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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST

Many HIV prevention strategies are currently under consideration where it is highly informative to know the study participants’ times of infection. These can be estimated using viral sequence data sampled early in infection. However, there are several scenarios that, if not addressed, can skew timing estimates. These include multiple transmitted/founder (TF) viruses, APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like)-mediated mutational enrichment, and recombination. Here, we suggest a pipeline to identify these problems and resolve the biases that they introduce. We then compare two modeling strategies to obtain timing estimates from sequence data. The first, Poisson Fitter (PF), is based on a Poisson model of random accumulation of mutations relative to the TF virus (or viruses) that established the infection. The second uses a coalescence-based phylogenetic strategy as implemented in BEAST. The comparison is based on timing predictions using plasma viral RNA (cDNA) sequence data from 28 simian-human immunodeficiency virus (SHIV)-infected animals for which the exact day of infection is known. In this particular setting, based on nucleotide sequences from samples obtained in early infection, the Poisson method yielded more accurate, more precise, and unbiased estimates for the time of infection than did the explored implementations of BEAST. The inference of the time of infection is a critical parameter in testing the efficacy of clinical interventions in protecting against HIV-1 infection. For example, in clinical trials evaluating the efficacy of passively delivered antibodies (Abs) for preventing infections, accurate time of infection data are essential for discerning levels of the Abs required to confer protection, given the natural Ab decay rate in the human body. In such trials, genetic sequences from early in the infection are regularly sampled from study participants, generally prior to immune selection, when the viral population is still expanding and genetic diversity is low. In this particular setting of early viral growth, the Poisson method is superior to the alternative approach based on coalescent methods. This approach can also be applied in human vaccine trials, where accurate estimates of infection times help ascertain if vaccine-elicited immune protection wanes over time.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Bayesian estimation of HIV acquisition dates for prevention trials

Accurate timing estimates of when participants acquire HIV in HIV prevention trials are necessary for determining antibody levels at acquisition. The Antibody-Mediated Prevention (AMP) Studies showed that a passively administered broadly neutralizing antibody can prevent the acquisition of HIV from a neutralization-sensitive virus. We developed a pipeline for estimating the date of detectable HIV acquisition (DDA) in AMP Study participants using diagnostic and viral sequence data. Using a Bayesian strategy that combines three streams of data (REN [rev/vpu/env/Δnef] sequence, GP [gag/Δpol] sequence, and diagnostic) where their 95% credible intervals overlap based on pre-specified criteria and decision rules. We evaluated the performance of our AMP pipeline using PacBio viral sequence data from 41 participants across two prospective acute HIV acquisition cohort studies, FRESH and RV217, with twice-weekly sampling. These cohort studies enrolled young women in South Africa and men and women in Kenya and Thailand, respectively, with a high likelihood of HIV acquisition. In evaluating performance, “true DDA” was the center of bounds between last-negative and first-positive RNA diagnostic tests (median time 4 days, range 2–7 days); bias was the mean difference between estimated and true DDA. Using diagnostic data alone yielded timing estimates with a bias of 2.4 days and root mean square error (RMSE) of 7.9 days. These results were improved using sequence + diagnostic data (bias 1.5 days, RMSE 6.9 days), as well as by restricting sequence-based estimation to samples from ≤5 weeks post-DDA (bias 0.2 days, RMSE 7.8 days).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Error Bounds for Dynamical Spectral Estimation

Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood. Here we analyze the error of a dynamical spectral estimation method called “the variational approach to conformational dynamics" (VAC). We bound the approximation error and estimation error for VAC estimates. Our analysis establishes VAC's convergence properties and suggests new strategies for tuning VAC to improve accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism

Process monitoring (PM) for nuclear safeguards sometimes requires estimation of thresholds corresponding to small false alarm rates. Threshold estimation dates to the 1920s with the Shewhart control chart; however, because possible new roles for PM are being evaluated in nuclear safeguards, it is timely to consider modern model selection options in the context of threshold estimation. One of the possible new PM roles involves PM residuals, where a residual is defined as residual = data − prediction. This paper reviews alarm threshold estimation, introduces model selection options, and considers a range of assumptions regarding the data-generating mechanism for PM residuals. Two PM examples from nuclear safeguards are included to motivate the need for alarm threshold estimation. The first example involves mixtures of probability distributions that arise in solution monitoring, which is a common type of PM. The second example involves periodic partial cleanout of in-process inventory, leading to challenging structure in the time series of PM residuals.

