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Technical Advances for Sustainable Remediation: In Situ Monitoring and Climate Resiliency - 18436

Recent advances in sustainable remediation techniques show promise as solutions for contaminant removal in soil and groundwater media. Although passive remediation (or natural attenuation) has gained momentum as an acceptable technique, there are still challenges in regulatory frameworks as well as in public acceptance. Leaving contaminants in subsurface requires increased burden of proof to show that plumes are stable and residual contaminants do not pose a significant health risk. Climate changes and frequent extreme climate events contribute another level of complexity and make it difficult to demonstrate system stability and predict future plume conditions. In this study, we show two coupled approaches to tackle this challenge: (1) in situ groundwater monitoring based on advanced sensor and data analytics technologies (i.e., Big Data), and (2) state-of-art modeling for optimizing monitoring and site closure strategies. In the new monitoring approach, in situ sensors are used to continuously measure the key variables that control contaminant plume mobility and its spatial and temporal distribution (such as pH, redox potential, electrical conductivity, and groundwater level). Coupled with a limited number of groundwater sampling, the data analytics methods - data mining and machine learning - allow us to estimate contaminant concentrations, and also to detect any significant changes associated with the plume mobility. In parallel, the state-of-art numerical flow and reactive transport model - ASCEM software under DOE Office of Environmental Management - provides a physical and mechanistic understanding of the system behavior, and predicts the long-term plume migration for optimizing the monitoring and closure strategies. In particular, we focus on the effects of extreme precipitation or drought on the site closure as well as monitoring strategies. We demonstrate this approach at the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Novel Carbon (C)-Boron (B)-Nitrogen (N)-Containing H2 Storage Materials

