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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Summary of Remediated and Unremediated Nitrate Salt Surrogate Testing in Support of the Waste Treatment Permit Application to the New Mexico Environment Department (NMED)

The inadvertent creation of transuranic waste carrying hazardous waste codes D001 and D002 requires the treatment of the material to eliminate the hazardous characteristics and allow its eventual shipment and disposal at the Waste Isolation Pilot Plant (WIPP). This report briefly summarizes the surrogate testing that was done in support of our understanding of this waste form.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

False Negative Rates of a Macrofoam-Swab Sampling Method with Low Surface Concentrations of Two Bacillus anthracis Surrogates via Real-Time PCR

Surface sampling for Bacillus anthracis spores has traditionally relied on detection via bacterial cultivation methods. Although effective, this approach does not provide the level of organism specificity that can be gained through molecular techniques. False negative rates (FNR) and limits of detection (LOD) were determined for two B. anthracis surrogates with modified rapid viability-polymerase chain reaction (mRV-PCR) following macrofoam-swab sampling. This study was conducted in parallel with a previously reported study that analyzed spores using a plate-culture method. B. anthracis Sterne (BAS) or B. atrophaeus Nakamura (BG) spores were deposited onto four surface materials (glass, stainless steel, vinyl tile, and plastic) at nine target concentrations (2 to 500 spores/coupon; 0.078 to 19.375 colony-forming units [CFU] per cm2). Mean FNR values for mRV-PCR analysis ranged from 0 to 0.917 for BAS and 0 to 0.875 for BG and increased as spore concentration decreased (over the concentrations investigated) for each surface material. FNRs based on mRV-PCR data were not statistically different for BAS and BG, but were significantly lower for glass than for vinyl tile. FNRs also tended to be lower for the mRV-PCR method compared to the culture method. The mRV-PCR LOD95 was lowest for glass (0.429 CFU/cm2 with BAS and 0.341 CFU/cm2 with BG) and highest for vinyl tile (0.919 CFU/cm2 with BAS and 0.917 CFU/cm2 with BG). These mRV-PCR LOD95 values were lower than the culture values (BAS: 0.678 to 1.023 CFU/cm2 and BG: 0.820 to 1.489 CFU/cm2). The FNR and LOD95 values reported in this work provide guidance for environmental sampling of Bacillus spores at low concentrations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

High-Temperature Oxidation of Plutonium Surrogate Metals and Alloys

The Plutonium Management and Disposition Agreement (PMDA) is a nuclear non-proliferation agreement designed to remove 34 tons of weapons-grade plutonium from Russia and the United States. While several removal options have been proposed since the agreement was first signed in 2000, processing the weapons-grade plutonium to mixed-oxide (MOX) fuel has remained the leading candidate for achieving the goals of the PMDA. However, the MOX program has received its share of criticisms, which causes its future to be uncertain. One alternative pathway for plutonium disposition would involve oxidizing the metal followed by impurity down blending and burial in the Waste Isolation Pilot Plant (WIPP) in Carlsbad, New Mexico. This pathway was investigated by use of a hybrid microwave and a muffle furnace with Fe and Al as surrogate materials. Oxidation occurred similarly in the microwave and muffle furnace; however, the microwave process time was significantly faster.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Surrogate fuel assembly multi-axis shaker tests to simulate normal conditions of rail and truck transport

This report describes the third set of tests (the “DCLa shaker tests”) of an instrumented surrogate PWR fuel assembly. The purpose of this set of tests was to measure strains and accelerations on Zircaloy-4 fuel rods when the PWR assembly was subjected to rail and truck loadings simulating normal conditions of transport when affixed to a multi-axis shaker. This is the first set of tests of the assembly simulating rail normal conditions of transport.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Zirconium and Yttrium (p, d) Surrogate Nuclear Reactions: Measurement and determination of gamma-ray probabilities: Experimental Physics Report