Burr, Tom↗
DOE OSTI2020

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) [SWR-20-115]

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) can be used to estimate behind the meter (BTM) solar generation from limited datasets. Using distributed sets of diverse solar sites, but with close spatial proximity, and/or limited historical PV generation information can be used to generate of solar profiles. This is useful for applications where partial solar generation data is available, and on-site irradiance data is not available. GET-Solar calculates PV technical data parameters and calculates output by modeling shading and cloud cover impacts. Clear sky irradiance profiles are used to calculate base generation profiles on which the local shading and cloud cover layers are added. In order to arrive at clear sky generation data, PV mounting information like panel tilt and and angle is needed. For cases where PV mounting information is missing, panel tilt and azimuth angle can be estimated using a non-linear optimization algorithm. GET-Solar can also be used for forecasting applications where historical PV data is available to estimate local shading at various solar positions. This overall framework can be used for estimation of solar parameters, and assisting in analyzing and modeling diverse sets of DER where solar generation is also present in utility meter data. GET-Solar has been developed in Python and uses the Pyomo optimization language.

Abraham, Sherin Ann↗
DOE OSTI2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. Furthermore, the combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The effect of atmospheric transmissivity on model and observational estimates of the sea ice albedo feedback

The sea-ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS) – how much the surface albedo in sea-ice regions changes as the planet warms– and the radiative sensitivity (RS) – how much the top of atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite based RS is within the model range of RS that differs by a factor of two across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS which, in conjunction with the satellite-based RS yields an SIAF of 0.16 ± 0.04 W m-2 K-1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model specific RS and IS and find a model mean feedback parameter of 0.37 W m-2 K-1 which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.

Arctic, Antarctic, climate change, Climate & Earth↗
DOE OSTI2021

The Scale and Activity of Symmetric Instability Estimated from a Global Submesoscale-Permitting Ocean Model

Abstract Symmetric instability (SI) extracts kinetic energy from fronts in the surface mixed layer (SML), potentially affecting the SML structure and dynamics. Here, a global submesoscale-permitting ocean model named MITgcm LLC4320 simulation is used to examine the Stone linear prediction of the maximum SI scale to estimate grid spacings needed to begin resolving SI. Furthermore, potential effects of SI on the usable wind work are estimated roughly: this estimate of SI “activity” is useful for assessing if these modes should be resolved or parameterized. The maximum SI scale varies by latitude with median values from 568 to 23 m. Strong seasonality is observed in the SI scale and activity. The median scale in winter is 188 m globally, 2.5 times of that of summer (75 m). SI is more active in winter: 15% of the time compared with 6% in summer. The strongest SI activity is found in the western Pacific, western Atlantic, and Southern Oceans. The required grid spacings for a global model to begin resolving SI eddies in the SML are 24 m (50% of regions resolved) and 7.9 m (90%) in winter, decreasing to 9.4 m (50%) and 3.6 m (90%) in summer. It is also estimated that SI may reduce usable wind work by an upper bound of 0.83 mW m −2 globally, or 5% of the global magnitude. The sensitivity of these estimates to empirical thresholds is provided in the text.

