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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Best Practices for Shielding Analyses of Activated Metals and Spent Resins from Reactor Operation

This report investigated best practices for performing shielding evaluations of Type B waste packages, as defined in 10 CFR Part 71 regulations on packaging and transportation of radioactive material, or packages for which the contents are not defined before loading and may include a broad range of nuclides, geometries, and non-fuel materials. The following non-fuel waste streams were analyzed: Activated metals from decommissioned commercial power reactors, including Type 304 stainless steel, reactor vessel steel, and Inconel, Control blades from boiling water reactors, Neutron-activated corrosion products on surfaces of activated metals, and Spent resins from power plant operations. Measured elemental compositions, including major constituents and impurities, for steel and Inconel samples from commercial power reactors were used in activation calculations to determine radionuclide inventories in activated metals. For a simplified cask model, 60 Co contribution to the total external package dose rate at 30 days after shutdown varied from approximately 60% to 95%, depending on the activated metal, initial cobalt impurity concentration in the metal, and the thickness of the overpack gamma shield. Its maximum contribution to the total external package dose rate of approximately 100% was reached within the time interval of 2 to 5 years after shutdown and was maintained for up to 45 to 60 years after shutdown, depending on material, initial cobalt impurity concentration, and shield thickness. Thereafter, the 60 Co contribution to external package dose rate decreased with increasing decay time. Cobalt-60 is primarily produced by neutron reactions with the cobalt impurity in steel and Inconel. Other important radionuclides in activated metals contributing to package external dose rate are radionuclides with relatively short decay times, including 51 Cr, 59 Fe, 58 Co, and 54 Mn. These radionuclides may be represented as an equivalent 60 Co activity/source because 60 Co gamma ray emissions are bounding in terms of source strength and energy to other important radionuclides identified in the analyzed activated metals. Approaches for modeling the neutron-activated corrosion products that may be attached to activated reactor components were analyzed in this report. It was demonstrated that a surface source is more conservative than a uniform volumetric source for the treatment of neutron-activated corrosion products with respect to external package dose rates. An analysis of the maximum radionuclide loadings reported on spent resins identified the radionuclides 137 Cs, 60 Co, 134 Cs, 65 Zn, and 58 Co as the primary contributors to external package dose rate. For a resin cooled for 3.08 years, the external package dose rate was entirely produced by the reported 137 Cs and 60 Co inventory. The neutron sources from actinides found on spent resins or activated metals produced negligible dose rates and may be ignored in dose rate analyses. Effects of idealized waste material, source geometry, and spatial material/source distributions on external package dose rates were determined based on dose rate results for a simplified cask model under normal conditions of transport. Type 304 stainless steel, zirconium, and aluminum with adjusted mass densities based on a maximum content weight were analyzed for material modeling effects on external package dose rate. These materials produced identical external package dose rates within the statistical uncertainties of the dose rate estimates. Among four different source geometry configurations with homogeneous material of different mass densities, uniform volumetric source distribution, and the same source strength, the geometry configuration with lowest mass density (i.e., minimum self-shielding effect among the four cases) was most conservative. Spatial source distributions that better represent localized peak 60 Co activity values were more conservative than a uniform volumetric source distribution, assuming the same weight and total source strength per package. The increase in external dose rate caused by localized source peaks can be as much as the ratio between source peak activity density to average activity density, depending on the location of the activated metal with peak activity density. Therefore, the shielding analysis may be simplified if localized peak activities and the average activity per package can be measured/determined and documented at the time of cask loading. For simplicity, external package dose rates may be determined based on average source activity and a uniform volumetric source distribution. The dose rate results from that calculation model multiplied by the ratio of peak activity density to average activity density will produce maximum dose rate values for conservative estimates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Sources of Conservatism in Making Corrective Action Decisions at the NNSS - 20477

