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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based estimation applications.

Simon, Donald L.↗
NASA NTRS2011

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Simon, Donald L.↗
DOE OSTI2012

Contaminated Soil Volume Estimation at the Maywood Site - 12292

As part of the ongoing remediation process at the Maywood Formerly Utilized Sites Remedial Action Program properties, Argonne National Laboratory assisted the U.S. Army Corps of Engineers (USACE) New York District in revising contaminated soil volume estimates for the remaining areas of the Stepan/Sears properties that require soil remediation. As part of the volume estimation process, an initial conceptual site model (ICSM) was prepared for the entire site that captured existing information (with the exception of soil sampling results) pertinent to the possible location of surface and subsurface contamination above cleanup requirements. This ICSM was based on historical anecdotal information, aerial photographs, and the logs from several hundred soil cores that identified the depth of fill material and the depth to bedrock under the site. Specialized geostatistical software developed by Argonne was used to update the ICSM with historical sampling results and down-hole gamma survey information for hundreds of soil core locations; both sampling results and down-hole gamma data were coded to identify whether the results indicated the presence of contamination above site cleanup requirements. Significant effort was invested in developing complete electronic data sets for the site by incorporating data contained in various scanned documents, maps, etc. The updating process yielded both a best guess estimate of contamination volumes and upper and lower bounds on the volume estimate that reflected the estimate's uncertainty. The site-wide contaminated volume estimate (with associated uncertainty) was adjusted to reflect areas where remediation was complete; the result was a revised estimate of the remaining soil volumes requiring remediation that the USACE could use for planning. Other environmental projects may benefit from this process for estimating the volume of contaminated soil. A comparison of sample and DHG results for various stations with the site ICSM provides one measure of the quality of site understanding relative to the presence of contamination and its likely extent. A second measure of the quality of a contaminated soil volume estimate, in the case of BAASS, is a comparison of the best guess volume estimate with the conservative volume estimate. For Maywood, the conservative volume estimate is more than double the best guess. The data available for the site indicate that the preponderance of this difference is attributable to uncertainty about the exact depth of known contamination. The observed depth of contamination based on sample results and DHG data often varied significantly among adjacent soil cores. In the case of Maywood, where a significant amount of remediation has already taken place, a third measure of the quality of the volume estimates is the retrospective estimate of contaminated soil volumes for areas that have already been remediated [2]. The USACE is currently comparing the latest BAASS volume estimates for those areas with records indicating what was actually removed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Adaptive Reweighted Variance Estimation for Monte Carlo Eigenvalue Simulations

Monte Carlo (MC) simulation is used to solve the eigenvalue form of the Boltzmann transport equation to estimate various parameters such as fuel pin flux distributions that are crucial for the safe and efficient operation of nuclear systems (e.g., a nuclear reactor). Monte Carlo eigenvalue simulation uses a sample mean over many stationary cycles (iterations) to estimate various parameters important to nuclear systems. A variance estimate of the sample mean is often used for calculating the confidence intervals. However, MC eigenvalue simulation variance estimators that ignore the intercycle correlation underestimate the true variance of the estimated quantity. This paper presents novel data-adaptive approaches based on a simple autoregressive (AR) model and sigmoid functions to improve MC variance estimation. The standard MC sample-based variance estimator (or naïve estimator) and the spectral density–based MC variance estimator are enhanced by adding data-adaptive components that reduce their bias and improve performance. By investigating the frequency pattern of the AR(1) (order 1) model, two adaptive spectral estimators and one adaptive naïve estimator are proposed. The proposed estimators manifest superior performance when applied to three test problems compared to the standard spectral density–based estimator previously introduced by the authors. In conclusion, these new estimators are straightforward, as they use online algorithms and do not require storage of tallies from all active cycles.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Cost Estimates for NPP Decommissioning Projects and Insights from Cost Analysis - 18118