The following summarizes the research conducted for DOE project DE-EE0005658 “Novel Carbon(C)-Boron(B)-Nitrogen(N)-Containing H2 Storage Materials”. This work focused in part on the continued study of two materials identified from the preceding project DE-FG360GO18143 (“Hydrogen Storage by Novel CBN Heterocycle Materials”) as lead candidates to meet the DOE technical targets for either vehicular or non-automotive hydrogen storage applications. Specifically, a room-temperature liquid, 3-methyl-1,2-cyclopentane (B), and a high H2 capacity solid, 1,2-BN-cyclohexane (J), were selected for further characterization and performance optimization. In addition to these compounds, the current project also aimed to prepare several new materials predicted to be disposed towards direct reversibility of H2 release and uptake, a feature deemed critical to achieving efficient recycling of spent fuel end products. To assist in the rational design of these and other next-generation materials, this project undertook to investigate the mechanism of hydrogen release from established compounds (mainly B and J) using a combined experimental/computational approach. Among this project’s signature accomplishments, the preliminary synthetic route to B was optimized for production on decagram scale. With such quantities of material available, its performance in powering an actual 30 W proton exchange membrane (PEM) fuel cell stack was tested and found to be identical to that of facility H2. Despite this positive proof-of-concept achievement, however, further consideration of neat B as a potential hydrogen storage material was abandoned due to evidence of thermal instability. Specifically, mass spectrometry-coupled thermogravimetric analysis (TGA-MS) revealed significant H2 release from B to initiate at 50 °C, well below the 60 °C minimum threshold set by the DOE. This result prompted a more extensive investigation in the decomposition mechanism of B vis-à-vis that of J, which exhibited in neat form a substantially higher onset temperature for spontaneous H2 release (70 °C). Solution-phase kinetic experiments using ReactIR established a second-order dependence for the initial loss of H2 from both B and J; Arrhenius analysis, however, revealed the activation barrier for this reaction was lower for B than for J, which presumably contributes to the diminished thermal stability of the former. On the basis of these and other experimental results, extensive computational efforts yielded a reasonable mechanistic model for the dehydrogenation of 1,2-BN-cycloalkane materials. While the prospect of neat B as a suitable hydrogen storage material was discarded, it was proposed that the combination of B with more thermally stable amine-borane-based materials might afford mixtures with improved properties. Indeed, when B was combined with ammonia borane (AB) in a 2:1 molar ratio, the two materials formed a liquid. More significantly, this mixture remained liquid even after complete dehydrogenation, thus establishing the potential for a single-phase fuel cycle. (In contrast, the dehydrogenation product of neat B is a low melting solid (mp = 28-30 °C).) Another advantage conferred by the blend formulation was a dramatic reduction in the amount of borazine produced by AB. Borazine is a well-known contaminant of H2 produced by the thermal decomposition of neat AB, and exerts deleterious effects on fuel cell performance. Residual gas analysis (RGA) of the gas stream generated from the B-AB blend, however, detected just 0.01% borazine content when a Pt-Ni nanoparticle dehydrogenation catalyst was used. In all ii then, the 2:1 B-AB blend marks a major achievement in the effort to develop a suitable liquid amine-borane hydrogen storage material, and merits further investigation into the optimization for practical adoption. Similar realization of the potential of J as a high % wt. H2 material required a method to dehydrogenate the carbonaceous components of the molecule without the use of a sacrificial hydrogen acceptor, as had been reported in the previous project. Ultimately, this reaction was achieved for a B,N-disubstituted BN-cyclohexene model substrate using a gas flow system with a fixed Pd/C catalyst bed. Considerable work remains, however, to translate these initial results into a general protocol for complete dehydrogenation of fully saturated BN-cycloalkane materials such as J. With concrete confirmation of the possibility to perform both BN and CC dehydrogenation on a single theoretical substrate, COMSOL modeling was used to evaluate the effects of thermodynamically coupling the two reactions. It was hypothesized that the heat generated from exothermic BN dehydrogenation would partially drive the endothermic CC dehydrogenation reaction; this additional heat consumption was expected to in turn confer the benefit of lowering the maximum reactor temperature. A two-dimensional model of an axisymmetric reactor including experimental kinetic and calculated thermodynamic parameters for both reactions did indeed predict these outcomes. The extent to which the effects of thermodynamic coupling actually manifested, however, were also revealed to depend strongly on the relative rates of the two reactions, as well as the magnitude of the equilibrium constant governing the progress of the endothermic process. Given the evident complexity of attaining high effective % wt. H2 capacity with J, alternative systems were investigated for greater facility of extensive H2 release. Among those studied, 1,2,4,5-bis-BN-cyclohexane (H) demonstrated the most favorable properties, particularly with respect to thermal stability: rather than decompose, a neat sample instead sublimed when heated above 150 °C. Nevertheless, two commercially available catalytic systems were identified to effect release of two H2 equivalents from H. Release of further equivalents were apparently impeded by the formation of either polymeric material or one of two dimeric cage compounds depending on the catalyst used. Notably, a method to regenerate H from these product mixtures remains to be developed. Thus, while H may prove useful for certain long-term energy storage needs, it is currently less suited applications involving frequent fuel consumption. Similar difficulties were also encountered in attempts to realize the complete fuel cycle of 1,3-BN-cyclohexane (E) and B,N-substituted derivatives thereof. It had been initially proposed that E would provide for readily reversible BN dehydrogenation through a measure of frustrated Lewis pair-type character. Indeed, computations predicted this reaction would be essentially thermoneutral in solution. In the course of attempts to fully hydrogenate the spent fuel, however, dimeric species formed and proved resistant to further BN reduction. While a number of monomeric cyclic compounds were also successfully synthesized as formal boron-nitrogen frustrated Lewis pairs, none demonstrated any capacity to split H2 across the BN unit. The challenge of developing a practical amine-borane-based material for readily reversible hydrogen storage thus remains unresolved at this time. As such, it deserves consideration as a major objective of any future work.

08 HYDROGEN↗

Structural and magnetic properties of La2Ni1−xCoxMnO6 compounds

Highlights: • Monoclinic La2Ni1−xCoxMnO6 samples have been prepared using Sol-gel technique. • Rietveld analysis shows an octahedrally distorted double perovskite structure. • The nature of FM interaction and TC depend on the B-site ions. • The short range FM ordering above TC is induced by antisite disorders. • The existence of any spin glass like phase in such system is discarded. - Abstract: Single phase monoclinic La{sub 2}Ni{sub 1−x}Co{sub x}MnO{sub 6} (x = 0.2, 0.4, 0.8) compounds, henceforth, referred as LNC{sub x}MO (x = 0.2, 0.4, 0.8) have been prepared by Sol–gel technique. The structural study and redox titration suggest that the samples are both La and O deficient. The generated atomic models using Rietveld refined structural parameters show octahedral distortion in the double perovskite structure. X-ray photoelectron spectroscopy (XPS) study has been used to determine the predominant valence states of Ni, Mn and Co ions. LNC{sub x}MO (x = 0.2, 0.4) are found to be Heisenberg ferromagnets whereas LNC{sub 0.8}MO is a phase segregated system. In LNC{sub 0.4}MO and LNC{sub 0.8}MO, at temperatures above T{sub C}, χ{sup −1}(T) curve shows the characteristics of Griffiths phase. The study of ac susceptibility and isothermal remanent magnetization (IRM) discard the possibility of any glassy state at low temperatures. The antiphase boundaries (APBs), antisite disorders (ASDs) and oxygen vacancies play important roles in this system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗

CLASH: Extending galaxy strong lensing to small physical scales with distant sources highly magnified by galaxy cluster members

We present a complex strong lensing system in which a double source is imaged five times by two early-type galaxies. We take advantage in this target of the extraordinary multi-band photometric data set obtained as part of the Cluster Lensing And Supernova survey with Hubble (CLASH) program, complemented by the spectroscopic measurements of the VLT/VIMOS and FORS2 follow-up campaign. We use a photometric redshift value of 3.7 for the source and confirm spectroscopically the membership of the two lenses to the galaxy cluster MACS J1206.2–0847 at redshift 0.44. We exploit the excellent angular resolution of the HST/ACS images to model the two lenses in terms of singular isothermal sphere profiles and derive robust effective velocity dispersion values of 97 ± 3 and 240 ± 6 km s{sup –1}. Interestingly, the total mass distribution of the cluster is also well characterized by using only the local information contained in this lensing system, which is located at a projected distance of more than 300 kpc from the cluster luminosity center. According to our best-fitting lensing and composite stellar population models, the source is magnified by a total factor of 50 and has a luminous mass of approximately (1.0 ± 0.5) × 10{sup 9} M {sub ☉} (assuming a Salpeter stellar initial mass function). By combining the total and luminous mass estimates of the two lenses, we measure luminous over total mass fractions projected within the effective radii of 0.51 ± 0.21 and 0.80 ± 0.32. Remarkably, with these lenses we can extend the analysis of the mass properties of lens early-type galaxies by factors that are approximately two and three times smaller than previously done with regard to, respectively, velocity dispersion and luminous mass. The comparison of the total and luminous quantities of our lenses with those of astrophysical objects with different physical scales, like massive early-type galaxies and dwarf spheroidals, reveals the potential of studies of this kind for improving our knowledge about the internal structure of galaxies. These studies, made possible thanks to the CLASH survey, will allow us to go beyond the current limits posed by the available lens samples in the field.

79 ASTRONOMY AND ASTROPHYSICS↗

Whole metagenome sequencing and 16S rRNA gene amplicon analyses reveal the complex microbiome responsible for the success of enhanced in-situ reductive dechlorination (ERD) of a tetrachloroethene-contaminated Superfund site

The North Railroad Avenue Plume (NRAP) Superfund site in New Mexico, USA exemplifies successful chlorinated solvent bioremediation. NRAP was the result of leakage from a dry-cleaning that operated for 37 years. The presence of tetrachloroethene biodegradation byproducts, organohalide respiring genera (OHRG), and reductive dehalogenase (rdh) genes detected in groundwater samples indicated that enhanced reductive dechlorination (ERD) was the remedy of choice. This was achieved through biostimulation by mixing emulsified vegetable oil into the contaminated aquifer. This report combines metagenomic techniques with site monitoring metadata to reveal new details of ERD. DNA extracts from groundwater samples collected prior to and at four, 23 and 39 months after remedy implementation were subjected to whole metagenome sequencing (WMS) and 16S rRNA gene amplicon (16S) analyses. The response of the indigenous NRAP microbiome to ERD protocols is consistent with results obtained from microcosms, dechlorinating consortia, and observations at other contaminated sites. WMS detects three times as many phyla and six times as many genera as 16S. Both techniques reveal abundance changes in Dehalococcoides and Dehalobacter that reflect organohalide form and availability. Methane was not detected before biostimulation but appeared afterwards, corresponding to an increase in methanogenic Archaea. Assembly of WMS reads produced scaffolds containing rdh genes from Dehalococcoides, Dehalobacter, Dehalogenimonas, Desulfocarbo, and Desulfobacula. Anaerobic and aerobic cometabolic organohalide degrading microbes that increase in abundance include methanogenic Archaea, methanotrophs, Dechloromonas, and Xanthobacter, some of which contain hydrolytic dehalogenase genes. Aerobic cometabolism may be supported by oxygen gradients existing in aquifer microenvironments or by microbes that produce O 2 via microbial dismutation. The NRAP model for successful ERD is consistent with the established pathway and identifies new taxa and processes that support this syntrophic process. This project explores the potential of metagenomic tools (MGT) as the next advancement in bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗

