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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Calibration approach and range of observed sap flow influences transpiration estimates from thermal dissipation sensors

Calibrating thermal dissipation (TD) sap flow sensors has become increasingly important to accurately estimate whole-tree transpiration, but it is unclear how the calibration approach itself influences the resulting coefficients and estimates. Here, we compare the two most common calibration approaches, gravimetric and potometric, using TD sensors inserted into Eucalyptus benthamii tree stems. The gravimetric approach uses an excised stem segment devoid of branches and leaves and pushes water through the stem using gravity, a positive force. The potometric approach uses a severed stem containing an intact canopy placed upright in a reservoir where water is pulled through the stem via transpiration, a negative force. We hypothesized that the positive pressure associated with gravimetric calibration would overestimate conductive sapwood area relative to that estimated from potometric calibration and that coefficients from these different approaches would result in different estimates of transpiration when applied to intact trees. We also predicted that calibrations could improve transpiration estimates by targeting the range of observed sap flow rates (i.e., K values) in intact trees. Conductive sapwood area was higher under gravimetric calibrations and resulting estimates of transpiration were lower compared to potometric calibrations. Segmented calibration curves, which fit two separate curves for the relationship between sap flux density (Fd) and sap flux index (K) based on the range of sap flow rates observed in intact trees, increased transpiration estimates from both gravimetric and potometric coefficients and diminished the magnitude of difference in transpiration estimates between approaches. Researchers should be aware that calibration approach and range of observed sap flow profoundly influences transpiration estimates from TD sensors and this likely applies to calibrations of other heat-based sap flow sensors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Validating and Comparing Energy Estimation Methods at Water Resource Recovery Facilities

Water resource recovery facilities play a crucial role in the water-energy nexus, consuming a substantial amount of energy in the United States. Growing treatment volumes and more stringent water quality standards are expected to increase the amount of energy needed to treat wastewater, but accurately estimating energy consumption and potential remains challenging due to variability in scale, treatment methods, and effluent treatment standards. In this study, we used publicly available data to evaluate the accuracy of methods for estimating energy consumption and generation, then quantified uncertainty based on key factors like flow rate, treatment level, and geographic location. To validate methods, we estimated energy consumption and generation at the facility-level, then compared estimates to self-reported data from utilities in major U.S. cities. We found that process models of treatment trains under best practice configurations were accurate relative to other methods for estimating electricity use, total energy use, and electricity generation from biogas utilization, and less complex methods based on effluent treatment level and prime movers also performed well for estimating electricity consumption and generation, respectively. Applying the evaluated methods to a national inventory of treatment facilities, we estimate that annual energy consumption ranged from 56.3 x 10^3 to 82.5 x 10^3 TJ in 2012 and 83.6 x 10^3 to 127 x 10^3 TJ in 2042. Our results indicate that not all estimation methods are suited for every use case, so we recommend that researchers and practitioners select an estimation method based on data availability and desired computational intensity.

Hodson, Abigayle↗
DOE OSTI2022

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

Abstract Motivation Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways. Results In this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl’s do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation. Availability and implementation The code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗
DOE OSTI2020

Ordovician-Cambrian Units: Hierarchical Evaluation of Geologic Carbon Storage Resource Estimates

The Indiana Geological and Water Survey (IGWS) led subtask 1.1 to assess the regional distribution and estimate the storage capacity of Ordovician-Cambrian stratigraphic units located within the partnership region. A comprehensive data set of wireline logs and petrophysical information was used to generate these interpretations. These data include core analysis for porosity and permeability, mercury injection capillary pressure (MICP), and existing well data including location and stratigraphic information. This report includes storage resource estimates (SREs) for three potential storage reservoirs(limestone and dolostone from the Upper Ordovician Trenton Limestone/Black River Group and equivalent units, the Middle Ordovician St. Peter Sandstone, and primary target reservoir rocks of the Lower Ordovician and Upper Cambrian Knox Supergroup and equivalent units) calculated using six methodologies: (1) a fixed value of porosity of 10 percent in all units evaluated; (2) a unique average porosity (per well) from wireline-derived porosity (neutron, sonic, and/or density porosity for each unit); (3) porosity values from core analysis; (4) a depth-dependent porosity model (Knox Supergroup only); (5) porosity based on a model based on petrophysical facies; and (6) SREs using National Energy Technology Laboratory’s CO2 Storage prospeCtive Resource Estimation Excel aNalysis (CO2-SCREEN beta V2). All methods used the same values for thickness for each unit. However, the areal extent of each assessment was limited by the data available for each method. Estimated volumes were calculated in 1-by-1 kilometer grid cells and summarized as county and total stratigraphic unit volumes. The resultant SREs mass are displayed using boxplots, which allow for comparing data statistics (mean values and variability) between methods. Differences observed in SRE results from the six methods are mainly attributable to differences in the data and conceptual models used to interpret or estimate porosity in each method. Based on this systematic variability between methods, it is inferred that methods 1, 4, and 6 are best used for regional-scale reconnaissance estimates of storage capacity while methods 2, 3, and 5 are more appropriate for local scales where more data is required. All estimates are data-density dependent and different methods require different amounts of data for reasonable assessments. ArcMap 10.5.1 software was used to portray SREs to help visualize spatial variance of estimates for each methodology, and more importantly, to highlight those areas having the greatest total storage potential estimates.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes

The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the methane fluxes and emissions in northern latitudes but when atmospheric observations are sparse, the models are only as good as their a priori estimates. Thus, improving a priori estimates is a competent way to reduce uncertainties and enhance emission estimates in the sparsely sampled regions. Here, we use a novel way to integrate remote sensing soil freeze/thaw (F/T) status from SMOS satellite to better capture the seasonality of methane emissions in the northern high latitude. The SMOS F/T data provide daily information of soil freezing state in the northern latitudes, and in this study, the data is used to define the cold season in the high latitudes and, thus, improve our knowledge of the seasonal cycle of biospheric methane fluxes. The SMOS F/T data is implemented to LPX-Bern DYPTOP model estimates and the modified fluxes are used as a biospheric a priori in the inversion model CarbonTracker Europe-CH 4 . The implementation of the SMOS F/T soil state is shown to be beneficial in improving the inversion model’s cold season biospheric flux estimates. Our results show that cold season biospheric CH4 emissions in northern high latitudes are approximately 0.60 Tg lower than previously estimated, which corresponds to 17% reduction in the cold season biospheric emissions. This reduction is partly compensated by increased anthropogenic emissions in the same area (0.23 Tg), and the results also indicates that the anthropogenic emissions could have even larger contribution in cold season than estimated here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

Abstract. Uncertainties in an output of interest that depends on the solution of a complex system (e.g., of partial differential equations with random inputs) are often, if not nearly ubiquitously, determined in practice using Monte Carlo (MC) estimation. While simple to implement, MC estimation fails to provide reliable information about statistical quantities (such as the expected value of the output of interest) in application settings such as climate modeling, for which obtaining a single realization of the output of interest is a costly endeavor. Specifically, the dilemma encountered is that many samples of the output of interest have to be collected in order to obtain an MC estimator that has sufficient accuracy – so many, in fact, that the available computational budget is not large enough to effect the number of samples needed. To circumvent this dilemma, we consider using multifidelity Monte Carlo (MFMC) estimation which leverages the use of less costly and less accurate surrogate models (such as coarser grids, reduced-order models, simplified physics, and/or interpolants) to achieve, for the same computational budget, higher accuracy compared to that obtained by an MC estimator – or, looking at it another way, an MFMC estimator obtains the same accuracy as the MC estimator at lower computational cost. The key to the efficacy of MFMC estimation is the fact that most of the required computational budget is loaded onto the less costly surrogate models so that very few samples are taken of the more expensive model of interest. We first provide a more detailed discussion about the need to consider an alternative to MC estimation for uncertainty quantification. Subsequently, we present a review, in an abstract setting, of the MFMC approach along with its application to three climate-related benchmark problems as a proof-of-concept exercise.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamic parameter estimation of generators

Various examples are provided extended Kalman filtering estimators for generators. In one example, among others an extended Kalman filtering estimator includes a dynamics estimator configured to calculate an estimated variable such as an estimated dynamic state or an estimated parameter of a generator, a geometry estimator configured to estimate generator values, and a Kalman filter component configured to determine a correction to the estimated state variable.

Miao, Zhixin↗
DOE OSTI2022

System and method of performing a range estimation for hybrid and electric vehicles

A system is provided for performing an automated range estimation process for an electric vehicle using a processor. Included in the system is a range estimator configured to estimate an initial value of an energy required to travel a unit distance for the electric vehicle. The range estimator generates a first estimation model based on a correlation between a maximum all-electric-range and the energy required to travel a unit distance. Then, the first estimation model is adjusted based on one or more predetermined driving conditions. The maximum all-electric-range of the electric vehicle is updated based on the adjusted first estimation model. An estimated range of the electric vehicle is calculated based on the updated maximum all-electric-range of the electric vehicle and a fraction of total energy capability remaining in the electric vehicle. The estimated range of the electric vehicle is outputted and is used to control the electric vehicle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<10 3 km 2 ) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. Furthermore, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations

Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as multiple force values can correspond to the same SOC. The traditional linear quadratic estimator can be driven to an incorrect SOC value. To address these difficulties, a novel switching estimation gain is used based on determining the operating region that corresponds to the actual SOC. Moreover, a drift in the measured force associated with a shift of the cell SOC–expansion behavior over time is addressed with a bias estimator for the force signal. The performance of Voltage-based (V) and Voltage and Force-based (V&F) SOC estimation algorithms are then compared and evaluated against a desired ±5% absolute error bound of the SOC using a dynamic stress test current protocol that tests the proposed estimation scheme across wide range of SOC and current rates.

