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Thermal neutron scattering cross sections for amorphous carbon

Carbon materials are commonly found in both nuclear reactors and experimental systems. Various carbon structures occur in nuclear applications ranging from crystalline and nuclear graphite to the amorphous carbon seen in next-generation advanced reactor designs. Amorphous carbon is based on a randomized graphite-like structure and offers the unique ability to disperse impurities throughout the bulk composition. A graphite-like amorphous carbon system was modeled using the classical molecular dynamics (MD) code LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). An improved version of the temperature-dependent Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential was used to model the carbon-carbon atomic interactions for the temperature at 300 K along with densities 1.60, 1.70, 1.85, and 2.23 g/cm{sup 3}. From the normalized velocity autocorrelation function (VACF), the phonon density of state (DOS) was then calculated as the Fourier transform of the normalized VACF. This DOS was then used as the primary input for the evaluation of the thermal scattering law (TSL, i.e. S(α,β)) and associated neutron thermal scattering cross sections. The TSL was analyzed using the Full Law Analysis Scattering System Hub (FLASSH). The amorphous structure results in shifts of the phonon DOS to lower energy modes than typically displayed for ideal crystalline graphite. This impact on the DOS is directly reflected in the TSL. Furthermore, the typical optical peak at 0.25 eV for the ideal graphite disappears for amorphous carbon, in good agreement with the expected structure. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

One-dimensional simulation and validation of divertor detachment induced through nitrogen seeding on HL-2A

Divertor detachment is a promising method to solve the power exhaust problem in tokamak devices or even in future magnetic fusion reactors. In this work, a detachment experiment (HL-2A shot #38008) with mixed gas seeding (60% nitrogen and 40% deuterium) is simulated using the SD1D module in BOUT++. In the process from attachment to detachment, the target electron temperature and the target ion saturation current in simulations are found to be consistent with the experimental results measured by Langmuir probes on the target plate. In order to understand the underlying detachment mechanism on HL-2A, this work analyses the role of different particle species in the cases with different seeding rates. It shows that the plasma density varies little and the density of neutrals (D and D 2 ) slightly decreases with the increase of nitrogen seeding rate, such that plasma–neutral interactions cannot effectively reduce plasma energy and plasma momentum in the divertor. The case with a high seeding rate predicts that increasing seeding rate cannot reach a target temperature lower than 2.5 eV, which is the required temperature for strong plasma–molecule interactions. Thus, the plasma–molecule interactions may not be important in the divertor during nitrogen seeding. Here this work also studies the parallel forces including (1) force due to the parallel electric field, (2) friction forces, (3) ion- and electron-thermal forces, and (4) collisional reactions (e.g. charge exchange recombination and ionisation). It is found that the friction force between nitrogen ions and other particle species (primarily D + ) is the dominant force towards the target, while ion- and electron-thermal forces are the dominant force pushing nitrogen ions back to upstream. Parallel forces determine the parallel distribution of nitrogen impurities, and therefore decide the region of nitrogen radiation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Evaluation of a potassium filled UO{sub 2} core in a beryllium-reflected space reactor - 14371

The critical configuration of the small, compact critical assembly (SCCA) experiments performed at the Oak Ridge Critical Experiments Facility (ORCEF) in 1962-1965[1,2] have been evaluated for the International Criticality Safety Benchmark Evaluation Project (ICSBEP) and were determined to be acceptable benchmark experiments and are included in the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook)[3]. The initial intent of these experiments was to support the design of the Medium Power Reactor Experiment (MPRE) program, whose purpose was to study 'power plants for the production of electrical power in space vehicles'.[4] The third configuration in this series of experiments was a beryllium-reflected mockup of a stainless-steel-clad, highly enriched uranium (HEU)-O{sub 2} fuel, potassium-cooled, space power reactor. Reactivity measurements, cadmium ratio spectral measurements, and fission rate measurements were measured through the core and top reflector. Fuel effect worth measurements and neutron moderating and absorbing material worths were also measured in the assembly fuel region. The cadmium ratios, fission rate, and worth measurements were evaluated for the International Reactor Physics Experiment Evaluation Project (IRPhEP) and are published in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook)[5]. More recently, two additional critical configurations have been evaluated and added to the existing evaluation report in the ICSBEP Handbook. The new critical configurations measure the effect of filling the core with potassium. The critical configuration consisted of 30.48 cm tall fuel tubes of 93.15 wt.% enriched uranium dioxide (UO{sub 2}) pellets. Each fuel tube had 26 pellets with a total mass of 295.8 g UO{sub 2} per tube. There were 253 tubes were arranged with 1.506-cm center-to-center, triangular spacing and surrounded on all side by a beryllium reflected. The reactivity of the system was measured with the core tank empty and filled with potassium. The system reactivity was increased from 13.4 c to 32 c. This change in system reactivity represents the worth of the potassium. Simple benchmark models for the empty and potassium filled cores have been derived and evaluated and found to be acceptable as benchmark experiments. The benchmark experiment eigenvalues are 1.0001 ± 0.0008 and 1.0011 ± 0.0009 for the empty and filled cores, respectively. When calculated with MCNP5 and ENDF/B-VII.0 the calculated eigenvalues are 0.98906 ± 0.00002 and 0.98883 ± 0.00002, respectively. These results are concerning due to the drop of the calculated system reactivity when potassium is added to the system rather than the expected increase in system reactivity. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Rapid Computer Aided Ligand Design and Screening of Precious Metal Extractants from TRUEX Raffinate with Experimental Validation