This technical report documents the surrogate reaction method and experimental results used to determine the desired neutron induced cross sections of 87 Y(n,g) and the known 90 Zr(n,g) cross section. This experiment was performed at the STARLiTeR apparatus located at Texas A&M Cyclotron Institute using the K150 Cyclotron which produced a 28.56 MeV proton beam. The proton beam impinged on Y and Zr targets to produce the nuclear reactions 89 Y(p,d) 88 Y and 92 Zr(p,d) 91 Zr. Both particle singles data and particle-gamma ray coincident data were measured during the experiment. This data was used to determine the γ-ray probability as a function of energy for these reactions. The results for the γ-ray probabilities as a function of energy for both these nuclei are documented here. For completeness, extensive tabulated and graphical results are provided in the appendices.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

239Pu(n, 2n) and 241Pu(n, 2n) surrogate cross section measurements using NeutronSTARS

The goal of this project was to develop a new approach to measuring (n,2n) reactions for isotopes of interest. We set out to measure the 239 Pu(n,2n) and 241 Pu(n,2n) cross sections by directly detecting the 2n neutrons that are emitted. With the goal of improving the 239 Pu(n,2n) cross section and to measure the 241 Pu(n,2n) cross section for the first time. To that end, we have constructed a new neutron-charged-particle detector array called NeutronSTARS. It has been described extensively in Casperson et al. [1] and in Akindele et al. [2]. We have used this new neutron-charged-particle array to measure the 241 Pu and 239 Pu fission neutron multiplicity as a function of equivalent incident-neutron energy from 100 keV to 20 MeV. We have made a preliminary determination of the 239 Pu(n,2n) and 241 Pu(n,2n) cross sections from the surrogate 240 Pu(α,α’2n) and 242 Pu(α,α’2n) reactions respectively. The experimental approach, detector array, data analysis, and results to date are summarized in the following sections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Surrogate fuel assembly multi-axis shaker tests to simulate normal conditions of rail and truck transport

This report describes the third set of tests (the “DCL a shaker tests”) of an instrumented surrogate PWR fuel assembly. The purpose of this set of tests was to measure strains and accelerations on Zircaloy-4 fuel rods when the PWR assembly was subjected to rail and truck loadings simulating normal conditions of transport when affixed to a multi-axis shaker. This is the first set of tests of the assembly simulating rail normal conditions of transport.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps.

We present a preliminary investigation of the use of Multi-Layer Perceptrons (MLP) and Recurrent Neural Networks (RNNs) as surrogates of parameter-to-prediction maps of com- putational expensive dynamical models. In particular, we target the approximation of Quan- tities of Interest (QoIs) derived from the solution of a Partial Differential Equations (PDEs) at different time instants. In order to limit the scope of our study while targeting a rele- vant application, we focus on the problem of computing variations in the ice sheets mass (our QoI), which is a proxy for global mean sea-level changes. We present a number of neural network formulations and compare their performance with that of Polynomial Chaos Expansions (PCE) constructed on the same data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps

We present a preliminary investigation of the use of Multi-Layer Perceptrons (MLP) and Recurrent Neural Networks (RNNs) as surrogates of parameter-to-prediction maps of computational expensive dynamical models. In particular, we target the approximation of Quantities of Interest (QoIs) derived from the solution of a Partial Differential Equations (PDEs) at different time instants. In order to limit the scope of our study while targeting a relevant application, we focus on the problem of computing variations in the ice sheets mass (our QoI), which is a proxy for global mean sea-level changes. We present a number of neural network formulations and compare their performance with that of Polynomial Chaos Expansions (PCE) constructed on the same data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Developing Surrogate Far-Field Nuclear Fallout and its Rapid Decontamination from Aircraft Surfaces

