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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Estimation of inflow uncertainties in laminar hypersonic double-cone experiments

We propose herein a probabilistic framework for assessing the consistency of an experimental dataset, i.e., whether the stated experimental conditions are consistent with the measurements provided. In case the dataset is inconsistent, our framework allows one to hypothesize and test sources of inconsistencies. This is crucial in model validation efforts. The framework relies on Bayesian inference to estimate experimental settings deemed uncertain, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements. We test the correctness of the framework on three double-cone experiments conducted in the CUBRC Inc.'s LENS-I shock tunnel, which have also been numerically simulated successfully. Thereafter, we use the framework to investigate two double-cone experiments (executed in the LENS-XX shock tunnel) which have encountered difficulties when used in model validation exercises. We detect an inconsistency with one of the LENS-XX experiments. In addition, we hypothesize two causes for our inability to simulate LEXS-XX experiments accurately and test them using our framework. We find that there is no single cause that explains all the discrepancies between model predictions and experimental data, but different causes explain different discrepancies, to larger or smaller extent. We end by proposing that uncertainty quantification methods be used more widely to understand experiments and characterize facilities, and we cite three different methods to do so, the third of which we present in this paper.

Thermophysics↗
DOE OSTI2018

Effects of exposure estimation errors on estimated exposure-response relations for PM2.5

Highlights: • Recent literature has identified associations between concentrations of fine particulate matter (PM2.5) in ambient air and adverse health effects at estimated exposure concentrations below current air ambient quality standards. • The models and analyses used do not usually quantify uncertainty about exposure estimates. • Realistic errors in exposure estimates can be estimated from available air quality monitoring stations by predicting concentrations at each station from concentrations at neighboring stations. • Exposure estimation errors are large enough to obscure low-dose nonlinearities or thresholds in the true concentration-response (C-R) function, artificially making them appear to be linear at low doses. • Better estimates of C-R functions will require modeling exposure distributions and estimation errors, rather than just estimating average exposure concentrations. Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes, from autism and auto theft to elderly mortality, suicide, and violent crime, have been widely reported. Influential articles have argued that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes. Yet, other studies have found that reducing black smoke and other particulate matter by as much as 70% and dozens of micrograms per cubic meter has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them. This paper examines whether this disconnect between association and causation might be explained in part by ignored estimation errors in estimated exposure concentrations. We use EPA air quality monitor data from the Los Angeles area of California to examine the shapes of estimated C-R functions for PM2.5 when the true C-R functions are assumed to be step functions with well-defined response thresholds. The estimated C-R functions mistakenly show risk as smoothly increasing with concentrations even well below the response thresholds, thus incorrectly predicting substantial risk reductions from reductions in concentrations that do not affect health risks. We conclude that ignored estimation errors obscure the shapes of true C-R functions, including possible thresholds, possibly leading to unrealistic predictions of the changes in risk caused by changing exposures. Instead of estimating improvements in public health per unit reduction (e.g., per 10 µg/m{sup 3} decrease) in average PM2.5 concentrations, it may be essential to consider how interventions change the distributions of exposure concentrations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Hybrid approach for estimating dynamic states of synchronous generators

Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this paper proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter (EKF) based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system shows that the proposed approach can improve estimation accuracy and increase robustness against model errors.

Akhlaghi, Shahrokh↗
DOE OSTI2022

Precise ROCOF estimation algorithm for low inertia power grids

The precise estimation of Rate Of Change Of Frequencies (ROCOFs) in a generation trip event can be helpful on power system inertia estimation, fast system response, and accurate event capturing. However, the ROCOF estimations from the existing Synchronized Measurement Devices (SMDs) are usually simply calculated by the finite difference between two adjacent frequency measurement points. The ineluctable noises, disturbances, and spikes from real-world frequency measurements can bring large dynamics to the ROCOF estimation and thus can result in an inaccurate estimation of the initial ROCOF. This issue becomes more serious when the target power grid has low inertia, and a large amount of distributed energy sources are deployed. To address this issue, a precise ROCOF estimation algorithm is designed based on least square method with flexible window size. In addition, a median filter is also designed and applied on the frequency measurements before using the proposed algorithm. The window size and thresholds in the proposed algorithm are determined with historical event data analysis. The proposed algorithm is deployed in a low cost, flexible, and distribution level universal grid analyzer (UGA) platform. In conclusion, multiple experiments are conducted in both a laboratory and the Hawaiian Islands to verify the effectiveness of the proposed algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Comparison of equilibrium climate sensitivity estimates from slab ocean, 150-year, and longer simulations