Rhodium is the most extensively used metal in catalytic applications; it occurs in mixed ores with platinum group metals (PGMs) in the earth’s crust in low concentrations (0.4 - 10 ppb). It is resistant to aerial oxidation and insoluble in all acids, including aqua regia, making classical purification methods time-consuming and inefficient. To ensure adequate purity, several precipitation and dissolution steps are necessary during separation. Low abundance, high demand, and extensive processing make rhodium the most expensive of all PGMs. From alternative sources, rhodium is also produced in sufficient quantities (0.47 kg per ton initial heavy metal (tIHM)) during the fission of U-235 in nuclear reactors along with other PGMs (i.e., Ag, Pd, Ru). A typical power water reactor operating with UO 2 fuel after cooling can generate PGMs in quantities greater than found in the earth’s crust (0.5-2 kg/tIHM). This currently untapped supply of PGMs has the potential to yield $5,000-30,000/tIHM. It is estimated that by the year 2030, the amount of rhodium generated in reactors could exceed natural reserves. Typical SNF processing removes the heavier lanthanides and actinides and can leave PGMs at ambient temperatures in aqueous acidic (Cl⁻ or NO 3 ⁻; pH < 1) solutions at various activities. While the retrieval of these precious metals from SNF would minimize waste generation and improve resource utilization, it has been difficult to achieve thus far. Two general strategies have been utilized to extract Rh(III) from chloride media: ion pairing and coordination complexation. Ion pairing mechanisms have been studied primarily with the tertiary and quaternary amines. Additionally, mixed mechanism extractions have been observed in which ion pairing is the initial mechanism, and longer extraction equilibrium time generated coordination complexes. Very few coordination complexation extraction ligands have been studied. This project approached this problem through the design of a software program that uses state-of-the-art computational combinatorial chemistry, and is developed and validated with experimental data acquisition; the resulting tool allows for rapid design and screening of new ligands for the extraction of precious metals from SNF. This document describes the software that has been produced, ligands that have been designed, and fundamental new understandings of the extraction process of Rh(III) as a function of solution phase conditions (pH, nature of acid, etc.).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Collaborative Research: A Model of Partially Ionized Plasma Flows with Kinetic Treatment of Neutral Atoms and Nonthermal Ions

Interactions of flows of partially ionized, magnetized plasma are frequently accompanied by the presence of both thermal and non-thermal (pickup) ion components. Such interactions cannot be modeled using traditional MHD equations and require more advanced approaches to treat them. If a nonthermal component of ions is formed due to charge exchange and collisions between the thermal (core) ions and neutrals, it experiences the action of magnetic field, its distribution function is isotropized, and it soon acquires the velocity of the ambient plasma without being thermodynamically equilibrated. This situation, e. g., takes place in the outer heliosphere –- the part of interstellar space beyond the solar system whose properties are determined by the solar wind interaction with the local interstellar medium. This is also possible in laboratory, at million degrees and above, when plasma is conducting electricity far too well, which makes Ohmic heating ineffective. To attain the target temperatures one needs additional heating eventually playing a dominant role. Among such sources is a so-called neutral particle beam heating. This is a wide-spread technique (Joint European Torus and International Thermonuclear Experimental Reactor experiments) based on the injection of powerful beams of neutral atoms into ohmically preheated plasma. In this project we have investigated the energy and density separation between the thermal and nonthermal components in the solar wind and interstellar plasmas. A new model has been developed in which we solve the ideal MHD equations for mixture of all ions and the kinetic Boltzmann equation to describe the transport of neutral atoms. As a separate capability, we can treat the flow of neutral atoms in a multi-component fashion, where neutral atoms born in each thermodynamically distinct region are governed by the Euler gas dynamic equations. We also describe the behavior of pickup ions either kinetically, using the Fokker--Planck equation, or as a separate fluid. Our numerical simulations have demonstrated that pickup ions play a major role in the interaction of the solar wind and (partially ionized) interstellar medium plasmas. Our teams have investigated the stability of the surface (the heliopause) that separates the solar wind from the local interstellar medium, the transport of galactic cosmic rays, the properties of the heliotail flow, and modifications to the bow wave in front of the heliopause due to charge exchange between the neutral H atoms born in the solar wind and interstellar ions. Modeling results have been validated against observational data, such as obtained by the Interstellar Boundary Explorer (IBEX), and made it possible to shed light on the structure of energetic neutral atom maps created by this spacecraft.. We have also demonstrated that charge-exchange modulated heliosphere is a source of anisotropy of the multi-TeV cosmic ray flux observed in a number of Earth-bound air shower experiments. Newly developed codes are implemented within a Multi-Scale Fluid-Kinetic Simulation Suite (MS-FLUKSS), a publicly available code being developed by our team for over 12 years. MS-FLUKSS scales well up to 160,000 computing cores and has been ported on major supercomputers in the country. Efficient parallelization and data choreography in the continuum simulation modules are provided by Chombo, an adaptive mesh refinement framework managed by Phillip Colella's team at LBNL. We have implemented in-house, hybrid (MPI+OpenMP) parallelization of the kinetic modules that solve the Boltzmann equation with a Monte Carlo method. Currently, the kinetic modules are being rewritten to take advantage of the modern CPU-GPU supercomputer architecture. The scope of the project allowed us to enhance plasma research and education in such broad, multidisciplinary field as physics of partially ionized plasma and its application to space physics and fusion science. Besides the impact on the modeling of complex physical systems, our approach to computational resource management for complex codes utilizing multiple algorithm technologies appears to be a major advance on current approaches. The development of sophisticated resource management will be essential for all future modeling efforts that incorporate a diversity of scales and physical processes. Our effort provided leadership in promoting computational science and plasma physics within the UAH and FIT campuses and, through the training of a broad spectrum of scientists and engineers, fostering new technologies across the country.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Collaborative Research: A Model of Partially Ionized Plasma Flows with Kinetic Treatment of Neutral Atoms and Nonthermal Ions

