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DOE OSTI2017

Exploring the interplay of resilience and energy consumption for a task-based partial differential equations preconditioner

Here, we discuss algorithm-based resilience to silent data corruptions (SDCs) in a task-based domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm exploits a reformulation of the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to SDCs. The implementation is based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Scalability tests run up to ~51K cores show a parallel efficiency greater than 90%. We use a 2D elliptic PDE and a fault model based on random single and double bit-flip to demonstrate the resilience of the application to synthetically injected SDC. We discuss two fault scenarios: one based on the corruption of all data of a target task, and the other involving the corruption of a single data point. We show that for our application, given the test problem considered, a four-fold increase in the number of faults only yields a 2% change in the overhead to overcome their presence, from 7% to 9%. We then discuss potential savings in energy consumption via dynamic voltage/frequency scaling, and its interplay with fault-rates, and application overhead.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Semi-Supervised Information Extraction for Cancer Pathology Reports

Pathology reports are a main source of data for cancer surveillance programs. Manual coding of pathology reports is labor-intensive but necessary for obtaining labeled data to train automated information extraction systems. In this study, we investigated semi-supervised deep learning, improving the performance of a multitask information extraction system for automated annotation of pathology reports. We used a set of over 374,000 pathology reports from the Louisiana Tumor Registry and a novel convolutional attention-based auto-encoder. We performed a set of experiments comparing supervised training augmented with unlabeled data at 1%, 5%, 10%, and 50% of the original data size. We also compared the impact of extending text processing to include unlabeled tokens. We find that semi-supervised training consistently improved individual performance with increased micro-averaged F-scores between 0.012 and 0.064 and increased macro-averaged F-scores of up to 0.158. This demonstrates that semantic information learned via unsupervised learning can be used to improve supervised clinical task performance.

Qiu, John X.↗

DOE OSTI2018

International Good Practice in Preparing for Decommissioning - 18404

The transition of a nuclear facility from operation to the implementation of dismantling is a critical phase in every decommissioning project. Several organizational and technical modifications are needed to adapt the facility to meet the new objectives and requirements. A variety of activities need to be planned and performed to support the transition and to prepare the dismantling of the facility. Experience has shown that it is essential to start with preparations for decommissioning at a very early stage, at best already during the design stage of the facility but at least during the operational stage. The preparation of the transition for decommissioning and dismantling (D and D) is a key issue for the success of the global D and D project to minimize delays and undue costs; to optimize personnel and other resources; and to initiate preparatory activities for decommissioning in a planned, timely and cost-effective manner and thus to ensure safe and efficient decommissioning. With the growing number of nuclear facilities reaching the decommissioning stage, the Working Party on Decommissioning and Dismantling (WPDD) within the Nuclear Energy Agency of OECD formed the Task Group on Preparing for Decommissioning. This group of experts include regulators, nuclear operators and independent experts to review the strategic aspects to optimize the preparations for decommissioning from the last years of operation onwards. This paper gives and overview of the work carried out by TGPFD between 2015 and 2017 and covers strategic approaches, issues and risks and observations of good practice rather than providing detailed descriptions of methods or technologies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Status Report of Joint RPI/ORNL NCSP Task for Thermal Neutron Total Cross Section Measurements

A new cold neutron moderator system was developed and constructed at the Rensselaer Polytechnic Institute (RPI) Gaerttner Linear Accelerator. This system was then used to measure the neutron total cross section in the thermal energy range for polyethylene, polystyrene, Lucite, and yttrium hydride. In tandem with these measurements, a fitting procedure was developed at Oak Ridge National Laboratory to vary computationally simulated phonon density of states until a fit with differential and integral data was achieved. This model, combined with the new RPI total cross section data, was used to generate thermal scattering law files for the materials measured at RPI.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Quench Detection for High-Temperature Superconductor Conductors Using Acoustic Thermometry