Naval aircraft and vehicles deployed in response to the tsunami of 2011 and subsequent nuclear meltdown at the Fukushima Daiichi nuclear power station were exposed to and contaminated with radioactive materials released to the atmosphere. In response, the Navy and Argonne National Laboratory are testing various decontamination methods to clean aircraft surfaces exposed to far-field nuclear fallout. Far-field fallout, which can travel hundreds of miles, includes dissolved radionuclides and fine particulate matter. To simulate contamination with such materials, dissolved cesium-137 ( 137 Cs) and radiolabeled particles were deposited on coupons (1 x 1 in. sections) of tire, acrylic, and painted metal surfaces. These coupons were subjected to six different decontamination methods, including ones involving four strippable products, a soap solution, and tap water. Removals of dissolved 137 Cs were generally high: over 90% of the deposited radionuclide was removed after three decontaminations. To simulate exposure to inert, radioactive fallout or other inert, airborne particulate, silica (SiO 2 ) particles were used as surrogate fine particulate matter. The silica particles were radio-labeled by first depositing europium-152 ( 152 Eu) or americium-241 ( 241 Am) on the particle surface as sesquioxides (Eu 2 O 3 or Am 2 O 3 ), then coating it with sodium silicate. Scanning electron microscopy confirmed that particle size was not altered by the treatment, while leaching studies into numerous solutions and decontamination agents confirmed that minimal amounts of the radiolabel dissolved into solution. Three different methods to apply the radioactive particles to the silica were evaluated. Pressing the coupons into a dish containing the particles resulted in particle clumping on the surface, while applying the particles within water also resulted in heterogeneous surface coverage. Suspending the particles in ethanol and then applying them to the surface supplied homogeneous surface coverage that provided acute and precise removal percentages. Finally, particle-surface interactions seemingly play an important role in radiological decontamination as the removal percentages were lower than those of soluble 137 Cs for aqueous decontamination methods. These decreased removal percentages indicate that more complex methods such as strippable decontamination agents are required to remove particulate far-field fallout from aircraft and similar vehicle surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Isomeric Cross Sections For 87 Y(n,$γ$) From Surrogate (p,d$γ$) Data

Cross sections for compound-nuclear reactions involving unstable targets are important for many applications, but can often not be measured directly. Recently, I developed a method for extracting cross sections for neutron-capture on unstable isotopes from indirect (surrogate) reaction data. I applied the method to $89$ Y(p,$γ$) data, which were supplied by the LLNL experimental nuclear physics program, to determine the $87$ Y(n,$γ$) (activation) cross section. Here, I extend the approach further, in order to determine cross sections for reactions that start and/or end at isomeric states. In particular, I determine cross sections, along with uncertainties, for seven additional reactions: $87$ Y(n,$γ$) $88gs$ Y, $87$ Y(n,$γ$) $88m1$ Y, $87$ Y(n,$γ$) $88m2$ Y, $87m$ Y(n,$γ$) $88$ Y (activation), $87m$ Y(n,$γ$) $88gs$ Y, $87m$ Y(n,$γ$) $88m1$ Y, $87m$ Y(n,$γ$) $88m2$ Y. These cross sections cannot be measured directly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

On-line Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification

Uncertainty quantification is recognized as a fundamental task to obtain predictive numerical simulations. However, many realistic engineering applications require complex and computationally expensive high-fidelity numerical simulations for the accurate characterization of the system responses. Moreover, complex physical models and extreme operative conditions can easily lead to hundreds of uncertain parameters that need to be propagated through high-fidelity codes. Under these circumstances, a single fidelity approach, i.e. a workflow that only uses high-fidelity simulations to perform the uncertainty quantification task, is unfeasible due to the prohibitive overall computational cost. In recent years, multifidelity strategies have been introduced to overcome this issue. The core idea of this family of methods is to combine simulations with varying levels of fidelity/accuracy in order to obtain the multifidelity estimators or surrogates with the same accuracy of their single fidelity counterparts at a much lower computational cost. This goal is usually accomplished by defining a prioria sequence of discretization levels or physical modeling assumptions that can be used to decrease the complexity of a numerical realization and thus its computational cost. However ,less attention has been dedicated to low-fidelity models that can be built directly from the small number of high-fidelity simulations available. In this work we focus our attention on Reduced-Order Models that can be considered a particular class of data-driven approaches. Our main goal is to explore the combination of multifidelity uncertainty quantification and reduced-order models to obtain an efficient framework for propagating uncertainties through expensive numerical codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Analysis of Neural Network Combustion Surrogate Models

