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Flux Synthesis of Lattice‐Engineered Rutile Solid Solutions for Acidic Oxygen Evolution
Developing efficient and stable electrocatalysts for the acidic oxygen evolution reaction (OER) is vital for advancing proton exchange membrane water electrolysis (PEMWE) technologies. Here, in this study, we report a flux synthesis of nitrogen-doped Ti–Ru rutile-type solid-solution oxides (M-TiRu 4 ) using molten NaNO 3 as the flux medium. The flux medium promotes the low-temperature conversion of TiN to rutile TiO 2 , while in situ-formed RuO 2 nanoparticles facilitate lattice templating and couple with interfacial ion migration, enabling the formation of homogeneous solid solutions with abundant lattice heterogeneity. Simultaneously, nitrogen atoms are stably incorporated into the lattice of solid solutions, inducing bandgap narrowing, which enhances electronic conductivity. The developed M-TiRu 4 catalyst exhibits exceptional acidic OER performance, delivering a low overpotential of 194 mV at 10 mA cm −2 , superior durability over 600 h, and a Ru mass activity 7.8 times that of commercial RuO 2 . At the device level, M-TiRu 4 enables PEMWE operation at 1.64 V @ 2 A cm −2 and maintains stable performance at 500 mA cm −2 for 200 h with a minimal degradation rate of 20 µV h −1 . This work demonstrates a robust approach for designing high-performance, durable acidic OER catalysts via synergistic lattice and electronic structure engineering, paving the way for next-generation water-splitting technologies.
Wave function methods for canonical ensemble thermal averages in correlated many-fermion systems
We present a wave function representation for the canonical ensemble thermal density matrix by projecting the thermofield double state against the desired number of particles. Furthermore, the resulting canonical thermal state obeys an imaginary-time evolution equation. Starting with the mean-field approximation, where the canonical thermal state becomes an antisymmetrized geminal power (AGP) wave function, we explore two different schemes to add correlation: by number-projecting a correlated grand-canonical thermal state and by adding correlation to the number-projected mean-field state. As benchmark examples, we use number-projected configuration interaction and an AGP-based perturbation theory to study the hydrogen molecule in a minimal basis and the six-site Hubbard model.
Snow ALbedo eVOlution (SALVO) Campaign Broadband Albedo from April - June, 2024 in Utqiagivk, AK level a1
A field-portable broadband (285 – 2800 nm) albedometer was used to make spatially distributed albedo measurements on tundra and sea ice surfaces. The albedometer consists of paired upward-looking and downward-looking pyranometers, which were both connected to a data logger. The instrument was mounted approximately 1 m above the surface using a tripod and was placed on a 1.4 m-long boom to minimize the impacts of shading from the operator and to observe surfaces undisturbed by footprints (see Appendix for photos of measurement setup and uncertainty assessment). Albedo measurements were taken parallel to the 200-m albedo lines at 5-m increments (41 measurements) ~1.2 m south of the line. On the operator’s end of the boom, there was a bubble level that was aligned with the bubble level on the upward-looking pyranometer. To take a measurement, the operator first relocated the tripod to the measurement location, then leveled the instrument and held it level for at least twice the pyranometers’ response time (5 or 15 seconds, see below), and finally depressed a trigger on the data logger. The data logger recorded the instantaneous voltage on both pyranometers, the measurement number, and the time. The data logger also converted the voltages to irradiances, and from these computed the ratio (outgoing/incoming) for albedo, which could be checked in the field. The operator recorded in a field notebook the measurement number that corresponded with the locations on the line and any pertinent notes (e.g., invalid measurements). With this setup, a trained operator could measure a 200-m albedo line (41 measurements) in approximately 30 minutes. Measurements were made within 3 hours of solar noon. The data logger had sufficient storage capacity to record all measurements from the campaign, but data were downloaded to a computer after each measurement day.
