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DOE OSTI2020

A New Estimator to Correct for Bias from Tag Rate Expansion on Natural-Origin Fish Attributes in Mixed-Stock Analysis Using Parentage-Based Tagging

Abstract In fisheries where hatchery- and natural-origin conspecifics occur as mixed stocks, it is often important to estimate both the natural-origin proportion of the mixture and the composition of attributes within the natural-origin portion (e.g., genetic stock, sex, and age-class). These estimates are facilitated by parentage-based tagging, which allows large numbers of hatchery fish to be efficiently tagged and later identified. When tag rates are less than 100%, the untagged fish in the mixture include both untagged hatchery fish and natural-origin fish. Unbiased estimation of the abundance and attribute composition of the natural-origin portion requires an estimator that accounts for tag rates. Two estimators are described: one “accounting-style” estimator similar to previously described approaches and one maximum likelihood method. These estimators were evaluated and compared using simulations mimicking estimation of the composition of fish migrating past a dam. The two estimators performed similarly at reducing bias when tag rates were high, but the maximum likelihood method had smaller mean square error when tag rates were low. We provide an R package to allow usage of this estimator in a wide variety of fisheries applications.

Delomas, Thomas A. (ORCID:000000015154759X)↗

DOE OSTI2018

Assessing the recent estimates of the global burden of disease for ambient air pollution: Methodological changes and implications for low- and middle-income countries

Highlights: • From 2013 to 2015, the Global Burden of Disease estimates for ambient air pollution increased by over one million deaths. • We reconstructed the global estimates to quantify the impact of changes in model inputs. • Much of the change was due to new studies and improved methods in calculating dose response functions. • For certain low-income countries the improved air pollution exposure assessment had a significant impact. • Complete documentation and implications of future input changes are essential. The Global Burden of Disease (GBD) is a comparative assessment of the health impact of the major and well-established risk factors, including ambient air pollution (AAP) assessed by concentrations of PM2.5 (particles less than 2.5 µm) and ozone. Over the last two decades, major improvements have emerged for two important inputs in the methodology for estimating the impacts of PM2.5: the assessment of global exposure to PM2.5 and the development of integrated exposure risk models (IERs) that relate the entire range of global exposures of PM2.5 to cause-specific mortality. As a result, the estimated annual mortality attributed to AAP increased from less than 1 million in 2000 to roughly 3 million for GBD in years 2010 and 2013, to 4.2 million for GBD 2015. However, the magnitude of the recent change and uncertainty regarding its rationale have resulted, in some cases, in skepticism and reduced confidence in the overall estimates. To understand the underlying reasons for the change in mortality, we examined the estimates for the years 2013 and 2015 to determine the quantitative implications of alternative model input assumptions. We calculated that the year 2013 estimates increased by 8% after applying the updated exposure data used in GBD 2015, and increased by 23% with the application of the updated IERs from GBD 2015. The application of both upgraded methodologies together increased the GBD 2013 estimates by 35%, or about one million deaths. We also quantified the impact of the changes in demographics and the assumed threshold level. Since the global estimates of air pollution-related deaths will continue to change over time, a clear documentation of the modifications in the methodology and their impacts is necessary. In addition, there is need for additional monitoring and epidemiological studies to reduce uncertainties in the estimates for low- and medium-income countries, which contribute to about one-half of the mortality.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Comparison of Global Aboveground Biomass Estimates From Satellite Observations and Dynamic Global Vegetation Models

The global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO 2 and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite-based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite-based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite-based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high-quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite- and model-based AGB estimates. Furthermore, we suggest the carbon-modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Development on electromagnetic impedance function modeling and its estimation