Detecting local heat-dissipating zones in high-temperature superconductor (HTS) magnets is a challenging task due to slow propagation of such zones in HTS conductors. For long conductor lengths, voltage-based methods may not provide a sufficient sensitivity or redundancy, and therefore nonvoltage-based detection alternatives are being sought. One of those is the recently proposed method of Eigen Frequency Thermometry (EFT), which is an active acoustic technique for a fast and nonintrusive detection of 'hot spots,' utilizing temperature dependence of the conductor elastic moduli. In this work, we demonstrate the efficiency of EFT for detecting localized heating in a 1.2-m-long sample of REBCO tape immersed in liquid nitrogen, and benchmark sensitivity of the acoustic detection with respect to voltage, hot spot temperature, and power dissipation in the conductor. Modifying the original technique for differential mode of operation enables a much improved sensitivity, and adds a hot spot localization capability. Furthermore, we adapt this technique to subscale coils wound with REBCO CORC conductor built in the framework of U.S. Magnet Development Program. A successful thermal-based detection of dissipation onset at the critical current for a two-layer canted CORC dipole assembly is discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Nuclear Computational Low Energy Initiative (NUCLEI)

This is the Final Report for Ohio State University for the NUCLEI SciDAC3 project. In addition to the local OSU summary given here, we have attached a global NUCLEI summary and a list of papers and talks by the full collaboration, with OSU contributions marked in red. The NUCLEI project, as defined by the scope of work, developed, implemented and ran codes for largescale computations of many topics in lowenergy nuclear physics. Physics studied included the properties of nuclei and nuclear decays, nuclear structure and reactions, and the properties of nuclear matter. The computational techniques used included Quantum Monte Carlo, Configuration Interaction, Coupled Cluster, InMedium SRG, and Density Functional methods. The research program emphasized areas of high interest to current and possible future DOE nuclear physics facilities, including ATLAS and FRIB (nuclear structure and reactions, and nuclear astrophysics), TJNAF (neutron distributions in nuclei, few body systems, and electroweak processes), NIF (thermonuclear reactions), MAJORANA and FNPB (neutrinoless doublebeta decay and physics beyond the Standard Model), and LANSCE (fission studies). Research at Ohio State University played an integral role in NUCLEI by providing input microscopic interactions and operators needed for ab initio structure and reaction calculations including uncertainty quantification, independent ab initio calculations using the inmedium similarity renormalization group (IMSRG), and developing microscopic constraints for the nextgeneration energy density functionals (EDF). Much of the theoretical work at OSU was closely coordinated with the theory team at Michigan State University. Although the OSU group did not usually directly develop codes for the highest performance computing components of NUCLEI, we provided needed inputs for much of the effort. In particular, our work contributes to the “Validated Nuclear Interactions” task as well as to both of the “Structure and Reactions” tasks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Background studies and normalization of signal events in the Mu2e experiment

The Mu2e experiment is currently being constructed at Fermilab to search for the neutrino-less conversion of negative muons into electrons in the field of an aluminum nucleus. The experiment aims at a sensitivity of four orders of magnitude higher than previous related experiments, which implies highly demanding accuracy requirements both in the design and during the operation. To achieve such a goal, two important tasks should be accomplished. First, it is essential to estimate precisely the particle yields and all the backgrounds that could mimic the monoenergetic conversion electron signal. Second, it is necessary to normalize the signal events accurately. The normalization of the signal events is planned to be done using a detector system made of an HPGe detector and a Lanthanum Bromide detector, which will measure the rate of muons stopped on the aluminum target by looking at the emitted characteristic X-and γ-rays of energies up to 1809 keV. Therefore, it is essential to evaluate the detector system's performance before the start of the actual experiment. In this study, the first task was addressed by an extensive campaign of Monte Carlo simulations to investigate the relevant parameters and their impact on the experiment's sensitivity. The second task was handled by taking advantage of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) pulsed Bremsstrahlung photon beam at the ELBE facility. The detector system was tested at the ELBE facility under timing and background conditions similar to the ones expected at the Mu2e experiment. The study presents and discusses the simulation results and the detector system testing campaign.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Draft ASME Boiler and Pressure Vessel Code Cases and Technical Bases for Use of Alloy 617 for Constructions of Nuclear Component Under Section III, Division 5