Center for Efficiency in Sustainable Energy Systems

The main goal of the Center for Efficiency in Sustainable Energy Systems is to produce a methodology that evaluates a variety of energy systems. Task I. Improved Energy Efficiency for Industrial Processes: This task, completed in partnership with area manufacturers, analyzes the operation of complex manufacturing facilities to provide flexibilities that allow them to improve active-mode power efficiency, lower standby-mode power consumption, and use low cost energy resources to control energy costs in meeting their economic incentives; (2) Identify devices for the efficient transformation of instantaneous or continuous power to different devices and sections of industrial plants; and (3) use these manufacturing sites to demonstrate and validate general principles of power management. Task II. Analysis of a solid oxide fuel cell operating on landfill gas: This task consists of: (1) analysis of a typical landfill gas; (2) establishment of a comprehensive design of the fuel cell system (including the SOFC stack and BOP), including durability analysis; (3) development of suitable reforming methods and catalysts that are tailored to the specific SOFC system concept; and (4) SOFC stack fabrication with testing to demonstrate the salient operational characteristics of the stack, including an analysis of the overall energy conversion efficiency of the system. Task III. Demonstration of an urban wind turbine system: This task consists of (1) design and construction of two side-by-side wind turbine systems on the YSU campus, integrated through power control systems with grid power; (2) preliminary testing of aerodynamic control effectors (provided by a small business partner) to demonstrate improved power control, and evaluation of the system performance, including economic estimates of viability in an urban environment; and (3) computational analysis of the wind turbine system as an enabling activity for development of smart rotor blades that contain integrated sensor/actuator/controller modules to enhance energy capture and reduce aerodynamic loading and noise by way of virtual aerodynamic shaping. Accomplishments: Task I. Improved Energy Efficiency for Industrial Processes: We organized an energy management training session held on February 22, 2011, which was advertised through a regional manufacturing association to provide wide-ranging notification. Over two dozen companies were represented a the seminar, ranging from heavy manufacturing businesses with $5,000,000 per year energy expenses, to small, light manufacturing facilities. Task 2. Landfill Fuel Cell Power Generation Solid Oxide Fuel Cells (SOFCs) were constructed and evaluated as a means of obtaining electrical energy from landfill gas. Analysis of landfill gas. Attempts at collecting gas samples at the landfill and evaluating them on campus were still unsuccessful. Even a Teflon® sample bag would lose its H2S content. Evaluation of Gas Clean-up We consider this a confirmation of the CO2 effect on the solubility of H2S in water making much less sulfide available for the photocatalyst. It also means that another method should be employed to clean up landfill gas. Nonetheless, composition of impurities in landfill gas was reduced sufficiently to allow successful operation of the test fuel cell. Comparison to a PEM fuel cell system. If a PEMFC were to be operated with landfill gas as the fuel, the gas would have to be treated for sulfur removal, and then processed in a reformer large enough to drive the equilibrium far toward the products, so that negligible CO would flow into the fuel cell. Analysis of a fuel cell running on landfill gas. Using a Gow-Mac gas chromatograph with a thermal conductivity detector, unambiguous determination of CO can be made, at least as a primary constituent Task 3: Task 3 Plasma Controlled Turbine Blades Wind Turbine Selection. After carefully reviewing the various model available in the market the team selected the ARE 110 (2.5kW). The ARE 110 provides a very long life with little maintenance due to their relatively low rotational speeds (low RPM). The turbines large swept area (10.2ms2/110sq.ft), high-efficiency blades, purpose built alternator, and optimized power electronics ensure maximum energy capture from a wide range of wind speeds. Two wind turbines were installed side-by-side at the Melnick Hall site to compare their performance. Evaluate and Optimize Aerodynamically Enhanced Turbine Blades Due to delays in the installation of the wind turbines, no actual data was obtained within the contract period. At this time, the turbines are installed and operational at YSU with standard blades. We are in contact with Orbital Research and in discussion as to how best the required data can be obtained.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Boosting Big National Lab Data

