Revisiting the bias factor methodologies for the validation of fast test reactors
Not Available
Palmiotti, Giuseppe (ORCID:0000000243120892)↗
SEARCH · Engineering Papers
Results for “Bias factors”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Investigation of the 176 Yb Interference Correction during Determination of the 176 Hf/ 177 Hf Ratio by Laser Ablation and Solution Analysis on the Neoma MC-ICP-MS
We utilized the Neoma™, a recently released MC-ICP-MS platform offered by ThermoFisher Scientific, to assess the behavior of the Lu-Yb-Hf system during laser ablation analyses of various zircon standards as well as solution-based analyses of the JMC-475 Hf standard doped with varying quantities of Yb and Lu. The primary goal of this work was to characterize the behavior of the Yb interference correction on the Neoma™ platform since this is one of the biggest issues in the Hf isotope analysis community and because the Neoma™ platform will supplant the Neptune™ series instrument. During laser ablation analysis, we found that the overall data quality scales proportionally with the total Hf signal intensity, with higher signal analyses producing extremely accurate (within 1 ε Hf unit) and precise (sub ε Hf unit within-run standard errors) data. At low Yb signals (<0.1 V 173 Yb), we were not able to produce an accurate internal Yb mass bias factor. However, utilizing an empirical approach allows for the application of session-specific relationships between the Yb and Hf mass bias factors, determined by analysis of standards of varying Yb content, to produce accurate ε Hf values from zircons with higher Yb/Hf ratios even where the total Hf signal intensity is relatively low. Similar behavior was observed in the solution analyses. Lastly, while the behavior of the Yb interference correction on the Neoma™ platform appears comparable to the Neptune™ series MC-ICP-MS, further work will help refine the understanding of the controls on mass bias behavior, oxide formation, session-to-session stability, etc.
36 MATERIALS SCIENCE↗
Selection-bias effects on high-𝑝 𝑇 yield and correlation measurements in oxygen + oxygen collisions
Oxygen + oxygen (O+O) collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC) offer a unique experimental opportunity to observe the onset of jet quenching in intermediate relativistic collision systems. As with the smaller proton-nucleus or larger nucleus-nucleus systems, measurements of centrality-selected high-𝑝 T processes in O+O collisions are expected to be sensitive to selection bias effects, which will be necessary to quantify or mitigate before a definitive conclusion on the presence of jet quenching. Using two Monte Carlo heavy-ion event generators, we provide a survey of centrality bias effects on high-𝑝 T yield and correlation measurements. Some highlights of our findings include that (1) bias factors for the accessible kinematic range at RHIC show a nontrivial 𝑝 T dependence, compared to a negligible one at the LHC given the smaller accessible Bjorken-𝑥 range, (2) centrality definitions based on multiplicity are less sensitive to bias effects than those based on the transverse energy, (3) the Angantyr generator gives qualitatively similar but larger-magnitude bias factors than Hijing, and (4) correlation measurements have a much smaller sensitivity to bias effects than do yield measurements. Furthermore, the findings here are intended to guide the experimental design and interpretation of O+O jet quenching and other hard-process measurements.
Hard scattering↗
Adiabatic and radiative cooling are both important causes of aerosol activation in simulated fog events in Europe
Aerosol–fog interactions affect the visibility in, and life cycle of, fog and are difficult to represent in weather and climate models. Here we explore processes that impact the simulation of fog droplet number concentrations (N d ) at sub-kilometer scale horizontal grid resolutions in the UK Met Office Unified Model. We modify the parameterization of aerosol activation to include droplet activation by radiative cooling in addition to adiabatic cooling and determine the relative importance of the two cooling mechanisms. We further test the sensitivity of simulated N d to: (a) interception of droplets by trees and buildings, (b) overestimation of updrafts in temperature inversions (which leads to artificially high N d values), and (c) potential mechanisms for droplet deactivation due to downward fluctuations in supersaturation. We evaluate our model against observations from the ParisFog and LANFEX field campaigns, building on evaluation described in the companion paper. Including radiative cooling in the activation mechanism improves how accurately we represent the liquid water path and the vertical structure of the fog in our LANFEX case study. However, with radiative cooling, the N d are overestimated for most of the ParisFog cases and for the LANFEX case. The time-averaged overestimate exceeds a factor of three (the normalized mean bias factor exceeds 2.0) in 4 out of 11 ParisFog cases. Our sensitivity studies demonstrate how these overestimates can be mitigated. Assuming the overestimate affects both radiative and adiabatic cooling, we find that although radiative cooling is more often the dominant source, both cooling sources can sometimes dominate activation.