The technical ability to decommission NPPs shut down after normal operation has been well demonstrated. However, concerns remain about the ability to accurately calculate and demonstrate the validity of decommissioning cost estimates, and to control costs during decommissioning. This paper describes recent international guidance on quality cost estimates for nuclear decommissioning projects and explores the potential added value that could be attained through more systematic analysis and reporting of decommissioning project costs. The OECD Nuclear Energy Agency (NEA) and the International Atomic Energy Agency (IAEA) have long been concerned about the need to promote greater transparency in decommissioning costing and have collaborated in a number of initiatives in pursuit of this objective. Together with the European Commission, they published 'The International Structure for Decommissioning Costing (ISDC)' in 2012. The ISDC provides a useful common reporting format for decommissioning costing. The NEA has also published guidance on the international peer reviews of decommissioning cost studies (2014) and on the practice of decommissioning cost estimation (2015). In September 2017, the NEA and IAEA published detailed guidance on addressing uncertainties in decommissioning cost estimates (the 'Addressing Uncertainties' report). This joint report sets out an approach to treating uncertainties reflecting current good practices in cost estimating. Specifically, it describes how uncertainties in decommissioning cost estimation can be addressed using standardized methods of estimating uncertainty and risk analysis. Its recommendations aim at enabling a consistent and comprehensive treatment of risk and uncertainty in the preparation of decommissioning cost estimates. Together these publications establish a good basis for producing comprehensive and high-quality cost estimates for nuclear decommissioning projects and for enhancing confidence in the estimates through improving transparency concerning the underpinning data and calculations, and more explicit representation of the uncertainties that may impact on project costs. There is increasing attention on understanding variations between cost estimates and apparent escalation of decommissioning costs. This arises both from the need to assure the adequacy of financing arrangements for future decommissioning projects and the ambition to improve the performance and ensure value-for-money in the delivery of decommissioning projects and services. A systematic analysis of actual decommissioning project costs in relation to the cost estimates would be expected to yield valuable insights about key cost drivers in actual decommissioning projects, and the extent to which current cost estimates accurately capture and reflect these. Aligning the reporting of decommissioning project costs with the underpinning cost estimates for the projects would also facilitate the identification of causes of major deviations between projected and actual costs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Estimating local noise power spectrum from a few FBP-reconstructed CT scans

Purpose: Traditional ways to estimate 2D CT noise power spectrum (NPS) involve an ensemble average of the power spectrums of many noisy scans. When only a few scans are available, regions of interest are often extracted from different locations to obtain sufficient samples to estimate the NPS. Using image samples from different locations ignores the nonstationarity of CT noise and thus cannot accurately characterize its local properties. The purpose of this work is to develop a method to estimate local NPS using only a few fan-beam CT scans. Methods: As a result of FBP reconstruction, the CT NPS has the same radial profile shape for all projection angles, with the magnitude varying with the noise level in the raw data measurement. This allows a 2D CT NPS to be factored into products of a 1D angular and a 1D radial function in polar coordinates. The polar separability of CT NPS greatly reduces the data requirement for estimating the NPS. The authors use this property and derive a radial NPS estimation method: in brief, the radial profile shape is estimated from a traditional NPS based on image samples extracted at multiple locations. The amplitudes are estimated by fitting the traditional local NPS to the estimated radial profile shape. The estimated radial profile shape and amplitudes are then combined to form a final estimate of the local NPS. We evaluate the accuracy of the radial NPS method and compared it to traditional NPS methods in terms of normalized mean squared error (NMSE) and signal detectability index. Results: For both simulated and real CT data sets, the local NPS estimated with no more than six scans using the radial NPS method was very close to the reference NPS, according to the metrics of NMSE and detectability index. Even with only two scans, the radial NPS method was able to achieve a fairly good accuracy. Compared to those estimated using traditional NPS methods, the accuracy improvement was substantial when a few scans were available. Conclusions: The radial NPS method was shown to be accurate and efficient in estimating the local NPS of FBP-reconstructed 2D CT images. It presents strong advantages over traditional NPS methods when the number of scans is limited and can be extended to estimate the in-plane NPS of cone-beam CT and multislice helical CT scans.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1992

A model for the cost of doing a cost estimate

A model for estimating the cost required to do a cost estimate for Deep Space Network (DSN) projects that range from $0.1 to $100 million is presented. The cost of the cost estimate in thousands of dollars, C(sub E), is found to be approximately given by C(sub E) = K((C(sub p))(sup 0.35)) where C(sub p) is the cost of the project being estimated in millions of dollars and K is a constant depending on the accuracy of the estimate. For an order-of-magnitude estimate, K = 24; for a budget estimate, K = 60; and for a definitive estimate, K = 115. That is, for a specific project, the cost of doing a budget estimate is about 2.5 times as much as that for an order-of-magnitude estimate, and a definitive estimate costs about twice as much as a budget estimate. Use of this model should help provide the level of resources required for doing cost estimates and, as a result, provide insights towards more accurate estimates with less potential for cost overruns.

