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DOE OSTI2021

Low Temperature Water-Gas Shift: Enhancing Stability through Optimizing Rb Loading on Pt/ZrO 2

Recent studies have shown that appropriate levels of alkali promotion can significantly improve the rate of low-temperature water gas shift (LT-WGS) on a range of catalysts. At sufficient loadings, the alkali metal can weaken the formate C–H bond and promote formate dehydrogenation, which is the proposed rate determining step in the formate associative mechanism. In a continuation of these studies, the effect of Rb promotion on Pt/ZrO 2 is examined herein. Pt/ZrO 2 catalysts were prepared with several different Rb loadings and characterized using temperature programmed reduction mass spectrometry (TPR-MS), temperature programmed desorption (TPD), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), an X-ray absorption near edge spectroscopy (XANES) difference procedure, extended X-ray absorption fine structure spectroscopy (EXAFS) fitting, TPR-EXAFS/XANES, and reactor testing. At loadings of 2.79% Rb or higher, a significant shift was seen in the formate ν(CH) band. The results showed that a Rb loading of 4.65%, significantly improves the rate of formate decomposition in the presence of steam via weakening the formate C–H bond. However, excessive rubidium loading led to the increase in stability of a second intermediate, carbonate and inhibited hydrogen transfer reactions on Pt through surface blocking and accelerated agglomeration during catalyst activation. Optimal catalytic performance was achieved with loadings in the range of 0.55–0.93% Rb, where the catalyst maintained high activity and exhibited higher stability in comparison with the unpromoted catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

OC6 Phase Ib: Floating Wind Component Experiment for Difference-Frequency Hydrodynamic Load Validation

A new validation campaign was conducted at the W2 Harold Alfond Ocean Engineering Laboratory at the University of Maine to investigate the hydrodynamic loading on floating offshore wind substructures, with a focus on the low-frequency contributions that tend to drive extreme and fatigue loading in semisubmersible designs. A component-level approach was taken to examine the hydrodynamic loads on individual parts of the semisubmersible in isolation and then in the presence of other members to assess the change in hydrodynamic loading. A variety of wave conditions were investigated, including bichromatic waves, to provide a direct assessment of difference-frequency wave loading. An assessment of the impact of wave uncertainty on the loading was performed, with the goal of enabling validation with this dataset of numerical models with different levels of fidelity. The dataset is openly available for public use and can be downloaded from the U.S. Department of Energy Data Archive and Portal.

17 WIND ENERGY↗

DOE OSTI2024

SAGAbg. I. A Near-unity Mass-loading Factor in Low-mass Galaxies via Their Low-redshift Evolution in Stellar Mass, Oxygen Abundance, and Star Formation Rate

Measuring the relation between star formation and galactic winds is observationally difficult. In this work we make an indirect measurement of the mass-loading factor (the ratio between the mass outflow rate and star formation rate) in low-mass galaxies using a differential approach to modeling the low-redshift evolution of the star-forming main sequence and mass–metallicity relation. We use Satellites Around Galactic Analogs (SAGA) background galaxies, i.e., spectra observed by the SAGA Survey that are not associated with the main SAGA host galaxies, to construct a sample of 11,925 spectroscopically confirmed low-mass galaxies from 0.01 ≲ z ≤ 0.21 and measure auroral line metallicities for 120 galaxies. The crux of the method is to use the lowest-redshift galaxies as the boundary condition of our model, and to infer a mass-loading factor for the sample by comparing the expected evolution of the low-redshift reference sample in stellar mass, gas-phase metallicity, and star formation rate against the observed properties of the sample at higher redshift. We infer a mass-loading factor of η m = $0.92^{+1.76}_{-0.74}$, which is in line with direct measurements of the mass-loading factor from the literature despite the drastically different sets of assumptions needed for each approach. While our estimate of the mass-loading factor is in good agreement with recent galaxy simulations that focus on resolving the dynamics of the interstellar medium, it is smaller by over an order of magnitude than the mass-loading factor produced by many contemporary cosmological simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Ducted Fuel Injection Provides Consistently Lower Soot Emissions in Sweep to Full-Load Conditions