The American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code currently only allows five materials for use in construction of nuclear components for high temperature service. These are: 2.25Cr-1Mo and V-modified 9Cr-1Mo steels, Types 304 and 316 stainless steels and the high nickel Alloy 800H. Since 2005, the US high temperature gas-cooled reactor program has been characterizing elevated temperature mechanical properties of Alloy 617 as the leading candidate construction material for the intermediate heat exchanger. After analysis of these experimental results, along with historical data and additional results available through the Generation IV International Forum, Very High Temperature Reactor, Materials Program Management Board Materials Handbook, a draft ASME Code Case to allow nuclear construction with Alloy 617 for temperatures up to 1750°F (954°C) has been developed. This report contains the Code Case for Low Temperature Service Construction of Section III, Division 5, Subsection HB, Subpart A, Class A and Subsection HC, Subpart A, Class B components, which has been approved in Section II, Materials, and Section III, Rules for Construction of Nuclear Facility Components. Supporting technical justification for the low temperature Code Case is also included. This Code Case allows use of Alloy 617 up to 800°F (425°C). This report also contains an updated draft of a Section III, Division 5, Subsection HB, Subpart B, Class A Code Case for Alloy 617 to qualify it for use in construction of nuclear components up to 1750°F (954°C) for service life up to 100,000 hours. The draft contained in Appendix 4, subject to editorial revision and approval by the ASME Special Task Group on Alloy 617 Code Qualification, will be submitted for approval by letter ballot by the appropriate ASME Committees. The technical justification supporting the Code Case is presented in Appendix 5 of this report. This background document is part of the information package that will be submitted with the Code Case for ballot.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Design of Multi-Stage Solvent Extraction Process for Separation of Rare Earth Elements

Flowsheet design and stage determination for the separation of rare earth elements (REEs) using solvent extraction (SX) is a challenging task because of the chemical similarity of the REEs. Low separation factors between the elements and complex equilibrium chemistry provide unique challenges to designing an efficient flowsheet for the separation of elements. The multi-stage nature of the SX process adds further complexity, making the assessment of products for a proposed design and stage combination difficult. Therefore, to develop a SX flowsheet, it is essential to quantify the performance for various design and separation conditions. This paper attempts to address the challenge by utilizing an equilibrium and process modeling approach. Results from a bench-scale study performed on a 10 g/L rare earth salt mixture were used in studying the extraction/stripping behavior and developing equilibrium models. DEHPA with TBP as a phase modifier was used as an extractant, while hydrochloric acid was utilized as a stripping agent. The results obtained were used in developing extraction/stripping models, which were integrated into a process framework of a SX train in a Matlab/Simulink environment. The models were programmed as a function block routine and used for developing a flowsheet, which was simulated for differing separation and design conditions. To identify optimum stage combinations, a particle swarm optimization (PSO) routine was developed and implemented for each SX train. Recovery and purity of elements of interest were used as objective function criteria. The stage combination leading to the minimization of the objective function was used to identify the optimum stage combination for a series of SX trains to attempt a balance of purity and recovery. The models and optimization method were implemented to separate a feed mixture containing REEs, which indicated that 99.52 and 85.41 percent purity is achievable for Yttrium and Lanthanum separation using 8-12-3 and 10-3-5 stage combination for loading, scrubbing, and striping. The model also indicated difficult separability between neodymium, praseodymium, and cerium.