Introduction: Big data. Love it or hate it, solving the world’s most intractable problems requires the ability to make sense of huge and complex sets of data and do it quickly. Speeding up the process – from hours to minutes or from weeks to days – is key to our success. One major source of such big data are physical experiments. As many will know, these physical experiments are commonly used to solve challenges in fields such as energy security, manufacturing, medicine, pharmacology, environmental protection and national security. Experiments use different instruments and sensor types to research for example the validity of new drugs, the base cause for diseases, more efficient energy sources, new materials for every day goods, effective methods for environmental cleanup, the optimal ingredients composition for chocolate or determine how to preserve valuable antics. This is done by experimentally determining the structure, properties and processes that govern biological systems, chemical processes and materials. The speed and quality at which we can acquire new insights from experiments directly influences the rate of scientific progress, industrial innovation and competitiveness. And gaining new groundbreaking insights, faster, is key to the economic success of our nations. Recent years have seen incredible advances in sensor technologies, from house size detector systems in large experiments such as the Large Hadron Collider and the ‘Eye of Gaia’ billion pixel camera detector to high throughput genome sequencing. These developments have led to an exponential increase in data volumes, rates and variety produced by instruments used for experimental work. This increase is coinciding with a need to analyze the experimental results at the time they are collected. This speed is required to optimize the data taking and quality, and also to enable new adaptive experiments, where the sample is manipulated as it is observed, e.g. a substance is injected into a tissue sample and the gradual effect is observed as more of the substance is injected, providing better insights into the natural processes that are occurring, as well as result driven sampling adjustment to capture particularly interesting features --- as they emerge. The Department of Energy’s Pacific Northwest National Laboratory (PNNL) is recognized for it’s expertise in the development of new measurement techniques and their application to challenges of national importance. So it was obvious to us to address the need for in-situ analysis of large scale experimental data. We have a wide range of experimental instruments on site, in facilities such as DOE’s national scientific user facility, the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). Commonly, scientists would create an individual analysis pipeline for each of those instruments; but even the same type of instrument would not necessarily share the same analysis tools. With the rapid increase of data volumes and rates we were facing two key challenges: how to bring a wider set of capabilities to bear to achieve in-situ analysis, and how to do so across a wide range of heterogeneous instruments at affordable costs and in a reasonable timeframe. We decided to take an unconventional approach to the problem, rather than developing customized, one-off solutions for specific instruments we wanted to explore if a more common solution could be found that would go beyond shared, basic infrastructures such as data movement and workflow engines.

97 MATHEMATICS AND COMPUTING↗

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗

Evaluation on the Effect of Composition on Radiation Hardening and Embrittlement in Model FeCrAl Alloys

This report details the findings of post-radiation mechanical testing and microstructural characterization performed on a series of model and commercial FeCrAl alloys to assist with the development of a cladding technology with enhanced accident tolerance. The samples investigated include model alloys with simple ferritic grain structure and two commercial alloys with minor solute additions. These samples were irradiated in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) up to nominal doses of 7.0 dpa near or at Light Water Reactor (LWR) relevant temperatures (300-400 C). Characterization included a suite of techniques including small angle neutron scattering (SANS), atom probe tomography (APT), and transmission based electron microscopy techniques. Mechanical testing included tensile tests at room temperature on sub-sized tensile specimens. The goal of this work was to conduct detailed characterization and mechanical testing to begin establishing empirical and/or theoretical structure-property relationships for radiation-induced hardening and embrittlement in the FeCrAl alloy class. Development of such relationships will provide insight on the performance of FeCrAl alloys in an irradiation environment and will enable further development of the alloy class for applications within a LWR environment. A particular focus was made on establishing trends, including composition and radiation dose. The report highlights in detail the pertinent findings based on this work. This report shows that radiation hardening in the alloys is primarily composition dependent due to the phase separation in the high-Cr FeCrAl alloys. Other radiation induced/enhanced microstructural features were less dependent on composition and when observed at low number densities, were not a significant contributor to the observed mechanical responses. Pre-existing microstructure in the alloys was found to be important, with grain boundaries and pre-existing dislocation networks acting as defect sinks, resulting in variations in the observed microstructures after irradiation. Dose trends were also observed, with increasing radiation dose promoting changes in the size and number density of the Cr-rich α' precipitates. Based on the microstructural analysis, performed tensile testing, and prior knowledge from FeCr literature it was hypothesized that the formation of the Cr-rich α' precipitates could lead to significant radiation-induced embrittlement in the alloys, and this could be composition dependent, a result which would mirror the trends observed for radiation-induced hardening. Due to the limited database on embrittlement in the FeCrAl alloy class after irradiation, a series of radiation experiments have been implemented. The overarching point of view within this report is the radiation tolerance of FeCrAl is complex, with many mechanisms and factors to be considered at once. Further development of the FeCrAl alloy class for enhanced accident tolerant applications requires detailed, single (or at least limited) variable experiments to fully comprehend and predict the performance of this alloy in LWRs. This report has been submitted as fulfillment of milestone M2FT-15OR0202321 titled, Summary report on the effect of composition on the irradiation embrittlement of Gen 1 ATF FeCrAl for the Department of Energy Office of Nuclear Energy, Advanced Fuel Campaign of the Fuel Cycle R&D program.