Electron Cloud Studies at Fermilab
The presence of unwanted electrons in an accelerator vacuum chamber, known as E-cloud (E-cloud) can potentially cause operational problems in the Fermilab Main Injector (MI) and Recycler Ring (RR). E-cloud has caused instability in the RR in the past, and although it is not currently a problem, there is measurable E-cloud in the MI accelerator. There are planned beam intensity increases due to upgrades of the Fermilab accelerator complex, so E-cloud could become a problem. Some work has been done by others previously to understand how low SEY (Secondary Electron Yield coefficient) coatings might mitigate production of E-cloud, and to model the mechanism whereby E-cloud causes beam instability. Using previous studies as a base, this research took several approaches toward understanding the risk of E-cloud at Fermilab. The evolution of the SEY of the SS316L (stainless steel), TiN coated SS316L, and amorphous carbon coated SS316L were measured in-situ using a SEY meas urement station in the MI tunnel. The SEY of these materials change over time either due to bombardment of the E-cloud, or disruption of vacuum conditions. The SEY evolution was tracked over a several year period to find out how long it takes for the SEY of each material to reach its lowest level, and how much the SEY rises during deconditioning periods of poor vacuum. The SEY measurement results can be used to determine whether the SS316L will be a problem at upgrade intensities, and if so, whether or not TiN and A-Carbon coatings can mitigate E-cloud related problems sufficiently. Direct measurements of the E-cloud were done as well, and compared to simulation. The E-cloud bombardment rate was measured at different beam intensities and bunch lengths. It was possible to get detailed information on how the E-cloud varies over the acceleration cycle, where sensitivity to bunch length is reflected in the evolution of the E-cloud. The Retarding Field Analyzer (RFA) measuring th e E-cloud bombardment rate was near the instrument that is u! sed to measure the SEY of the beam pipe material. This proximity provided an accurate SEY value for simulations, so that the simulated E-cloud bombardment rate could be better compared to measurement. Bunch length measurements and computations generated accurate bunch length information also needed as input for simulations. After this careful control of the input parameters, the POSINST simulations of E-cloud were a good match to direct measurements. This gave confidence that predictions could be made concerning the E-cloud densities at upgrade intensities. These densities were compared against corresponding proton densities to predict the SEY required to avoid instabilities. That prediction and the information provided by the SEY measurements provide helpful information regarding the risks of E-cloud effects at future beam intensities at Fermilab.
Fast-forwarding quantum evolution
We investigate the problem of fast-forwarding quantum evolution, whereby the dynamics of certain quantum systems can be simulated with gate complexity that is sublinear in the evolution time. We provide a definition of fast-forwarding that considers the model of quantum computation, the Hamiltonians that induce the evolution, and the properties of the initial states. Our definition accounts for any asymptotic complexity improvement of the general case and we use it to demonstrate fast-forwarding in several quantum systems. In particular, we show that some local spin systems whose Hamiltonians can be taken into block diagonal form using an efficient quantum circuit, such as those that are permutation-invariant, can be exponentially fast-forwarded. We also show that certain classes of positive semidefinite local spin systems, also known as frustration-free, can be polynomially fast-forwarded, provided the initial state is supported on a subspace of sufficiently low energies. Last, we show that all quadratic fermionic systems and number-conserving quadratic bosonic systems can be exponentially fast-forwarded in a model where quantum gates are exponentials of specific fermionic or bosonic operators, respectively. Our results extend the classes of physical Hamiltonians that were previously known to be fast-forwarded, while not necessarily requiring methods that diagonalize the Hamiltonians efficiently. We further develop a connection between fast-forwarding and precise energy measurements that also accounts for polynomial improvements.
Improving Schrödinger Equation Implementations with Gray Code for Adiabatic Quantum Computers
We reformulate the continuous-space Schrödinger equation in terms of spin Hamiltonians. For the kinetic energy operator, the critical concept facilitating the reduction in model complexity is the idea of position encoding. A binary encoding of position produces a spin-1/2 Heisenberg-like model and yields exponential improvement in space complexity when compared to classical computing. Encoding with a binary reflected Gray code (BRGC), and a Hamming-distance-2 Gray code (H2GC) reduces the model complexity down to the 𝑋𝑍 and transverse Ising model, respectively. For 𝐴 qubits BRGC yields 2 𝐴 positions and is reduced to its 2-local form with O(𝐴) ancillary qubits. H2GC yields 2 𝐴/2+1 positions with O(𝐴 2 ) three-local penalty terms. We also identify the bijective mapping between diagonal unitaries and the Walsh series, producing the mapping of any real potential to a series of 𝑘 -local Ising models through the fast Walsh transform. Finally, in a finite volume, we provide some numerical evidence to support the claim that the total time needed for adiabatic evolution is protected by the infrared cutoff of the system. As a result, initial state preparation from a free-field wave function to an interacting system is expected to exhibit polynomial time complexity with volume and constant scaling with respect to lattice discretization for all encodings. For H2GC, if the evolution starts with the transverse Hamiltonian due to hardware restrictions, then penalties are dynamically introduced such that the low-lying spectrum reproduces the energy levels of the Laplacian. The adiabatic evolution of the penalty Hamiltonian is therefore sensitive to the ultraviolet scale. It is expected to exhibit polynomial time complexity with lattice discretization, or exponential time complexity with respect to the number of qubits given a fixed volume.
