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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effects of surface runoff and infiltration partition methods on hydrological modeling: A comparison of four schemes in two watersheds in the Northeastern US

The ratio according to which the precipitation/snowmelt is divided into surface and subsurface water is of primary importance to watershed scale hydrological modeling. The present study compared four different surface runoff and infiltration partition methods in the Richards-equation-based SWAT model (RSWAT), i.e., daily curve number method (DC-RSWAT), hourly curve number method (HC-RSWAT), Green-Ampt method (GA-RSWAT), and effective infiltration capacity method (EIC-RSWAT). These four versions of RSWAT and a daily-curve-number-based SWAT (DC-SWAT) were used to simulate daily flow rate (from 2001 to 2015) at the outlets of the Tuckahoe Creek Watershed (TCW) and Greensboro Watershed (GW) of the Chesapeake Bay in Maryland. Parameter sensitivity analysis, calibration, and prediction uncertainty analysis were conducted. The results indicate that surface runoff and infiltration partition methods have pronounced impacts on model performance. Comparative analysis showed that DC-RSWAT outperformed other versions of RSWAT for both calibration (from 2001 to2007) and validation (from 2008 to 2015) periods in the two test watersheds. In general, DC-RSWAT achieved more accurate deterministic flow simulation and reliable uncertainty estimation than other versions. As such, daily-curve-number method is recommended for surface runoff and infiltration partition in RSWAT for simulating stream flow.

hydrology, modeling, streamflow↗
DOE OSTI2021

Machine-learning predictions of the shale wells’ performance

The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). In this work, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k-means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime.

03 NATURAL GAS↗
NASA NTRS1971

The role and use of the stochastic linear-quadratic-Gaussian problem in control system design.

The role of the linear-quadratic stochastic control problem in engineering design is reviewed in tutorial fashion. The design approach is motivated by considering the control of a nonlinear uncertain plant about a desired input-output response. It is demonstrated how a design philosophy based on (1) deterministic perturbation control, (2) stochastic state estimation, and (3) linearized stochastic control leads to an overall closed-loop control system. The emphasis of the paper is on the philosophy of the design process, the modeling issue, and the formulation of the problem; the results are given for the sake of completeness, but no proofs are included. The systematic off-line nature of the design process is stressed throughout.

Athans, M.↗
NASA NTRS1995

A PC-based magnetometer-only attitude and rate determination system for gyroless spacecraft

This paper describes a prototype PC-based system that uses measurements from a three-axis magnetometer (TAM) to estimate the state (three-axis attitude and rates) of a spacecraft given no a priori information other than the mass properties. The system uses two algorithms that estimate the spacecraft's state - a deterministic magnetic-field only algorithm and a Kalman filter for gyroless spacecraft. The algorithms are combined by invoking the deterministic algorithm to generate the spacecraft state at epoch using a small batch of data and then using this deterministic epoch solution as the initial condition for the Kalman filter during the production run. System input comprises processed data that includes TAM and reference magnetic field data. Additional information, such as control system data and measurements from line-of-sight sensors, can be input to the system if available. Test results are presented using in-flight data from two three-axis stabilized spacecraft: Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) (gyroless, Sun-pointing) and Earth Radiation Budget Satellite (ERBS) (gyro-based, Earth-pointing). The results show that, using as little as 700 s of data, the system is capable of accuracies of 1.5 deg in attitude and 0.01 deg/s in rates; i.e., within SAMPEX mission requirements.

Challa, M.↗
DOE OSTI2012

Prioritization Risk Integration Simulation Model (PRISM) For Environmental Remediation and Waste Management - 12097