Earlier studies have proven how ducted fuel injection (DFI) substantially reduces soot for low- and mid-load conditions in heavy-duty engines, without significant adverse effects on other emissions. Nevertheless, no comprehensive DFI study exists showing soot reductions at high- and full-load conditions. This study investigated DFI in a single-cylinder, 1.7-L, optical engine from low- to full-load conditions with a low-net-carbon fuel consisting of 80% renewable diesel and 20% biodiesel. Over the tested load range, DFI reduced engine-out soot by 38.1–63.1% compared to conventional diesel combustion (CDC). This soot reduction occurred without significant detrimental effects on other emission types. Thus, DFI reduced the severity of the soot–NO x tradeoff at all tested conditions. While DFI delivered considerable soot reductions in thepresent study, previous DFI studies at low- and mid-load conditions delivered larger soot reductions (>90%) compared to CDC operation at the same conditions. Therefore, the DFI configuration used here has been deemed nonoptimal (in terms of parameters such as the injector-spray and piston geometries), and several improvements are recommended for future studies with high-load DFI. Further, these improvements include employing better spray-duct alignment, a deeper piston bowl with a smaller injector umbrella angle, and a fuel injector that opens and closes faster. The study also suggests future research to make DFI ready for commercialization, such as metal-engine tests to ensure desirable DFI performance over an engine’s complete speed/load map. Overall, this study supports the continued development and commercialization of DFI to meet upcoming emissions regulations for heavy-duty vehicles. Specifically, multicylinder engine experiments and CFD simulations should be utilized to optimize the performance and clarify the full potential of DFI.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

On-Track Measurement of Road Load Changes in Two Close-Following Vehicles: Methods and Results

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances. Through trial and error, test methods were developed that appear to provide satisfactory results, considering the challenges of track testing under real-world conditions (wind, weather, temperature changes, etc.). We found that total road load was reduced by about 10-12% at an optimum gap time of 0.25 to 0.4 s. Challenges that were encountered included less repeatability of load measurements at short gap distances and powertrain mode switching (from a hybrid vehicle) that rendered the 88-km/h results less useful. This study will provide the framework for further investigations on coordinated-driving road load using track testing methods.

Duoba, Michael↗

DOE OSTI2015

Optical based tactile shear and normal load sensor

Various technologies described herein pertain to a tactile sensor that senses normal load and/or shear load. The tactile sensor includes a first layer and an optically transparent layer bonded together. At least a portion of the first layer is made of optically reflective material. The optically transparent layer is made of resilient material (e.g., clear silicone rubber). The tactile sensor includes light emitter/light detector pair(s), which respectively detect either normal load or shear load. Light emitter(s) emit light that traverses through the optically transparent layer and reflects off optically reflective material of the first layer, and light detector(s) detect and measure intensity of reflected light. When a normal load is applied, the optically transparent layer compresses, causing a change in reflected light intensity. When shear load is applied, a boundary between optically reflective material and optically absorptive material is laterally displaced, causing a change in reflected light intensity.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Method and system employing finite state machine modeling to identify one of a plurality of different electric load types

A system is for a plurality of different electric load types. The system includes a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor. The processor acquires a voltage and current waveform from the sensors for a corresponding one of the different electric load types; calculates a power or current RMS profile of the waveform; quantizes the power or current RMS profile into a set of quantized state-values; evaluates a state-duration for each of the quantized state-values; evaluates a plurality of state-types based on the power or current RMS profile and the quantized state-values; generates a state-sequence that describes a corresponding finite state machine model of a generalized load start-up or transient profile for the corresponding electric load type; and identifies the corresponding electric load type.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Fast DR: Controlling Small Loads over the Internet

