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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantification of Land-Use/Land Cover Change as Driver of Earth System Dynamics (Final Technical Report)

Human land-use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth surface, with impacts on climate. In the future, land-use activities are likely to expand and/or intensify further to meet future demands for food, feed, fiber, and energy. The proposal has three major objectives: 1: Develop metrics to quantify climate sensitivity to land-use/land-cover change using DOE-ESM/CESM; 2: develop new essential input data products and algorithms for land-use in DOE-ESM/CESM (past-future); and 3: design model experiments to isolate and quantify the effects of land-use on climate in DOE- ESM/CESM. The science supported by this award has generated numerous accomplishments, and is generating major impacts on the topic of land-use/climate more broadly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Extreme Weather and Climate Vulnerabilities of the Electric Grid: A Summary of Environmental Sensitivity Quantification Methods

Climate hazards and extreme weather affect all components of the electric grid system, from generation to end use. Increasing temperatures, decreasing water availability, more intense storm events, and sea level rise affect the ability of the electric grid to produce and transmit electricity from fossil, nuclear, and existing and emerging renewable energy sources. Most electricity infrastructure is built for past or current climate conditions. Due to long lifetimes, electricity systems are likely to be exposed more frequently to more extreme climate conditions than those for which they were designed, and may not operate as intended under changing climate conditions. Utilities, regulators, state energy offices, and other electricity system planners are beginning to conduct environmental risk assessments, develop climate resilience plans, and incorporate changing climate conditions into long-term planning processes. Here, we highlight the analytical resources available for sensitivity assessment of electrical grid components under extreme weather and climate, and identify gaps in the literature on quantitative methods available for assessment of component vulnerability.

Dumas, Melissa↗
DOE OSTI2019

Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output

In experiments conducted on the Z-machine at Sandia National Laboratories, dynamic material properties cannot be analyzed using traditional analytic methods, necessitating solving an inverse problem. Bayesian model calibration is a statistical framework for solving an inverse problem to estimate parameters input into a computational model in the presence of multiple uncertainties. Disentangling input parameter uncertainty and model misspecification is often poorly identified problem. When using computational models for physical parameter estimation, the issue of parameter identifiability must be carefully considered to obtain accurate and precise estimates of physical parameters. Additionally, in dynamic material properties applications, the experimental output is a function, velocity over time. While we can sample an arbitrarily large number of points from the measured velocity, these curves only contain a finite amount of information about the calibration parameters. In this report, we propose modifications to the Bayesian model calibration framework to simplify and improve the estimation of physical parameters with functional outputs. Specifically, we propose scaling the likelihood function by an effective sample size rather than modeling the discrepancy function; and modularizing input nuisance parameters with weakly identified parameters. We evaluate the performance of these proposed methods using a statistical simulation study and then apply these methods to estimate parameters of the tantalum equation of state. We conclude that these proposed methods can provide simple, fast, and statistically valid alternatives to the full Bayesian model calibration procedure; and that these methods can be used to estimate parameters of the equation of state for tantalum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Quantification of MagLIF Morphology using the Mallat Scattering Transformation

The morphology of the stagnated plasma resulting from Magnetized Liner Inertial Fusion (MagLIF) is measured by imaging the self-emission x-rays coming from the multi-keV plasma, and the evolution of the imploding liner is measured by radiographs. Equivalent diagnostic response can be derived from integrated rad-MHD simulations from programs such as Hydra and Gorgon. There have been only limited quantitative ways to compare the image morphology, that is the texture, of simulations and experiments. We have developed a metric of image morphology based on the Mallat Scattering Transformation (MST), a transformation that has proved to be effective at distinguishing textures, sounds, and written characters. This metric has demonstrated excellent performance in classifying ensembles of synthetic stagnation images. We use this metric to quantitatively compare simulations to experimental images, cross experimental images, and to estimate the parameters of the images with uncertainty via a linear regression of the synthetic images to the parameter used to generate them. This coordinate space has proved very adept at doing a sophisticated relative back-ground subtraction in the MST space. This was needed to compare the experimental self emission images to the rad-MHD simulation images. We have also developed theory that connects the transformation to the causal dynamics of physical systems. This has been done from the classical kinetic perspective and from the field theory perspective, where the MST is the generalized Green's function, or S-matrix of the field theory in the scale basis. From both perspectives the first order MST is the current state of the system, and the second order MST are the transition rates from one state to another. An efficient, GPU accelerated, Python implementation of the MST was developed. Future applications are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Status Report on Uncertainty Quantification and Sensitivity Analysis Tools in the Geologic Disposal Safety Assessment (GDSA) Framework

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Development of Innovative Measurement Techniques for Fission Product Transport Quantification

This document provides the progress of a research project at Texas A&M University (TAMU), led by Idaho National Laboratory (INL), in support of the Versatile Test Reactor (VTR) program and work scope area of gas-cooled fast reactors. The VTR TAMU INL project started in October 2018. The document includes the main objectives of the project, scaling analysis applied to the experimental facilities, experimental methodology including instrumentation and techniques applied, and up-to-date results from the experimental and numerical studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Energy Saving Quantification on Ductless Heat Pump (DHP) in Existing Homes

