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At least 559 records · Page 31

Charging, aggregation, and electrostatic dispersion of radioactive and nonradioactive particles in the atmosphere

Electrostatic dispersion can significantly impact the microphysical behavior of charged particles and ions until reaching zero space charge. However, although radioactive particles can be strongly charged in air, the influence of electrostatic dispersion has been neglected in understanding their behavior. This study is aimed at investigating the time evolution of the charge and size distributions of radioactive and nonradioactive particles in air and developing simple approaches for applications. With processes involving charging, aggregation, and electrostatic dispersion, a comprehensive population balance model (PBM) has been developed to examine particle charge/size distribution dynamics. It is shown that compared to nonradioactive particles, the charge and size distributions of radioactive particles may evolve differently with time because radioactivity and electrostatic dispersion can significantly affect the charging and aggregation kinetics of the particles. It is found that, after the Fukushima accident, background aerosols in the pathway of radioactive plumes might be highly charged due to ionizing radiation, suggesting that radiation fields may strongly influence in situ measurements of charged atmospheric particles. The comprehensive PBM is simplified, and then the verification and application of the simplified PBMs are discussed. This study provides useful insight into how radioactivity can affect the dynamic behavior of particles in atmospheric systems including radiation sources.

54 ENVIRONMENTAL SCIENCES↗

Synchronous High-frequency Distributed Readout For Edge Processing At The Fermilab Main Injector And Recycler

The Main Injector (MI) was commissioned using data acquisition systems developed for the Fermilab Main Ring in the 1980s. New VME-based instrumentation was commissioned in 2006 for beam loss monitors (BLM)[2], which provided a more systematic study of the machine and improved displays of routine operation. However, current projects are demanding more data and at a faster rate from this aging hardware. One such project, Real-time Edge AI for Distributed Systems (READS), requires the high-frequency, low-latency collection of synchronized BLM readings from around the approximately two-mile accelerator complex. Significant work has been done to develop new hardware to monitor the VME backplane and broadcast BLM measurements over Ethernet, while not disrupting the existing operations critical functions of the BLM system. This paper will detail the design, implementation, and testing of this parallel data pathway.

43 PARTICLE ACCELERATORS↗

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems

With the increased adoption of distributed energy resources (DER), they are expected to contribute to power system reliability services and enhance power system stability. This paper presents a distributed consensus control approach for the real-time active power reserve estimation and power management in distributed PV (photovoltaic) systems. The proposed method estimates the cumulative active power reserve from numerous PV generators using only the sparse communication network for a DER aggregator to provide active power regulation services. The real-time active power reserve estimation using the proposed approach can be used by DER aggregators to manage curtailment in distributed PV systems to maintain stipulated reserves and further to provide power system frequency regulation. Using the proposed approach, the DER aggregator only requires measurements at the feeder substation and it does not require information from individual DER units, thereby improving system resiliency. The proposed distributed consensus control method is validated on IEEE 123 testbed using PSCAD simulations.

active power reserve↗

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems: Preprint

With the increased adoption of distributed energy resources (DER), they are expected to contribute to power system reliability services and enhance power system stability. This paper presents a distributed consensus control approach for the real-time active power reserve estimation and power management in distributed PV (photovoltaic) systems. The proposed method estimates the cumulative active power reserve from numerous PV generators using only the sparse communication network for a DER aggregator to provide active power regulation services. The real-time active power reserve estimation using the proposed approach can be used by DER aggregators to manage curtailment in distributed PV systems to maintain stipulated reserves and further to provide power system frequency regulation. Using the proposed approach, the DER aggregator only requires measurements at the feeder substation and it does not require information from individual DER units, thereby improving system resiliency. The proposed distributed consensus control method is validated on IEEE 123 testbed using PSCAD simulations.

active power reserve↗

Evolution of the marker distribution in gyrokinetic $δf$ particle-in-cell simulations

The evolution of the particle weight in a δf particle-in-cell simulation depends on the marker distribution that can evolve in a turbulent field due to turbulent diffusion. When Monte Carlo methods are used to implement the test particle collision operator, or when the particle motion is not strictly Hamiltonian in a collisionless simulation, the marker distribution will evolve along the particle trajectory and, in general, cannot be known exactly. A two-dimensional numerical marker distribution is proposed as an approximation. It is shown to be advantageous over other common methods for evaluating the marker distribution in long-time turbulence simulations. A generalized two-weight δf-method is proposed to mitigate the marker evolution problem.