We compare equilibrium climate sensitivity (ECS) estimates from pairs of long (= 800-year) control and abruptly quadrupled CO2 simulations with shorter (150, 300 year) coupled atmosphere-ocean simulations and Slab Ocean Models (SOM). Consistent with previous work, ECS estimates from shorter coupled simulations based on annual averages for years 1-150 underestimate those from SOM (-8% ± 13%) and long (-14% ± 8%) simulations. Analysis of only years 21-150 improved agreement with SOM (-2% ± 14%) and long (-8% ± 10%) estimates. Use of pentadal averages for years 51-150 results in improved agreement with long simulations (-4% ± 11%). While ECS estimates from current generation US models based on SOM and coupled annual averages of years 1-150 range from 2.6°C to 5.3°C, estimates based longer simulations of the same models range from 3.2°C to 7.0°C. Such variations between methods argues for caution in comparison and interpretation of ECS estimates across models.

Dunne, John P.↗
DOE OSTI2014

Using LiF:Mg,Cu,P TLDs to estimate the absorbed dose to water in liquid water around an {sup 192}Ir brachytherapy source

Purpose: The absorbed dose to water is the fundamental reference quantity for brachytherapy treatment planning systems and thermoluminescence dosimeters (TLDs) have been recognized as the most validated detectors for measurement of such a dosimetric descriptor. The detector response in a wide energy spectrum as that of an{sup 192}Ir brachytherapy source as well as the specific measurement medium which surrounds the TLD need to be accounted for when estimating the absorbed dose. This paper develops a methodology based on highly sensitive LiF:Mg,Cu,P TLDs to directly estimate the absorbed dose to water in liquid water around a high dose rate {sup 192}Ir brachytherapy source. Methods: Different experimental designs in liquid water and air were constructed to study the response of LiF:Mg,Cu,P TLDs when irradiated in several standard photon beams of the LNE-LNHB (French national metrology laboratory for ionizing radiation). Measurement strategies and Monte Carlo techniques were developed to calibrate the LiF:Mg,Cu,P detectors in the energy interval characteristic of that found when TLDs are immersed in water around an{sup 192}Ir source. Finally, an experimental system was designed to irradiate TLDs at different angles between 1 and 11 cm away from an {sup 192}Ir source in liquid water. Monte Carlo simulations were performed to correct measured results to provide estimates of the absorbed dose to water in water around the {sup 192}Ir source. Results: The dose response dependence of LiF:Mg,Cu,P TLDs with the linear energy transfer of secondary electrons followed the same variations as those of published results. The calibration strategy which used TLDs in air exposed to a standard N-250 ISO x-ray beam and TLDs in water irradiated with a standard{sup 137}Cs beam provided an estimated mean uncertainty of 2.8% (k = 1) in the TLD calibration coefficient for irradiations by the {sup 192}Ir source in water. The 3D TLD measurements performed in liquid water were obtained with a maximum uncertainty of 11% (k = 1) found at 1 cm from the source. Radial dose values in water were compared against published results of the American Association of Physicists in Medicine and the European Society for Radiotherapy and Oncology and no significant differences (maximum value of 3.1%) were found within uncertainties except for one position at 9 cm (5.8%). At this location the background contribution relative to the TLD signal is relatively small and an unexpected experimental fluctuation in the background estimate may have caused such a large discrepancy. Conclusions: This paper shows that reliable measurements with TLDs in complex energy spectra require a study of the detector dose response with the radiation quality and specific calibration methodologies which model accurately the experimental conditions where the detectors will be used. The authors have developed and studied a method with highly sensitive TLDs and contributed to its validation by comparison with results from the literature. This methodology can be used to provide direct estimates of the absorbed dose rate in water for irradiations with HDR{sup 192}Ir brachytherapy sources.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