Srivastava, Vaibhav (ORCID:0000000212645987)↗

DOE OSTI2021

Software Quality Assurance for EBR-II Fuels Irradiation and Physics Database (FIPD)

The Fuels Irradiation and Physics Database (FIPD) is an ongoing DOE project on archival of the EBR-II metal-alloy fuel irradiation experiments. As part of its use in support of license applications, the Quality Assurance Program Plan (QAPP) was drafted and endorsed by NRC in an effort to demonstrate its compliance with regulatory expectations. Software Quality Assurance (SQA) for the physics portion of FIPD is intended to qualify the calculated quantities such as fuel and cladding temperatures, neutron fluence and axially varying burnup estimates for irradiated fuel elements. This report covers the initial evaluation of SQA status of three neutron physics and thermo-fluid codes (REBUS, RCT and SE2RCT) that form the basis of calculated quantities for as-irradiated characteristics of the tested metallic fuel elements. The report also introduces an SQA plan to address the identified deficiencies. The REBUS, RCT, and SE2RCT codes are all part of the Argonne Reactor Code (ARC) code system. There is considerable knowledge and experience on REBUS and RCT but relatively less on SE2RCT. During FY2021, efforts focused on an assessment of how the data in the EBR-II Physics and Analysis DataBase (PADB) is generated with SE2RCT and used in FIPD. Additional tasks included considerations of uncertainties for power estimates in REBUS and RCT calculations and their impact on the combined RCT methodology. The RCT software usage in FIPD was assessed this year and the input/output details studied. A “requirements” document was created that identifies the key features of the RCT software being used in FIPD that need to have SQA documentation. A brief discussion on the history of RCT and its input is included in this report along with the basic SQA roadmap laid out in the requirements document. The SE2RCT software usage in FIPD is still being studied noting that there is no current manual. As part of the work done this year, two bugs were identified in the SE2RCT software which have a minor impact on the accuracy of the results it produces. No requirements document has been created, but one identified feature of SE2RCT being used that needs verification was its fuel pin temperature calculation. The work completed this year confirms that the approximations which will be included in the software verification report for SE2RCT are accurate. In addition to software quality assurance work for RCT and SE2RCT, an automated verification framework is proposed to simplify the software quality assurance process. The purpose of this framework is to streamline code verification and documentation while minimizing repetitive tasks for code developers and reviewers. The reduction of repeated input (between reference solution, software, and documentation input) throughout the SQA process reduces potential for human errors during the preparation of the supporting software quality records. The automation of the verification and documentation process proposed for this project leverages the existing verification structure already in place for the SAS4A/SASSYS-1 code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman

Abstract Motivation Multiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Results Here, we implement a smooth and differentiable version of the Smith–Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood. Availability and implementation Our code and examples are available at: https://github.com/spetti/SMURF. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Lamellar: A Rust-based Asynchronous Tasking and PGAS Runtime for High Performance Computing

Cybersecurity is one of the largest concerns in modern computing, impacting and dictating how governments, private corporations, and individuals interact with and live in an increasingly digital world. The NSA has recently released a memo [ 1] on “Software Memory Safety” where they highlight that both Microsoft and Google have stated around 70% of software vulnerabilities were due to memory safety issues. Although languages such as C and C++ provide freedom and flexibility with memory management, guaran- teeing safety falls mostly on the developer. The NSA recommends using “memory safe” languages whenever possible. In this paper we introduce Lamellar, an asynchronous tasking and PGAS HPC runtime written in Rust, one such "memory safe" language. We describe the entire Lamellar stack, from network interfaces to high- level abstractions such as distributed LamellarArrays and Active Messages. We conclude by showing comparable performance to legacy PGAS runtimes (e.g. OpenSHMEM) on a subset of the BALE kernel suite while maintaining strong memory safety principles.