, Monte Carlo methods↗

Picosecond measurements of plastic scintillator pulse shapes from gamma-ray interactions

An understanding of the pulse shape of organic scintillators can provide insight into scintillation mechanisms and inform the selection of the optimal detector material for a given application. Although the timing properties of organic scintillators have been extensively studied, significant discrepancies persist in reported rise and decay times. New plastic scintillating media have also been developed in recent years for which no literature data exist. The goal of this work is to provide high-precision measurements of the pulse shape of a suite of fast plastic organic scintillators from Eljen Technology—EJ-200, EJ-204, EJ-208, EJ-230, EJ-232, and EJ-232Q (with 0.5% benzophenone)—under excitation from $γ$-ray sources. The contributors to the system temporal resolution were quantified, and the dominant source of uncertainty was identified as the determination of the start time of a scintillation event. A pulse shape model was applied to the reconstructed temporal distributions, and rise and decay times were extracted and compared with manufacturer specifications. This work outlines a measurement approach to obtain the pulse shape of organic scintillators in response to $γ$-rays with temporal resolution on the order of tens of picoseconds, yielding data required for modeling plastic scintillator based detection systems for a range of applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Precise Linker Length and Dynamic Bond Exchange Control Penetrant Diffusion in Dense Vitrimers

Polymer networks with dynamic covalent bonds have been investigated for their self-healing ability, recyclability, and potential as more sustainable materials. Recent results have indicated that in some cases, bond exchange can enhance the transport of penetrants in dense networks, pointing to their potential for separations of membranes. Here, imine dynamic bonds in ethylene oxide (EO) networks with precise linker lengths were synthesized to investigate the transport of N,N′-bis(2,5-di-tert-butylphenyl)-3,4,9,10-perylenedicarboximide (BTBP), a large, anisotropic dye molecule. Networks with mesh sizes smaller than, comparable to, and greater than the size of the penetrant axes were investigated to probe the effects of bond exchange and network confinement on transport. Mesh sizes, which ranged from 0.5 to 1.62 nm, were determined from shear rheology, glass transitions by calorimetry, and probe diffusion coefficients by fluorescence recovery after photobleaching. Permanent networks with identical EO chain lengths were prepared as control samples, and up to a 3 orders of magnitude increase in diffusion coefficient is observed in the dynamic systems for short linkers containing 13 backbone atoms. The longest linkers with 71 backbone atoms show no difference between the permanent and dynamic networks. Linkers shorter than 11 backbone atoms, corresponding to a mesh size smaller than the penetrant small axis, diffusion is no longer observable on the experimental time scale, indicating a sharp cutoff attributed to the precise linkers and narrow mesh size distribution. The dynamic imine exchange time scales were compared to the diffusive hopping times of penetrants and indicate that exchange can occur during a diffusive displacement. Furthermore, these findings provide insights into the factors affecting penetrant transport in dense polymers and inspire the development of next-generation selective polymer membranes.

Diffusion↗

An Operational Resilience Metric for Modern Power Distribution Systems

The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics alone do not provide adequate energy security. As a result, researchers have focused their efforts on a new set of metrics based on the concept of resilience to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose a real-time resilience metric for modern power distribution systems. The metric is an aggregation of the controllable assets adaptive capacity, or their temporal flexibility in real and reactive power. This metric gives information to the magnitude and duration of a disturbance which the system can respond to without having to drop loads or have stability issues in voltage or frequency. We demonstrate the impact to resilience in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages.