Ghosh, Pratapaditya [Carnegie Mellon University, P↗
Coupled cluster and dislocation dynamics modeling of microstructure evolution in irradiated materials
We develop here a coupled cluster and dislocation dynamics framework to study the microstructure evolution of irradiated materials. The framework not only accounts for the three dimensional diffusion of radiation-generated clusters, but also their interaction with dislocation networks and the resultant climb motion of discrete dislocations within finite crystals. The framework is solved with a superposition solution scheme, and is applied to investigate the evolution of the irradiation-induced dislocation loops in zirconium (Zr), considering the effects of various bias factors including the diffusion anisotropy difference (DAD) of interstitials and interstitial clusters, the dislocation bias of defects to discrete dislocation segments, and the production bias of defects from the radiation cascade. We find that the DAD is the most critical factor influencing the kinetics of the loop evolution in Zr, while the recombination/interaction of mobile defects can induce a strong spatial dependence of the loop evolution together with the DAD. Here, the method is also adopted to study the evolution of interstitial $\langle$a$\rangle$ and vacancy $\langle$c$\rangle$ dislocation loop ensembles consistent with the microstructure observed during irradiation-induced growth of Zr. Our findings not only reveal the spatial dependence of the size and ellipticity of the dislocation loops, but also suggest a limit on the anisotropy factor of interstitials to reproduce the co-growth of $\langle$a$\rangle$ and $\langle$c$\rangle$ loops in zirconium, in good agreement with experimental observations and other simulation results.
Bias factors↗
E-Area Low Level Waste Generator Inventory Uncertainty Estimation
This report describes the method used to estimate the uncertainty and bias in the low-level waste inventory, as reported by Waste Generators, for waste disposed in the E-Area Low-Level Waste Facility at SRS. The waste cut uncertainty and bias is summed to calculate the current total uncertainty and bias in each disposal unit in the E-Area Low-Level Waste Facility. This information is used to estimate the future uncertainty and bias when all disposal units are closed. For most waste cuts, there are two areas of inventory uncertainty and bias: in the isotopic characterization of the waste stream and in the measurement of total waste cut activity. Waste stream characterization is accomplished through a combination of process knowledge and analytical measurements. This characterization is documented by a technical baseline that describes the relative quantity of each isotope present in the waste stream. The total activity of each individual waste cut is usually measured directly or is calculated through a ratio of activity per weight or activity per volume. The waste cut total activity is multiplied by the waste stream isotopic fraction to calculate the total isotopic activity present in the waste cut. The waste cut uncertainty and bias is calculated by analyzing the uncertainties and biases in the waste stream characterization and waste cut measurements and adding the uncertainties in quadrature and multiplying the reported isotopic activities by the bias factors. The current isotopic inventory, uncertainty, and bias of a disposal unit is the sum of the currently disposed individual waste cut isotopic activities, uncertainties, and biases. The future uncertainty and bias is estimated based on the average “worst-case” (largest) uncertainties and biases of currently closed and operational disposal units. This report details the method, data sources, and calculation techniques used to calculate these values. It also presents some of the difficulties in working with the reported data and gives examples of the calculations and analysis. The software used to carryout the analysis is included and the location of the complete individual analyses is given. After discussion with waste generators, examination of multiple waste characterization documents, and a review of SRS Manual 1S, the reported ELLWF radionuclide activities are clearly conservative and likely represent reasonable upper bounds for the true activities. However, quantifying the bias and uncertainty in each waste cut requires detailed analysis of the documentation for each waste cut measurement and waste stream characterization.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Dislocation loop bias and void swelling in irradiated α-iron from mesoscale and atomistic simulations
Abstract Dislocation loops are ubiquitous in irradiated materials, and dislocation loop bias plays a critical role in void swelling. However, due to complicated interactions between dislocation loops and point defects, it is challenging to evaluate the bias factors of dislocation loops. Here, we determine the bias of sessile < 100 > loops in α-iron using a recently developed atomistic approach based on the lifetime of point defects. We establish a mechanistic understanding of the loop interaction based on the diffusion tendency of point defects near the loop core region. Mobile self-interstitial atoms tend to be absorbed from the edge of the loop, and a trapping region perpendicular to the habit plane of the loop exists. The dislocation loop bias is found to be substantially lower than those of straight dislocations in α-iron and should be included in swelling rate estimates. With the obtained sink strength and bias values, agreement is achieved with experimental results for both absolute values and temperature dependence.
