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DOE OSTI2017

Preliminary Analysis of FFTF Loss-of-Flow Tests with SAS4A/SASSYS-1

The Fast Flux Test Facility (FFTF) was the most recent Sodium-cooled Fast Reactor (SFR) built in the United States. The Passive Safety Test (PST) program was carried out on FFTF between 1984 and 1986 to demonstrate the capability of passive reactor shutdown and decay heat removal in response to protected and unprotected transients. In order to recover the information and knowledge that has been gained from FFTF, a research project was initiated focusing on the simulation of Unprotected Loss of Flow Without Scram (LOFWOS) tests similar to the knowledge preservation activities conducted for EBR-II by Argonne National Laboratory in earlier years. These knowledge preservation activities are intended to support the validation of computational tools and methods in the area of SFR passive safety. Comparisons with experimental data and other safety codes provide opportunities to improve SFR design and safety analysis codes and methods.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2014

Analysis of multicrystal pump–probe data sets. I. Expressions for the RATIO model

The RATIO method in time-resolved crystallography [Coppens et al. (2009). J. Synchrotron Rad. 16 , 226–230] was developed for use with Laue pump–probe diffraction data to avoid complex corrections due to wavelength dependence of the intensities. The application of the RATIO method in processing/analysis prior to structure refinement requires an appropriate ratio model for modeling the light response. The assessment of the accuracy of pump–probe time-resolved structure refinements based on the observed ratios was discussed in a previous paper. In the current paper, a detailed ratio model is discussed, taking into account both geometric and thermal light-induced changes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

WE-AB-207A-04: Random Undersampled Cone Beam CT: Theoretical Analysis and a Novel Reconstruction Method

Purpose: Reducing x-ray exposure and speeding up data acquisition motived studies on projection data undersampling. It is an important question that for a given undersampling ratio, what the optimal undersampling approach is. In this study, we propose a new undersampling scheme: random-ray undersampling. We will mathematically analyze its projection matrix properties and demonstrate its advantages. We will also propose a new reconstruction method that simultaneously performs CT image reconstruction and projection domain data restoration. Methods: By representing projection operator under the basis of singular vectors of full projection operator, matrix representations for an undersampling case can be generated and numerical singular value decomposition can be performed. We compared properties of matrices among three undersampling approaches: regular-view undersampling, regular-ray undersampling, and the proposed random-ray undersampling. To accomplish CT reconstruction for random undersampling, we developed a novel method that iteratively performs CT reconstruction and missing projection data restoration via regularization approaches. Results: For a given undersampling ratio, random-ray undersampling preserved mathematical properties of full projection operator better than the other two approaches. This translates to advantages of reconstructing CT images at lower errors. Different types of image artifacts were observed depending on undersampling strategies, which were ascribed to the unique singular vectors of the sampling operators in the image domain. We tested the proposed reconstruction algorithm on a Forbid phantom with only 30% of the projection data randomly acquired. Reconstructed image error was reduced from 9.4% in a TV method to 7.6% in the proposed method. Conclusion: The proposed random-ray undersampling is mathematically advantageous over other typical undersampling approaches. It may permit better image reconstruction at the same undersampling ratio. The novel algorithm suitable for this random-ray undersampling was able to reconstruct high-quality images.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

A novel analysis method for paired-sample microbial ecology experiments

Many microbial ecology experiments use sequencing data to measure a community s response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method s validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our method identified known oil degraders as well as two clades, Maricurvus and Rhodobacteraceae, that responded to amendment with oil but do not include known oil degraders. Furthermore, our approach is sensitive to organisms that increased in abundance only in the experimental unit but less sensitive to organisms that increased in both control and experimental units, thus mitigating the role of bottle effects .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Particle transport constraints via Bayesian spectral fitting of multiple atomic lines

Optimized operation of fusion devices demands detailed understanding of plasma transport, a problem that must be addressed with advances in both measurement and data analysis techniques. In this work, we adopt Bayesian inference methods to determine experimental particle transport, leveraging opportunities from high-resolution He-like ion spectra in a tokamak plasma. The Bayesian spectral fitting code is used to analyze resonance (w), forbidden (z), intercombination (x, y), and satellite (k, j) lines of He-like Ca following laser blow-off injections on Alcator C-Mod. This offers powerful transport constraints since these lines depend differently on electron temperature and density, but also differ in their relation to Li-like, He-like, and H-like ion densities, often the dominant Ca charge states over most of the C-Mod plasma radius. Using synthetic diagnostics based on the AURORA package, we demonstrate improved effectiveness of impurity transport inferences when spectroscopic data from a progressively larger number of lines are included.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Particle transport constraints via Bayesian spectral fitting of multiple atomic lines

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Probing cellulose structures with vibrational spectroscopy