Dong, Jihai↗
DOE OSTI2021

Evaluation of KDP Estimation Algorithm Performance in Rain Using a Known-Truth Framework

Accurate estimation of specific differential phase ( K DP ) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (Φ DP ) and estimate K DP , but the corresponding K DP estimate uncertainty is unquantified. This study provides guidance on how commonly used K DP estimation algorithms perform in various environments. Here, we create numerous synthetic (“true”) K DP profiles, integrate over them to obtain “smoothed” Φ DP , and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy Φ DP profiles and compared to the true K DP profile such that the errors and uncertainty are quantified. The synthetic K DP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing K DP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the Φ DP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic K DP profiles. Some K DP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Estimating contact rates at a mass gathering by using video analysis: a proof-of-concept project

Current approaches for estimating social mixing patterns and infectious disease transmission at mass gatherings have been limited by various constraints, including low participation rates for volunteer-based research projects and challenges in quantifying spatially and temporally accurate person-to-person interactions. We developed a proof-of-concept project to assess the use of automated video analysis for estimating contact rates of attendees of the GameFest 2013 event at Rensselaer Polytechnic Institute (RPI) in Troy, New York. Video tracking and analysis algorithms were used to estimate the number and duration of contacts for 5 attendees during a 3-minute clip from the RPI video. Attendees were considered to have a contact event if the distance between them and another person was ≤1 meter. Contact duration was estimated in seconds. We also simulated 50 attendees assuming random mixing using a geospatially accurate representation of the same GameFest location. The 5 attendees had an overall median of 2 contact events during the 3-minute video clip (range: 0 6). Contact events varied from less than 5 seconds to the full duration of the 3- minute clip. The random mixing simulation was visualized and presented as a contrasting example. We were able to estimate the number and duration of contacts for five GameFest attendees from a 3-minute video clip that can be compared to a random mixing simulation model at the same location. In conclusion, the next phase will involve scaling the system for simultaneous analysis of mixing patterns from hours-long videos and comparing our results with other approaches for collecting contact data from mass gathering attendees.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Consistent Estimation of Gibbs Energy Using Component Contributions

Standard Gibbs energies of reactions are increasingly being used in metabolic modeling for applying thermodynamic constraints on reaction rates, metabolite concentrations and kinetic parameters. The increasing scope and diversity of metabolic models has led scientists to look for genome-scale solutions that can estimate the standard Gibbs energy of all the reactions in metabolism. Group contribution methods greatly increase coverage, albeit at the price of decreased precision. We present here a way to combine the estimations of group contribution with the more accurate reactant contributions by decomposing each reaction into two parts and applying one of the methods on each of them. This method gives priority to the reactant contributions over group contributions while guaranteeing that all estimations will be consistent, i.e. will not violate the first law of thermodynamics. We show that there is a significant increase in the accuracy of our estimations compared to standard group contribution. Specifically, our cross-validation results show an 80% reduction in the median absolute residual for reactions that can be derived by reactant contributions only. We provide the full framework and source code for deriving estimates of standard reaction Gibbs energy, as well as confidence intervals, and believe this will facilitate the wide use of thermodynamic data for a better understanding of metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Plutonium in Manhattan Project workers: Using autopsy data to evaluate organ content and dose estimates based on urine bioassay with implications for radiation epidemiology

Purpose Radiation dose estimates in epidemiology typically rely on intake predictions based on urine bioassay measurements. The purpose of this article is to compare the conventional dosimetric estimates for radiation epidemiology with the estimates based on additional post-mortem tissue radiochemical analysis results. Methods The comparison was performed on a unique group of 11 former Manhattan Project nuclear workers, who worked with plutonium in the 1940s, and voluntarily donated their bodies to the United States Transuranium and Uranium Registries. Results Post-mortem organ activities were predicted using different sets of urine data and compared to measured activities. Use of urinalysis data collected during the exposure periods overestimated the systemic (liver+skeleton) deposition of 239 Pu by 155±134%, while the average bias from using post-exposure urinalyses was –4±50%. Committed effective doses estimated using early urine data differed from the best estimate by, on average, 196±193%; inclusion of follow-up urine measurements in analyses decreased the mean bias to 0.6±36.3%. Cumulative absorbed doses for the liver, red marrow, bone surface, and brain were calculated for the actual commitment period. Conclusion On average, post-exposure urine bioassay results were in good agreement with post-mortem tissue analyses and were more reliable than results of urine bioassays collected during the exposure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Caribbean deepwater snappers: Application of the bomb radiocarbon age estimation validation in understanding aspects of ecology and life history