36 MATERIALS SCIENCE↗

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗

First lithium experiments in HIDRA and evidence of helium retention during quasi-steady-state stellarator plasma operations

Recent experiments in Hybrid Illinois Device for Research and Applications (HIDRA) have had operational discharges between t discharge = 60 and 1000 s using electron cyclotron resonant heating (ECRH) of the plasma. This means that quasi-steady-state plasma discharges reach conditions to study long-pulse plasma material interactions (PMIs). The newly commissioned HIDRA-Material Analysis Test-stand PMI diagnostic is used to place a drop of lithium onto a heated tungsten surface, transfer the sample in-vacuo and expose it in a helium plasma. Helium is of interest as there is an open question to whether lithium will be able to remove helium ash in real fusion devices. The introduction of the W-Li sample in HIDRA resulted in evaporation of lithium into the helium plasma during a 600 s pulse and caused a reduction of over 90% in neutral pressure during the discharge. It was also observed that the plasma density and temperature increased by over 2.5 times. Using spectroscopy and a helium collisional radiative model, the peak temperature and density of the helium plasma can be monitored during the discharge. During lithium evaporation, as significant lithium ionization occurs, there is a 85% drop in the HIDRA vessel neutral pressure, despite a constant flow rate of He gas. This reduction in neutral pressure is supported by spectroscopy data with corresponding reductions in He I line intensities (587 nm, 667 nm, 706 nm, and 728 nm), as well as those of other impurities. At one point in the discharge a lithium plasma is created, as indicated by an increase in Li + emission and a complete reduction in He + emission, but the electron density jumps from ne = 3 × 10 18 m –3 to over ne = 8 × 10 18 m –3 while the core temperature stays relatively constant between Te = 16 eV and 20 eV. Once lithium has completely evaporated from the sample and the majority of the ionized lithium has diffused from the plasma to the vessel walls, pressure and spectroscopy data paired with He collisional radiative model calculations shows a re-establishment of a helium plasma in a low recycling regime. In this regime, the density drops down to ne = 2 × 10 18 m –3 and the electron temperature increases from T e = 20 eV to over T e = 50 eV indicating an increase in helium heating efficiency. This is also indicated by the He+ emission re-establishing and having a higher intensity. Here, we show the results from the first lithium campaign in HIDRA. In the presence of lithium, and in particular when lithium ions are present, the helium disappears from the plasma via an as of yet unknown complex relationship that needs to be further studied. The most likely explanation is that the lithium ions are distributed around the vessel and able to trap helium to the surface turning HIDRA into a large gettering surface. These results have potential implications on future plasma facing component design using liquid lithium for impurity and recycling control using limiters and divertors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Novel Instrumentation for Advanced Quantum Chromodynamics Studies with Flavor Sensitivity