Interactions of flows of partially ionized, magnetized plasma are frequently accompanied by the presence of both thermal and non-thermal (pickup) ion components. Such interactions cannot be modeled using traditional MHD equations and require more advanced approaches to treat them. If a nonthermal component of ions is formed due to charge exchange and collisions between the thermal (core) ions and neutrals, it experiences the action of magnetic field, its distribution function is isotropized, and it soon acquires the velocity of the ambient plasma without being thermodynamically equilibrated. This situation, e. g., takes place in the outer heliosphere - the part of interstellar space beyond the solar system whose properties are determined by the solar wind interaction with the local interstellar medium. This is also possible in laboratory, at million degrees and above, when plasma is conducting electricity far too well, which makes Ohmic heating ineffective. To attain the target temperatures one needs additional heating eventually playing a dominant role. Among such sources is a so-called neutral particle beam heating. This is a wide-spread technique (Joint European Torus and International Thermonuclear Experimental Reactor experiments) based on the injection of powerful beams of neutral atoms into ohmically preheated plasma. In this project we have investigated the energy and density separation between the thermal and nonthermal components in the solar wind and interstellar plasmas. A new model has been developed in which we solve the ideal MHD equations for mixture of all ions and the kinetic Boltzmann equation to describe the transport of neutral atoms. As a separate capability, we can treat the flow of neutral atoms in a multi-component fashion, where neutral atoms born in each thermodynamically distinct regions are governed by the Euler gas dynamic equations. We also describe the behavior of pickup ions either kinetically, using the Fokker–Planck equation, or as a separate fluid. Our numerical simulations have demonstrated that pickup ions play a major role in the interaction of the solar wind and (partially ionized) interstellar medium plasmas. Our teams have investigated the stability of the surface (the heliopause) that separates the solar wind from the local interstellar medium, the transport of galactic cosmic rays, the properties of the heliotail flow, and modifications to the bow wave in front of the heliopause due to charge exchange between the neutral H atoms born in the solar wind and interstellar ions. Modeling results have been validated against observational data, such as obtained by the Interstellar Boundary Explorer (IBEX), and made it possible to shed light on the structure of energetic neutral atom maps created by this spacecraft.. We have also demonstrated that charge-exchange modulated heliosphere is a source of anisotropy of the multi-TeV cosmic ray flux observed in a number of Earth-bound air shower experiments. Newly developed codes are implemented within a Multi-Scale Fluid-Kinetic Simulation Suite (MS-FLUKSS), a publicly available code being developed by our team for over 12 years. MS-FLUKSS scales well up to 160,000 computing cores and has been ported on major supercomputers in the country. Efficient parallelization and data choreography in the continuum simulation modules are provided by Chombo, an adaptive mesh refinement framework managed by Phillip Colella’s team at LBNL. We have implemented in-house, hybrid (MPI+OpenMP) parallelization of the kinetic modules that solve the Boltzmann equation with a Monte Carlo method. Currently, the kinetic modules are being rewritten to take advantage of the modern CPU-GPU supercomputer architecture. The scope of the project allowed us to enhance plasma research and education in such broad, multidis- ciplinary field as physics of partially ionized plasma and its application to space physics and fusion science. Besides the impact on the modeling of complex physical systems, our approach to computational resource management for complex codes utilizing multiple algorithm technologies appears to be a major advance on current approaches. The development of sophisticated resource management will be essential for all future modeling efforts that incorporate a diversity of scales and physical processes. Our effort provided leadership in promoting computational science and plasma physics within the UAH and FIT campuses and, through the training of a broad spectrum of scientists and engineers, foster new technologies across the country.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Experimental confirmation of first-principles thermal conductivity in Zirconium-doped ThO 2