42 ENGINEERING↗

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, we, in this paper, propose a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Investigating Wireless Quantum Key Distribution for Advanced Reactor Communications

Remote operation of small modular reactor (SMR) facilities is an appealing prospect for streamlined operation costs, safety concerns, and user convenience. The ability to guarantee security of communication channels between offsite users and nuclear reactor facilities is critical to enabling remote operations, given the sensitive nature of reactor state data. This report proposes a configuration implementing quantum key distribution (QKD) and advanced encryption standard (AES) protocols to establish secure reactor data feeds. The model outlined, which utilizes the Argonne-developed SeQUeNCe software package and custom MATLAB code, determines free-space channel losses for microwave-band communications in a variety of weather conditions and simulates QKD via ground-satellite links. This report demonstrates the feasibility of repeatable, real-time key generation and distribution for this configuration towards enabling secure wireless reactor communications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Pore-scale simulation of drying in porous media using a hybrid lattice Boltzmann: pore network model

In this work, a hybrid method coupling a pseudo-potential lattice Boltzmann model (LBM) and a pore network model (PNM) to simulate drying in porous media is proposed. Based on the watershed method, the porous medium is firstly decomposed into pore regions. According to the liquid–vapor phase distribution at a given time, the pore regions are further divided into four pore types, namely two-phase pores where a liquid–vapor interface exists, buffer pores next to the two-phase pores, single-liquid and single-vapor phase pores. The pseudo-potential LBM is used in the two-phase and buffer pores to simulate liquid drying and track the movement of the interfaces, while the single-phase PNM simulations are conducted in the buffer and single-phase pores to simulate vapor or liquid flow. LBM and PNM are coupled in the buffer pores through exchange of boundary information. The hybrid method is applied to simulate liquid drying in a porous medium. The whole-domain LBM simulation is considered as the reference solution to validate the hybrid method. Liquid saturation variation during the drying process and detailed phase and pressure distributions obtained by the two methods match quite well, demonstrating the accuracy of the hybrid method. For the specific case studied, the hybrid method saves more than 60% computational time compared to the whole-domain LBM simulation. In addition, the speedup of the hybrid method becomes more significant for a larger computational domain. In summary, the hybrid method developed in this work combines the accuracy of LBM and the efficiency of PNM to simulate drying in porous media at pore scale and can lead to significant reduction of computation time, thus allowing the pore-scale consideration of drying in larger porous systems.

36 MATERIALS SCIENCE↗

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

We develop a tractable nonparametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. Employing the inferred reproductive rates, we estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

97 MATHEMATICS AND COMPUTING↗

Time Disciplined Non-PLL Active Synchronization for Grid Forming Inverters

Two major issues facing grid-forming inverters are synchronism and phase reference inaccuracies. Prior literature has addressed these problems with solutions such as disciplining the phase reference using GPS and active synchronization modes but these methods have not yet been integrated together. This paper serves to unite solutions and develop a means for an inverter to remain synchronized and grid-forming without phase reference inaccuracies through a novel time-disciplined active synchronization phase reference. Further, this work expands upon prior literature on active synchronization to include black-start capabilities. Finally, the time disciplined phase reference is evaluated in Simulink as a grid-forming inverter capable of any synchronization circumstance and assessed by key metrics from modern standards.

blackstart↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Using real-time nuclear activation detectors for measuring neutron yields from D(D, T)n reactions on the national ignition facility (NIF)

The National Ignition Facility (NIF) has 48 Real-Time Nuclear Activation Detectors distributed around the target chamber capable of measuring deuterium-triton reaction neutron yields with high precision. Here, in this work, we extend this functionality to deuterium–deuterium (DD) reaction neutrons using a nuclear reaction that occurs in the detector’s scintillator material. The corresponding decay of the activated material has a very short half-life of 5 s, which necessitates rapid data collection immediately following an experiment. In this regime, dead time can be very high (>50%) adding significant uncertainty to the measurement. To combat this, we have developed a dead time model that can self-consistently describe the measured data. Initial results show reasonable agreement (within 20%) with DD neutron yields from neutron time-of-flight spectrometers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Impact of survey spatial variability on galaxy redshift distributions and the cosmological 3 × 2-point statistics for the Rubin Legacy Survey of Space and Time (LSST)