Feed the fish: A review of aquaculture feeders and their strategic implementation
Automatic feeders are revolutionizing aquaculture by enabling precise and controlled feeding regimens that optimize growth, reduce feed waste, and enhance the overall efficiency of farming operations. Demand feeders provide some similar benefits but with feeding tailored to fish appetites and, in some cases, feed dispersal without electricity. This review explores the evolution, technological advancements, and applications of these feeders with a survey of recent research. It looks at common feeder designs and compares the pros and cons of five automatic feeder configurations in commercial use today. The article contrasts feeders and feed sizes for farmed species in different life stages and facilities, from hatcheries to ponds and offshore cages. It discusses the importance of species-tailored feeding strategies as well as impacts on fish behavior and health, nutrient delivery, and environmental sustainability. Incorporating sensors and on-site computing enables optimization in feeding algorithms that feeders then implement precisely and reliably. The review compiles results from multiple studies comparing growth rate, weight, and feed conversion ratio (FCR) outcomes from automatic feeders and demand feeders versus traditional hand-feeding regimens. The integration of feeders with real-time monitoring and data analytics is also discussed, highlighting future directions for research and adoption across the industry.
Simulation of Divertor Performance in ST40 Under Dynamic Double-Null Plasmas
A power fraction model was implemented for the simultaneous prediction of 3-D surface temperature evolution at all four divertor targets in near-double-null (DN) tokamak configurations, which is especially important for compact high-field devices that may not have the ability to dissipate large amounts of power on the high-field side. Evaluating the power-sharing between the four divertor strike points in a disconnected DN configuration is important for understanding the overall power balance, as well as for optimizing the power exhaust performance and prolonging the survivability of the plasma facing components (PFCs). This power-sharing is typically evaluated in terms of the separation between the primary and secondary separatrices at the outboard midplane, $\textit {dR}_{\text {sep}}$. The Heat flux Engineering Analysis Toolkit (HEAT) is coupled with Brunner’s power fraction model to simulate the deposited heat flux and resultant temperature change on 3-D divertor targets in a dynamic DN (DDN) pulse operation in ST40, a high-field spherical tokamak. The simulation results showed that with DDN operation, the operation time has significantly increased compared with single-null geometry configurations.
Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models
One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.
Overview of the ultrasonic instrumentation research in the MYRRHA project
The Belgian Nuclear Research Centre SCK.CEN is in the process of developing MYRRHA, a new generation IV fast flux research reactor to replace the aging BR2. MYRRHA is conceptualized as an accelerator driven system cooled with lead bismuth eutectic mixture (LBE). As LBE is opaque to visual light, ultrasonic measurement techniques are employed as the main technology to provide feedback where needed. This paper we will give an overview of the R and D at SCK.CEN with respect to ultrasonic instrumentation in heavy liquid metals. High temperature ultrasonic transducers are deployed into the reactor to generate and receive the required ultrasonic signals. The ultrasonic waves are generated and sensed by means of a piezo-electric disc at the heart of the transducer. The acoustic properties of commonly used piezo-electric materials match rather well with the acoustic properties of heavy liquid metals, simplifying the design and construction of high bandwidth ultrasonic transducers for use in heavy liquid metals. The ultrasonic transducers will operate in a liquid metal environment, where radiation and high temperature limit the choice of materials for construction. Moreover, the high surface tension of the liquid metal hinders proper wetting of the transducer, required for optimal transmission and reception of the ultrasonic waves. In a first part of the paper, we will discuss the effect of these parameters on the performance of the overall ultrasonic system. In the second part of the paper, past, present and future ultrasonic experiments in LBE will be reviewed. We will show the results of an experiment where a transducer is scanned near the free surface of an LBE pool to render ultrasonic images of objects submerged in the heavy liquid metal. Additionally, the preliminary results of an ongoing experiment that measures the evolution of LBE wetting on different types of metals and various surface conditions will be reported. The evolution of wetting is an important parameter to assess the performance of ultrasonic systems that are submerged in LBE over longer time periods. In the last part we will briefly present the overall design of the two main ultrasonic systems that will be deployed in MYRRHA: the ultrasonic fuel identification system and the ultrasonic lost fuel localization system. (authors)
Final Targeting Strategy for the Sloan Digital Sky Survey IV Apache Point Observatory Galactic Evolution Experiment 2 North Survey