The degradation of thermal conductivity in advanced nuclear fuels due to the accumulation of fission products and irradiation-induced defects is inevitable, and must be considered as part of safety and efficiency analyses of nuclear reactors. Here, this study examines the thermal conductivity of a zirconium-doped ThO 2 crystal, synthesized via the hydrothermal method using a spatial domain thermoreflectance technique. Zirconium is one of the soluble fission products in oxide fuels that can effectively scatter heat-carrying phonons in the crystalline lattice of fuel. Thus, thermal property measurements of zirconium-doped ThO 2 single crystals provide insights into the effects of substitutional zirconium doping, isolated from extrinsic factors such as grain boundary scattering. The experimental results are compared with first-principles calculations of the lattice thermal conductivity of ThO 2 , employing an iterative solution of the Peierls-Boltzmann transport equation. Additionally, the non-perturbative Green's function methodology is utilized to compute phonon-point defect scattering rates, accounting for local distortions around point defects, including mass difference changes, interatomic force constants, and structural relaxation. The congruence between the predicted results from first-principles calculations and the measured temperature-dependent thermal conductivity validates the computational methodology. Furthermore, the methodologies employed in this study enable systematic investigations of thermal conductivity reduction by fission products, potentially leading to the development of more accurate fuel performance codes.

36 - MATERIALS SCIENCE↗

Quench detection using Hall sensors in high-temperature superconducting CORC ® -based cable-in-conduit-conductors for fusion applications

Advanced magnet systems for fusion applications would greatly benefit from the use of high-temperature superconductors (HTS). These materials allow fusion magnets to operate at higher magnetic fields, allowing for more compact fusion machines, and allow for operation at elevated temperatures, enabling demountable coils that provide access for maintenance of the fusion reactor. Quench detection remains a major challenge in the protection of HTS magnets that are vulnerable to localized conductor burnout due to their low quench propagation velocities. One of the methods explored is the use of Hall sensors that are incorporated in or near the magnet terminations that can detect local field variations that occur as a result of current redistribution within the conductor to bypass a hotspot within the magnet winding. This method is potentially well suited for Cable in Conduit Conductors, such as those made from Conductor on Round Core (CORC) cables, in which sub-cables containing HTS tapes are connected to the terminations at a low resistance. Here, to demonstrate the technique, a CORC ® triplet consisting of three sub-cables, rated for 4 kA operation at 77 K, was manufactured and Hall sensors were used to measure local field variations next to the terminations due to current redistribution between the cables. The Hall response was compared to voltages that developed over the cables and terminations as a local hotspot was applied to different cables in the triplet. It was found that the Hall sensors were faster and more sensitive than voltage contact measurements and were able to reliably detect current redistribution of only a few amperes caused by a hotspot, well before the triplet exceeded its critical current. The method also allowed the detection of heater-induced hotspots during high ramp rates of 2 kA s<:sup>–1 relevant for fusion applications. Hall sensors have a distinct benefit of being less sensitive to inductive pickup of AC interference compared to voltage contact measurements that make quench detection through voltage measurements in magnets especially challenging. The method can also be used for diagnostic measurements of current redistribution caused by other sources such as inhomogeneous current injection from faulty joints, or localized conductor damage. The Hall sensors are likely capable of detecting the onset of a quench that may occur a far distance away from the sensor location, presenting a breakthrough in HTS quench detection that potentially removes one of the remaining barriers to reliable operation of large HTS magnet systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Passive Safety Feature of KIPT Neutron Source Facility