Today the Electromagnetic methods such as magnetotellurics (MT) and controlled sources audio MT (CSAMT) is used in a broad variety of applications. Its usefulness in poor seismic areas and its negligible environmental impact are integral parts of effective exploration at minimum cost. As exploration was forced into more difficult areas, the importance of MT and CSAMT, in conjunction with other techniques, has tended to grow continuously. However, there are obviously important and difficult problems remaining to be solved concerning our ability to collect process and interpret MT as well as CSAMT in complex 3D structural environments. This talk aim at reviewing and discussing the recent development on MT as well as CSAMT impedance functions modeling, and also some improvements on estimation procedures for the corresponding impedance functions. In MT impedance modeling, research efforts focus on developing numerical method for computing the impedance functions of three dimensionally (3-D) earth resistivity models. On that reason, 3-D finite elements numerical modeling for the impedances is developed based on edge element method. Whereas, in the CSAMT case, the efforts were focused to accomplish the non-plane wave problem in the corresponding impedance functions. Concerning estimation of MT and CSAMT impedance functions, researches were focused on improving quality of the estimates. On that objective, non-linear regression approach based on the robust M-estimators and the Hilbert transform operating on the causal transfer functions, were used to dealing with outliers (abnormal data) which are frequently superimposed on a normal ambient MT as well as CSAMT noise fields. As validated, the proposed MT impedance modeling method gives acceptable results for standard three dimensional resistivity models. Whilst, the full solution based modeling that accommodate the non-plane wave effect for CSAMT impedances is applied for all measurement zones, including near-, transition-as well as the far-field zones, and consequently the plane wave correction is no longer needed for the impedances. In the resulting robust impedance estimates, outlier contamination is removed and the self consistency between the real and imaginary parts of the impedance estimates is guaranteed. Using synthetic and real MT data, it is shown that the proposed robust estimation methods always yield impedance estimates which are better than the conventional least square (LS) estimation, even under condition of severe noise contamination. A recent development on the constrained robust CSAMT impedance estimation is also discussed. By using synthetic CSAMT data it is demonstrated that the proposed methods can produce usable CSAMT transfer functions for all measurement zones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Optimal Bandwidth for Multitaper Spectrum Estimation

A systematic method for bandwidth parameter selection is desired for Thomson multitaper spectrum estimation. We give a method for determining the optimal bandwidth based on a mean squared error (MSE) criterion. When the true spectrum has a second-order Taylor series expansion, one can express quadratic local bias as a function of the curvature of the spectrum, which can be estimated by using a simple spline approximation. This is combined with a variance estimate, obtained by jackknifing over individual spectrum estimates, to produce an estimated MSE for the log spectrum estimate for each choice of time-bandwidth product. The bandwidth that minimizes the estimated MSE then gives the desired spectrum estimate. Additionally, the bandwidth obtained using our method is also optimal for cepstrum estimates. We give an example of a damped oscillatory (Lorentzian) process in which the approximate optimal bandwidth can be written as a function of the damping parameter. Furthermore, the true optimal bandwidth agrees well with that given by minimizing estimated the MSE in these examples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Effectiveness of external respiratory surrogates for in vivo liver motion estimation

Purpose: Due to low frame rate of MRI and high radiation damage from fluoroscopy and CT, liver motion estimation using external respiratory surrogate signals seems to be a better approach to track liver motion in real-time for liver tumor treatments in radiotherapy and thermotherapy. This work proposes a liver motion estimation method based on external respiratory surrogate signals. Animal experiments are also conducted to investigate related issues, such as the sensor arrangement, multisensor fusion, and the effective time period. Methods: Liver motion and abdominal motion are both induced by respiration and are proved to be highly correlated. Contrary to the difficult direct measurement of the liver motion, the abdominal motion can be easily accessed. Based on this idea, our study is split into the model-fitting stage and the motion estimation stage. In the first stage, the correlation between the surrogates and the liver motion is studied and established via linear regression method. In the second stage, the liver motion is estimated by the surrogate signals with the correlation model. Animal experiments on cases of single surrogate signal, multisurrogate signals, and long-term surrogate signals are conducted and discussed to verify the practical use of this approach. Results: The results show that the best single sensor location is at the middle of the upper abdomen, while multisurrogate models are generally better than the single ones. The estimation error is reduced from 0.6 mm for the single surrogate models to 0.4 mm for the multisurrogate models. The long-term validity of the estimation models is quite satisfactory within the period of 10 min with the estimation error less than 1.4 mm. Conclusions: External respiratory surrogate signals from the abdomen motion produces good performance for liver motion estimation in real-time. Multisurrogate signals enhance estimation accuracy, and the estimation model can maintain its accuracy for at least 10 min. This approach can be used in practical applications such as the liver tumor treatment in radiotherapy and thermotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT

Purpose: Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).Methods: The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.Results: Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left–right (L-R), anterior–posterior (A-P), and superior–inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively.Conclusions: The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections

Purpose: To develop a technique to estimate onboard 4D-CBCT using prior information and limited-angle projections for potential 4D target verification of lung radiotherapy.Methods: Each phase of onboard 4D-CBCT is considered as a deformation from one selected phase (prior volume) of the planning 4D-CT. The deformation field maps (DFMs) are solved using a motion modeling and free-form deformation (MM-FD) technique. In the MM-FD technique, the DFMs are estimated using a motion model which is extracted from planning 4D-CT based on principal component analysis (PCA). The motion model parameters are optimized by matching the digitally reconstructed radiographs of the deformed volumes to the limited-angle onboard projections (data fidelity constraint). Afterward, the estimated DFMs are fine-tuned using a FD model based on data fidelity constraint and deformation energy minimization. The 4D digital extended-cardiac-torso phantom was used to evaluate the MM-FD technique. A lung patient with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume, including changes of respiration amplitude, lesion size and lesion average-position, and phase shift between lesion and body respiratory cycle. The lesions were contoured in both the estimated and “ground-truth” onboard 4D-CBCT for comparison. 3D volume percentage-difference (VPD) and center-of-mass shift (COMS) were calculated to evaluate the estimation accuracy of three techniques: MM-FD, MM-only, and FD-only. Different onboard projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy.Results: For all simulated patient and projection acquisition scenarios, the mean VPD (±S.D.)/COMS (±S.D.) between lesions in prior images and “ground-truth” onboard images were 136.11% (±42.76%)/15.5 mm (±3.9 mm). Using orthogonal-view 15°-each scan angle, the mean VPD/COMS between the lesion in estimated and “ground-truth” onboard images for MM-only, FD-only, and MM-FD techniques were 60.10% (±27.17%)/4.9 mm (±3.0 mm), 96.07% (±31.48%)/12.1 mm (±3.9 mm) and 11.45% (±9.37%)/1.3 mm (±1.3 mm), respectively. For orthogonal-view 30°-each scan angle, the corresponding results were 59.16% (±26.66%)/4.9 mm (±3.0 mm), 75.98% (±27.21%)/9.9 mm (±4.0 mm), and 5.22% (±2.12%)/0.5 mm (±0.4 mm). For single-view scan angles of 3°, 30°, and 60°, the results for MM-FD technique were 32.77% (±17.87%)/3.2 mm (±2.2 mm), 24.57% (±18.18%)/2.9 mm (±2.0 mm), and 10.48% (±9.50%)/1.1 mm (±1.3 mm), respectively. For projection angular-sampling-intervals of 0.6°, 1.2°, and 2.5° with the orthogonal-view 30°-each scan angle, the MM-FD technique generated similar VPD (maximum deviation 2.91%) and COMS (maximum deviation 0.6 mm), while sparser sampling yielded larger VPD/COMS. With equal number of projections, the estimation results using scattered 360° scan angle were slightly better than those using orthogonal-view 30°-each scan angle. The estimation accuracy of MM-FD technique declined as noise level increased.Conclusions: The MM-FD technique substantially improves the estimation accuracy for onboard 4D-CBCT using prior planning 4D-CT and limited-angle projections, compared to the MM-only and FD-only techniques. It can potentially be used for the inter/intrafractional 4D-localization verification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

TU-EF-204-01: Accurate Prediction of CT Tube Current Modulation: Estimating Tube Current Modulation Schemes for Voxelized Patient Models Used in Monte Carlo Simulations

Purpose: Most patient models used in Monte Carlo-based estimates of CT dose, including computational phantoms, do not have tube current modulation (TCM) data associated with them. While not a problem for fixed tube current simulations, this is a limitation when modeling the effects of TCM. Therefore, the purpose of this work was to develop and validate methods to estimate TCM schemes for any voxelized patient model. Methods: For 10 patients who received clinically-indicated chest (n=5) and abdomen/pelvis (n=5) scans on a Siemens CT scanner, both CT localizer radiograph (“topogram”) and image data were collected. Methods were devised to estimate the complete x-y-z TCM scheme using patient attenuation data: (a) available in the Siemens CT localizer radiograph/topogram itself (“actual-topo”) and (b) from a simulated topogram (“sim-topo”) derived from a projection of the image data. For comparison, the actual TCM scheme was extracted from the projection data of each patient. For validation, Monte Carlo simulations were performed using each TCM scheme to estimate dose to the lungs (chest scans) and liver (abdomen/pelvis scans). Organ doses from simulations using the actual TCM were compared to those using each of the estimated TCM methods (“actual-topo” and “sim-topo”). Results: For chest scans, the average differences between doses estimated using actual TCM schemes and estimated TCM schemes (“actual-topo” and “sim-topo”) were 3.70% and 4.98%, respectively. For abdomen/pelvis scans, the average differences were 5.55% and 6.97%, respectively. Conclusion: Strong agreement between doses estimated using actual and estimated TCM schemes validates the methods for simulating Siemens topograms and converting attenuation data into TCM schemes. This indicates that the methods developed in this work can be used to accurately estimate TCM schemes for any patient model or computational phantom, whether a CT localizer radiograph is available or not. Funding Support: NIH Grant R01-EB017095; Disclosures - Michael McNitt-Gray: Institutional Research Agreement, Siemens AG; Research Support, Siemens AG; Consultant, Flaherty Sensabaugh Bonasso PLLC; Consultant, Fulbright and Jaworski; Disclosures - Cynthia McCollough: Research Grant, Siemens Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-BRA-08: A Novel Technique for Estimating Volumetric Cine MRI (VC-MRI) From Multi-Slice Sparsely Sampled Cine Images Using Motion Modeling and Free Form Deformation