25 ENERGY STORAGE↗
DOE OSTI2023

Linearization errors in discrete goal-oriented error estimation

This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered. The traditional method to derive an error estimate in this context requires linearizing both the nonlinear variational form and the nonlinear functional of interest which introduces linearization errors into the error estimate. In this paper, we investigate these linearization errors. In particular, we develop a novel discrete goal-oriented error estimate that accounts for traditionally neglected nonlinear terms at the expense of greater computational cost. We demonstrate how this error estimate can be used to drive mesh adaptivity. Here, we show that accounting for linearization errors in the error estimate can improve its effectivity for several nonlinear model problems and quantities of interest. We also demonstrate that an adaptive strategy based on the newly proposed estimate can lead to more accurate approximations of the nonlinear functional with fewer degrees of freedom when compared to uniform refinement and traditional adjoint-based approaches.

42 ENGINEERING↗
DOE OSTI2024

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. Further, there are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nonmagnetized Collisional Plasma Parameter Estimation From Two Frequency Signal Interrogation Attenuation

A nonmagnetized collisional plasma parameter estimator from two frequency signal interrogation attenuation is developed. The plasma parameters that are estimated are the plasma frequency, electron neutral momentum collision frequency, and the plasma thickness. The plasma frequency and electron neutral momentum collision frequency are considered uniform across the plasma thickness. The relative permittivity is defined, and the complex index of refraction is developed. Using this definition and applying the plasma frequency, electron neutral momentum collision frequency, the radial propagation frequency, and plasma thickness, an attenuation is determined for known cases. The development of the estimator is discussed. The estimator uses a performance index where the minimum difference between the plasma frequencies and electron neutral momentum collision frequencies is determined for the two signal interrogation frequencies under the constraint of the same plasma thickness. The estimator was developed in three stages which include iterative, sequential, and adaptive. The setups of the iterative, sequential, and adaptive approaches are discussed. The impact of the interrogation frequency and the estimator setup is investigated. The estimator in the three development stages is compared with known cases and the plasma parameter estimator performance is quantified.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Improvements to Contributions from Neutron Inelastic Scattering for Next-Event Estimators in MCNP ® Software

An improvement to the handling of contributions from neutron inelastic scattering to next-event estimators has been implemented in the MCNP6 ® software for release with version 6.3.1. The kinematic equations that govern the outgoing energy of inelastic neutron scattering contributions to next-event estimators have two roots. Historically, the implementation in the MCNP coding has only used the upper root to the quadratic equation. This includes all versions predating version 6.3.1 including all versions of MCNP5 and MCNPX software. However, a review of the neutron next-event estimator physics has shown that this does not reproduce the track-length estimator results at low energies. Several examples are presented that test single neutron inelastic scattering reaction types, Level Scattering (Law 3), Tabulated Energy Angle (Law 61), and Kalbach-Mann distribution (Law 44). The test problems compare the track-length estimator (f4 tally) in the MCNP software, with the existing implementation of the neutron next-event estimator (f5 tally), and the modified changes to the neutron next-event estimator implementation. As the MCNP implementation ignores the lower root, the unmodified neutron next-event estimators will generally underestimate the lower energy contribution. However, a second issue with the Kalbach-Mann distribution (Law 44) implementation allows contributions to backward scattering in the center-of-mass frame that is not kinematically possible, thus overestimating backward scattering contributions. A third issue with the way the MCNP implementation handles floating point comparison for scattering directly ahead or directly backward in the center-of-mass frame generally leads to underestimation (except for backwards scattering for Law 44).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Risk Ratio and Risk Difference Estimation in Case-cohort Studies

Background: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. Methods: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. Results: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. Conclusion: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities

Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high-throughput automated approach to univariate nonparametric density estimation based on maximum entropy and order statistics, improving accuracy over univariate KDE. This article presents an extension of the single variable case to multiple variables. The univariate estimator is used to recursively calculate a product array of one-dimensional conditional probabilities. In combination with interpolation methods, a complete joint probability density estimate is generated for multiple variables. Good accuracy and speed performance in synthetic data are demonstrated by a numerical study using known distributions over a range of sample sizes from 100 to 10 6 for two to six variables. Performance in terms of speed and accuracy is compared to KDE. The multivariate density estimate developed here tends to perform better as the number of samples and/or variables increases. As an example application, measurements are analyzed over five filters of photometric data from the Sloan Digital Sky Survey Data Release 17. The multivariate estimation is used to form the basis for a binary classifier that distinguishes quasars from galaxies and stars with up to 94% accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