HPC Software Systems, Rust Programming Language, P↗

DOE OSTI2022

Low-Cost Preform and Molding Processes

The entry cost for prototyping a composite component for manufacture using automated, high rate processes is prohibitively expensive in many cases, especially for small business, where tooling costs may be several $100k. Discussions with industry also indicate that many small companies, tier 1 and 2 suppliers, have an interest to mold composite parts but do not want to deal with the capital cost, material handling issues, and labor associated with dry fiber preforming operations. While the molders may locate near the end user for logistics reasons, it may be more cost effective for the performer to remain regional and invest in capital equipment to support preform automation, thus keeping costs to a minimum. This project was designed to explore and demonstrate several options to meet these industry needs. Dry fiber preforming approaches were evaluated which allow for low pressure resin infusion, single sided tooling options such a vacuum assisted resin transfer molding (VARTM) or low pressure resin transfer molding (RTM-light). Unlike sheet molding compound, SMC compression molding where typical molding pressures of 1000 psi are required to push material into the desired location; positioning of a dry fiber preform into the desired location on the tool allows for low molding pressures of 10-50 psi. Lower molding pressures allow for use of low cost, additive fabrication of polymeric tooling. Polymeric tooling is suitable for rapid part prototyping and limited production. Dry fiber preforming approaches evaluated included use of commercial chopped strand mat, robotic chopper gun deposition, and continuous fiber preform augmentation using tailored fiber placement (TFP). Use of chopped strand mat does not require a robotic deposition method, however a cutting table is generally required and there is typically 20-30% scrap generation. While various fiber areal weights are available, the preform is not readily optimized for minimal fiber use or weight savings. In contrast, a robotic chopper gun approach allows for localized deposition where fiber is required to meet structural requirements. The robotic method is highly automated and minimizes fiber scrap, however the capital cost of the equipment and engineering labor for programming can result in higher preform cost compared to chopped strand mat in certain cases depending on preform complexity. Dry fiber preforming using the robotic chopper gun method allows for creation of three dimensional forms. This approach may be ideal for molding in-house, or if the preforms stack together densely to allow for efficient shipping. Applications evaluated for this program considered trade-off between fabrication of a fully 3D preform versus production of a flat preform which is designed to readily drape into the final desired shape. Such a preform design greatly simplifies robotic programming and requires no specialized tooling. The flat preforms are easily stacked and shipped to the final molding location. Flat preforms are much easier to augment with TFP continuous fiber to provide local reinforcement. The demonstration and evaluation of these preforming and tooling methods were completed on three component applications. The first application was a battery box cover for an electric vehicle which was highly three dimensional. The second demonstrator article was comprised of complex contours and was used to demonstrate the use of TFP and RTM-light molding process. The third demonstration article was the roof of an operator’s cab for large construction equipment. The roof is relatively flat however it is comprised of complex changes in thickness which clearly demonstrate the advantage of robotic chopper gun approach as compared to using numerous preform layers of chopped strand mat. The cost trades for the various preforming methods are summarized to help guide the reader as to preforming method considerations. Finally, these demonstrations all used glass fiber roving. A fourth, exploratory task was added to evaluate the ability to make preforms using Zoltek’s carbon fiber split tow roving. We were able to adapt the chopper gun to make flat preforms for laminate testing, but further development effort would be required to make suitable preforms.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Managing Zirconium Chemistry and Phase Compatibility in Combined Process Separations for Minor Actinide Partitioning

In response to the NEUP Program Supporting Fuel Cycle R&D Separations and Waste Forms call DEFOA- 0000799, this report describes the results of an R&D project focusing on streamlining separation processes for advanced fuel cycles. An example of such a process relevant to the U.S. DOE FCR&D program would be one combining the functions of the TRUEX process for partitioning of lanthanides and minor actinides from PUREX(UREX) raffinates with that of the TALSPEAK process for separating transplutonium actinides from fission product lanthanides. A fully-developed PUREX(UREX)/TRUEX/TALSPEAK suite would generate actinides as product(s) for reuse (or transmutation) and fission products as waste. As standalone, consecutive unit-operations, TRUEX and TALSPEAK employ different extractant solutions (solvating (CMPO, octyl(phenyl)-N,Ndiisobutylcarbamoylmethylphosphine oxide) vs. cation exchanging (HDEHP, di-2(ethyl)hexylphosphoric acid) extractants), and distinct aqueous phases (2-4 M HNO 3 vs. concentrated pH 3.5 carboxylic acid buffers containing actinide selective chelating agents). The separate processes may also operate with different phase transfer kinetic constraints. Experience teaches (and it has been demonstrated at the lab scale) that, with proper control, multiple process separation systems can operate successfully. However, it is also recognized that considerable economies of scale could be achieved if multiple operations could be merged into a single process based on a combined extractant solvent. The task of accountability of nuclear materials through the process(es) also becomes more robust with fewer steps, providing that the processes can be accurately modeled. Work is underway in the U.S. and Europe on developing several new options for combined processes (TRUSPEAK, ALSEP, SANEX, GANEX, ExAm are examples). There are unique challenges associated with the operation of such processes, some relating to organic phase chemistry, others arising from the variable composition of the aqueous medium. This project targets in particular two problematic issues in designing combined process systems: managing the chemistry of challenging aqueous species (like Zr 4+ ) and optimizing the composition and properties of combined extractant organic phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Biofuels Refining Engineering