During the course of closing nuclear testing release sites at the Nevada National Security Site (NNSS) under the Federal Facility Agreement and Consent Order (FFACO) using the Risk-Based Corrective Action (RBCA) evaluation process, conservative assumptions and estimates were used in many phases of the work. These assumptions and estimates were agreed to during Data Quality Objective (DQO) meetings and published in the FFACO documents. However, the potential cumulative effect of these multiple sources of conservatism was not evaluated. The RBCA process ensures that corrective action decisions minimize the risk of making Type I errors (not applying corrective action to areas that exceed action levels) at the expense of making Type II errors (applying corrective action to areas that do not exceed action levels). Rather than fully characterizing the spatial distribution of contaminants throughout each site, the RBCA process uses a bounding methodology that conservatively estimates spatial boundaries beyond which contamination is not present that exceed corrective action levels. This process accepts that these boundaries may encompass areas that do not exceed corrective action levels and, therefore, may be somewhat larger than for fully characterized sites. This is justified at sites where the reduction in investigation costs are more than any potential increase in corrective action costs. To minimize the risk of making Type I errors, conservatism was used throughout the process. This study documents the many sources of conservatism used in the RBCA process. Conservatism was used in the RBCA process in determining potential receptors, determining action levels, collecting samples, converting sample results into receptor dose, and accounting for uncertainty. This provides decision-makers with a high level of confidence that unacceptable risk to receptors will not result from exposure to residual contamination outside the corrective action boundaries. However, it does not provide an estimate of the actual extent of areas exceeding action levels. This study provides an evaluation of the effects of compounding conservatism on RBCA process decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Distribution Grid Modeling Using Smart Meter Data

The knowledge of distribution grid models, including topologies and line impedances, is essential for grid monitoring, control and protection. However, such information is often unavailable, incomplete or outdated. The increasing deployment of smart meters (SMs) provides a unique opportunity to tackle this issue. This paper proposes a two-stage framework for distribution grid modeling using SM data. In the first stage, the network topology is identified by reconstructing a weighted Laplacian matrix of distribution networks. In the second stage, a least absolute deviations (LAD) regression model is developed for estimating line impedance of a single branch based on the nonlinear (inverse) power flow model, wherein a conductor library is leveraged to narrow down the solution space. The LAD regression model is originally a mixed-integer nonlinear program whose continuous relaxation is still non-convex. Furthermore, we specially address its convex relaxation and discuss the exactness. The modified regression model is then embedded within a bottom-up sweep algorithm to achieve the identification across the network in a branch-wise manner. Numerical results on the IEEE 13-bus, 37-bus and 69-bus test feeders validate the effectiveness of the proposed methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $\sqrt{s}=13\hbox{TeV}$ proton–proton collision data

A search for R-parity-violating supersymmetry in final states characterized by high jet multiplicity, at least one isolated light lepton and either zero or at least three b-tagged jets is presented. The search uses 139fb –1 of $\sqrt{s}=13\hbox{TeV}$ proton–proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the b-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine-learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine-learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

The Age of Evapotranspiration: Lower‐Bound Constraints From Distributed Water Fluxes Across the Continental United States

Abstract Unlike streamflow, which can be sampled in aggregate at the catchment outlet, evapotranspiration (ET) is spatially dispersed, challenging large‐scale age estimation. Here, we introduce an approach for constraining the age of ET via mass balance and present the minimum flux‐weighted age of ET across the continental United States using distributed, publicly available water flux data sets. The lower‐bound constraint on ET age can be calculated by assuming that ET is preferentially sourced from the most recent precipitation through a last‐in, first‐out algorithm. From 2012 to 2017, ET was at least several months old across large areas of the western continental United States, including in Mediterranean and (semi‐)arid climate zones and shrub and evergreen needleleaf plant communities. The primary limitation of this approach is that it provides only a minimum flux‐weighted average age to satisfy the mass balance of outgoing fluxes; true ET fluxes are composed of distributions of ages and may be composed of much older water. The primary advantage of the approach is that flux time series of precipitation and ET are sufficient to constrain ET age, and model parameterization is unnecessary. ET ages can be used to validate tracer‐aided and modeling approaches and inform studies of biogeochemistry, water‐rock interactions, and plant water sourcing under drought.