The true nature of ordinary matter has always fascinated humankind, whose imagination has been pointed to the right direction since the 5th century BC with the first known atomic theory by Leucippus and Democritus. They described the matter as formed by small, invisible, indivisible, and eternal particles: the atoms. A long investigation followed them, including contributions from several of history?s greatest philosophers and scientists. The progress led to the understanding that atoms have an internal structure consisting of a central nucleus composed of protons and neutrons, surrounded by a cloud of electrons. About 50 years ago, scientists discovered that nucleons also have an internal structure. Through the Deep Inelastic Scattering (DIS) experiments, on which a lepton is scattered off a nucleon, it was possible to access the distribution of partons inside the nucleon along the longitudinal direction defined by the hard-scale probe. It was a crucial step in developing Quantum Chromodynamics (QCD), currently the best theory describing the subatomic matter. Ultimately, DIS was recognized as a limited tool to investigate the nucleon structure because it is mainly sensitive to observables at the probe?s energy scale. In recent decades, theoretical developments have allowed defining soft-scale observables by measuring more complex processes. Reactions such as Semi-Inclusive Deep Inelastic Scattering (SIDIS) with single or di-hadron production, can provide information about the momentum of parton inside the nucleon. In particular, polarized SIDIS allows access to the Transverse-Momentum-Dependent (TMD) parton distributions describing the correlations of the quark and gluon degrees of freedom in transverse momentum and spin. This study can provide three-dimensional imaging of nucleon structure and inner dynamics, as long as the hadronic component in the final state can be measured. Identifying the species of the produced particles provides information on the quark flavor and assumes a crucial role in this context. The thesis describes the work carried out by the author in preparing novel instrumentation for particle identification in the final state of DIS experiments, to support modern analysis of the parton dynamics within the basic confined object (nucleon) with flavor sensitivity. The work has been focused on the CLAS12 spectrometer in operation at Jefferson Laboratory (JLab), Newport News, VA, USA, and the ePIC experiment in preparation for the future Electron-Ion Collider (EIC) at Brookhaven National Laboratory (BNL), Long Island, NY, USA. CLAS12 is a fixed-target experiment using a 12 GeV beam of polarized electrons scattering off polarized nuclear targets. In 2018 and 2022, two Ring Imaging Cherenkov (RICH) detector modules were installed to improve the ?±/K± separation in the high-momentum region (3÷8 GeV/c) of the experiment. The author contributed to the assembly, installation, and com- missioning of the second RICH module, to the efficiency studies of the first module, and to the first SIDIS analysis with high-momentum kaons identified by the RICH. These studies led to the observation of the first spin asymmetry with high-momentum kaons from the CLAS12 experiment, and an initial assessment of the systematic error associated with the hadron identification. ePIC will be the first experiment at the future EIC, the new collider designed to expand the frontiers of QCD. The author was involved in the development of the dual-radiator Ring Imaging Cherenkov (dRICH). This detector will interpolate the measurements of the Cherenkov angles of photons produced by relativistic particles crossing two different radiators to identify charged hadrons. The author contributed to the studies conducted with the dRICH prototype, developing the reconstruction and analysis software and simulations, characterizing the aerogel radiator samples, and being responsible for the tracking system used during the beam tests. The performance obtained by the prototype has been progressively improved and the results are now comparable with the expectation derived from the simulation and satisfy the requirements of the experiment. In Chapter 1, the SIDIS theory is briefly introduced, outlying the connection with the experimental measurement. Chapter 2 includes the description of the CLAS12 RICH, its assembly and installation, and the efficiency study. In Chapter 3, the analysis of the Beam-Spin Asymmetry associated with SIDIS kaons is described. Chapter 4 describes the contribution to the dRICH for EIC, starting from a description of the ePIC experiment and focusing on the studies performed for the dRICH prototype. The conclusions of this work are summarized at the end.

Vallarino, Simone↗

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗

The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

Galah is an ongoing high-resolution spectroscopic survey with the goal of disentangling the formation history of the Milky Way using the fossil remnants of disrupted star formation sites that are now dispersed around the Galaxy. It is targeting a randomly selected magnitude-limited ( V ≤ 14) sample of stars, with the goal of observing one million objects. To date, 300,000 spectra have been obtained. Not all of them are correctly processed by parameter estimation pipelines, and we need to know about them. We present a semi-automated classification scheme that identifies different types of peculiar spectral morphologies in an effort to discover and flag potentially problematic spectra and thus help to preserve the integrity of the survey results. To this end, we employ the recently developed dimensionality reduction technique t-SNE ( t -distributed stochastic neighbor embedding), which enables us to represent the complex spectral morphology in a two-dimensional projection map while still preserving the properties of the local neighborhoods of spectra. We find that the majority (178,483) of the 209,533 Galah spectra considered in this study represents normal single stars, whereas 31,050 peculiar and problematic spectra with very diverse spectral features pertaining to 28,579 stars are distributed into 10 classification categories: hot stars, cool metal-poor giants, molecular absorption bands, binary stars, H α /H β emission, H α /H β emission superimposed on absorption, H α /H β P-Cygni, H α /H β inverted P-Cygni, lithium absorption, and problematic. Classified spectra with supplementary information are presented in the catalog, indicating candidates for follow-up observations and population studies of the short-lived phases of stellar evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