The goal of this project is the development of novel catalysts and knowledge of reaction pathways and mechanisms for conversion of biomass-based compounds to fuels that are compatible with oil-based fuels and with acceptable or superior fuel properties. The research scope included both catalysts to convert lignocellulosic biomass-based molecules (from pyrolysis) and vegetable oil-based molecules (i.e., triglycerides and fatty acid methyl esters). This project comprised five technical tasks. Each task is briefly introduced below, and major technical accomplishments summarized. Technical accomplishments were described in greater detail in the quarterly progress reports, and in even more detail in the >50 publications acknowledging this DoE project funding (list of publications and presentations included at the end of this report). The results of this research added greatly to the knowledge base necessary for upgrading of pyrolysis oil to hydrocarbon fuels and chemicals, and for conversion of vegetable oils to fungible diesel fuel. Numerous new catalysts and catalytic reaction systems were developed for upgrading particular compounds or compound families found in the biomass-based pyrolysis oils and vegetable oils. Methods to mitigate catalyst deactivation were investigated, including novel reaction/separation systems. Performance and emission characteristics of biofuels in flames and engines were measured. Importantly, the knowledge developed from this project became the basis for a subsequent collaborative proposal led by our research group, involving researchers from the University of Wisconsin, the University of Pittsburg, and the Idaho National Lab, for the DoE Carbon, Hydrogen and Separations Efficiency (CHASE) program, which was subsequently funded (one of only four projects awarded in the CHASE program). The CHASE project examined novel catalytic processes for lignocellulosic biomass conversion as well as technoeconomic analyses for process options for maximum carbon capture and hydrogen efficiency. Our research approach combined catalyst synthesis, measurements of catalyst activity and selectivity in different reactor systems and conditions, and detailed catalyst characterization to develop fundamental understanding of reaction pathways and the capability to predict product distributions. Nearly all of the candidate catalysts were prepared in-house via standard techniques such as impregnation, co-impregnation, or chemical vapor deposition. Supports were usually purchased, but in some cases coprecipitation was used to simultaneously create the support and active component, which can be advantageous for strong active component-support interactions and for achieving high active component dispersion. In-house synthesis also allowed for studies of the effects on catalyst activity and selectivity of such factors as support porosity, calcination temperature, and reduction/activation conditions. Depending on the physical characteristics of the molecule, catalyst activity measurements were carried out in tubular flow reactors (for vapor phase reactions) or stirred tank reactors (for liquid phase reactions) over a wide range of pressures and temperatures. Reactant and product concentrations were measured using gas chromatography (both on-line and off-line, with TCD, FID, and/or mass spectrometric detection). For promising catalysts, detailed physicochemical characterization was carried out using FTIR, Raman, XPS, and XRD spectroscopies (all available in our laboratories) and TEM spectroscopy (available at OU). Additional methods included temperature programmed techniques (TPD, TPO) and surface area measurements by nitrogen adsorption techniques.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2019