In residential retrofit applications, ductless mini-split heat pumps (DHP) are often reported to have high-energy savings potential, depending on the system they are supplementing or replacing. However, recently, there have been a number of utility studies and analyses indicating these energy savings are not being achieved when the existing system is left in place for backup heating or air conditioning. The Pacific Northwest National Laboratory conducted a three-phase project to help determine which control strategies would have the most energy savings impact in various climate zones around the United States. The first phase developed the standalone simulation model for the PNNL Lab Homes and investigated the energy-saving potential from different control strategies and HVAC system configurations. The second phase focused on conducting experiments in the PNNL Lab Homes, which tested the most promising solutions that were modeled in the first phase. The third phase used the field data to calibrate the simulation model and then extrapolated the results to different climate locations and different building sizes. This report focuses on the third phase of the study, including five major parts: model calibration, parametric model setup, results, a sensitivity analysis of air leakage rate, and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Microwave digestion and quantification procedure

The samples were microwave digested to accomplish complete dissolution needed to perform quantitative analysis of their iron content. The diamond and Para B4 C samples did not dissolve during the microwave digestion and they were filtered. The supernatant was analyzed. The resulting solutions were analyzed by ICP-OES for the major cation content and by ICP-MS for trace element content.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast Computational Algorithms for Partial Differential Equations and Uncertainty Quantifications

This project concerned the construction, testing and analysis of computational algorithms for solving parameterized and stochastic partial differential equations. The study and understanding of equations of this type is of fundamental importance in numerous engineering and scientific applications. Examples include simulation of plasma dynamics in models of electric propulsion and nuclear fusion, simulation of multiphase flows, such as the flow of water, gas and oil in reservoirs, and structural analysis of the dependence of structures on materials. Parametrization is used in such settings when properties of the models such as viscosity of fluids or electric resistivity of materials are not precisely understood and instead are treated as random variables. The resulting solutions are themselves random, and having such solutions will enable engineers to use probabilistic methods to assess the likelihood of events, for example, whether a pollutant in a liquid will exceed a limit, and to use such analyses to develop ways to ensure positive outcomes. Construction of accurate (high resolution) computational solutions is expensive, requiring significant computer time and computational resources, and there is need to reduce computational cost to make simulation useful and effective. The aim of the project was to construct algorithms to efficiently compute surrogate solutions to parameterized problems to allow for efficient and accurate simulation. The technical approach used focused on two related strategies, based on rank-reduction methods and reduced-order models. These methods construct surrogate solutions of parameter-dependent models by projection or interpolation into low-dimensional approximation spaces. Cost savings are achieved if the low-dimensional spaces can be identified and constructed efficiently and if the resulting low-dimensional algebraic systems can be solved cheaply. Accomplishments include: Theoretical and empirical demonstration of the effectiveness of fast multigrid solution strategies for computing low-rank representations of parameter-dependent solutions to discrete partial differential equations, including the first proof establishing so-called textbook convergence properties for low-rank methods. Development of efficient solution algorithms for solving nonlinear parameter-dependent partial differential equations used in models of fluid dynamics. Developent of efficient algorithms for low-rank representation of solutions of time-dependent simulations of fluid dynamics using multi-dimensional tensor representations of solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development of a High-Energy Neutron Sniffer and Subsequent Dose Quantification for High-Energy Particle Accelerator Shielding Surveys

The objective of this research is to design a portable neutron detector that is lightweight and responds to neutrons with energies of 20 MeV and greater. The instrument will be used as a ‘sniffer’ probe for identifying shielding weaknesses at high-energy particle accelerators and be paired with a plastic scintillator to quantify the high-energy neutron dose equivalent. The sniffer probes are two different sizes and comprised of either cesium iodide or sodium iodide, all doped with thallium, making a total of four detectors. The probes were calibrated to respond only to radiations with 8 MeV electron equivalent energy or higher, vastly reducing detector response from background and lower energy radiations. The sniffer probes were tested using californium- 252 and americium-beryllium sources, followed by high-energy (up to 800 MeV) neutron exposures at the Los Alamos Neutron Science Center Weapons Neutron Research (WNR) facility. The sniffer probes were then used to identify areas around the accelerator facility where elevated levels of high-energy radiation was present and the plastic scintillators were placed in these locations to determine the high-energy neutron dose equivalent. The dose equivalent is calculated by measuring the 12 C(n,2n) 11 C reaction, which has a 20.4 MeV neutron threshold. Two locations behind the WNR facility were identified with the probes and the dose equivalent rates from neutron energies above 20 MeV were calculated to be 1.24 mrem/h behind flightpath 30-left and 1.53 behind flightpath 30-right. Of the four sniffer probes, the 2-inch x 2-inch cesium iodide is the recommended probe for performing high-energy shielding surveys. The larger crystal size yields higher sensitivity at lower count rates and the more dense material increases the likelihood of interaction within the crystal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Uncertainty Quantification of Classifying Mechanical Breach in the Crash and Burn Problem

The prediction of mechanical breach in the Crash and Burn problem is important for designing the full system to minimize the probability of loss of containment. Due to uncertainties in the impact angle, material properties, and material models, it is not possible to define an exact critical impact speed at which the system breaches. Furthermore, the cost of running a large scale sampling study to determine the empirical probability of breach is prohibitive. In this work, surrogate models from machine learning, namely logistic regression and artificial neural networks, are introduced to predict binary classification of pass versus breach from a limited set of samples. The structure and calibration of these classifiers is discussed, and a set of metrics for describing the performance of the classifiers is introduced. The classifiers are used on the UUR version of the Crash model to attempt prediction of failure probability and perform variable sensitivity analysis. Where a single sample of the computational model can take hundreds of CPU-hours, training and evaluating a classifier can take seconds or less, thus giving high predictive power in a relatively short time.

42 ENGINEERING↗