The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is a dual-hemisphere, near-infrared (NIR), spectroscopic survey with the goal of producing a chemodynamical mapping of the Milky Way. The targeting for APOGEE-2 is complex and has evolved with time. In this paper, we present the updates and additions to the initial targeting strategy for APOGEE-2N presented in Zasowski et al. (2017). These modifications come in two implementation modes: (i) “Ancillary Science Programs” competitively awarded to Sloan Digital Sky Survey IV PIs through proposal calls in 2015 and 2017 for the pursuit of new scientific avenues outside the main survey, and (ii) an effective 1.5 yr expansion of the survey, known as the Bright Time Extension (BTX), made possible through accrued efficiency gains over the first years of the APOGEE-2N project. For the 23 distinct ancillary programs, we provide descriptions of the scientific aims, target selection, and how to identify these targets within the APOGEE-2 sample. The BTX permitted changes to the main survey strategy, the inclusion of new programs in response to scientific discoveries or to exploit major new data sets not available at the outset of the survey design, and expansions of existing programs to enhance their scientific success and reach. After describing the motivations, implementation, and assessment of these programs, we also leave a summary of lessons learned from nearly a decade of APOGEE-1 and APOGEE-2 survey operations. Finally, a companion paper, F. Santana et al. (submitted; AAS29036), provides a complementary presentation of targeting modifications relevant to APOGEE-2 operations in the Southern Hemisphere.
Determination of a Generic Human Error Probability Distribution, Part 1: A Dynamic Formulation of SPAR-H
In almost all major nuclear power plants (NPP) events include a significant human error component. As an example, in Fukushima Daiichi (2011) nuclear meltdown, the human actions related to the high-pressure injection cooling strongly affected accident evolution. Furthermore, in the Three Mile Island accident (1979) (which was initiated by a stuck open valve and thus was triggered a loss of coolant accident) the unit operators made decisions based upon false assumptions that contributed negatively to the accident. Given these premises, the ability to accurately model human interactions with plant evolution and, in particular, dynamically calculate human error probability (HEP) plays a major role in a probabilistic risk analysis (PRA). Human reliability analysis (HRA) identifies and models the causes of human errors, and quantifies HEP by applying different methodologies. HRA methods identify errors and reflect on the understanding of error-prone people and systems. There are a number of HRA methods available that assist in identifying the vulnerabilities in the system and provide guidance in determining the HEP. HEP is the probability of a human failure event (HFE), and quantification of HEPs under various situations is utilized in PRA of large complex systems. One of the more prominent HRA methods is the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method that was developed to estimate HEPs for the use in the Standardized Plant Analysis Risk (SPAR) PRA models employed in U.S. NPPs. SPAR-H is implemented in U.S. NPPs by regulators at the U.S. Nuclear Regulatory Commission (NRC) to determine the HEP based on expert estimation through the use of calculation worksheets. In SPAR-H, the nominal HEPs (NHEPs) are multiplied by performance shaping factors (PSF) multiplier values to obtain the overall error probability. PSFs encompass the human components, individual characteristics, their environment, and aspects of the task. With PSFs, the goal is to capture all factors that can affect human performance. In SPAR-H there are eight PSFs used, each having respective levels corresponding to multipliers: Available Time, Stress/Stressors, Complexity, Experience/Training, Procedures, Ergonomics/Human Machine Interface, Fitness for Duty, and Work Processes. The PSF multipliers indicate enhanced, nominal, or degraded human performance. The SPAR-H method estimates the HEP through multiplying a NHEP with PSFs. Hence, SPAR-H is a traditional HRA method that assesses human performance at a fixed, or static, point in time. In a dynamic HRA framework, human performance is assessed on a continuous time basis. SPAR-H can be used within a dynamic PRA framework through the use of simulation and stochastic models. The goal of dynamic HRA is to build a more realistic model of the human-system interaction to enhance calculation of reliability, risk and safety analysis. The progression of events in dynamic SPAR-H considers that some PSFs may experience gradual change, while other change can be sudden due to individualistic differences or rapid plant status changes. The PSF changes are caused by contextual changes in the scenario. For example, as more time passes, the realization that a task should be completed sooner may cause the operator more stress. In another example, a sudden relay of information about sickness of a family member may alter the operator's general stress level and fitness for duty. Each PSF has a different dynamic nature; PSFs like stress and complexity change more quickly over the course of a task than experience and training. While the experience of an operator changes over the course of attaining the objective, the amount that this added experience changes the HEP is very minimal. In contrast, the stress, complexity, and procedures PSFs may change quite a bit over the course of task completion.