Argonne National Laboratory (ANL) of the United States and Kharkov Institute of Physics and Technology (KIPT) of Ukraine have cooperated on the development, design, and construction of a neutron source facility. The facility was constructed at Kharkov, Ukraine and its commissioning process is underway. The facility has an electron accelerator driven subcritical assembly. The electron beam power is 100 kW using 100 MeV electrons. Tungsten or natural uranium is the target material for generating neutrons driving the subcritical assembly. The subcritical assembly is composed of WWR-M2 Russian fuel assemblies with U-235 enrichment of 19.7 wt%, surrounded by beryllium reflector assembles and graphite blocks. The water coolant operates at room temperature during normal operation, when the facility is operating at full power using 100-KW electron beam power. The passive safety features of the facility are discussed in in this study. Monte Carlo computer code MCNPX was utilized in the analyses with ENDF/BVII.0 nuclear data libraries. Negative reactivity temperature feedback was consistently observed, which is important for the facility safety performance. Due to the design of WWR-M2 fuel assemblies, slight temperature increase and the corresponding density decrease in the water coolant would cause relatively large reactivity drop, which offset the reactivity gain by mistakenly loading an additional fuel assembly. The increase of fuel temperature also causes sufficiently large reactivity decrease. This enhances the facility safety performance because fuel temperature increase provides prompt negative reactivity feedback. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

Complete β-decay patterns of 142 Cs, 142 Ba, and 142 La determined using total absorption spectroscopy

Background: The β decays of fission products produced in nuclear fuel are important for nuclear energy applications and fundamental science of reactor antineutrinos. In particular, nuclear reactor safety is related to the decay modes of radioactive neutron-rich nuclei, primarily via the emission of γ rays, neutrons, and electrons. Additionally nuclear reactors are the most powerful man-made source of antineutrinos emitted during the β decay of fission products. These antineutrinos are used to inspect fundamental properties of leptons as well as informing reactor operation. However, the majority of data on complex decays of fission products collected in the evaluated nuclear data repositories like Evaluated Nuclear Structure Data File (ENSDF) and Evaluated Nuclear Data Files (ENDF) are based on low-efficiency and often incomplete measurements resulting in questionable reference reactor antineutrino flux predictions, see the analysis by [Nichols, J. Nucl. Sci. Technol. 52, 17 (2015)]. Various assessments like the one done under the auspices of the [Yoshida et al., Assessment of Fission Product Decay Data for Decay Heat Calculations: A report by the Working Party on International Evaluation Co-operation of the Nuclear Energy Agency Nuclear Science Committee (Nuclear Energy Agency, Organization for Economic Co-operation and Development, Paris, France, 2007), Vol. 25], as well as by [Sonzogni, Johnson, and McCutchan, Phys. Rev. C 91, 011301(R) (2015)] and [Dwyer and Langford, Phys. Rev. Lett. 114, 012502 (2015)], list the A = 142 isobars with high cumulative fission yield among the important nuclei where data for reactor decay heat and/or antineutrino production should be verified and/or improved. Purpose: Here, our goal is to improve the quality of β -decay measurements and evaluate the impact of modified decay schemes on reactor decay heat and antineutrino energy spectra, for fission products along the A = 142 isobaric chain. This work is an in depth follow-up on [Rasco et al., Phys. Rev. Lett. 117, 092501 (2016)]. which presented briefly the impact of the corrected decay scheme of 142 Cs . Here, we extend the data to full isobaric decay chain including the daughter nuclei, 142 Ba and 142 La, and present more details on the 142 Cs results. Method: The decays of neutron-rich isobars of mass A = 142 produced by means of proton-induced fission of 238 U were measured using the Modular Total Absorption Spectrometer (MTAS) array on-line at the mass separator and Tandem accelerator at Oak Ridge National Laboratory. Results: The β -decay schemes for 142 Cs and 142 La were modified with respect to the nuclear data repositories. A small β-delayed neutron branching ratio for 142 Cs emitter was remeasured as $0.10^{+5}_{–3}% %. Improved precision on the measured half-lives is reported. Small corrections to the low-energy decay of 142 Ba are made. The β-decay patterns for 142 La and 142 Cs are presented. The decay heat release and cross section for the detection of reactor antineutrinos are deduced and compared to earlier results. Conclusions: The β-feeding pattern for 142 Cs having decay energy value $Q_β$ of over 7 MeV was substantially modified with respect to the current ENSDF entry. Smaller changes were encountered for 142 La, but since this A = 142 isobar also has a large cumulative yield in fission, the changes influence both decay heat and the antineutrino spectra. The previously known β intensities for 142 Ba decay ($Q_β$ value of 2.2 MeV) were verified and slightly modified. Overall, increased decay heat values and lower flux of antineutrinos interacting with matter are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A Comparison of Monte Carlo and Deterministic Solvers for keff and Sensitivity Calculations