Systematic Technology Evaluation Program for SiC-based BWR Channel Box

Silicon carbide (SiC) –based accident-tolerant fuel (ATF) cladding of light water reactors (LWRs) has become a topic of great interest in nuclear material development since the Fukushima Dai-ichi Nuclear Power Plant accident. Extensive development of SiC cladding has identified benefits of the concept, including its inherent accident resistance and neutronic performance. Advancements achieved during the development of SiC-based ATF cladding have made it attractive for use in accident-tolerant cores of LWRs. In this document, we consider the use of SiC-based materials to replace the zirconium alloy used for the channel boxes of boiling water reactors. Both accident resistance and prevention of the bowing observed in current grades of channel boxes are needs driving the exploration of a new channel box system. To provide a pathway for channel box development, this document lays out (1) a current technology overview, (2) the technology gap, and (3) a systematic technology evaluation plan. The objectives of the technology evaluation plan are to address key feasibility issues revealed by the knowledge gap analysis and to contribute to advancing the technology readiness level. Further development will be required in the technology implementation phase. The technology evaluations include understanding the behavior of channel boxes under both normal and off-normal conditions, identifying potential mitigation strategies for the issues if necessary, and improving channel box performance based on the knowledge obtained. The structure of the technology evaluation plan and each task are shown in Figure S.1. The knowledge gap analysis identified key research areas, including irradiation effects, hydrothermal corrosion, accident behavior, and design and manufacture. These are common issues between SiC-based channel boxes and SiC-based fuel cladding, although technical evaluations specific for the channel box components are required. This document also describes details of the technical feasibility evaluation methods and plans.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Thermodynamics and Kinetics of Phase Transformations in Energy Materials (Final Report)

The primary goal of this program is to develop computational tools and methodology for enabling a shift from empirical trial-and-error driven materials discovery towards data and information technology driven design of materials with targeted functional properties. Both thermodynamic and kinetic factors need to be taken into account when designing new materials. Thermodynamics sets the necessary-but-not-sufficient conditions for the suitability of a particular material in a given application, while the kinetics controls the rates of microscopic processes and hence the performance in energy applications (e.g., power in energy storage, conversion efficiencies in solar cells and thermoelectrics, chemical activity in catalysis). The complexity of these phenomena makes computational design difficult, not least due to the lack of suitable theoretical methods and computational tools. To address these needs, we develop systematic, quantitatively accurate first-principles methods to address two important problems: (i) predict and optimize the thermodynamics of materials over a large composition and structure space, and (ii) model kinetic processes such as mass and heat transport in crystalline solids under non-equilibrium conditions. These tasks are addressed by combining high-throughput computation with automatic construction of accurate lattice models using mathematically rigorous methods of information theory, such as compressive sensing and Bayesian inference.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows

Deep Learning for Enhancing Fault TolerantCapabilities of Scientific WorkflowsAlok Singh, Ilkay AltintasSan Diego Supercomputer Center, UCSDLa Jolla, CA, USA{a1singh, ialtintas}@ucsd.eduMalachi Schram, Nathan TallentPacific Northwestern National LaboratoryRichland, WA, USA{malachi.schram, nathan.tallent}@pnnl.govAbstract-In the history of Computer Science, the act of ‘delegation’ has been the greatest multiplier of society’s problem-solving ability. A scientist working on detecting anomalies in a phenomenon, does not need to re-invent matrix multiplication techniques to solve her problem. Scientific workflows provide ultimate ‘delegation’ mechanism -where a domain scientist can completely forget the specifics of ‘how’ her program will execute on a large cluster in an efficient and cost-effective manner and can instead focus on the mathematical formulation and theoretical robustness of her solution. We present here an approach that directly aims to make the execution of Scientific Workflows more reliable, robust and efficient. We aim that the work presented in this paper will propel the larger effort, from the scientific workflow community, of making scientific workflow execution as simple, efficient and robust as a JOIN operation in a modern database. Specifically, we apply Deep Learning techniques to develop a mechanism that forecasts the final state (success or failure) of a dynamic job in a large-scale particle physics experiment, with minimal data gathering, and as early as possible in job’s life cycle. The key advantage of having a predictive mechanism to identify and anticipate failure-prone jobs is the potential for designing intelligent Fault Tolerance mechanisms to handle anomalous events. We achieve a 14% improvement in computational resources utilization, and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics Computing workflow.To the best of our knowledge, this is the most exhaustive and first of its kind study of neural network architectures in context of a real-dataset profiled from a large-scalescientific workflow.

deep learning↗