The PRISM (Prioritization Risk Integration Simulation Model), a computer model was developed to support the Department of Energy's Office of Environmental Management (DOE-EM) in its mission to clean up the environmental legacy from the Nation's nuclear weapons materials production complex. PRISM provides a comprehensive, fully integrated planning tool that can tie together DOE-EM's projects. It is designed to help DOE managers develop sound, risk-informed business practices and defend program decisions. It provides a better ability to understand and manage programmatic risks. The underlying concept for PRISM is that DOE-EM 'owns' a portfolio of environmental legacy obligations (ELOs), and that its mission is to transform the ELOs from their current conditions to acceptable conditions, in the most effective way possible. There are many types of ELOs - - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. For a given suite of planned activities, PRISM simulates the outcomes as they play out over time, allowing for all key identified uncertainties and risk factors. Each contaminated building, land area and waste stream is tracked from cradle to grave, and all of the linkages affecting different waste streams are captured. The progression of the activities is fully dynamic, reflecting DOE-EM's prioritization approaches, precedence requirements, available funding, and the consequences of risks and uncertainties. The top level of PRISM is the end-user interface that allows rapid evaluation of alternative scenarios and viewing the results in a variety of useful ways. PRISM is a fully probabilistic model, allowing the user to specify uncertainties in input data (such as the magnitude of an existing groundwater plume, or the total cost to complete a planned activity) as well as specific risk events that might occur. PRISM is based on the GoldSim software that is widely used for risk and performance assessment calculations. PRISM can be run in a deterministic mode, which quickly provides an estimate of the most likely results of a given plan. Alternatively, the model can be run probabilistically in a Monte Carlo mode, exploring the risks and uncertainties in the system and producing probability distributions for the different performance measures. The PRISM model demonstrates how EM can evaluate a portfolio of ELOs, and transform the ELOs from their current conditions to acceptable conditions, utilizing different strategic approaches. There are many types of ELOs - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. This scope of work for the PRISM process and the development of a dynamic simulation model are a logical extension of the GoldSim simulation software used by the OCRWM to assess the long-term performance for the Yucca Mountain Project and by NNSA to assess project risk at its sites. Systems integration modeling will promote better understanding of all project risks, technical and nontechnical, and more defensible decision-making for complex projects with significant uncertainties. It can provide effective visual communication and rapid adaptation during interactions with stakeholders (Administration, Congress, State, Local, and NGO). It will also allow rapid assessment of alternative management approaches. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

17 WIND ENERGY↗
NASA NTRS1971

An estimator for the standard deviation of a natural frequency. I.

A brief review of mean-square approximate systems is given. The case in which the masses are deterministic is considered first in the derivation of an estimator for the upper bound of the standard deviation of a natural frequency. Two examples presented include a two-degree-of-freedom system and a case in which the disorder in the springs is perfectly correlated. For purposes of comparison, a Monte Carlo simulation was done on a digital computer.

Schiff, A. J.↗
NASA NTRS2019

Refining Lucy Mission Delta-V During Spacecraft Design Using Trajectory Optimization Within High-Fidelity Monte Carlo Maneuver Analysis

Recent advances linking medium-fidelity trajectory optimization and high-fidelity trajectory propagation/maneuver design software with Monte Carlo maneuver analysis and parallel processing enabled realistic statistical delta-V estimation well before launch. Completing this high-confidence, refined statistical maneuver analysis early enabled release of excess delta-V margin for increased dry mass margin for the Lucy Jupiter Trojan flyby mission. By 3.3 years before launch, 16 of 34 TCMs had 1000 re-optimized trajectory design samples, yielding tens of m/s lower 99%-probability delta-V versus targeting maneuvers to one optimal trajectory. One year later, 1000 re-optimized samples of all deterministic maneuvers and subsequent flybys further lowered estimated delta-V.