Hahm, W. J.↗
DOE OSTI2022

Anticipating water distribution service outages from increasing temperatures

With projected temperature increases and extreme events due to climate change for many regions of the world, characterizing the impacts of these emerging hazards on water distribution systems is necessary to identify and prioritize adaptation strategies for ensuring reliability. To aid decision-making, new insights are needed into how water distribution system reliability to climate-driven heat will change, and the proactive maintenance strategies available to combat failures. To this end, we present the model Perses, a framework that joins a water distribution network hydraulic solver with reliability models of physical assets or components to estimate temperature increase-driven failures and resulting service outages in the long term. A theoretical case study is developed using Phoenix, Arizona temperature profiles, a city with extreme temperatures and a rapidly expanding infrastructure. By end-of-century under hotter futures there are projected to be 1%–5% more pump failures, 2%–5% more PVC pipe failures, and 3%–7% more iron pipe failures (RCP 4.5–8.5) than a baseline historical temperature profile. Service outages, which constitute inadequate pressure for domestic and commercial use are projected to increase by 16%–26% above the baseline under maximum temperature conditions. The exceedance of baseline failures, when compounded across a large metro region, reveals potential challenges for budgeting, management, and maintenance. An exploration of the mitigation potential of adaptation strategies shows that expedited repair times are capable of offsetting the additional outages from climate change, but will come with a cost.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

KaRMMa – kappa reconstruction for mass mapping

ABSTRACT We present KaRMMa, a novel method for performing mass map reconstruction from weak-lensing surveys. We employ a fully Bayesian approach with a physically motivated lognormal prior to sample from the posterior distribution of convergence maps. We test KaRMMa on a suite of dark matter N-body simulations with simulated DES Y1-like shear observations. We show that KaRMMa outperforms the basic Kaiser–Squires mass map reconstruction in two key ways: (1) our best map point estimate has lower residuals compared to Kaiser–Squires; and (2) unlike the Kaiser–Squires reconstruction, the posterior distribution of KaRMMa maps is nearly unbiased in all summary statistics we considered, namely: one-point and two-point functions, and peak/void counts. In particular, KaRMMa successfully captures the non-Gaussian nature of the distribution of κ values in the simulated maps. We further demonstrate that the KaRMMa posteriors correctly characterize the uncertainty in all summary statistics we considered.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Converting sWeights to probabilities with density ratios

The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The sPlot formalism is a common tool used to isolate distributions from different sources. However, the negative sWeights produced by the sPlot technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert sWeights to event probabilities, which we call drWeights. The drWeights can then be applied to produce the distributions of interest and are consistent with direct use of the sWeights. This article will also show how decision trees are particularly well suited to convert sWeights, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial drWeights are reweighted by an additional converter gives substantially better results.

Glazier, Derek I. [Univ. of Glasgow, Scotland (Uni↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Measurement of $d^2 \sigma/d|\vec{q}|d E_{avail}$ and 2p2h contribution using charged current $\nu_\mu$ interactions in the NOvA Near Detector

This Thesis presents the analysis methods and measurement of the $d^2 \sigma/d|\vec{q}|d E_{avail}$-CC inclusive cross section that describes neutrino scattering in a predominantly hydrocarbon medium. Studies that are prerequisite for the analysis including, variable determinations, event selections, efficiency, purity determinations, and systematic uncertainty estimations, are summarized. Tests of the analysis are also detailed, and shown to reproduce the input distributions. The double-differential cross-section measurement provides the foundation for determination of the inclusive rate for the excess induced by 2p2h processes together with nuclear medium effects that are not described by the Fermi gas model. The methodology for the extraction of this rate, defined as the excess observed relative to the rate estimated for known single-nucleon interactions, is described in detail. The following new measurements are reported in this Thesis: (1) The $\nu_\mu-$CC inclusive double-differential cross section as a function of three-momentum transfer and available hadronic energy, for an average $E_\nu$ of 1.8 GeV, is obtained. The double-differential cross section value that obtains when the bin widths are taken to be the dimensions of the entire analysis domain, e.g. $0.2 \le |\vec{q}| \le 2.0 GeV/c$ and $0.0 \le E_{avail} \le 2.0$ GeV, is inclusive cross section is $(6.05 \pm 0.75)\times10^{-39}$ cm$^2$/GeV/GeV/$c$/nucleon. An event excess attributed to 2p2h-MEC processes is observed; most of the rate occurs in a contiguous phase space region $0.3 \le |\vec{q}| \le 1.0$ GeV$/c$ $0.0 \le E_{avail} \le 0.35$ GeV. This rate represents $12.0\pm 6.5\%$ of the observed CC inclusive cross section; its cross section ratio relative to CCQE scattering is estimated to be $44.3 \pm 23.9\%$.