Remer, D. S.↗
NASA NTRS1993

The cost of doing a cost estimate

A model for estimating the cost required to do a cost estimate for Deep Space Network (DSN) projects that range from $0.1 to $100 million is presented. The cost of the cost estimate in thousands of dollars, C(sub E), is found to be approximately given by C(sub E) = K(/C(sub p)/(sup 0.35)) where C(sub p) is the cost of the project being estimated in millions of dollars and K is a constant depending on the accuracy of the estimate. For an order-of-magnitude estimate, K = 24; for a budget estimate, K = 60; and for a definitive estimate, K = 115. That is, for a specific project, the cost of doing a budget estimate is about 2.5 times as much as that for an order-of-magnitude estimate, and a definitive estimate costs about twice as much as a budget estimate. Use of this model should help provide the level of resources required for doing cost estimates and, as a result, provide insights towards more accurate estimates with less potential for cost overruns.

Remer, Donald S.↗
DOE OSTI2016

Unbiased Ratio Estimator for k-Eigenvalue in Monte Carlo Criticality Calculations

In criticality calculations, the effective multiplication factor (k{sub eff}) is an important parameter for reactor analysis. It can be interpreted as the ratio of the neutron production rate to the loss rate in a reactor core. In Monte Carlo (MC) criticality calculations, k{sub eff} is obtained for each active cycle i as: k-i = P{sub i}/L{sub i} for I = 1 to I where P{sub i} and L{sub i} are the production rate and the loss rate at cycle i, respectively, and they are normalized by the nominal source size for cycle. Since all neutron particles in a cycle will be absorbed or leaks out at the end, the loss rate, L{sub i}, can be set as the source rate, which is the unity. Thus, in MCNP5, k{sub i} is simply calculated with P{sub i} by using three conventional estimators; collision, absorption, and track-length estimators. In domain decomposed MC criticality calculations based on the fission and surface source (FSS) iteration method, k{sub i} is calculated by a ratio of P{sub i} and L{sub i}, where both P{sub i} and L{sub i} are random variables, sampled by using the same particle histories. When the numerator and the denominator are correlated, the variance of the ratio can be smaller than that of the numerator. Then, the final estimate of k{sub eff} is provided as the sample mean of k{sub i}, which will be referred as 'simple' ratio estimator in this paper. In Ref., it is shown that this ratio estimator converges faster and fluctuates less than the conventional estimator. A similar ratio estimator is also used in Serpent 2, as an implicit estimate of k{sub eff}. However, this type of 'simple' ratio estimator is biased, which means that the expectation of the ratio is different from the ratio of the expectation of the numerator and that of the denominator. In this paper, an unbiased ratio estimator which was originally derived in the cytometry analysis is introduced to estimate k{sub eff} and is tested on a continuous-energy UO{sub 2} pin-cell problem. In this test problem, it is shown that the variance of the unbiased ratio estimator is smaller than those of the conventional estimators provided by MCNP5, while the simple ratio estimator can be significantly biased when the number of histories per cycle is too small. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps

Soils and other unconsolidated deposits in the northern circumpolar permafrost region store large amounts of soil organic carbon (SOC). This SOC is potentially vulnerable to remobilization following soil warming and permafrost thaw, but SOC stock estimates were poorly constrained and quantitative error estimates were lacking. This study presents revised estimates of permafrost SOC stocks, including quantitative uncertainty estimates, in the 0–3 m depth range in soils as well as for sediments deeper than 3 m in deltaic deposits of major rivers and in the Yedoma region of Siberia and Alaska. Revised estimates are based on significantly larger databases compared to previous studies. Despite this there is evidence of significant remaining regional data gaps. Estimates remain particularly poorly constrained for soils in the High Arctic region and physiographic regions with thin sedimentary overburden (mountains, highlands and plateaus) as well as for deposits below 3 m depth in deltas and the Yedoma region. While some components of the revised SOC stocks are similar in magnitude to those previously reported for this region, there are substantial differences in other components, including the fraction of perennially frozen SOC. Upscaled based on regional soil maps, estimated permafrost region SOC stocks are 217 ± 12 and 472 ± 27 Pg for the 0–0.3 and 0–1 m soil depths, respectively (±95% confidence intervals). Storage of SOC in 0–3 m of soils is estimated to 1035 ± 150 Pg. Of this, 34 ± 16 Pg C is stored in poorly developed soils of the High Arctic. Based on generalized calculations, storage of SOC below 3 m of surface soils in deltaic alluvium of major Arctic rivers is estimated as 91 ± 52 Pg. In the Yedoma region, estimated SOC stocks below 3 m depth are 181 ± 54 Pg, of which 74 ± 20 Pg is stored in intact Yedoma (late Pleistocene ice- and organic-rich silty sediments) with the remainder in refrozen thermokarst deposits. Total estimated SOC storage for the permafrost region is ∼1300 Pg with an uncertainty range of ∼1100 to 1500 Pg. Of this, ∼500 Pg is in non-permafrost soils, seasonally thawed in the active layer or in deeper taliks, while ∼800 Pg is perennially frozen. In conclusion, this represents a substantial ∼300 Pg lowering of the estimated perennially frozen SOC stock compared to previous estimates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