Trojan↗
DOE OSTI2018

Challenges to Evaluating Ecological Risks in Performance Assessments - 18424

Performance assessments (PAs), whether probabilistic or deterministic, have largely focused on evaluating human health effects of radionuclides. In the past, it was asserted that protecting human health would automatically protect the environment. This blanket assertion has been questioned, so tools and methodologies to directly estimate non-human biota dose have been developed and made available since the early 2000's. For example, the Biota Dose Assessment Committee (BDAC) guidance led to the development of the RESRAD-BIOTA (RESRAD stands for Residual Radioactive materials) software to implement this guidance. The BDAC was a US Department of Energy (DOE) led working group with members from academia and consultancies in addition to DOE technical staff. Parallel efforts have been in place in Europe with the development of the ERICA tool (Environmental Risk from Ionising Contaminants: Assessment and Management). More recently, interpretation of the requirements of US Department of Energy (DOE) Order O458.1 have led some waste management facilities to include ecological risks in addition to human health effects in their PAs. We have recently developed methodologies to assess ecological risks within probabilistic PAs (PPAs) for two Federal facilities. One of the challenges in applying the non-human biota assessment models to PPAs is that ecological risk assessments following US Environmental Protection Agency (EPA) or DOE guidance start with protective screening assessments before moving into more site-specific ecological risk assessments. Conservative assumptions bias the screening assessments and greatly overestimate the risks posed by contaminants. However, although the screens are biased they can be useful in ruling out model spatial domain areas or types of biota from requiring further assessment. In some cases, the results of the screen could entirely rule out ecological risks from further assessment. For a more refined and unbiased estimate of dose to non-human biota, one would typically conduct site-specific studies to better define exposure through the food web or adverse effects based on eco-toxicity studies of environmental, and generally weathered, contaminant mixtures. However, for PPAs that consider future site conditions after the loss of institutional control, such site-specific studies are not possible. Rather, site-specific future ecological risk models use probabilistic inputs that result in a distribution of population doses or exposures and calculate the distribution of possible adverse effects to ecological populations. This approach considers both radionuclide and chemical inventory. For both Federal facilities, the model spatial domains include riparian and aquatic environments located downstream of the waste management site. These riparian and aquatic environments represent linear exposure elements for some wildlife populations and require special consideration of exposures to individuals and to populations along such spatial domains. At a minimum, wildlife foraging should be focused along the axis of such water bodies. Another challenge is how to incorporate ecologically relevant spatial scales into models that have contaminant transport as the primary focus. Riparian and aquatic environments also have threatened and endangered (T and E) species, which necessitate greater levels of protection given their rarity. A final challenge for conducting ecological risk assessments is how to evaluate T and E species in the future. This might be addressed by evaluating potential for impacts on individuals of feeding guilds with species that are currently threatened or endangered. We also have to consider how ecological populations might change as a result of climate change. It is a more difficult problem to predict what species might become rare in the future, and how to best assess future populations of threatened or endangered species. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1983

Simplified analysis techniques to support the determination of Shuttle aerodynamics

The use of deterministic and regression methods to supplement the maximum likelihood estimation programs for the determination of the stability and control parameters of the Shuttle vehicle is discussed. Examples taken from STS-2 and STS-3 flight data show the use of deterministic methods to verify the effectiveness of the pitch and roll Reaction Control System (RCS), to verify the model structure used in the maximum likelihood estimator and to indicate regions of the descent trajectory where unmodeled moments dominate the vehicle motion and the application of maximum likelihood estimation would lead to incorrect parameter values. The use of regression methods to determine selected parameters when the more sophisticated maximum likelihood programs are not required, to determine the form of the vehicle mathematical model and to select the data time intervals for most effective parameter identification are also discussed.

Suit, W. T.↗
DOE OSTI2014

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This paper presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Investigation of methodology for uncertainty quantification of model parameters

Quantifying the uncertainty contributors of best-estimate thermal hydraulic code is getting more and more attention in safety analysis of nuclear industry during recent decades. Yet for uncertainties of certain model parameters which may not be directly measured, the quantification process is usually subjective and inaccurate. This paper investigates the statistical methodologies in order to get the probability density function (pdf) of model parameters more objectively based on observed experimental responses. The simplification of mathematical model is described for the parameter estimation, and solutions based on Bayesian theorem and Maximum Likelihood Estimation (MLE) are classified and demonstrated for both deterministic approach and stochastic approach. As an application, the uncertainties of model parameters related to reflood phenomena implemented in RELAP5 code are quantified. It is indicated that the statistical method which is independent of best-estimate code is feasible and easy to apply. And through the check of uncertainty propagation, it demonstrates that the uncertainty bands can envelope most of the experiment measurement with advantage of accuracy. (authors)

42 ENGINEERING↗
DOE OSTI2017

Monte Carlo Estimates of Eigenvalue Sensitivity to System Dimensions using Kernel Density Estimators

In the last decade there has been an increasing interest in calculating sensitivity coefficients in order to quantify the impact of uncertainty in material properties and system parameters on quantities of interest. Extensive work has been done developing Monte Carlo methods to estimate eigenvalue sensitivity to material properties. While deterministic methods exist for determining the eigenvalue sensitivity to system dimensions, the use of Monte Carlo methods to efficiently calculate eigenvalue sensitivity coefficients to system dimensions is still an open topic. Current Monte Carlo methods for calculating eigenvalue sensitivity coefficients to system dimensions are inefficient. Direct Monte Carlo perturbations require two additional simulations of high statistical precision in order to determine the eigenvalue sensitivity to the location of a single boundary. If the sensitivity to the location of multiple surfaces are desired, two additional simulations must be run for each surface. Kiedrowski and Favorite extended continuous energy Monte Carlo methods for sensitivities of nuclear cross sections to calculate sensitivities to interface boundaries or system dimensions to create the only current alternative to direct Monte Carlo calculations. However this method relies on the simulation of additional random walks launched at each surface crossing event. The present method extends upon Kiedrowski's work and eliminates the inefficient portions of the calculation through the use of Kernel Density Estimators (KDEs). The new method efficiently estimates eigenvalue sensitivities to the uniform expansion or contraction of material interface boundaries or system dimensions, negligibly impacting the runtime performance of the Monte Carlo code. The new estimator is tested on bare and reflected critical configurations in spherical geometry and is compared to estimates obtained from direct Monte Carlo perturbations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Investigation of Heterogeneity Effects on Calculated Sensitivities of the ZPPR-15B Assemblies