Olson, Travis Grant↗
DOE OSTI2024

Climate change-resilient snowpack estimation in the Western United States

Abstract In the 21st century, warmer temperatures and changing atmospheric circulation will likely produce unprecedented changes in Western United States snowfall 1–3 , with impacts on the timing, amount, and spatial patterns of snowpack 4–7 . The ~900 snow pillow stations are indispensable to water resource management by measuring snow-water equivalent (SWE) 8,9 in strategic but fixed locations 10,11 . However, this network may not be impacted by climate change in the same way as the surrounding area 12 and thus fail to accurately represent unmeasured locations; climate change thereby threatens our ability to measure the effects of climate change on snow. In this work, we show that maintaining the current peak SWE estimation skill is nonetheless possible. We find that explicitly including spatial correlations—either from gridded observations or learned by the model—improves skill at predicting distributed snowpack from sparse observations by 184%. Existing artificial intelligence methods can be useful tools to harness the many available sources of snowpack information to estimate snowpack in a nonstationary climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Investigation of Poorly Studied Open Cluster NGC 4337 Using Multicolor Photometric and Gaia DR2 Astrometric Data

We present a comprehensive analysis (photometric and kinematical) of the poorly studied open cluster NGC 4337 using 2MASS, WISE, APASS, and Gaia DR2 databases. By determining the membership probabilities of stars, we identified the 624 most probable members with membership probability higher than 50% by using proper motion and parallax data taken from Gaia DR2. The mean proper motion of the cluster is obtained as μ{sub x}=−8.83±0.01 and μ {sub y} = 1.49 ± 0.006 mas yr{sup −1}. We find the normal interstellar extinction toward the cluster region. The radial distribution of members provides a cluster radius of 7.′75 (5.63 pc). The estimated age of 1600 ± 180 Myr indicates that NGC 4337 is an old open cluster with a bunch of red giant stars. The overall mass function slope for main-sequence stars is found as 1.46±0.18 within the mass range 0.75−2.0 M{sub ⊙}, which is in fair agreement with Salpeter’s value (x = 1.35) within uncertainty. The present study demonstrates that NGC 4337 is a dynamically relaxed open cluster. Using the Galactic potential model, Galactic orbits are obtained for NGC 4337. We found that this object follows a circular path around the Galactic center. Under the kinematical analysis, we compute the apex coordinates (A, D) by using two methods: (i) the classical convergent point method and (ii) the AD-diagram method. The obtained coordinates are (A {sub conv}, D {sub conv}) = (96.°27 ± 0.°10, 13.°14 ± 0.°27) and (A {sub ◦}, D {sub ◦}) = (100.°282 ± 0.°10, 9.°577 ± 0.°323) respectively. We also computed the Velocity Ellipsoid Parameters, matrix elements (μ {sub ij}), direction cosines (l {sub j}, m {sub j}, n {sub j}), and the Galactic longitude of the vertex (l {sub 2}).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