Purpose: To develop a technique to estimate on-board VC-MRI using multi-slice sparsely-sampled cine images, patient prior 4D-MRI, motion-modeling and free-form deformation for real-time 3D target verification of lung radiotherapy. Methods: A previous method has been developed to generate on-board VC-MRI by deforming prior MRI images based on a motion model(MM) extracted from prior 4D-MRI and a single-slice on-board 2D-cine image. In this study, free-form deformation(FD) was introduced to correct for errors in the MM when large anatomical changes exist. Multiple-slice sparsely-sampled on-board 2D-cine images located within the target are used to improve both the estimation accuracy and temporal resolution of VC-MRI. The on-board 2D-cine MRIs are acquired at 20–30frames/s by sampling only 10% of the k-space on Cartesian grid, with 85% of that taken at the central k-space. The method was evaluated using XCAT(computerized patient model) simulation of lung cancer patients with various anatomical and respirational changes from prior 4D-MRI to onboard volume. The accuracy was evaluated using Volume-Percent-Difference(VPD) and Center-of-Mass-Shift(COMS) of the estimated tumor volume. Effects of region-of-interest(ROI) selection, 2D-cine slice orientation, slice number and slice location on the estimation accuracy were evaluated. Results: VCMRI estimated using 10 sparsely-sampled sagittal 2D-cine MRIs achieved VPD/COMS of 9.07±3.54%/0.45±0.53mm among all scenarios based on estimation with ROI-MM-ROI-FD. The FD optimization improved estimation significantly for scenarios with anatomical changes. Using ROI-FD achieved better estimation than global-FD. Changing the multi-slice orientation to axial, coronal, and axial/sagittal orthogonal reduced the accuracy of VCMRI to VPD/COMS of 19.47±15.74%/1.57±2.54mm, 20.70±9.97%/2.34±0.92mm, and 16.02±13.79%/0.60±0.82mm, respectively. Reducing the number of cines to 8 enhanced temporal resolution of VC-MRI by 25% while maintaining the estimation accuracy. Estimation using slices sampled uniformly through the tumor achieved better accuracy than slices sampled non-uniformly. Conclusions: Preliminary studies showed that it is feasible to generate VC-MRI from multi-slice sparsely-sampled 2D-cine images for real-time 3D-target verification. This work was supported by the National Institutes of Health under Grant No. R01-CA184173 and a research grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗

DOE OSTI2017

Comparison of methane emission estimates from multiple measurement techniques at natural gas production pads

This study presents the results of a campaign that estimated methane emissions at 268 gas production facilities in the Fayetteville shale gas play using onsite measurements (261 facilities) and two downwind methods - the dual tracer flux ratio method (Tracer Facility Estimate - TFE, 17 facilities) and the EPA Other Test Method 33a (OTM33A Facility Estimate - OFE, 50 facilities). A study onsite estimate (SOE) for each facility was developed by combining direct measurements and simulation of unmeasured emission sources, using operator activity data and emission data from literature. The SOE spans 0-403 kg/h and simulated methane emissions from liquid unloadings account for 88% of total emissions estimated by the SOE, with 76% (95% CI [51%-92%]) contributed by liquid unloading at two facilities. TFE and SOE show overlapping 95% CI between individual estimates at 15 of 16 (94%) facilities where the measurements were paired, while OFE and SOE show overlapping 95% CI between individual estimates at 28 of 43 (65%) facilities. However, variance-weighted least-squares (VWLS) regressions performed on sets of paired estimates indicate statistically significant differences between methods. The SOE represents a lower bound of emissions at facilities where onsite direct measurements of continuously emitting sources are the primary contributor to the SOE, a sub-selection of facilities which minimizes expected inter-method differences for intermittent pneumatic controllers and the impact of episodically-emitting unloadings. At 9 such facilities, VWLS indicates that TFE estimates systematically higher emissions than SOE (TFE-to-SOE ratio = 1.6, 95% CI [1.2 to 2.1]). At 20 such facilities, VWLS indicates that OFE estimates systematically lower emissions than SOE (OFE-to-SOE ratio of 0.41 [0.26 to 0.90]). Given that SOE at these facilities is a lower limit on emissions, these results indicate that OFE is likely a less accurate method than SOE or TFE for this type of facility.