In past years, sensitivity studies of ZPR and ZPPR assemblies were carried out with deterministic codes for several purposes, such as uncertainty estimation of calculated parameter values, cross section adjustment, representativity analysis. etc. However, these sensitivity studies used simplified R-Z models of ZPR/ZPPR assemblies where the multi-group cross section data was obtained from the homogenized material compositions of each reactor zone. The main reason for this simplification was the lack of a deterministic path able to compute sensitivity coefficients for complicated 3D geometries in transport theory. Also, each ZPR and ZPPR assembly consists of several different drawers each containing different plate types and plate arrangements and it was felt that performing sensitivity calculations with an equivalent R-Z reactor model would capture most of the feedback while the heterogeneity effects were believed to be of second order. Recently a new code, BuildZPRModel, was developed at ANL that automatically generates the inputs for cross section processing and core calculation of heterogeneous ZPR and ZPPR assemblies for 3D transport calculations. Additionally, the PERSENT code (recently developed at ANL as well) is able to carry out sensitivity calculations in transport theory on 3D geometries. With the benefit of the newly developed codes, a sensitivity study was performed for the heterogeneous model of ZPPR-15B assemblies 96 and 102 generated by BuildZPRModel. The results were compared with the sensitivities computed with the use of fully homogenized R-Z reactor models, in order to quantify the impact of heterogeneity effects neglected in the analysis performed in past years.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1977

A new method for predicting response in complex linear systems. II

The paper describes convergence and response studies in the low frequency range of complex systems, particularly with low values of damping of different distributions, and reports on the modification of the relaxation procedure required under these conditions. A new method is presented for response estimation in complex lumped parameter linear systems under random or deterministic steady state excitation. The essence of the method is the use of relaxation procedures with a suitable error function to find the estimated response; natural frequencies and normal modes are not computed. For a 45 degree of freedom system, and two relaxation procedures, convergence studies and frequency response estimates were performed. The low frequency studies are considered in the framework of earlier studies (Kayser and Bogdanoff, 1975) involving the mid to high frequency range.

Bogdanoff, J. L.↗
DOE OSTI2016

Investigation of Heterogeneity Effects on Calculated Sensitivities of the ZPPR-15B Assemblies

In past years, sensitivity studies of ZPR and ZPPR assemblies were carried out with deterministic codes for several purposes, such as uncertainty estimation of calculated parameter values, cross section adjustment, representativity analysis. etc.. However, these sensitivity studies used simplified R-Z models of ZPR/ZPPR assemblies where the multi-group cross section data was obtained from the homogenized material compositions of each reactor zone. The main reason for this simplification was the lack of a deterministic path able to compute sensitivity coefficients for complicated 3D geometries in transport theory. Also, each ZPR and ZPPR assembly consists of several different drawers each containing different plate types and plate arrangements and it was felt that performing sensitivity calculations with an equivalent R-Z reactor model would capture most of the feedback while the heterogeneity effects were believed to be of second order. Recently a new code, BuildZPRModel, was developed at ANL that automatically generates the inputs for cross section processing and core calculation of heterogeneous ZPR and ZPPR assemblies for 3D transport calculations. Additionally, the PERSENT code (recently developed at ANL as well) is able to carry out sensitivity calculations in transport theory on 3D geometries. With the benefit of the newly developed codes, a sensitivity study was performed for the heterogeneous model of ZPPR-15B assemblies 96 and 102 generated by BuildZPRModel. The results were compared with the sensitivities computed with the use of fully homogenized R-Z reactor models, in order to quantify the impact of heterogeneity effects neglected in the analysis performed in past years. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.

97 MATHEMATICS AND COMPUTING↗