ML-Based Pebble Power Reconstruction for Pebble Bed Reactor Analysis

Pebble power reconstruction has been explored to complement the conventional homogenized modeling approach in pebble bed reactor (PBR) analysis, as detailed heterogeneous geometry calculations are computationally expensive. The random distribution of pebble fuels within the core challenges the application of conventional pin power reconstruction methods. To address this, we introduce a machine learning approach based on the transformer model, composed of encoder and decoder layers, to estimate the flux and power form functions for reconstructing individual pebble neutron fluxes and powers. The homogeneous neutron flux distribution within each spectral zone (SZ) is obtained from finite element solutions of global diffusion or transport calculations. Verification tests demonstrate that the trained transformer model accurately predicts power form functions over a range of conditions, including variations in pebble enrichment, location, type, SZ size, and burnup. In particular, verification using a three-dimensional PBR benchmark with burned pebbles shows good agreement in heterogeneous pebble power distributions between Griffin and Serpent. These results highlight the potential of applying conventional pin power reconstruction approaches to PBR cores with randomly distributed pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Impact of crowding on the diversity of expanding populations

Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population’s range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria–Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically “pushed out” of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter—the characteristic growth layer thickness—and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantifying the Financial Impacts of Electric Vehicles on Utility Ratepayers and Shareholders [Slides]

Widespread electric vehicle (EV) adoption is critical for meeting economy-wide decarbonization goals and, as a result, states are considering enabling policies and rate designs to accelerate EV deployment. EVs can provide possible financial upside to electric utilities and ratepayers in several ways. For example, from the utility perspective, EVs could drive increased electricity sales and new earnings opportunities through increased capital investments. From the ratepayer perspective, increased electric loads from EVs could reduce average all-in retail rates. The degree to which there are net benefits or costs to shareholders and/or ratepayers depends on how EVs are integrated and managed through enabling grid investments and charging strategies. Using Berkeley Lab’s Financial Impacts of Distributed Energy Resources (FINDER) model that mimics the electric utility investment planning and ratemaking processes, we estimate the utility earnings and customer rate impacts of EVs using a bookend approach of “managed” (i.e., best case) and “mismanaged” (i.e., worst case) charging strategies for a generic summer-peaking, investor-owned, and vertically integrated utility. The analysis also examines the sensitivity of results to different assumptions of EV deployment characteristics, EV impacts on retail electricity sales, incremental distribution system costs, EV charging location, and utility EV enablement costs (i.e., utility costs to invest in EV charging, controls, and communication to deliver and administer EV programs). The results are intended to inform EV policies and deployment strategies that maximize utility system benefits and minimize ratepayer costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Studies of laser stimulated photodetachment from nanoparticles for particle charge measurements

Determining nanoparticle charge is more challenging than that for microparticles due to change in the particle size during the synthesis substantial plasma property variations, and difficulties in visualizing individual particles, rendering conventional microparticle charge diagnostics ineffective in dusty plasma. In this work, we utilized laser-stimulated photodetachment (LSPD) to deduce the mean charge of nanoparticles. Nanoparticles were grown in an Ar/C 2 H 2 mixture using a capacitively coupled RF discharge and the LSPD induced changes in the electron current monitored by a cylindrical Langmuir probe. LSPD signals were obtained and analyzed across different dust growth phases. The prolonged decay of electron current pulses was attributed to the presence of residual negative ions, caused by the effective electrostatic trapping of these ions and the potential post—LSPD re-formation of new ones. The particle charge was estimated by combining the laser-stimulated photodetachment signal from the probe with the dust density obtained from laser-light extinction using the measured nanoparticle size distribution. For a nanoparticles size range of approximately 100–250 nm and mean diameter of $d$ p ∼154.37 nm, the effective mean charge was estimated to be $\langle$$Q$$\rangle$ d $≈$ 37 elementary charge units. The measured charge values are lower than those predicted by orbital motion limited theory, which may be attributed to significant electron depletion in the nanodusty plasma. LSPD results in Ar/C 2 H 2 nano-dusty plasma confirm the applicability of this method for estimating individual nanoparticle charges. However, it has also been demonstrated that electron detachment from residual background negative ions can influence the detachment current decay and must be carefully considered.

dust particle charge↗