Verification and validation of our solutions for calculating the neutron reactivity for nuclear materials is a key issue to address for many applications, including criticality safety, research reactors, power reactors, and nuclear security. Neutronics codes solve variations of the Boltzmann transport equation. The two main variants are Monte Carlo versus deterministic solutions, e.g. the MCNP [1] versus PARTISN [2] codes, respectively. There have been many studies over the decades that examined the accuracy of such solvers and the general conclusion is that when the problems are well-posed, either solver can produce accurate results. However, the devil is always in the details. The current study examines the issue of self-shielding and the stress it puts on deterministic solvers. Most Monte Carlo neutronics codes use continuous-energy descriptions of the neutron interaction data that are not subject to this effect. The issue of self-shielding occurs because of the discretisation of data used by the deterministic solutions. Multigroup data used in these solvers are the average cross section and scattering parameters over an energy range. Resonances in cross sections can occur that change the likelihood of interaction by one to three orders of magnitude over a small energy range. Self-shielding is the numerical effect that the average cross section in groups with strong resonances can be strongly affected as neutrons within that material are preferentially absorbed or scattered out of the resonance energies. This affects both the average cross section and the scattering matrix.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Development of a Severe Accident Analysis Engine Using Approximate Reasoning

In hypothetical severe accident scenarios, nuclear power plant safety is challenged by core degradation and release of fission products. To aid the plant operator and technical support center staff, research has been performed to develop understanding of severe accident phenomenology, assessing their risk and devise measures to mitigate accident consequences (Sehgal, 2011). Nuclear power plant accidents involve highly complex physical and chemical interactions, a large number of safety related systems, and emergency operating procedures (EOP) and severe accident management guidelines (SAMG). Large number of factors with uncertainty and stress can overwhelm the operators. The overall objective of the present research is to develop a methodology and tools that an operator can use to predict severe accident progression and make effective decisions to arrest and manage the accident under large uncertainties. Towards this goal, this study examines a novel approach that brings to bear a combined use of techniques in artificial intelligence (Russel and Norvig, 2010), including Bayesian Network (BN), Answer Set Programming (ASP) and Fuzzy Inference (FI) to achieve faster-than-real-time, yet sufficiently accurate analysis of severe accident progression. Bayesian network (BN) is a type of probabilistic graphical models that can be used to represent knowledge and conditional dependencies about an uncertain realm. It is useful for capturing the causal relationships in a complex system. It also allows prediction via Bayesian inference (Kelly and Smith, 2012). It is composed of nodes and edges, which express random variables and probabilistic relationships between the nodes, respectively. This relationship, given by conditional probability is calculated using stochastic methods. The BN is used in several systems for analysis and management of accident risk; e.g. (Knochenhauer and Frid, 2010). In those systems, the nodes or so-called 'effective factors' are presumably known within the network by system descriptions in SAMGs. There are two steps to configure the BN, namely (i) identifying compositions within the network and (ii) defining conditional dependency between nodes. The first step is performed following the spirit of ASP (Baral, 2003) using Prolog (Wielemaker, 2005). Knowledge representation is used to build databases required for processing by ASP. Knowledge representation is defined as the expression of knowledge in a way that is understandable to both human and computers. Originally knowledge indicates data, information and facts. To focus, knowledge should be formed to achieve a goal. In a severe accident, key physics and safety related components status are defined according to a failure mode. The conditional dependencies between nodes are generated by approximate reasoning using Fuzzy inference (Zadeh, 1995). Fuzzy logic is a multiple-valued logic to treat ambiguous features such as linguistic description. It indicates possibility, which is distinguished from probability. Fuzzy set consists of linguistic variables according to the fixed rule. In other words, even uncertain states could be expressed as not binary logic, but as many-value logic by rule-based technology. Fuzzy control system is comprised of Fuzzy inference engine that is performed based on the 'If-Then Rule'. The 'If-Then Rule' is of the following form. If Y1 is B11 and Y2 is B12 then Z is C1. If Y1 is B21 and Y2 is B22 then Z is C2. Y1 and Y2 are inputs, which indicate conditional variables. Z indicates decision-making and both B and C are linguistic variables. With this rule-based technique, the approximate reasoning can be realized by Fuzzy inference. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools and data for modeling this risk is highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data’s high uncertainty. This report outlines initial work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant models. This research paves the way for the development of methods to reduce fire data uncertainty used in modeling. The Nuclear Regulatory Commission has mandated that nuclear power plants perform fire risk modeling. However, there are several issues with the current risk modeling implementation that affect the results. Approved modeling methods can be overly conservative and often do not match plant experience. Also, the data used in the modeling can have high uncertainties and is influenced by expert judgement. To evaluate input data uncertainty, researchers performed an initial review of several fire experiments done at Sandia National Laboratories. Uncertainties for fire data can come from many sources, such as experiment design constraints, environmental conditions, or other plant-specific aspects. There are many different significant and insignificant parameters driving the uncertainty. Additionally, the uncertainty of the different input data used in the fire modeling could have a significant or insignificant effect on the entire plant risk. A four-step methodology was developed to perform Integrated Probabilistic Risk Assessment Importance Ranking. A demonstration case using these steps was set up and three of the four steps were completed in fiscal year (FY) 2019 and the fourth step done FY 2020. These steps are: 1. The qualitative analysis of potential sources was conducted with the following items identified for the demonstration. • Maximum heat release rate • Time to maximum heat release rate • Duration of max heat release rate • Time to decay • Thermal conductivity of concrete • Specific heat of concrete • Density of concrete • Cable jacket thickness 2. A quantitative characterization of dominant sources of uncertainty was performed. A list of distributions and determined values of the dominant sources is shown in Appendix A. 3. A quantitative screening of the potential sources of uncertainty using Morris Elementary Effects Analysis was completed. An experimental model using the physics-based fire modeling tool Fire Dynamics Simulator was developed and coupled with the Risk Analysis Virtual Environment. The Morris analysis identified at least two parameters that can be eliminated as significant contributors (specific heat of concrete and cable jacket thickness). 4. Global importance measure (Global IM) analysis to generate a comprehensive ranking based on their influence on the plant risk. In this research, a moment-independent Global IM is used since it can address (a) uncertainty in the input parameters of the fire model, (b) uncertainty in the risk outputs, and (c) non-linearity and interactions among input parameters in the fire model, more accurately than the correlation-based and variance-based global methods. The observations from the research showed that, depending on the initial and boundary conditions of the fire scenarios, fire-induced damage could have a very small probability and could be dominated by the tail of the uncertainty distribution; hence, the accuracy of the correlation-based and variance-based methods is questionable. The moment-independent Global IM analysis in this research provides a better understanding of how experimental uncertainty data affects industry’s plant models and where improvements in that data will have the largest benefit for improving fire modeling accuracy in causing core damage. Among the five unscreened parameters obtained from the Morris EE analysis, the Global IM analysis results for the case study indicated that max heat release rate and fire location are the most important parameters. The report also outlines benefits of using a unified computational platform that integrates the underlying simulations (e.g., a fire progression model), quantitative screening (using the Morris EE method), and the Global IM analysis. A unified platform can (i) facilitate the ranking of input parameters considering multiple key fire scenarios simultaneously, rather than considering one scenario at a time, (ii) contribute to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, (iii) facilitate the sampling-based uncertainty quantification for Fire PRA, and (iv) help generating both “industry-wide” and "plant-specific" ranking of uncertainty sources in Fire PRA. Future research should be done to include additional parameters such as detection/suppression or cable fire spread. Adding a PRA software such as SAPHIRE to the RAVEN platform would help with plant model integration and improve treatment of fire-induced dependency. The I-PRA risk importance ranking methodology offered in this report can provide valuable information for efficiently (a) enhancing the realism of Fire PRA for existing plants and (b) supporting the development of Dynamic Fire PRA for advanced reactors and new plants.