National soil organic carbon estimates can improve global estimates

Global inventories of spatial and vertical distribution of soil organic carbon (SOC) stocks are being used in national and global initiatives targeted to mitigate climate change and land degradation impacts. Yet, national level high-resolution estimates of SOC stocks can be useful for improving the accuracy of global SOC inventories. We estimated spatially resolved SOC stocks of surface 0–30 cm and subsurface 30–100 cm layers at a spatial resolution of 30m in tropical Island, Sri Lanka using a legacy harmonized soil database of 122 soil profiles. The national estimates were compared with two global estimates derived from WISE30sec and SoilGrids250m. The tropical Island (land area=64,610 km2) occupying 0.03% of global land area showed a considerable heterogeneity in SOC stocks ranging from 2.0-342.5 Mg ha -1 and 2.7-391.7 Mg ha -1 in the surface and subsurface soil layers, respectively. We found, elevation, precipitation and slope angle as main environmental controllers of the spatial distribution of SOC stocks under tropical climate. Incorporating the pedogenic information (derived from soil series level legacy map, soil orders and suborders) with environmental controllers resulted in better regression models of predicting surface (R 2 =0.61) and subsurface (R 2 =0.81) SOC stocks. Geographically weighted regression kriging derived maps of SOC stocks revealed that 0–100 cm soil layer of the tropical Island stored 500 Tg C contributing for 0.04% of the global SOC stocks. The validation results of our estimates showed low Mean Estimation Error (MEE: surface -1.6 and subsurface -1.6 Mg ha -1 ) and Root Mean Square Error (RMSE: surface 29.5 and subsurface 24.9 Mg ha -1 ) indicating a low bias and satisfactory predictions. The relative improvement of the prediction accuracy of the SOC stocks of our geospatial estimates in the 0–30 cm layer in comparison to SoilGrids250m and WISE30sec data derived SOC stocks were 51.7% and 35.2%, respectively. The SOC stocks predictions of the 30-100 cm soil layer showed even better relative improvement compared to SoilGrids250m (78.4%) and WISE30sec (57.4%) SOC estimates. Compared to estimates of total SOC stocks resulted in this study, WISE30sec data derived SOC stock maps showed 30% over estimation of the C stock in surface 0-30 cm (332 Tg C) and 41% overestimation in 30-100 cm layer (343 Tg C). The over estimation of total SOC stocks by the SoilGrids250 SOC stocks map for the surface 0-30 cm layer was 122% (567 Tg C) and for the 30-100 cm layer it was 209% (750 Tg C). We conclude that the fusion of legacy soil information of SOC stocks with appropriate environmental covariates and pedogenic information derived from legacy area-class soil maps at national level can produce more accurate inventories of spatial and vertical distribution of SOC stocks. As a result, these national inventories have a great potential of upgrading global inventories of SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data-Driven Distributed Algorithms for Estimating Eigenvalues and Eigenvectors of Interconnected Dynamical Systems

Here, the paper presents data-driven algorithms to estimate in a distributed manner the eigenvalues, right and left eigenvectors of an unknown linear (or linearized) interconnected dynamic system. In particular, the proposed algorithms do not require the identification of the system model in advance before performing the estimation. As a first step, we consider interconnected dynamical system with distinct eigenvalues. The proposed strategy first estimates the eigenvalues using the well-known Prony method. The right and left eigenvectors are then estimated by solving distributively a set of linear equations. One important feature of the proposed algorithms is that the topology of communication network used to perform the distributed estimation can be chosen arbitrarily, given that it is connected, and is also independent of the structure or sparsity of the system (state) matrix. The proposed distributed algorithms are demonstrated via a numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Kalman filter data assimilation: Targeting observations and parameter estimation

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A Method to Estimate the Atomic Number and Mass Thickness of Intervening Materials in Uranium and Plutonium Gamma-Ray Spectroscopy Measurements