Investigation of grid-based vorticity-velocity large eddy simulation off-body solvers for application to overset CFD
Accurately predicting unsteady wakes and vortex-dominated flows is essential to a wide range of engineering applications, including aircraft, rotorcraft, shipboard operations, bio-inspired unsteady flight and propulsion, wind turbines, and urban flows. While current CFD software can model the complete flow field and wake system, the computational costs incurred in high Reynolds number unsteady turbulent flow simulations often remain prohibitive for routine engineering use, particularly for applications involving moving components. Prior work has demonstrated that by adopting a vorticity-velocity formulation in a grid-based off-body flow solver (VorTran-M and VorTran-M2) one can lower these costs by several orders of magnitude when compared to conventional approaches. This paper describes the extensions made to VorTran-M2 to support turbulent flows, and associated benchmarking activity to assess its performance for problems involving strong stretching and diffusion processes, whose competing contributions to the vorticity field are core drivers of turbulent flow evolution. Predictions are presented for: (i) the Kida-Pelz problem whose inviscid form is of mathematical interest due to its apparent formation of singular flow in finite time; and (ii) the Taylor Green vortex arrangement, which has been extensively studied as a fundamental simulation challenge in the turbulent modeling community. Here, the results are used to evaluate the overall predictive ability and performance of two sub-grid scale models incorporated into VorTran-M2. Results indicate that the computational cost savings seen previously for inviscid and convection dominated problems extend to turbulent flow simulations supporting the viability of VorTran-M2 as a low cost means for accurately modeling the far-field and background flow, particularly when long duration vorticity evolution is of interest.
The quenching of galaxies, bulges, and disks since cosmic noon
Here, we present an analysis of the quenching of star formation in galaxies, bulges, and disks throughout the bulk of cosmic history, from z = 2 – 0. We utilise observations from the Sloan Digital Sky Survey and the Mapping Nearby Galaxies at Apache Point Observatory survey at low redshifts. We complement these data with observations from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey at high redshifts. Additionally, we compare the observations to detailed predictions from the LGalaxies semi-analytic model. To analyse the data, we developed a machine learning approach utilising a Random Forest classifier. We first demonstrate that this technique is extremely effective at extracting causal insight from highly complex and inter-correlated model data, before applying it to various observational surveys. Our primary observational results are as follows: at all redshifts studied in this work, we find bulge mass to be the most predictive parameter of quenching, out of the photometric parameter set (incorporating bulge mass, disk mass, total stellar mass, and B/T structure). Moreover, we also find bulge mass to be the most predictive parameter of quenching in both bulge and disk structures, treated separately. Hence, intrinsic galaxy quenching must be due to a stable mechanism operating over cosmic time, and the same quenching mechanism must be effective in both bulge and disk regions. Despite the success of bulge mass in predicting quenching, we find that central velocity dispersion is even more predictive (when available in spectroscopic data sets). In comparison to the LGalaxies model, we find that all of these observational results may be consistently explained through quenching via preventative ‘radio-mode’ active galactic nucleus feedback. Furthermore, many alternative quenching mechanisms (including virial shocks, supernova feedback, and morphological stabilisation) are found to be inconsistent with our observational results and those from the literature.