97 MATHEMATICS AND COMPUTING↗

Elemental Characterization of Neutron-Irradiated Tungsten Using the GD-OES Technique

In reactor-relevant fusion divertor conditions, tungsten (W) will be used as an armor material due to its excellent thermal properties. It will be exposed to impurities from numerous sources, including ion implantation and mixing, neutron transmutation, low-Z plasma-facing-component (PFC) redeposition and codeposition of deuterium and tritium fuel, and trapped helium bubbles. The impurity plasma material–interaction effects are a concern because they can cause gradual degradation of the material and of plasma performance due to dust formation, fuel retention, and even changes to the thermal and mechanical properties of the W armor. It is crucial to measure the amount of impurities in W, and the glow discharge–optical emission spectroscopy (GD-OES) technique is exceptionally well suited for analysis of irradiated samples. GD-OES can measure a sample’s elemental composition by sputtering the surface of the sample, ionizing the eroded material, and measuring the optical emission of the excited atoms. In order for the GD-OES technique to be applied to neutron-irradiated tungsten samples, a mounting system for miniature samples was designed. The sample mounting and centering procedure was successful in measuring the depth distribution of control W and W alloy sample elemental concentrations. These control depth spectra will be used as elemental references for postirradiated samples. The residence time of surface layers was measured, a comparison of signals from different anodes was completed, and the influence of initial surface roughness or nonuniformity was understood. The depth distribution of an arc-welded W-0.4% rhenium (Re) alloy was measured to have a stable Re signal that was distributed evenly in the W matrix. Here, the methods developed here will allow for quantification of impurities and transmutation amounts in neutron-irradiated W. GD-OES is a powerful tool but requires calibration and careful optimization of the parameters to obtain meaningful results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Cu‐Catalyzed Aerobic Oxidative C─C Cleavage in Lignin‐Derived Oligomers and Biological Funneling of the Monomeric Products