Reef fishes have been utilized as food fish throughout the U.S. Caribbean and Gulf of Mexico waters for centuries, with increasing fishing effort in recent decades. As a result, many species have experienced declines in landings, including deepwater snappers such as queen snapper Etelis oculatus and blackfin snapper Lutjanus buccanella . However, little to no peer-reviewed published research exists on basic life history parameters for either species. Confirming the accuracy of an age estimation method for a fish species is essential for ensuring sustainable fisheries management. This is because in the assessment of fisheries species population age-based parameters, including longevity, age at sexual maturity, growth rate, mortality, age-specific reproductive output, and lifetime reproductive output, are important in understanding overall life history strategies of managed stocks. The past stock assessment on U.S. Caribbean queen snapper utilized an estimated longevity of 8 y, derived from length frequencies for fish from St. Lucia. Blackfin snapper has an estimated longevity of 27 y based on a relatively small study from offshore waters of the southeastern U.S. The focus of our investigation was to estimate maximum longevity of two data-poor species in the U.S. Caribbean. The accuracy of ageing methods was tested via bomb radiocarbon age estimation validation and effects of depth on Δ 14 C in otolith cores and eye lens core values were examined. Results from our work indicate a maximum validated age of 45 y for queen snapper, and 43 y for blackfin snapper. Our findings indicate queen snapper and blackfin snapper are long-lived (> 40 y). The resulting Δ 14 C comparison between eye lens cores and otolith cores has important implications for the study of age validation, specifically when deepwater species are involved.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Evaluation of WSR-88D Level III and MRMS Rainfall Estimates Against Rain Gauge Observations at the Savannah River Site

Rainfall data for the Savannah River Site (SRS) has been historically measured by rain gauges. These instruments serve as ground truth for most climatological and weather applications; however, gauge measurements are prone to errors or biases under certain weather conditions. Rainfall estimates from radar reflectivity values have been developed and improved over the years and serve as an alternative or supplement for gauge measurements. This study compares measurements from tipping bucket rain gauges with rainfall estimates from the NOAA NWS WSR-88D Level III hourly rainfall product and the Multi-Radar Multi-Sensor (MRMS) gauge-corrected precipitation estimate. Results show good agreement between radar derived amounts and ground measurements, with MRMS values showing correlation coefficients of 0.60-0.95 and RMSE values of less than 1.0 cm (0.4 in). The WSR-88D Level III estimates result in correlation coefficients of 0.46-0.86 and RMSE values of less than 1.3 cm (0.5 in). Few outliers are observed for each data pair and are evaluated against precipitation classification products (WSR-88D Hybrid Hydrometeor Classification product and MRMS Precipitation Flag product). The rain gauges used in this study are not part of the Hydrometeorological Automated Data System (HADS) network used to correct the MRMS estimates and therefore, results of this work provide an independent validation of the MRMS gauge correction scheme.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Estimation of Radiation Doses to U.S. Military Test Participants from Nuclear Testing: A Comparison of Historical Film-Badge Measurements, Dose Reconstruction and Retrospective Biodosimetry