Yu, Ziang↗
Capture efficiency and bias from the defect dynamics near grain boundaries in BCC Fe using mesoscale simulations
The capture efficiency describes the capability of a sink, such as a grain boundary (GB), dislocation, and void, to absorb point defects (PDs). The bias defines the difference in capture efficiency between the absorption of a vacancy and dumbbell at a sink. Complete kinetic information on PDs, including diffusion barriers and diffusion orientations, as well as accurate saddle points, are needed to determine the capture efficiency and bias at a sink accurately, which is computationally demanding. Here, in the present study, the Self-Evolving Atomistic Kinetic Monte Carlo (SEAKMC) method was used to investigate the defect dynamics of PDs near different types of grain boundaries (GBs) (with both $\langle$100$\rangle$ and $\langle$110$\rangle$ families) accurately in body-centered cubic (BCC) iron (Fe). The capture efficiency, sink strength, and bias factor of different types of GBs were determined in Fe, which, different from traditional rate theory estimation, showed a distinct capture efficiency, sink strength, and bias in different GBs. The results demonstrate a strong positive correlation between the capture efficiency and the GB strain width, instead of the GB misorientation, GB energy, or GB-PD binding energy, which have been investigated previously. This work provides valuable insight into the radiation-induced microstructural evolution of GBs.
36 MATERIALS SCIENCE↗
Using lifetime of point defects for dislocation bias in bcc Fe
The interaction between dislocations and point defects is key to deformation processes and microstructural evolution of structural materials. In this work, we compute the lifetime of point defects to describe their interaction with dislocations. This approach can accurately account for the effects of the dislocation core and anisotropic defect dynamics to accumulatively determine the capture efficiency, sink strength, and dislocation bias at different temperatures and dislocation densities. Particularly, the absorption of point defects by straight screw and edge dislocations in a model bcc iron system is studied. The maximum swelling rates based on the obtained bias factors are in close agreement with a variety of experimental measurements, including both neutron and ion-irradiation data, especially when considering the survival fraction for point defects from displacement cascades. Further, this approach applies to many other processes and sinks, such as dislocation loops and interfaces, providing a powerful means to develop fundamental insights critical for improving radiation resistance and mechanical properties of structural materials through controlling defect interaction and evolution.
36 MATERIALS SCIENCE↗
Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and galaxy-galaxy lensing using the MagLim lens sample
The cosmological information extracted from photometric surveys is most robust when multiple probes of the large scale structure of the Universe are used. Two of the most sensitive probes are the clustering of galaxies and the tangential shear of background galaxy shapes produced by those foreground galaxies, so-called galaxy-galaxy lensing. Combining the measurements of these two two-point functions leads to cosmological constraints that are independent of the way galaxies trace matter (the galaxy bias factor). The optimal choice of foreground, or lens, galaxies is governed by the joint, but conflicting requirements to obtain accurate redshift information and large statistics. We present cosmological results from the full 5000 deg 2 of the Dark Energy Survey’s first three years of observations (Y3) combining those two-point functions, using for the first time a magnitude-limited lens sample (MagLim) of 11 million galaxies, especially selected to optimize such combination, and 100 million background shapes. We consider two flat cosmological models, the Standard Model with dark energy and cold dark matter ( Λ CDM ) a variation with a free parameter for the dark energy equation of state ( w CDM ). Both models are marginalized over 25 astrophysical and systematic nuisance parameters. In Λ CDM we obtain for the matter density Ω m = 0.320 - 0.034 + 0.041 and for the clustering amplitude S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.778 - 0.031 + 0.037 , at 68% C.L. The latter is only 1 σ smaller than the prediction in this model informed by measurements of the cosmic microwave background by the Planck satellite. In w CDM we find Ω m = 0.32 - 0.046 + 0.044 , S 8 = 0.777 - 0.051 + 0.049 and dark energy equation of state w = - 1.031 - 0.379 + 0.218 . We find that including smaller scales, while marginalizing over nonlinear galaxy bias, improves the constraining power in the Ω m - S 8 plane by 31% and in the Ω m - w plane by 41% while yielding consistent cosmological parameters from those in the linear bias case. These results are combined with those from cosmic shear in a companion paper to present full DES-Y3 constraints from the three two-point functions ( 3 × 2 pt ).