Assessing Impacts on Pressure Stabilization and Leasing Acreage for CO 2 Storage Utilizing Oil Migration Concepts
Favorable geological storage for CO 2 has long been pictured as large anticlines with thick sandstones, similar to oil reservoirs in the petroleum system. Unlike oil, however, stored CO 2 does not need to be recoverable, which raises the possibility of using dissolution and residual trapping to augment the capacity of buoyant traps and tap more of the bulk rock volume. The work presented builds on that idea, asking the following question: If we inject CO 2 down to a syncline – analogous to the carrier bed in the petroleum system – how would this injection mechanism impact storage capacity and plume shape, migration, and stabilization? To address this question, we built a reservoir model, based on seismic interpretation of Middle Miocene strata, offshore Galveston, Texas. 3-D seismic and well logs were used to characterize key intervals. Reservoirs chosen for modeling are progradational-aggradational sands with mud intercalation. They have a higher degree of heterogeneity than the more conventional reservoirs commonly targeted for CO 2 storage. Modeling investigated how far the CO 2 plume would migrate under two scenarios: (1) injecting CO 2 at the base of the salt withdrawal basin (syncline scenario) and (2) injecting CO 2 at the base of the structural closure, similar to a common injection well location for EOR purposes (base scenario). For each scenario, we separately simulated injection of 30 MT of CO 2 and 60 MT of CO 2 continuously for 30 years and observe the plume and pressure evolution for 100 years after the injection stops. The simulation shows that injecting the CO 2 into a syncline limits the vertical migration of CO 2 , thus making synclinal injection more secure. In the syncline scenario, the geological layer around the injection point is more heterogeneous than the layer in the base scenario; thus, the CO 2 tends to migrate laterally. Additionally, in the syncline scenario, the plume does not even reach the upper part of the anticline, allowing us to safely store an additional amount of CO 2 into the reservoir. Furthermore, the simulation also shows that in the syncline scenario, the times needed for the reservoir to reach its stabilized pressure after the end of injections are faster. To summarize, CO 2 injection at the base of a syncline could provide additional storage, increase the safety of the project from the limited vertical plume migration, and expedite plume stabilization, which could result in the decrease of monitoring frequency as the project runs, thus lowering the operating cost of the project in the long run.
High-Temperature Gas-Cooled Pebble-Bed Reactors Running In And Transient Modeling Capabilities Demonstration
This study presents a comprehensive benchmarking and verification effort of several thermal-hydraulic and multiphysics capabilities for high-temperature gas-cooled reactor (HTGR) applications. The first part of this effort focuses on the running-in verification of Griffin's multiphysics capabilities, specifically for simulating the evolution of Pebble Bed reactor cores from startup to equilibrium. In the absence of validation data, code-to-code comparisons are conducted with Kugelpy, showing good agreement for key quantities like maximum power density and fresh core k-eigenvalue predictions. However, discrepancies in equilibrium core predictions suggest potential issues with cross sections, underscoring the need for further refinement and evaluation. The HTTF system analysis code benchmark involves RELAP5-3D, SAM, and GAMMA+ to assess their predictive capabilities for HTTF behavior under both normal operation and pressurized conduction cooldown (PCC) transient conditions. While there is good agreement in predicting major parameters such as coolant temperature, solid temperature, and flow distribution, discrepancies in transient behavior highlight differences in modeling approaches, nodalizations, and heat transfer models. The HTTF lower plenum CFD benchmark employs nekRS to simulate flow mixing phenomena, successfully capturing relevant flow physics and demonstrating mesh independence in complex geometries. Preliminary results suggest a relatively uniform temperature field but significant unsteadiness in the flow, requiring time-averaging analyses. The GPBR200 system analysis code benchmark uses SAM's core channel and porous media models, incorporating an RCCS loop for decay heat removal. During steady-state and transient conditions, including protected de-pressurized and pressurized loss of forced cooling (DLOFC and PLOFC), both models show good agreement in predicting temperature profiles and key parameters. Notably, while the core channel model underpredicts convective heat transfer effects, both models maintain temperatures well below the TRISO fuel safety limit. These benchmarking efforts collectively enhance the predictive capabilities of the tools used in HTGR design and safety analysis, guiding developments to improve their accuracy and applicability.
Nominal and adversarial synthetic PMU data for standard IEEE test systems
GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.