A novel multistage estimation of the signal parameters of a possibly data-modulated sinusoid under very high dynamics

A novel multistage estimation scheme is presented for estimating the parameters of a received carrier signal possibly phase-modulated by unknown data, and experiencing very high Doppler, Doppler rate, etc. Such a situation arises, for example, in the case of Global Positioning Systems (GPS) where the signal parameters are directly related to the position, velocity, acceleration and jerk of the GPS receiver. In the proposed multistage scheme, the first stage estimator operates as a coarse estimator resulting in higher rms estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency (an event termed cycle slip). The second state of the estimator operates on the error signal available from the first stage, refining the overall estimates, and in the process also reduces the number of cycle slips. The first stage algorithm is selected to be a modified least squares algorithm operating upon the differential signal model and referred to as differential least squares. This estimation stage provides relatively coarse estimates of the frequency and its derivatives. The second algorithm is simply an extended Kalman filter which also yields the estimate of the phase along with a more refined estimate of frequency as well. A major advantage of the proposed algorithm is a reduction in the threshold on received carrier power-to-noise power spectral density ratio as compared to the threshold achievable by either of these algorithms alone. In fact, it appears from the simulations that for the case of an unmodulated carrier, the proposed scheme achieves the same threshold as for an almost exact and computationally intensive implementation of the maximum likelihood estimator.

Kumar, Rajendra↗
NASA NTRS1996

An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.

Keel, Byron M.↗
DOE OSTI2025

Comparison of removal and spatial mark‐resight models for estimating wild pig density

Density estimation is critical to effectively manage invasive species and elucidate areas of highest concern. For wild pigs (Sus scrofa), the ability to estimate density is complicated because of their variable home range sizes and social structure. Common methods for estimating density (e.g., mark-recapture) may be unsuitable in management applications because additional data needs to be collected before and after management. Removal models offer a suitable alternative to estimate density changes following management and can be applied broadly across areas where management of wild pigs is ongoing. We collected wild pig removal and camera trap data from 25 private properties ranging in size from approximately 0.5 km 2 to 95 km 2 across 3 ecoregions in South Carolina, USA, from 2020–2023. We compared factors affecting consistency and precision of property-level density estimates between removal and spatial mark-resight (SMR) models. In general, excluding 1 large outlier, density estimates from removal models were between 0.60 and 15.85 wild pigs/km 2 (median = 5.34) with a median coefficient of variation (CV) of 0.76 and 95% confidence intervals for the CV between 0.70 and 0.94. Similarly, excluding 1 large outlier, density estimates from SMR were between 0.22 and 30.97 wild pigs/km 2 (median = 5.48) with a median CV of 0.39 and 95% confidence intervals for the CV between 0.38 and 1.20. We found the precision of removal models was affected primarily by the number of wild pigs dispatched in the removal period (3 months) and the ecoregion in which they were removed. None of the covariates, including the number of recaptures (a corresponding measure of sample size), influenced precision of the SMR models, although recaptures did influence the density estimates. At the individual property level, density estimates from our 2 estimators were dissimilar from each other in approximately 80% of instances, although none of the covariates we examined influenced dissimilarity. Our results provide unique insight into how sample size affects density estimates using 2 common methods and into novel SMR models that incorporate both marked and unmarked detections. In addition, the density estimates in this study can be used as a reference for wild pig densities in common land cover types throughout the southeastern United States.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Kumaraswamy distribution: different methods of estimation

This paper addresses different methods of estimation of the unknown parameters of a two-parameter Kumaraswamy distribution from a frequentist point of view. We briefly describe ten different frequentist approaches, namely, maximum likelihood estimators, moments estimators, L-moments estimators, percentile based estimators, least squares estimators, weighted least squares estimators, maximum product of spacings estimators, Cramér–von-Mises estimators, Anderson–Darling estimators and right tailed Anderson–Darling estimators. Monte Carlo simulations and two real data applications are performed to compare the performances of the estimators for both small and large samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Monte Carlo volumetric-ray-casting estimator for global fluence tallies on GPUs