This paper reviews principles, data interpretations, and applications of vibrational spectroscopic methods used for analysis of cellulose in the isolated state and in plant cell walls or lignocellulose biomass. The paper begins with reviewing the crystalline structures of crystalline cellulose polymorphs and the principles of three different vibrational spectroscopy methods—infrared (IR), Raman, and sum frequency generation (SFG)—complemented with density functional theory calculations. Then, it discusses the vibrational modes of crystalline celluloses, how the chain orientation in crystalline domain is analyzed in each method, and how the concentration and spatial distribution of crystalline cellulose domains interspersed in amorphous matrices are manifested or analyzed differently in these three methods. Finally, the paper discusses examples of analyzing crystalline cellulose in plant cell walls or lignocellulose biomass with IR, Raman, and SFG including spectroscopic imaging. One review cannot cover all vibrational spectroscopy literatures on cellulose; this review aims at providing tutorial information, using selected literatures and experimental data, needed to interpret nano-, meso-, and micro-scale structures of cellulose in plant cell walls and lignocellulose biomass.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Quantitative phase analysis of Mg:ZrO{sub 2} nanoparticles by Rietveld refinement method

To quantify the structural phases of nanocrystalline ZrO{sub 2} doped with Mg ions of varying concentrations (3, 5, 10, 15 and 20%) and annealed at different temperatures. Magnesia doped zirconia was prepared by chemical co-precipitation method and annealed up to 1000°C. The monoclinic and tetragonal phases present in Mg:ZrO{sub 2} were quantified using Rietveld refinement analysis of the X-ray diffraction data and compared with the Direct method based on peak intensity calculations. Tetragonal phase was dominant in the 600°C annealed Mg:ZrO{sub 2} for all Mg concentrations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗

DOE OSTI2016

DECIPHERING CONTRIBUTIONS TO THE EXTRAGALACTIC GAMMA-RAY BACKGROUND FROM 2 GeV TO 2 TeV

Astrophysical sources outside the Milky Way, such as active galactic nuclei and star-forming galaxies, leave their imprint on the gamma-ray sky as nearly isotropic emission referred to as the extragalactic gamma-ray background (EGB). While the brightest of these sources may be individually resolved, their fainter counterparts contribute diffusely. In this work, we use a recently developed analysis method, called the Non-Poissonian Template Fit, on up to 93 months of publicly available data from the Fermi Large Area Telescope to determine the properties of the point sources (PSs) that comprise the EGB. This analysis takes advantage of photon-count statistics to probe the aggregate properties of these source populations below the sensitivity threshold of published catalogs. We measure the source-count distributions and PS intensities, as a function of energy, from ∼2 GeV to 2 TeV. We find that the EGB is dominated by PSs, likely blazars, in all seven energy sub-bins considered. These results have implications for the interpretation of IceCube’s PeV neutrinos, which may originate from sources that contribute to the non-blazar component of the EGB. Additionally, we comment on implications for future TeV observatories such as the Cherenkov Telescope Array. We provide sky maps showing locations most likely to contain these new sources at both low (≲50 GeV) and high (≳50 GeV) energies for use in future observations and cross-correlation studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Soils Activity Mobility Study: Methodology and Application