Existing methods for lignin deconstruction to aromatic monomers primarily cleave carbon–oxygen bonds within the polymer, resulting in sub-optimal monomer yields and formation of oligomers that retain intact carbon–carbon bonds. Here, we demonstrate that copper-catalyzed aerobic oxidation under aqueous alkaline conditions promotes oxidative cleavage of carbon–carbon bonds in lignin oligomers derived from reductive catalytic fractionation (RCF) of pine and poplar biomass. Fundamental insights are gained from reactions of model compounds that resemble subunits present in RCF oligomers. Optimal results are achieved in a flow reactor that provides precise control over O2 delivery, temperature, and reaction residence time. The Cu-catalyzed aerobic oxidation conditions access aromatic monomers in 19 and 34 wt% monomer yields, respectively, from pine- and poplar-derived RCF oligomers. Overall, the sequence consisting of biomass RCF into monomers and oligomers followed by oxidative deconstruction of the RCF oligomers generates substantially higher yields of aromatic monomers from lignin. Engineered strains of Pseudomonas putida support biological funneling of the oligomer-derived oxygenated aromatic compounds into cis,cis-muconic acid from pine or 2-pyrone-4,6-dicarboxylic acid from poplar.

09 BIOMASS FUELS↗

Transient Pulse-Response Time-of-Flight Mass Spectrometry for Complex, Deactivating Heterogeneous Catalytic Systems: Application to Ethane Dehydroaromatization

The study of complex, multistep bond-forming and -breaking reactions in heterogeneous catalytic systems often encounters challenges associated with the involvement of large numbers of intermediates among branching pathways. Kinetic information obtained from traditional steady-state measurements can be complemented with that from time-resolved methods to uncover details of the underlying chemistry. Herein, we describe an approach for tracking the complete time-resolved chemical composition (ca. 4–200 u) of a reactor effluent in response to a reactant pulse. We use a six-port rotary valve with a metered sampling loop to pulse reactants at ambient pressure into a flow reactor packed with a catalyst bed within the isothermal region of a heated furnace. The temporal evolution of effluent species is tracked using time-resolved molecular-beam time-of-flight mass spectrometry. We highlight the possibilities that this method has to offer by studying the complex bifunctional mechanism of ethane dehydroaromatization over an HZSM-5-supported platinum catalyst. We demonstrate that energy-tunable ionization sources, which facilitate isomer resolution, enable the measurement of the full mass spectral time-dependent system response. This includes the evolution of major products and mechanistically relevant reactive intermediates such as 1,3-butadiene and cyclopentadiene; these species have not previously been observed from this reaction. In additional studies, we also assess the role of platinum in the catalyst by examining temporal responses to ethane and ethylene feeds. Results show that two temporally distinct formation pathways exist for methane and benzene, and that their importance depends on both catalyst composition and reactant identity. Additionally, characteristics of catalyst deactivation are uniquely observable in the time-resolved mass spectral response, including the selective deactivation of a benzene formation pathway. The combination of time-of-flight mass spectrometry with tunable ionization enables the simultaneous observation of all effluents in a complex mixture of intermediates with isomer/isobar differentiation capabilities that can be applied to any complex reaction system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Characterization of methane oxidation in a simulated landfill cover system by comparing molecular and stable isotope mass balances

Highlights: • Quantification of bacterial methane oxidation was investigated by isotope analysis. • Independent approaches (mass balancing, stoichiometry, and stable isotope analysis). • Robust statistical validation of calculated biodegradation in experimental reactor. • Thermal imaging to locate bacterial methane oxidation along with isotope analysis. - Abstract: Biological methane oxidation may be regarded as a method of aftercare treatment for landfills to reduce climate relevant methane emissions. It is of social and economic interest to estimate the behavior of bacterial methane oxidation in aged landfill covers due to an adequate long-term treatment of the gas emissions. Different approaches assessing methane oxidation in laboratory column studies have been investigated by other authors recently. However, this work represents the first study in which three independent approaches, ((i) mass balance, (ii) stable isotope analysis, and (iii) stoichiometric balance of product (CO{sub 2}) and reactant (CH{sub 4}) by CO{sub 2}/CH{sub 4}-ratio) have been compared for the estimation of the biodegradation by a robust statistical validation on a rectangular, wide soil column. Additionally, an evaluation by thermal imaging as a potential technique for the localization of the active zone of bacterial methane oxidation has been addressed in connection with stable isotope analysis and CO{sub 2}/CH{sub 4}-ratios. Although landfills can be considered as open systems the results for stable isotope analysis based on a closed system correlated better with the mass balance than calculations based on an open system. CO{sub 2}/CH{sub 4}-ratios were also in good agreement with mass balance. In general, highest values for biodegradation were determined from mass balance, followed by CO{sub 2}/CH{sub 4}-ratio, and stable isotope analysis. The investigated topsoil proved to be very suitable as a potential cover layer by removing up to 99% of methane for CH{sub 4} loads of 35–65 g m{sup –2} d{sup –1} that are typical in the aftercare phase of landfills. Finally, data from stable isotope analysis and the CO{sub 2}/CH{sub 4}-ratios were used to trace microbial activity within the reactor system. It was shown that methane consumption and temperature increase, as a cause of high microbial activity, correlated very well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