Current state-of-the-art demand response systems are expensive due to extreme reliability constraints in telemetry, control, and metering. The typical cost of enabling a site for regulation services is between $50k and $80k. These costs are orders of magnitude too high for small loads to participate in high-value grid services. Traditional demand response (DR) schedules events in advance and provides basic on/off control. We developed fast DR measurement and communication technologies, which are scalable cost-effective demand response systems with sufficient reliability for real-time control and monitoring of loads over the internet, and we demonstrated an example system for under $100. We installed FastDR systems for lighting, motor, and thermostat control along with power metering at a total of 10 sites, including 6 commercial buildings and 4 residential buildings. This demonstration addressed the following important methodological questions: 1) Can we reliably control loads simultaneously and with low latency across multiple sites within a specified response time; 2) How reliable is using the internet or 4G cellular network as the mode of control and metering; and 3) Can existing Smart Meters provide data for real-time telemetry services. Testing shows fast DR is capable of control to response in 4 seconds and loads complete transition in seconds to less than one minute depending on load type. We demonstrated 4s regulation services and synchronized load control, and we successfully met specifications for ancillary services across the US. Telemetry via the 4G cellular network demonstrated 94.5% success rate, and residential Internet demonstrated a 98% success rate.

Kiliccote, Sila↗

DOE OSTI2020

Codisposal Waste Package Loading Options for DOE SNF and HLW PPT

The U.S. Department of Energy (DOE) is responsible for managing spent nuclear fuel (SNF) that is currently in, or will in the future come into, its possession. DOE must continue to safely store that SNF, transport it to an interim storage site or a repository, and dispose of it. These fuels come from a wide range of reactor types with various cladding materials and enrichments. Many of these reactors, now decommissioned, had unique design features, such as core configuration, fuel element and assembly geometry, moderator and coolant materials, operational characteristics, and neutron spatial and spectral properties, resulting in a large diversity of reactor and fuel designs. Because of the wide variety and conditions of SNF, a robust DOE Standard Canister was proposed that would confine radionuclides and preclude moderator. The DOE Standard Canister had four variations: 3.05-meter (10-foot) or 4.57-meter (15-foot) length, and 45.7-cm (18-inch) or 61.0-cm (24-inch) diameter. For ultimate disposal in the Yucca Mountain Repository, these canisters were to be grouped with 61.0-cm (24-inch) diameter high level waste (HLW) canisters in a 2.13-meter (84-inch) diameter co-disposal waste package. The smaller 45.7-cm (18-inch) diameter DOE Standard Canister could be placed in the middle of five HLW canisters. The larger 61.0-cm (24-inch) diameter DOE Standard Canister would take the place of one of the five HLW canisters on the outer ring in the co-disposal waste package. No DOE Standard Canisters have been loaded. A preliminary evaluation has estimated the number of elements of a fuel type that can fit into the different sizes of the DOE Standard Canister, but no definitive loading configuration has been selected. Changing the loading configuration could impact the number of loadable DOE Standard Canisters and the number of co-disposal waste packages needed for eventual disposition. This paper conveys the ranges of DOE Standard Canisters and HLW canisters that may be produced under certain conditions. It also examines the differences in the estimated number of co-disposal waste packages produced for eventual disposal when using different loading strategies in the DOE Standard Canister for Advanced Test Reactor (ATR), Peach Bottom, and High Flux Isotope Reactor (HFIR) SNF. Changing the loading configurations of ATR, Peach Bottom, and HFIR SNF slightly impacted the number of co-disposal waste packages that may be needed for ultimate disposal. The change in loading configuration was more impactful when a different canister was used, as opposed to varying the number of elements that could fit inside the same size canister. In one case, a reduction of co-disposal waste packages could be achieved by allowing mixing of short HLW canisters with long DOE Standard Canisters. The main conclusion from this analysis is that the ratio between HLW canisters and DOE Standard Canisters will drive the total number of co-disposal waste packages. If too many HLW canisters (i.e., more than five times the number of 18-inch DOE standard canisters) or DOE Standard Canisters are produced, some co-disposal waste packages may not have all positions filled. A co-disposal waste package may be filled with all HLW with no DOE Standard Canister, or a co-disposal waste package could be filled with a single DOE Standard Canister. A ratio that does not closely align to optimum could allow for the design of waste packages that hold just HLW canisters or just DOE SNF canisters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration: Preprint