This report presents a three-level approach for estimation of sediment transport to provide an assessment of potential erosion risk for sites at the Nevada National Security Site (NNSS) that are posted for radiological purposes and where migration is suspected or known to occur due to storm runoff. Based on the assessed risk, the appropriate level of effort can be determined for analysis of radiological surveys, field experiments to quantify erosion and transport rates, and long-term monitoring. The method is demonstrated at contaminated sites, including Plutonium Valley, Shasta, Smoky, and T-1. The Pacific Southwest Interagency Committee (PSIAC) procedure is selected as the Level 1 analysis tool. The PSIAC method provides an estimation of the total annual sediment yield based on factors derived from the climatic and physical characteristics of a watershed. If the results indicate low risk, then further analysis is not warranted. If the Level 1 analysis indicates high risk or is deemed uncertain, a Level 2 analysis using the Modified Universal Soil Loss Equation (MUSLE) is proposed. In addition, if a sediment yield for a storm event rather than an annual sediment yield is needed, then the proposed Level 2 analysis should be performed. MUSLE only provides sheet and rill erosion estimates. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) provides storm peak runoff rate and storm volumes, the inputs necessary for MUSLE. Channel Sediment Transport (CHAN-SED) I and II models are proposed for estimating sediment deposition or erosion in a channel reach from a storm event. These models require storm hydrograph associated sediment concentration and bed load particle size distribution data. When the Level 2 analysis indicates high risk for sediment yield and associated contaminant migration or when there is high uncertainty in the Level 2 results, the sites can be further evaluated with a Level 3 analysis using more complex and labor- and data-intensive methods. For the watersheds analyzed in this report using the Level 1 PSIAC method, the risk of erosion is low. The field reconnaissance surveys of these watersheds confirm the conclusion that the sediment yield of undisturbed areas at the NNSS would be low. The climate, geology, soils, ground cover, land use, and runoff potential are similar among these watersheds. There are no well-defined ephemeral channels except at the Smoky and Plutonium Valley sites. Topography seems to have the strongest influence on sediment yields, as sediment yields are higher on the steeper hill slopes. Lack of measured sediment yield data at the NNSS does not allow for a direct evaluation of the yield estimates by the PSIAC method. Level 2 MUSLE estimates in all the analyzed watersheds except Shasta are a small percentage of the estimates from PSIAC because MUSLE is not inclusive of channel erosion. This indicates that channel erosion dominates the total sediment yield in these watersheds. Annual sediment yields for these watersheds are estimated using the CHAN-SEDI and CHAN-SEDII channel sediment transport models. Both transport models give similar results and exceed the estimates obtained from PSIAC and MUSLE. It is recommended that the total watershed sediment yield of watersheds at the NNSS with flow channels be obtained by adding the washload estimate (rill and inter-rill erosion) from MUSLE to that obtained from channel transport models (bed load and suspended sediment). PSIAC will give comparable results if factor scores for channel erosion are revised towards the high erosion level. Application of the Level 3 process-based models to estimate sediment yields at the NNSS cannot be recommended at this time. Increased model complexity alone will not improve the certainty of the sediment yield estimates. Models must be calibrated against measured data before model results are accepted as certain. Because no measurements of sediment yields at the NNSS are available, model validation cannot be performed. This is also true for the models used in the Level 2 analyses presented in this study. The need to calibrate MUSLE to local conditions has been discussed. Likewise, the transport equations of CHAN-SEDI and CHAN-SEDII need to be calibrated against local data to assess their applicability under semi-arid conditions and for the ephemeral channels at the NNSS. Before these validations and calibration exercises can be undertaken, a long-term measured sediment yield data set must be developed. Development of long-term measured sediment yield data cannot be overemphasized. Long-term monitoring is essential for accurate characterization of watershed processes. It is recommended that a long-term monitoring program be set up to measure watershed erosion rates and channel sediment transport rates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Pair distribution function analysis of nano-object assemblies

The atomic pair distribution function (aPDF) analysis technique, also known as the total scattering method, which considers both Bragg and diffuse scattering, has been used extensively to probe local atomic arrangements in crystalline and disordered materials. In contrast, there have been limited applications of the PDF in self-assembled nanomaterials, which represent a class of materials built from nanoscale objects, such as nano-colloids, micelles and proteins. As distinguished from atoms, nano-objects have polydispersity in size and shape, and such form-factor effects complicate the application of PDF analysis to nano-systems. Here, in this paper, the application of the PDF is extended to spherical nano-object assemblies and the formulae for the nano-PDF (nPDF) are derived, showing some differences from the aPDF. By numerical simulations, the properties of the nPDF (peak broadening and pattern profile) are studied systematically as a function of structural features, such as nano-object parameters (size and size polydispersity) and assembly structural features (size, shape, structure type and lattice disorder), and of data processing parameters ( q cut-off and `missing' data in ultra-small-angle regions). The nPDF analysis method is found to provide an effective route to revealing not only nanoscale but also mesoscale structural properties, for example the morphology of a nano-assembly.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

An evaluation for geometries, formation enthalpies, and dissociation energies of diatomic and triatomic (C, H, N, O), NO 3 , and HNO 3 molecules from the PAW DFT method with PBE and optB88-vdW functionals

The structural geometries, formation enthalpies, and dissociation energies of all diatomic and triatomic molecules consisting of the four basic elements C, H, N, and/or O are calculated using the projector augmented wave density functional theory (DFT) method with the Perdew–Burke–Ernzerhof and optB88-vdW exchange-correlation functionals. The calculations are also extended to two larger molecules NO 3 and HNO 3 , which consist of four and five atoms, respectively. In total, 82 molecules or isomers are considered in the calculations. The geometric parameters including 42 bond lengths and 15 bond angles of these molecules calculated using the planewave DFT method are highly satisfactory, relative to the available experimental data. The error analysis is also performed for 49 formation enthalpies and 138 dissociation energies (including 51 atomization energies as well as the corresponding bond dissociation energies). The results are also compared with the previous data from various atomic-orbital-based methods for molecules and from similar or different planewave DFT methods for various solids and other molecules. This provides an informative and instructive evaluation especially for calculating the large-size material systems containing these small molecules as well as for developing the DFT methods further.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2015