Establishing a geochemical heterogeneity model for a contaminated vadose zone – Aquifer system

A large set of sediment samples from a 1600 m2 experimental plot within a 2.2 km2 vadose zone and groundwater uranium (VI) plume were subject to physical, chemical, and mineralogic characterization. The plot is being used for field experimentation on U(VI) recharge and transport processes within a persistent groundwater plume that exists in the groundwater-river interaction zone of the Columbia River at the U.S. DOE Hanford site. The samples were obtained during the installation of 35 tightly spaced (10 m separation) groundwater monitoring wells. The characterization measurements for each sample included total contaminant concentrations (U and Cu primarily), bicarbonate extractable U(VI), sequential 238U(VI) contaminant desorption Kd, 233U(VI) adsorption Kd, grain size distribution, surface area, extractable poorly crystalline Fe(III) oxides, and mineralogy. The characterization objective was to inform a conceptual model of coupled processes controlling the anomalous longevity of the plume, and to quantify the spatial heterogeneity of the contaminant inventory and the primary properties effecting reactive transport. Correlations were drawn between chemical, physical, and reaction properties, and Gaussian simulation was used to compute multiple 3-D realizations of extractable U(VI), the 233U(VI) adsorption Kd, and the distribution of the reactive < 2 mm fraction. Adsorbed contaminant U(VI) was highest in the vadose zone and the zone of seasonal water table fluctuation lying at its base. Adsorbed U(VI) was measureable, but low, in the groundwater plume region where very high hydraulic conductivities existed. The distribution of adsorbed U(VI) displayed no apparent correlation with sediment physical or chemical properties. Desorption [238U(IV)] and adsorption [233U(VI)] Kd values showed appreciable differences due to mass transfer controlled surface complexation and the effects of long subsurface residence times. The 233U(VI) adsorption Kd, a combined measure of surface complexation strength and site concentration, was relatively uniform throughout the domain, displaying correlation with fines distribution and surface area. The characterization results revealed U(VI) supplied to the groundwater plume through spatially heterogeneous recharge from residual contamination in the zone of seasonal water table fluctuation, and transport of U(VI) controlled by weak, kinetically-controlled surface complexation in the coarse-textured saturated zone. Geostatistical relationships for the adsorbed contaminant U distribution in the characterization domain allow an extrapolation to inventory at the plume scale, a critical unknown for remedial action.

contaminant heterogeniety, uranium groundwater plu↗

The identification of filaments on far-infrared and submillimiter images: Morphology, physical conditions and relation with star formation of filamentary structure

Observations of molecular clouds reveal a complex structure, with gas and dust often arranged in filamentary, rather than spherical geometries. The association of pre- and proto-stellar cores with the filaments suggests a direct link with the process of star formation. Any study of the properties of such filaments requires representative samples from different environments for an unbiased detection method. We developed such an approach using the Hessian matrix of a surface-brightness distribution to identify filaments and determine their physical and morphological properties. After testing the method on simulated, but realistic, filaments, we apply the algorithms to column-density maps computed from Herschel observations of the Galactic plane obtained by the Hi-GAL project. We identified ∼500 filaments, in the longitude range of l = 216.°5 to l = 225.°5, with lengths from ∼1 pc up to ∼30 pc and widths between 0.1 pc and 2.5 pc. Average column densities are between 10{sup 20} cm{sup –2} and 10{sup 22} cm{sup –2}. Filaments include the majority of dense material with N{sub H{sub 2}} > 6 × 10{sup 21} cm{sup –2}. We find that the pre- and proto-stellar compact sources already identified in the same region are mostly associated with filaments. However, surface densities in excess of the expected critical values for high-mass star formation are only found on the filaments, indicating that these structures are necessary to channel material into the clumps. Furthermore, we analyze the gravitational stability of filaments and discuss their relationship with star formation.

79 ASTRONOMY AND ASTROPHYSICS↗