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions, i.e., the reserve requirement, renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained off-line using historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine and battery. Case studies demonstrated that the proposed method outperforms other operating reserve determination methods.

distribution system↗

DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗

DOE OSTI2022

Model predictive control for matrix converter operating in current control mode with load current estimation

A matrix converter system operating in current control mode is provided. The matrix converter includes a switching matrix coupled between a low voltage side and a high voltage side, wherein the matrix converter is coupled at its low voltage side to a generator for receiving an input power including an input current and transforming the input power into an output power at its high voltage side, wherein a load is coupled to the high voltage side. The control system includes a load observer and a model predictive controller (MPC). The load observer is configured to estimate a load current that flows to the load from the high voltage side of the matrix converter as a function of a switching state of the switching matrix, an output voltage output at the high voltage side of the matrix converter, and the input current. The MPC is configured to select the switching state of the switching matrix to meet one or more control objectives defined by minimization of a multi-objective function that tracks the output voltage and the input current using the estimated load current.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A multi-level load shape clustering and disaggregation approach to characterize patterns of energy consumption behavior

This study presents representative electrical load shapes, disaggregated to the end-use level, for over 5000 customer clusters across California’s residential, commercial, industrial and agricultural sectors. We developed a novel, multi-level load shape clustering approach for residential and commercial sectors leveraging interval meter data for over 350,000 California utility customers collected as a part of the Phase 4 California Demand Response (DR) Potential Study. The clustering approach allowed us to identify typical consumption patterns and categorize customers based on their daily load shape displayed throughout the year. For example, we were able to identify customers with particular energy technologies such as electric vehicles and rooftop solar, as well as building occupancy types such as restaurants, grocery stores and even unoccupied buildings, based solely on whole-building interval data. We then combined the load shape-based clusters with other customer information including building type, climate, geographical area, total consumption and low-income status, to create a set of customer clusters based on both demographics and usage patterns. Total cluster electricity demand was then disaggregated into a wide variety of end-uses using weather normalization and other publicly available end-use load shape datasets. The resulting disaggregated cluster load shapes will be released in anonymized form as part of the Phase 4 DR Potential Study. They will have wide-ranging applications in energy research and policy analysis, including estimation of energy efficiency (EE) and DR potential on the end-use level, time-dependent valuation of EE savings, building stock modeling, and developing customer targeting strategies for EE and DR programs.

Murthy, Samanvitha↗

DOE OSTI2023

Pulsed source for driving non-linear current dependent loads

A pulsed current source comprises a power source, a discharge capacitor, and an inductive element. The discharge capacitor is selectively coupled to either of the power source or the inductive element. When coupled to the power source, the discharge capacitor is charged. The inductive element can be connected to a load. The load can have a current-dependent impedance. When the discharge capacitor is coupled to the inductive element, the discharge capacitor discharges through the inductive element and the load. The discharge capacitor and the inductive element are configured so that the current through the load exhibits a substantially linear rise in a linear operational region. The inductive element is configured to saturate during discharge of the capacitor through the load, so that the saturation of the inductive element causes the current through the load to continue to rise in a substantially linear fashion.

