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At least 1,045 records · Page 58

Structure-Reactivity Relationships in Multi-Component Transition Metal Oxide Catalysts FINAL Report

The focus of the project was on developing an atomic-level understanding of how transition metal oxide catalysts function. Over the course of several renewals the specific emphases shifted from understanding how local structure and oxidation state affect how molecules adsorb and react on the surfaces of binary oxide crystals to more complex systems where interactions between different transition metal oxide cations in an oxide catalyst can affect reactivity, and finally to the impact of cluster size on oxide stability and reactivity. Hallmarks of the work were the use of epitaxial growth methods to create surfaces relevant to catalysis yet tractable for fundamental surface science approaches, and the use of scanning tunneling microscopy to follow structural changes induced by reactions and to pinpoint adsorption sites. Key early findings included the identification of oxidation and reduction mechanisms on a tungsten oxide catalyst surface that determine the sites available for reaction, identification of C-O bond cleavage as the rate limiting step in alcohol dehydration reactions on the tungsten oxide surface, and demonstration that reduction does not change the favored reaction pathway but rather eases C-O bond cleavage and thus reduces the reaction barrier. Subsequently, a new reconstruction on the anatase phase of TiO 2 relevant to catalysis was discovered and shown to create sites with distinct reactivity compared to other TiO 2 surfaces. Building on this work on anatase, the mechanism by which TiO 2 enhances the reactivity of vanadium oxide layers was characterized and it was found that the TiO 2 substrate can force thin vanadia layers to adopt structures they would not ordinarily form in the bulk which in turn creates differences in reactivity between supported layers and bulk samples. From there, the work progressed to studying well-defined ternary oxides where synergistic effects between the two cations can induce catalytic properties not seen for the individual binary oxides and to the structure and properties of transition metal oxide clusters. For the latter, surprising results were found including the observation that small clusters can actually be orders of magnitude more difficult than bulk materials to oxidize and that even weak substrate interactions can dictate the structure and reactivity of the oxide clusters. It was shown that these results could be explained in terms of simple thermodynamic arguments that extend to materials beyond the Co oxide system studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

What is the current state of the science of Cyber defense?

My overall sense of the cyber defense field is one of an adolescent discipline currently bogged down in a cloud of issues, the most iconic of which is the great diversity of approaches that are being aggregated to form a coherent field. Because my own expertise is complex systems and materials physics research, I have limited direct experience in cyber security sciences except as a user of secure networks and computing resources. However, in producing this report, I have found with certainty that there exists no calculus for cyber risk assessment, mitigation, and response, although some hopeful precepts toward this end are emerging.

Computer Science↗

Interim Status Report for Risk Management for SFRs

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of passive, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to take advantage of natural phenomena to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a variety of beyond design basis events with the intent of exploring the utility of a Dynamic Bayesian Network to infer the state of the reactor to inform the operator's corrective actions. These inferences also serve to identify the instruments most critical to informing an operator's actions as candidates for hardening against radiation and other extreme environmental conditions that may exist in an accident. This reduction in uncertainty serves to inform ongoing discussions of how small sodium reactors would be licensed and may serve to reduce regulatory risk and cost for such reactors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Developing and Implementing the Data Mining Algorithms in RAVEN

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.

97 MATHEMATICS AND COMPUTING↗

3D Simulation of External Flooding Events for the RISMC Pathway

Incorporating 3D simulations as part of the Risk-Informed Safety Margins Characterization (RISMIC) Toolkit allows analysts to obtain a more complete picture of complex system behavior for events including external plant hazards. External events such as flooding have become more important recently – however these can be analyzed with existing and validated simulated physics toolkits. In this report, we describe these approaches specific to flooding-based analysis using an approach called Smoothed Particle Hydrodynamics. The theory, validation, and example applications of the 3D flooding simulation are described. Integrating these 3D simulation methods into computational risk analysis provides a spatial/visual aspect to the design, improves the realism of results, and can prove visual understanding to validate the analysis of flooding.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

RAVEN Beta Release

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN has now increased in maturity enough for the Beta 1.0 release.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focusing on the input structure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Two-Dimensional Neutronic and Fuel Cycle Analysis of the Transatomic Power Molten Salt Reactor

This status report presents the results from the first phase of the collaboration between Transatomic Power Corporation (TAP) and Oak Ridge National Laboratory (ORNL) to provide neutronic and fuel cycle analysis of the TAP core design through the Department of Energy Gateway for Accelerated Innovation in Nuclear, Nuclear Energy Voucher program. The TAP design is a molten salt reactor using movable moderator rods to shift the neutron spectrum in the core from mostly epithermal at beginning of life to thermal at end of life. Additional developments in the ChemTriton modeling and simulation tool provide the critical moderator-to-fuel ratio searches and time-dependent parameters necessary to simulate the continuously changing physics in this complex system. Results from simulations with these tools show agreement with TAP-calculated performance metrics for core lifetime, discharge burnup, and salt volume fraction, verifying the viability of reducing actinide waste production with this design. Additional analyses of time step sizes, mass feed rates and enrichments, and isotopic removals provide additional information to make informed design decisions. This work further demonstrates capabilities of ORNL modeling and simulation tools for analysis of molten salt reactor designs and strongly positions this effort for the upcoming three-dimensional core analysis.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Application of spectroscopy and super-resolution microscopy: Excited state