This report details initial investigations of neural network surrogate models for combustion applications. The models are assessed with respect to averaged predictive fidelity over a test data set as well as with respect to the accuracy of resolving landmark statistics of interest, as a function of increasing training data volume.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Incorporating physical constraints into Gaussian process surrogate models (LDRD Project Summary)

This report summarizes work done under the Laboratory Directed Research and Development (LDRD) project titled "Incorporating physical constraints into Gaussian process surrogate models?' In this project, we explored a variety of strategies for constraint implementations. We considered bound constraints, monotonicity and related convexity constraints, Gaussian processes which are constrained to satisfy linear operator constraints which represent physical laws expressed as partial differential equations, and intrinsic boundary condition constraints. We wrote three papers and are currently finishing two others. We developed initial software implementations for some approaches. This report summarizes the work done under this LDRD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Automated Discovery of DOminaNt physics Informed Surrogates (ADDONIS) Framework for Improving Water Cycling Predictability

Focal Areas: The primary alignment of the proposed framework is with focal area 2, because it guides the construction of a hierarchy of predictive models generated from Statistical and AI/ ML techniques. There are connections to focal areas 1 and 3 because surrogates allow for coupled data assimilation techniques where the full model is too expensive (1) and the discovered balance laws can be used as an insightful analysis tool to validate the structure of models (3).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Selection and Characterization of Surrogate Material for Developing Zr Barrier Coatings for Uranium Nitride Particles

In support of UC 1-x N x fuel kernels (~ 800 micrometer diameter) development work ongoing at Oak Ridge national laboratory (ORNL), where the fuel kernels will be embedded inside a possible zirconium based matrix, Argonne National Laboratory (ANL) is contributing to help develop a robust metal diffusion barrier coating. Zirconium nitride (ZrN) and Zirconium oxide (ZrO2) were selected as possible diffusion barrier candidates, and the atomic layer deposition (ALD) has been chosen as a candidate technology to deposit the film. Development of such a barrier coating with the ALD process entails significant number of trials to study relevant parameters. Therefore, to prevent needless wastage, instead of coating directly over fuel kernels, a surrogate candidate with similar dimensions and physical properties were been selected and coated to develop the diffusion barrier coating.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from each other. When an application imposes new constraints, for example, by requiring samples in a non-rectangular domain or the addition of new samples to an existing set, a common solution is to modify the algorithm currently in use, often with less than satisfactory results. As an alternative, we propose the concept of intelligent sampling, where we devise algorithms specifically tailored to meet our sampling needs, either by creating new algorithms or by modifying suitable algorithms from other fields. Surprisingly, both qualitative and quantitative comparisons indicate that some relatively simple algorithms can be easily modified to meet the many sampling requirements of surrogate modeling, hyperparameter optimization, and data analysis; these algorithms outperform their more sophisticated counterparts currently in use, resulting in better use of time and computer resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Optimization of artificial viscosity in production codes based on Gaussian Regression surrogate models

To accurately model flows with shock waves using staggered-grid Lagrangian hydrodynamics, artificial viscosity has to be introduced to convert kinetic energy into internal energy, thereby increasing the entropy across shocks. Determining the appropriate strength of the artificial viscosity is an art and strongly depends on the particular problem and experience of the researcher. The objective of this study is to pose the problem of finding the appropriate strength of artificial viscosity as an optimization problem and solve this problem using machine learning (ML) tools, specifically using surrogate models based on Gaussian Process regression and Bayesian analysis. We describe the optimization method and discuss various practical details of its implementation. The shock-containing problems for which we apply this method all have been implemented in the LANL code FLAG. First, we apply ML to find optimal values to isolated shock problems of different strengths. Second, we apply ML to optimize viscosity for a 1D propagating detonation problem based on Zel’dovich-von Neumann-Doring (ZND) detonation theory using a reactive burn model. We compare results for default (currently used values in FLAG) and optimized values of artificial viscosity for these problems demonstrating the potential for significant improvement in the accuracy of computations.

42 ENGINEERING↗