Curtis, III, William C.↗

DOE OSTI2019

Simulation of Spent Nuclear Fuel loading into a Final Waste Repository

Previous work towards studying repository loading have used: - spent nuclear fuel (SNF) with an average burnup composition; - a lumped capacitance thermal model for calculating temperature in a Cyclus repository model. The goal of this work is to improve on the repository models and use U.S. historical SNF inventory data in simulations to more accurately study the loading of a waste repository. These goals will be achieved by: - using UNF-ST-and-DARDS Unified Database (UDB) that has historic assembly-specific data (e.g, isotopic composition, heat) in Cyclus simulations; - implementing a more accurate thermal model within a Cyclus repository model. Objectives: - Create a Cyclus spent fuel conditioning model that packages spent fuel bundles into packages which have user-defined properties. - Create a Cyclus medium-fidelity repository model that gives accurate time and spatial dependent temperature values and loads the repository based on a user-selected loading strategy. Cyclus is an agent-based extensible framework for modeling flow of material through user-defined nuclear fuel cycles. In Cyclus, each facility in the fuel cycle is modeled individually and the facilities interact with one another as independent agents. The spent fuel conditioning model accepts spent fuel bundles and puts them into a cylindrical waste package. In the spent fuel conditioning model, the user can define variables: For each layer, radius, thermal conductivity, thermal diffusivity; For each package, Number of spent fuel bundles, Radius and height. The waste repository model accepts waste packages and emplaces them into available positions within the waste repository based on a thermal criteria. The thermal criteria is a temperature limit at the interface between the waste package surface and the host geology, that is set based on the repository's host geology. In the waste repository model, the user can define the variables: Capacity, Distance between waste packages, Distance between drifts, Repository host geology, Loading Strategy. After the addition of new waste packages at each time step, the waste repository model recalculates the temperature at each location in the repository. If the addition of this new package causes its temperature to exceed the thermal limit, it will be placed back into the buffer. A thermal model that relies on a transient 'outside' model and quasi-steady-state 'inside' model is used to accurately determine the temperature in the repository. The 'outside' model assumes a homogenous medium with the Engineered Barrier System (EBS) replaced by the geologic medium. Temperature solutions for the central waste package, adjacent point and line sources are superimposed to calculate the temperature at specific points in the repository. The central drift consists of one finite line source which represents the central waste package. The central drift also consists of point sources that represent neighboring waste packages in the central drift. The neighboring drifts are represented by infinite line sources. The 'inside' model is considered to be at a quasi-steady-state condition because EBS has a relatively low thermal mass compared to the infinite geologic medium. The steady state calculation is performed at each time step with the heat source and interface temperature as boundary conditions. Future Work: Run Cyclus simulations with U.S. historical SNF inventory data, the spent fuel conditioning and repository models to study how waste package acceptance strategies impact repository loading.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Co-Disposal Waste Package Loading Options for DOE SNF and HLW - 20331

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

A Methodology for Loading the Advanced Test Reactor Driver Core for Experiment Analysis

The Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) is one of the premier irradiation facilities in the world. The ATR hosts experiments from a variety of laboratories, companies, and disciplines. Because of its flexibility in providing customized irradiation conditions to experiments, such as coolant chemistry, temperature, and reactor power, analysis for experiments, both from a programmatic and safety perspective, can be a challenging endeavor. Because of these experiment variables, there is always an effort to improve upon the existing analysis methodology in order to provide the sponsor of any given experiment with the most accurate predictions possible. The paper discusses the effort to improve upon the methodology for loading the ATR driver core for experiment analysis. Experiment analysis at INL is generally broken up into three separate disciplines: neutronics, thermal-hydraulics, and structural. The focus of this paper is neutronics analysis. After the experiment has a preliminary design, the neutronics analyst will create a model in the Monte Carlo N-Particle (MCNP) transport code to calculate fuel and material heating rates from MCNP tallies and provide those to the thermal-hydraulics analyst, and calculate reactivity worths, perturbation on the axial fission profile of the ATR driver core by the experiment, void worths, and experiment backup worths. Also, there are usually several programmatic parameters that the experiment sponsor requests that the neutronics analyst calculates. These can include heat generation rates, burnup and displacements per atom (DPA). In order to accurately analyze these parameters, an important input into the physics analyst's MCNP model is the loading of the ATR driver core. The ATR driver core consists of 40, 19-plate highly-enriched uranium (HEU) fuel elements, arranged in a serpentine shape, as pictured in Figure 1. The ATR driver core is responsible for the vast majority of the thermal power of the reactor and the nuclear interactions with the experiments being irradiated in ATR. A unique feature of ATR is the 'power tilt' across the reactor. A power tilt means that the Northwest, Northeast, Southwest, and Southeast Lobes of ATR frequently operate at different powers, providing different irradiation conditions for the experiments in and around the lobes of the reactor. In order to achieve lobe power splits, different fuel elements for each of the 40 positions are chosen by ATR Reactor Engineering so that lobe power splits can be maintained during an operating cycle. A typical fuel loading for an ATR cycle consists of 14 to 18 new fuel elements and 2 to 26 depleted fuel elements. These loadings are typically chosen from three different types of fuel elements. All three ATR fuel element types share a common design, spelled out in Chapter 4 of the ATR Safety Analysis Report (SAR). The fuel element meat is HEU UAlx dispersion fuel sandwiched between aluminum cladding with a length of 121.92 cm (4 ft), with 19 plates per fuel element. The three variations of this common design are designated by the first two letters in their serial numbers: XA, YA, and NB. XA elements are most commonly used. These elements have nominally 1075 g of uranium-235 and 0.66 g of boron-10. The NB elements have the same uranium-235 loading as the XA elements, but no boron in any plates. These elements are used when excess reactivity is needed. The YA elements have the same loading as the XA elements, except that the outermost plate (Plate 19) has no boron or uranium. These elements are used to mitigate aged reflector damage. In order to accurately model the ATR driver core, all three types of fuel elements must be accounted for in the MCNP model. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Demonstration of Load-Follow Operations with Standalone BISON through VERA

As part of the Consortium for Advanced Simulation of Light Water Reactors (CASL), the Virtual Environment for Reactor Applications (VERA) is being developed to provide high-fidelity multiphysics simulations of nuclear reactor cores. VERA contains a number of single physics packages that are coupled together. MPACT is the primary deterministic neutron transport solver, CTF provides thermal hydraulics solutions, and BISON - which is built off of the MOOSE framework - is used for fuel performance simulations. Over the past year, a standalone BISON capability has been developed using a one-way coupling between MPACT/CTF coupling calculations and BISON. This has allowed CASL contributors to gain insight into the fuel performance characteristics while a more tightly coupled methodology between MPACT, CTF, and BISON has been under development. The standalone capability has been used to simulate Watts Bar Unit 1 (WBN1) Cycles 1-3 and to screen for quantities of interest to pellet-clad interaction in Cycles 6-7. The simulation and screening results are presented here. CASL recently partnered with Exelon and the University of Illinois at Urbana Champaign (UIUC) to begin simulating some of Exelon's plants, with particular focus on load-follow operations. In such operations, the power output from the plant will vary based on the demand or anticipated demand at the time. For example, demand is much lower late at night than during the day when people are awake, so it operating the plant at lower power in the evenings could be advantageous. Historically, nuclear power generation has been among the cheapest sources of energy, so most plants in the United States have been operated at full power except for outages and various small scale events. However, in the current economic climate, priorities have changed. A number of questions arise when load-follow operations are considered, particularly with respect to VERA's tools and capabilities. Can the coupled neutronics and thermal hydraulics simulations with MPACT and CTF perform well without substantial convergence issues? Can BISON handle the somewhat rapid power changes present with load-follow? Will clad hoop stresses be alarmingly high as a result of these power changes? This work addresses these questions and demonstrates operations to assess feasibility and robustness before Exelon and UIUC proceed with their simulations using VERA. To demonstrate load-follow capability, two test problems were developed. The first is a single rod that experiences nominal operations during Cycle 1, and then it simulates 18 months of representative load-follow operations in Cycle 2. This single-rod case was used to test the robustness of MPACT, CTF, and BISON on a typical rod. The second problem is a quarter-core model based on WBN1, Cycle 3, with a single month of load-follow operations inserted into the middle of the cycle. This provides better understanding of the stresses to the clad during the numerous power changes. Sections of this article are adapted from the original, full technical report on this capability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