A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-ray-casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-ray-casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Finally, simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Evaluation of top-down and bottom-up global terrestrial respiration estimates and their mismatch with model simulations

Terrestrial respiration is one of the most poorly understood processes in the global carbon cycle, making respiration predictions uncertain. However, expanding observations and machine learning approaches have led to a proliferation of estimates. We compiled total ecosystem and heterotrophic respiration estimates derived from top-down atmospheric inversions and bottom-up upscaling of ecosystem observations and compared them with dynamic vegetation models (DGVM) simulations over the 1980-2020 period. Our analysis revealed a convergence in mean annual global total ecosystem respiration estimates between top-down 97.1 (± SD 6.8) PgC yr-1 and bottom-up 98.5 (+/-13.4) PgC yr-1, which were both significantly lower than the ensemble mean from DGVMs estimates 133.7 (±4.7) PgC yr-1. We also found similar temporal trends between top-down estimates with a mean of 0.075 (±0.05) PgC yr-2, and bottom-up estimates of 0.05 (±0.05) PgC yr-2, which were 5 to 7 times smaller than the ensemble mean trend of 0.34 PgC yr-2 simulated by DGVMs. Global heterotrophic respiration showed much less agreement, ranging from top-down estimates of 42.7 (±4.0) PgC yr-1 to bottom-up estimates of 51.5 (±4.0) PgC yr-1 and a significantly larger ensemble model mean estimate of 60.8 PgC yr-1 (±1.9). The temporal trends in observation-based bottom-up estimates of heterotrophic respiration of 0.03 PgC yr-2 were five times lower than the model ensemble mean 0.15 PgC yr-2. Large regional disagreements in heterotrophic respiration estimates and simulations were evident in tropical and boreal latitudes. Therefore, improved regional and heterotrophic respiration estimates are necessary to reduce uncertainties regarding the future vulnerability of soil carbon.

Ballantyne, Ashley↗
DOE OSTI2012

COMPARING SINGLE-EPOCH VIRIAL BLACK HOLE MASS ESTIMATORS FOR LUMINOUS QUASARS

Single-epoch virial black hole (BH) mass estimators utilizing broad emission lines have been routinely applied to high-redshift quasars to estimate their BH masses. Depending on the redshift, different line estimators (H{alpha}, H{beta}, Mg II {lambda}2798, C IV {lambda}1549) are often used with optical/near-infrared spectroscopy. Here, we use a homogeneous sample of 60 intermediate-redshift (z {approx} 1.5-2.2) Sloan Digital Sky Survey quasars with optical and near-infrared spectra covering C IV through H{alpha} to investigate the consistency between different single-epoch virial BH mass estimators. We critically compare rest-frame UV line estimators (C IV {lambda}1549, C III] {lambda}1908, and Mg II {lambda}2798) with optical estimators (H{beta} and H{alpha}) in terms of correlations between line widths and between continuum/line luminosities, for the high-luminosity regime (L{sub 5100} > 10{sup 45.4} erg s{sup -1}) probed by our sample. The continuum luminosities of L{sub 1350} and L{sub 3000}, and the broad-line luminosities are well correlated with L{sub 5100}, reflecting the homogeneity of quasar spectra in the rest-frame UV-optical, among which L{sub 1350} and the line luminosities for C IV and C III] have the largest scatter in the correlation with L{sub 5100}. We found that the Mg II FWHM correlates well with the FWHMs of the Balmer lines and that the Mg II line estimator can be calibrated to yield consistent virial mass estimates with those based on the H{beta}/H{alpha} estimators, thus extending earlier results on less luminous objects. The C IV FWHM is poorly correlated with the Balmer line FWHMs, and the scatter between the C IV and H{beta} FWHMs consists of an irreducible part ({approx}0.12 dex), and a part that correlates with the blueshift of the C IV centroid relative to that of H{beta}, similar to earlier studies comparing C IV with Mg II. The C III] FWHM is found to correlate with the C IV FWHM, and hence is also poorly correlated with the H{beta} FWHM. While the C IV and C III] lines can be calibrated to yield consistent virial mass estimates as H{beta} on average, the scatter is substantially larger than Mg II, and the usage of C IV/C III] FWHM in the mass estimators does not improve the agreement with the H{beta} estimator. We discuss controversial claims in the literature on the correlation between C IV and H{beta} FWHMs, and suggest that the reported correlation is either a result based on small samples or only valid for low-luminosity objects.

79 ASTRONOMY AND ASTROPHYSICS↗