We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Time (LSST). Specifically, we construct a mock photometry data set degraded by the Rubin OpSim observing conditions, and estimate photometric redshifts of the sample using a template-fitting photo-z estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold sample, defined as $i\lt 25.3$ for 10 yr LSST data, with an adjusted magnitude cut for each year and divide it into five tomographic redshift bins for the weak lensing lens and source samples. We quantify the change in the number of objects, mean redshift, and width of each tomographic bin as a function of the coadd i-band depth for 1-yr (Y1), 3-yr (Y3), and 5-yr (Y5) data. In particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of LSST, hence provide a worst-case scenario of the impact from non-uniformity. We find that these quantities typically increase with depth, and the variation can be $10\!-\!40~{{\rm per\ cent}}$ at extreme depth values. Using Y3 as an example, we propagate the variable depth effect to the weak lensing $3\times 2$ pt analysis, and assess the impact on cosmological parameters via a Fisher forecast. We find that galaxy clustering is most susceptible to variable depth, and non-uniformity needs to be mitigated below 3 per cent to recover unbiased cosmological constraints. There is little impact on galaxy–shear and shear–shear power spectra, given the expected LSST Y3 noise.

cosmology↗

The weathered bedrock vadose zone: A hidden control on water availability in the western United States (Final Technical Report)

This report summarizes the research completed during the project entitled "The weathered bedrock vadose zone: A hidden control on water availability in the western United States" (Award: DE-SC0018039). The report covers work completed between 9/15/17 and 9/14/20. The objective of the exploratory project work was to evaluate the overarching hypothesis that the subsurface underlying actively eroding landscapes is systematically structured as a result of a balance between weathering and erosion and that this structure leads to dynamic moisture storage within weathered rock. To accomplish project objectives, we characterized hillslope weathering profile structure and conducted subsurface moisture monitoring across four study sites with comparable bedrock types but differing erosion rate and hydroclimate: The Angelo Coast Range Reserve (ACRR), Sagehorn-Russell Ranch (SRR), Antelope Valley Ranch (AVR), and the LBNL Watershed Function Scientific Focus Area (SFA). New characterization and monitoring infrastructure at the AVR and SFA sites was established through this project and enables long-term monitoring of hydrologic and carbon fluxes at the sites for comparison with long-term monitoring in other sites. Deep drilling, downhole logging, and characterization of chemistry and porosity of rock recovered from drilling reveals two weathering fronts across the sites. A shallower front is marked by the extent of pervasive fracturing and oxidation of pyrite and organic carbon. A deeper weathering front is marked by the appearance of open fractures and discoloration. At the AVR site, multiple ridge tops of different hillslope length were investigated to establish the relationship between the weathering front and topography. Drilling reveals that the fraction of the hillslope relief that is weathered scales with hillslope length, consistent with the theoretical framework that guided the project relating groundwater drainage with landscape evolution. With the establishment of new boreholes, we have documented significant water storage in weathered bedrock using low-field borehole nuclear magnetic resonance (NMR) across all four sites. In the upper 4 m of bedrock, the average water contents at ACRR, AVR, SRR, and SFA are approximately 25%, 16%, 25%, 23% respectively. At the ACRR site, where long-term monitoring via neutron probe surveys and time-domain transmission sensors are available, we have demonstrated that NMR reliably records moisture content and importantly, moisture content dynamics in weathered bedrock. Across all sites, our NMR monitoring has also revealed that a considerable fraction of the water storage occurs in fractures or pores that are significantly larger than the fine-grained bedrock matrix of the parent rock. To identify the proportion of water storage occurring in fractures and large pores, we analyzed the NMR signal by evaluating both the sum of echoes and an inversion of the NMR signal for a distribution of T2 relaxation times. Both methods reveal that at least 20% of the water storage occurs in fractures and larger pores across all sites. To constrain the interpretation of the in-situ field-scale NMR measurements, we compare the NMR response in the field to independent pore-structure information obtained via helium pycnometry. Results support the inference that dynamic seasonal water storage is dominantly restricted to the fracture network, which can reach 7% of the total volume. This dynamic range in water content is comparable to that of some soils, underscoring the need to understand how this region of the Earth System is structured, and functions as a hydrologic reservoir.

58 GEOSCIENCES↗

A distributed voltage inference framework for cyber-physical attacks detection and localization in active distribution grids

The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.

active distribution grids↗