To accurately characterize shielded special nuclear materials (SNM) using passive gamma-ray spectroscopy measurement techniques, the effective atomic number and the thickness of shielding materials must be measured. Intervening materials between the source and detector may affect the estimated source isotopics (uranium enrichment and plutonium grade) for techniques which rely on raw count rates or photopeak ratios of gamma-ray lines separated in energy. Furthermore, knowledge of the surrounding materials can provide insight regarding the configuration of a device containing SNM. The described method was developed using spectra recorded using high energy resolution CdZnTe detectors, but can be expanded to any gamma-ray spectrometers with energy resolution of better than 1% FWHM at 662 keV. The effective atomic number, Z, and mass thickness of the intervening shielding material are identified by comparing the relative attenuation of different gamma-ray lines and estimating the proportion of Compton scattering interactions to photoelectric absorptions within the shield. While characteristic Kα x-rays can be used to identify shielding materials made of high Z elements, this method can be applied to all shielding materials. This algorithm has adequately estimated the effective atomic number for shields made of iron, aluminum, and polyethylene surrounding uranium samples using experimental data. The mass thicknesses of shielding materials have been estimated with a standard error of less than 1.3 g/cm2 for iron shields up to 2.5 cm thick. The effective atomic number was accurately estimated to 26 ± 5 for all iron thicknesses.

CdZnTe detectors, gamma-ray imaging, gamma-ray spe↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
DOE OSTI2020

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, due to for example cyber attacks, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars: Radar and Lidar Turbulence Estimation

Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. Likewise, the agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. And, based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. Furthermore, the uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Multicollinearity in simultaneous equations system: evaluation of estimation performance of two-parameter estimator

In simultaneous equations model, two-stage least squares estimator is easy to apply and commonly preferred. When multicollinearity exists, two-stage least squares estimator has some drawbacks and it is no longer favorable. In this context, biased estimation methods are recommended. Two-parameter estimator of Özkale and Kaçıranlar (Commun Stat Theory Methods 36(15):2707–2725, 2007) had been established to be superior to the ordinary least squares estimator under some conditions in linear regression model suffering from multicollinearity. In this paper, the idea of two-parameter estimation in linear regression model is carried out to the simultaneous equations model. For this model, two-stage two-parameter estimator is proposed to remedy the problem of multicollinearity. Estimation performance of this new estimator is evaluated by means of two real-life data analyses. In addition to the numerical example, an extensive Monte Carlo experiment is conducted.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Online Static Load Model Estimation in Distribution Systems: Preprint

The paper focuses on parameter identification for time varying voltage dependent polynomial load models, namely the constant impedance (Z), constant current (I), and constant power (P), in the distribution systems. Typically, parameter identification has been done for aggregated loads at substations for the transmission planning studies. Given the higher voltage volatility faced by active distribution systems, this paper takes the load model estimation to the distribution networks. The paper assumes the presence of measurements at the distribution nodes from various sources, each at varying temporal resolutions. Least squares estimation (LSE) method with bounded variables is used to convert the measurements into ZIP parameter estimations for loads at various locations of the feeder. The results and discussions focus on some of the estimation issues faced in a distribution system due to varying voltage sensitivities of loads along a feeder, measurement resolution, impact of window size and sampling rate of measurement data, and the use of dynamic simulations to create synthetic measurement data in the absence of real ones.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

This paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and PMU measurements are calculated separately through unscented transformation and a vector autoregression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case of synchronization issues between SCADA and PMU measurements, either forecasted PMU measurements or prior SCADA measurements from the last estimation run are leveraged to restore system observability. After that, robust generalized maximum-likelihood (GM)-estimator is developed to integrate full measurement correlations and handle bad data in SCADA and PMU measurements. Thanks to the robustness and flexibility of the proposed robust estimator, bad data as well as imperfect measurement synchronization are mitigated, yielding high statistical efficiency. Comprehensive comparison results with other alternatives under various conditions demonstrate the benefits of the proposed framework.

Zhao, Junbo (ORCID:0000000284989666)↗