79 ASTRONOMY AND ASTROPHYSICS↗
The Atacama Cosmology Telescope: SZ-based masses and dust emission from IR-selected cluster candidates in the SHELA survey
Here we examine the stacked thermal Sunyaev–Zel’dovich (SZ) signals for a sample of galaxy group and cluster candidates from the 24 deg 2 infrared Spitzer-HETDEX Exploratory Large Area (SHELA) survey. We identify the objects in combination with optical data using the redMaPPer algorithm, and divide them into three richness bins (λ in 10–20, 20–30, and 30–76 with average photometric redshifts of 0.80, 0.73, and 0.70, respectively). All richness bins show evidence for dust emission, which we fit using stacked profiles from Herschel Stripe 82 data. We fit for synchrotron emission using stacked profiles created by binning source fluxes from NRAO VLA Sky Survey data. We can confidently detect the SZ decrement only in the highest richness bin, finding M SZ,500 = $8.7^{+1.7}_{-1.3} \times 10^{13}\, \mathrm{ M}_\odot$. Neglecting the correction for dust and synchrotron depresses the inferred mass by 26 percent, indicating a partial fill-in of the SZ decrement from dust and synchrotron emission. We compare our corrected SZ masses to two redMaPPer mass–richness scaling relations and find that the SZ mass is lower than predicted by the richness. For the lower richness bins, mass bias factors as low as 1 – b = 0.6 are not enough to bring the mass limits into agreement. We discuss possible explanations for this discrepancy. The SHELA richnesses may differ from previous richness measurements due to the inclusion of infrared data in redMaPPer. To connect the SZ signal to the mass, we use a universal gas pressure profile that is calibrated to massive clusters at low redshift. It may not be applicable to our lower mass, higher redshift sample.
79 ASTRONOMY AND ASTROPHYSICS↗
Setup and verification of a SCALE/KENO platform for generic FHR benchmark calculations
The work presented in this article is preliminary to downstream analysis of a generic fluoride salt-cooled high-temperature reactor (gFHR) core performed by the University of Tennessee in collaboration with Kairos Power (KP). A Monte Carlo transport model of the publicly available gFHR equilibrium core is developed in SCALE/KENO with multigroup energy treatment. Several output quantities of interest are used to verify the simulations against a benchmark model developed by KP using the continuous energy Monte Carlo code Serpent 2. Good agreement is seen in flux and fission rate profiles with a maximum relative difference of 1.4% and 2.8% respectively. Furthermore, an effective multiplication factor bias of 44 pcm was observed between the two simulations. The fuel temperature reactivity coefficient calculated with SCALE is within uncertainty to the reference model. This verification acts as a publicly reproducible benchmark for the gFHR in SCALE/KENO. A simplified depletion model is also presented where a single fuel pebble is depleted to discharge burnup through the equilibrium core while the equilibrium core is assumed to be invariant. This method produces results that intercept the equilibrium core concentrations in every case, however, an interesting artifact of this particular depletion model is uncovered. The phenomenon is shown to be a fundamental feature of the differential rate equations and inspires questions about how this system behaves when the time evolution of the equilibrium core is considered. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Anomaly detection in collider physics via factorized observables
To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called : factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions. Published by the American Physical Society 2024
Astronomy & Astrophysics↗
Reconstructing Hanford worker external doses from photons for epidemiology
The accurate reconstruction of external photon doses is essential for credible radiation epidemiology. This article presents the methodology used to derive dose estimates for 37 012 Hanford Site workers included in the Million Person Study. The approach employs historical dose records from the Hanford Radiation Exposure database and a previous epidemiology study. Bias correction factors specific to dosimeter type and period of use were applied and missing annual doses were estimated using a hierarchical nearby method to estimate deep dose equivalent for each worker. For early years with limited detection sensitivity, missed doses were quantified based on expected time-period-specific, low-dose statistical distributions. The revised dose estimates resulted in lower median and mean career doses than unadjusted data, while increasing the number of person-years with nonzero dose. Sensitivity analyses assessed the influence of bias in dosimetry measurements, missed doses and gap years on dose estimates. Differences in cumulative dose estimates between unadjusted and revised annual estimates are most prominent in the early operational years due to the highest bias during that time period.
dose reconstruction↗
Earth-Centered, Earth-Fixed Inertial Navigation System & Error-State Kalman Filter Reference Manual
This is a self-contained reference document that derives the equations necessary to build a combined inertial navigation system and error-state Kalman filter. Coordinate transform, linear time invariant system, inertial sensing, and error-state Kalman filtering theory is built up from first principles. This theory is then leveraged to derive the system equations for two combined inertial navigation system and error-state Kalman filters: (1) a 15-state system modeling white-noise-integrating accelerometer and gyroscope biases, and (2) a 39-state system modeling static and first-order Gauss-Markov accelerometer and gyroscope biases, scale factor errors, and cross-axis sensitivity errors.
42 ENGINEERING↗
Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica
A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka-band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12-hour increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in-cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid-containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), i.e., high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid-containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. Finally, this diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein.
54 ENVIRONMENTAL SCIENCES↗
Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach
The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. Finally, this work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.
97 MATHEMATICS AND COMPUTING↗
Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign
Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.
54 ENVIRONMENTAL SCIENCES↗