03 NATURAL GAS↗

DOE OSTI2014

Sub-Second Parallel State Estimation

This report describes the performance of Pacific Northwest National Laboratory (PNNL) sub-second parallel state estimation (PSE) tool using the utility data from the Bonneville Power Administrative (BPA) and discusses the benefits of the fast computational speed for power system applications. The test data were provided by BPA. They are two-days’ worth of hourly snapshots that include power system data and measurement sets in a commercial tool format. These data are extracted out from the commercial tool box and fed into the PSE tool. With the help of advanced solvers, the PSE tool is able to solve each BPA hourly state estimation problem within one second, which is more than 10 times faster than today’s commercial tool. This improved computational performance can help increase the reliability value of state estimation in many aspects: (1) the shorter the time required for execution of state estimation, the more time remains for operators to take appropriate actions, and/or to apply automatic or manual corrective control actions. This increases the chances of arresting or mitigating the impact of cascading failures; (2) the SE can be executed multiple times within time allowance. Therefore, the robustness of SE can be enhanced by repeating the execution of the SE with adaptive adjustments, including removing bad data and/or adjusting different initial conditions to compute a better estimate within the same time as a traditional state estimator’s single estimate. There are other benefits with the sub-second SE, such as that the PSE results can potentially be used in local and/or wide-area automatic corrective control actions that are currently dependent on raw measurements to minimize the impact of bad measurements, and provides opportunities to enhance the power grid reliability and efficiency. PSE also can enable other advanced tools that rely on SE outputs and could be used to further improve operators’ actions and automated controls to mitigate effects of severe events on the grid. The power grid continues to grow and the number of measurements is increasing at an accelerated rate due to the variety of smart grid devices being introduced. A parallel state estimation implementation will have better performance than traditional, sequential state estimation by utilizing the power of high performance computing (HPC). This increased performance positions parallel state estimators as valuable tools for operating the increasingly more complex power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

IDC reengineering Phase 2 & 3 US industry standard cost estimate summary

Sandia National Laboratories has prepared a ROM cost estimate for budgetary planning for the IDC Reengineering Phase 2 & 3 effort, using a commercial software cost estimation tool calibrated to US industry performance parameters. This is not a cost estimate for Sandia to perform the project. This report provides the ROM cost estimate and describes the methodology, assumptions, and cost model details used to create the ROM cost estimate. ROM Cost Estimate Disclaimer Contained herein is a Rough Order of Magnitude (ROM) cost estimate that has been provided to enable initial planning for this proposed project. This ROM cost estimate is submitted to facilitate informal discussions in relation to this project and is NOT intended to commit Sandia National Laboratories (Sandia) or its resources. Furthermore, as a Federally Funded Research and Development Center (FFRDC), Sandia must be compliant with the Anti-Deficiency Act and operate on a full-cost recovery basis. Therefore, while Sandia, in conjunction with the Sponsor, will use best judgment to execute work and to address the highest risks and most important issues in order to effectively manage within cost constraints, this ROM estimate and any subsequent approved cost estimates are on a 'full-cost recovery' basis. Thus, work can neither commence nor continue unless adequate funding has been accepted and certified by DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA

We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.

09 BIOMASS FUELS↗

NASA NTRS1985

Maximum Likelihood Estimation with Emphasis on Aircraft Flight Data

Accurate modeling of flexible space structures is an important field that is currently under investigation. Parameter estimation, using methods such as maximum likelihood, is one of the ways that the model can be improved. The maximum likelihood estimator has been used to extract stability and control derivatives from flight data for many years. Most of the literature on aircraft estimation concentrates on new developments and applications, assuming familiarity with basic estimation concepts. Some of these basic concepts are presented. The maximum likelihood estimator and the aircraft equations of motion that the estimator uses are briefly discussed. The basic concepts of minimization and estimation are examined for a simple computed aircraft example. The cost functions that are to be minimized during estimation are defined and discussed. Graphic representations of the cost functions are given to help illustrate the minimization process. Finally, the basic concepts are generalized, and estimation from flight data is discussed. Specific examples of estimation of structural dynamics are included. Some of the major conclusions for the computed example are also developed for the analysis of flight data.

Iliff, K. W.↗