Photophysics of inorganic materials and organic molecules in complex systems have been extensively studied with absorption and emission spectroscopy.1-4 Steady-state and time-resolved fluorescence studies are commonly carried out to characterize excited-state properties of fluorophores. Although steady-state fluorescence measurements are widely used for analytical applications, time-resolved fluorescence measurements provide more detailed information about excited-state properties and the environment in the vicinity of the fluorophore. Many photophysical processes, such as photoinduced electron transfer (PET), rotational reorientation, solvent relaxation, and energy transfer, occur on a nanosecond (10 -9 s) timescale, thus affecting the lifetime of the fluorophores. Moreover, time-resolved microscopy methods, such as lifetimeimaging, combine the benefits of the microscopic measurement and information-rich, timeresolved data. Thus, time-resolved fluorescence spectroscopy combined with microscopy can be used to quantify these processes and to obtain a deeper understanding of the chemical surroundings of the fluorophore in a small area under investigation. This thesis discusses various photophysical and super-resolution microscopic studies of organic and inorganic materials, which have been outlined below.

36 MATERIALS SCIENCE↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

SRF cavity testing using a FPGA Self Excited Loop

Various authors have previously studied the theory and practice of cavity testing, notably an extensive treatment by Powers [1] and Padamsee [2]. The advent of the digital Low Level RF (LLRF) electronics based on Field Programmable Logic Arrays (FPGA) provides various improvements over the rather complex systems used in the past as well as enabling new measurement techniques.In this document we reintroduce a technique that seems to have fallen out of practice in recent times, that is obtaining the coupling constant β through measurements from just one port, the reflected power port, of the directional coupler placed in front of the cavity.

43 PARTICLE ACCELERATORS↗

Assessment of the Neutronic and Fuel Cycle Performance of the Transatomic Power Molten Salt Reactor Design

This report presents results from a collaboration between Transatomic Power Corporation (TAP) and Oak Ridge National Laboratory (ORNL) to provide neutronic and fuel cycle analysis of the TAP core design through the Department of Energy Gateway for Accelerated Innovation in Nuclear (GAIN) Nuclear Energy Voucher program. The TAP concept is a molten salt reactor using configurable zirconium hydride moderator rod assemblies to shift the neutron spectrum in the core from mostly epithermal at beginning of life to thermal at end of life. Additional developments in the ChemTriton modeling and simulation tool provide the critical moderator-to-fuel ratio searches and time-dependent parameters necessary to simulate the continuously changing physics in this complex system. The implementation of continuous-energy Monte Carlo transport and depletion tools in ChemTriton provide for full-core three-dimensional modeling and simulation. Results from simulations with these tools show agreement with TAP-calculated performance metrics for core lifetime, discharge burnup, and salt volume fraction, verifying the viability of reducing actinide waste production with this concept. Additional analyses of mass feed rates and enrichments, isotopic removals, tritium generation, core power distribution, core vessel helium generation, moderator rod heat deposition, and reactivity coeffcients provide additional information to make informed design decisions. This work demonstrates capabilities of ORNL modeling and simulation tools for neutronic and fuel cycle analysis of molten salt reactor concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Final Technical Report of the project "Controlling Quantum Information by Quantum Correlations"

The report describes hypotheses, aims, methods and results of the project 20170675PRD2, “Controlling Quantum Information by Quantum Correlations”, which has been run from July 31, 2017 to January 7, 2018. The technical work has been performed by Director’s Fellow Davide Girolami of the T-4 Division, Physics of Condensed Matter and Complex Systems, under the supervision of Wojciech Zurek (T-4), Lukasz Cincio (T-4), and Marcus Daniels (CCS-7). The project ended as Davide Girolami has been converted to J. R. Oppenheimer Fellow to work on the project 20180702PRD1, “Optimal Control of Quantum Machines”, started on January 8, 2018.

97 MATHEMATICS AND COMPUTING↗

Abstract - Cooperative Research and Development Agreement between Ames National Laboratory and National Energy Technology Laboratory AGMT-0609

The goal of this project is to develop a merged environment for simulation and analysis (MESA) at the National Energy Technology Laboratory’s (NETL) Hybrid Performance (Hyper) project laboratory. The MESA sensor lab developed as a component of this research will provide a development platform for investigating: 1) advanced control strategies, 2) testing and development of sensor hardware, 3) various modeling in-the-loop algorithms and 4) other advanced computational algorithms for improved plant performance using sensors, real-time models, and complex systems tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗

How to ADAPT

The ADAPT software allows for the examination of aleatory and epistemic uncertainties in complex system transients using the Dynamic Event Tree (DET) methodology. This manual outlines the principles of operation of ADAPT and provides directions for its use. Future plans for the code are briefly outlined.

97 MATHEMATICS AND COMPUTING↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