Retrospective radiation dose estimations, whether based on physical or biological measurements, or on theoretical dose reconstruction, are limited in their precision and reliability, particularly for exposures that occurred many decades ago. Here, we studied living U.S. military test participants, believed to have received high-dose radiation exposures during nuclear testing-related activities approximately six decades ago, with two primary goals in mind. The first was to compare three different approaches of assessing past radiation exposures: 1. Historical personnel monitoring data alone; 2. Dose reconstruction based on varying levels of completeness of individual information, which can include film badge data; and 3. Retrospective biodosimetry using chromosome aberrations in peripheral blood lymphocytes. The second goal was to use the collected data to make the best possible estimates of bone marrow dose received by a group with the highest military recorded radiation doses of any currently living military test participants. Six nuclear test participants studied had been on Rongerik Atoll during the 1954 CASTLE Bravo nuclear test. Another six were present at the Nevada Test Site (NTS) and/or Pacific Proving Ground (PPG) and were believed to have received relatively high-dose exposures at those locations. All were interviewed, and all provided a blood sample for cytogenetic analysis. Military dose records for each test participant, as recorded in the Defense Threat Reduction Agency's Nuclear Test Review and Information System, were used as the basis for historical film badge records and provided exposure scenario information to estimate dose via dose reconstruction. Dose to bone marrow was also estimated utilizing directional genomic hybridization (dGH) for high-resolution detection of radiation-induced chromosomal translocations and inversions, the latter being demonstrated for the first time for the purpose of retrospective biodosimetry. As the true dose for each test participant is not known these many decades after exposure, this study gauged the congruence of different methods by assessing the degree of correlation and degree of systematic differences. Overall, the best agreement between methods, defined by statistically significant correlations and small systematic differences, was between doses estimated by a dose reconstruction methodology that exploited all the available individual detail and the biodosimetry methodology derived from a weighted average dose determined from chromosomal translocation and inversion rates. Employing such a strategy, we found that the Rongerik veterans who participated in this study appear to have received, on average, bone marrow equivalent doses on the order of 300–400 mSv, while the NTS/ PPG participants appear to have received approximately 250–300 mSv. The results show that even for nuclear events that occurred six decades in the past, biological signatures of exposure are still present, and when taken together, chromosomal translocations and inversions can serve as reliable retrospective biodosimeters, particularly on a group-average basis, when doses received are greater than statistically-determined detection limits for the biological assays used.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Local-Distance Seismic Event Relocation and Relative Magnitude Estimation, Applications to Mining Related Seismicity in the Powder River Basin, Wyoming

Recent efforts to characterize small ( M w < 3 ) seismic events at local distances have become more important because of the increased observation of human-triggered and induced seismicity and the need to advance nuclear explosion monitoring capabilities. The signals generated by low-magnitude seismic sources necessitate the use of nearby short-period observations, which are sensitive to local geological heterogeneity. Local to near-regional distance ( < 300 km ) surface and shear waves can dominate short-period observations from small, shallow seismic sources. In this work, we utilize these observations to estimate precise, relative locations and magnitudes of ~ 700 industrial mining events in Wyoming, using nearly 360,000 observations. The precise, relative location estimates (with formal location uncertainty estimates of less than 1 km) collapse a diffuse collection of mining events into discrete clusters associated with individual blasting operations. We also invert the cross-correlation amplitudes to estimate precise, relative moment magnitude estimates, which help validate and identify disparities in the event sizes reported by regional network catalogs. Joint use of multiple phases allows for the inclusion of more seismic events due to the increase in the number of observations. In some cases, using a single phase allowed us to relocate only 50% of the original reported seismic events within a cluster. Combining shear- and surface-wave phases increased the number of events to above 90% of the original events, allowing us to characterize a broader range of event sizes, source to station distances, and event distributions. This analysis takes a step toward making a fuller characterization of small industrial seismic events observed at local distances.

58 GEOSCIENCES↗
DOE OSTI2013

Flux and Reaction Rate Kernel Density Estimators in OpenMC

Current methods for obtaining flux and reaction rate profiles in a Monte Carlo simulation rely on histogram tallies that can suffer from large uncertainties when fine detail is required. Recently, another density estimation technique has been applied to Monte Carlo radiation transport sim ulations: the Kernel Density Estimator (KDE) [Banerjee, K.]. KDEs are capable of calculating the flux at a point without the necessity of tracking particles on a spatial mesh, making KDEs ideal tools for multi-physics problems. Furthermore, KDEs allow for one collision or track length to contribute to the estimate of the flux at multiple points, circumventing the large statistical uncertainties associated with fine-mesh histogram tallies. However, KDEs have only been proven to be capable of estimating flux values at a series of points; no work has been done to estimate reaction rates or other quantities of interest. Furthermore, little work has been done in optimizing the KDE bandwidth for the energy-dependence of neutron transport simulations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