for generating any spectra given an appropriate model and mapping of source to model layer in the batch inject tool. This way, spectra from field sample counts, mobile laboratories, or even fixed laboratories can be generated in bulk given an estimate of the radioactivity concentration or total radioactivity in an import file. This expands the capabilities of this tool a great deal and will make it a more useful tool for CM and others to help estimate detector response for nearly any situation. This user guide will explain the steps needed to perform a batch inject file generation.
Electronic parts used in Nuclear Security Enterprise (NSE) applications have varying pedigrees. Understanding the differences among these "part classes" will better enable Kansas City National Security Campus (KCNSC) and Sandia National Laboratories (SNL, or Sandia) to effectively manage factors such as risk, effort, cost, etc. across all functional areas which have a shared interest in the definition and acquisition process. Regardless of the pedigree and complexity, all parts are expected to meet necessary quality and reliability requirements. This activity has been conducted as part of the COTS Transformation Initiative (CTI).
Low friction is demonstrated with pure polycrystalline tantalum sliding contacts in both molecular dynamics simulations and ultrahigh vacuum experiments. This phenomenon is shown to be correlated with deformation occurring primarily through grain boundary sliding and can be explained using a recently developed predictive model for the shear strength of metals. Specifically, low friction is associated with grain sizes at the interface being smaller than a critical, material-dependent value, where a crossover from dislocation mediated plasticity to grain-boundary sliding occurs. Low friction is therefore associated with inverse Hall-Petch behavior and softening of the interface. Direct quantitative comparisons between experiments and atomistic calculations are used to illustrate the accuracy of the predictions.
Battery energy storage systems are often controlled through an energy management system (EMS), which may not have access to detailed models developed by battery manu-facturers. The EMS contains a model of the battery system's performance capabilities that enables it to optimize charge and discharge decisions. In this paper, we develop a process for the EMS to calculate and improve the accuracy of its control model using the operational data produced by the battery system. This process checks for data salience and quality, identifies candidate parameters, and then calculates their accuracy. The process then updates its model of the battery based on the candidate parameters and their accuracy. We use a charge reservoir model with a first order equivalent circuit to represent the battery and a flexible open-circuit-voltage function. The process is applied to one year of operational data from two lithium-ion batteries in a battery system located in Sterling, MA USA. Results show that the process quickly learns the optimal model parameters and significantly reduces modeling uncertainty. Applying this process to an EMS can improve control performance and enable risk-averse control by accounting for variations in capacity and efficiency.