Modeling and Simulations for the High Flux Isotope Reactor Cycle 400

A concerted effort over the past few years has been focused on enhancing the core model for the High Flux Isotope Reactor (HFIR), as part of a comprehensive study for HFIR conversion from high-enriched uranium (HEU) to low-enriched uranium (LEU) fuel. At this time, the core model used to perform analyses in support of HFIR operation is an MCNP model for the beginning of Cycle 400, which was documented in detail in a 2005 technical report. A HFIR core depletion model that is based on current state-of-the-art methods and nuclear data was needed to serve as reference for the design of an LEU fuel for HFIR. The recent enhancements in modeling and simulations for HFIR that are discussed in the present report include: (1) revision of the 2005 MCNP model for the beginning of Cycle 400 to improve the modeling data and assumptions as necessary based on appropriate primary reference sources HFIR drawings and reports; (2) improvement of the fuel region model, including an explicit representation for the involute fuel plate geometry that is characteristic to HFIR fuel; and (3) revision of the Monte Carlo-based depletion model for HFIR in use since 2009 but never documented in detail, with the development of a new depletion model for the HFIR explicit fuel plate representation. The new HFIR models for Cycle 400 are used to determine various metrics of relevance to reactor performance and safety assessments. The calculated metrics are compared, where possible, with measurement data from preconstruction critical experiments at HFIR, data included in the current HFIR safety analysis report, and/or data from previous calculations performed with different methods or codes. The results of the analyses show that the models presented in this report provide a robust and reliable basis for HFIR analyses.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Dispersion Validation for Flow Involving a Large Structure Revisited: 45 Degree Rotation

The atmospheric dispersion of contaminants in the wake of a large urban structure is a challenging fluid mechanics problem of interest to the scientific and engineering communities. Magnetic Resonance Velocimetry (MRV) and Magnetic Resonance Concentration (MRC) are relatively new techniques that leverage diagnostic equipment used primarily by the medical field to make 3D engineering measurements of flow and contaminant dispersal. SIERRA/Fuego, a computational fluid dynamics (CFD) code at Sandia National Labs is employed to make detailed comparisons to the dataset to evaluate the quantitative and qualitative accuracy of the model. This work is the second in a series of scenarios. In the prior work, a single large building in an array of similar buildings was considered with the wind perpendicular to a building face. In this work, the geometry is rotated by 45 degrees and improved studies are performed for simulation credibility. The comparison exercise shows conditionally good comparisons between the model and experiment. Model uncertainties are assessed through parametric variations. Various methods of quantifying the accuracy between experiments and data are examined Three-dimensional analysis of accuracy is performed. The effort helped identify deficiencies in the techniques used to make these comparisons, and further methods development therefore becomes one of the main recommendations for follow-on work.

42 ENGINEERING↗

DOE OSTI2025

3D Continuous Forcing Dataset from 3D Constrained Variational Analysis at SGP

The continuous 3D large-scale forcing (VARANAL3D) data set derived from 3D constrained variational analysis (3DCVA) extends the conventional constrained variational analysis method by incorporating multiple sub-columns within the analysis domain. This advancement introduces spatial variability into the large-scale forcing fields, thereby enriching the data set’s applicability. The VARANAL3D data set spans from 2004 to 2018 and covers a region of 5˚×4.5˚ domain around the ARM SGP site. The analysis domain is divided into 10×9 sub-columns with 0.5˚ resolution. The 3D large-scale forcing data provides necessary variables to drive and evaluate single-column models (SCM), cloud-resolving models (CRM) ,and large-eddy simulations (LES), as well as information for testing model sensitivity to spatial variability of the large-scale forcing data, facilitating more rigorous testing and refinement of physical processes in SCM/CRM/LES.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Determining Patterns from Radiation Portal Monitor Data: Enabling Data Insight with Visual and Interactive Exploratory Data Analysis

Smuggling of nuclear and radiological material is a complex problem that will need to be analyzed from a variety of angles in order to effectively combat something so rare, yet so hazardous. One crucial tool in the effort to detect and deter smuggling of rad/nuc materials is the radiation portal monitor (RPM)—machines that scan passengers at airports, vehicles and trains at border crossings, and shipping containers at ports throughout the world. While data from RPMs are regularly analyzed at the individual lane level to ensure monitor health and good operation, aggregate analysis of the data has not gone far beyond summary statistics such as number of occupancies and alarm rates. In particular, there are opportunities to carry out interactive exploratory data analysis with the help of tools like the R package Shiny and methods that lend themselves to the visual display of spatiotemporal correlations such as spatial PCA and detrended cross-correlation analysis. By doing so, we discover interesting spatiotemporal patterns in the data that point to specific events or anomalous moments in time that warrant further analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