Results for “task programming” — Page 60

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↗

DOE OSTI2021

ARM Aerosol Measurement Science Group 2019 Strategic Planning Workshop Report

This report summarizes the results of a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility workshop held in November 2019 to advance a science-based strategy for ARM’s aerosol measurement program. This was the second such workshop since the Aerosol Measurement Science Group (AMSG) was chartered in 2015 to enhance coordination of ARM observations of aerosols and atmospheric trace gases with the needs of ARM users. The results presented here reflect the AMSG’s focus in recent years on science-based strategies that will contribute to the increased use of ARM data to fulfill its mission of improving process representations and predictability in climate models. Sessions held during the workshop range from interfacing with models through aerosol sampling strategies to calibration protocols and data products. The AMSG workshops have been designed to recommend actions that will enable ARM to evolve and continue to meet its mission. To that end, the AMSG will develop an actionable plan from the recommendations outlined here. Some are well defined and can reasonably be accomplished in the short term. Others are less definite or of a larger scope that calls for a longer-term implementation. Further discussion will be required to develop and prioritize actionable items related to such areas. Task teams comprising the appropriate expertise and perspective from the AMSG and other members of the community will be formed to achieve this outcome. Some particular topics are recognized as high priority, so plans are underway to develop task teams and to hold follow-on discussions to address them. Four areas currently being considered for short, focused discussion are 1) aerosol measurements on the North Slope of Alaska, 2) improving data usability for modeling, 3) strategies for advancing remote sensing, vertical profiling, and distributed measurements of aerosols, and 4) aerosol sampling strategies at existing ARM sites to provide intensive modes of operation to promote data usage for process and modeling studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗

DOE OSTI2016

TH-C-BRC-00: Emerging Technologies in SRS/SBRT Delivery

The delivery techniques for SRS/SBRT have been under rapid developments in recent years, which pose new challenges to medical physicists ranging from planning and quality assurance to imaging and motion management. This educational course will provide a general overview of the latest delivery techniques in SRS/SBRT, and discuss the clinical processes to address the challenges of each technique with special emphasis on dedicated gamma-ray based device, robotic x-band linac-based system and conventional C-arm s-band linac-based SRS systems. (1). Gamma-ray based SRS/SRT: This is the gold standard of intracranial SRS. With the advent of precision imaging guidance and frameless patient positioning capabilities, novel stereoscopic CBCT and automatic dose adaption solution are introduced to the Gamma-ray based SRS for the first time. The first North American system has been approved by the US regulatory for patient treatments in the spring of 2016. (2). Robotic SRS/SBRT system: A number of technological milestones have been developed in the past few years, including variable aperture collimator, sequential optimization technique, and the time reduction technique. Recently, a new robotic model allows the option of a multi-leaf collimator. These technological advances have reduced the treatment time and improved dose conformity significantly and could potentially expand the application of radiosurgery for the treatment of targets not previously suitable for robotic SRS/SBRT or fractionated stereotactic radiotherapy. These technological advances have created new demanding mandates on hardware and patient quality assurance (QA) tasks, as well as the need for updating/educating the physicists in the community on these requirements. (3). Conventional Linac based treatments: Modulated arc therapy (MAT) has gained wide popularities in Linac-based treatments in recent years due to its high delivery efficiency and excellent dose conformities. Recently, MAT has been introduced to deliver highly conformal radiosurgery treatments to multiple targets simultaneously via a single isocenter to replace the conventional multi-iso multi-plan treatments. It becomes important to understand the advantages and limitations of this technique, and the pitfalls for implementing this technique in clinical practice. The planning process of single-iso multi-target MAT will be described, and its plan quality and delivery efficiency will be compared with multi-iso plans. The QA process for verifying such complex plans will be illustrated, and pitfalls in imaging and patient set up will be discussed. Overall, this session will focus on the following areas: 1) Update on the emerging technology in current SRS/SBRT delivery. 2) New developments in treatment planning and Quality Assurance program. 3) Imaging guidance and motion management. Learning Objectives: To understand the SRS/SBRT principles and its clinical applications, and gain knowledge on the emerging technologies in SRS/SBRT. To review planning concepts and useful tips in treatment planning. To learn about the imaging guidance procedures and the quality assurance program in SRS/SBRT. National Institutes of Health, Varian Medical System; L. Ren, The presenter is funded by National Institutes of Health and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-C-BRC-01: An Overview of Emerging Technologies in SRS/SBRT Delivery

The delivery techniques for SRS/SBRT have been under rapid developments in recent years, which pose new challenges to medical physicists ranging from planning and quality assurance to imaging and motion management. This educational course will provide a general overview of the latest delivery techniques in SRS/SBRT, and discuss the clinical processes to address the challenges of each technique with special emphasis on dedicated gamma-ray based device, robotic x-band linac-based system and conventional C-arm s-band linac-based SRS systems. (1). Gamma-ray based SRS/SRT: This is the gold standard of intracranial SRS. With the advent of precision imaging guidance and frameless patient positioning capabilities, novel stereoscopic CBCT and automatic dose adaption solution are introduced to the Gamma-ray based SRS for the first time. The first North American system has been approved by the US regulatory for patient treatments in the spring of 2016. (2). Robotic SRS/SBRT system: A number of technological milestones have been developed in the past few years, including variable aperture collimator, sequential optimization technique, and the time reduction technique. Recently, a new robotic model allows the option of a multi-leaf collimator. These technological advances have reduced the treatment time and improved dose conformity significantly and could potentially expand the application of radiosurgery for the treatment of targets not previously suitable for robotic SRS/SBRT or fractionated stereotactic radiotherapy. These technological advances have created new demanding mandates on hardware and patient quality assurance (QA) tasks, as well as the need for updating/educating the physicists in the community on these requirements. (3). Conventional Linac based treatments: Modulated arc therapy (MAT) has gained wide popularities in Linac-based treatments in recent years due to its high delivery efficiency and excellent dose conformities. Recently, MAT has been introduced to deliver highly conformal radiosurgery treatments to multiple targets simultaneously via a single isocenter to replace the conventional multi-iso multi-plan treatments. It becomes important to understand the advantages and limitations of this technique, and the pitfalls for implementing this technique in clinical practice. The planning process of single-iso multi-target MAT will be described, and its plan quality and delivery efficiency will be compared with multi-iso plans. The QA process for verifying such complex plans will be illustrated, and pitfalls in imaging and patient set up will be discussed. Overall, this session will focus on the following areas: 1) Update on the emerging technology in current SRS/SBRT delivery. 2) New developments in treatment planning and Quality Assurance program. 3) Imaging guidance and motion management. Learning Objectives: To understand the SRS/SBRT principles and its clinical applications, and gain knowledge on the emerging technologies in SRS/SBRT. To review planning concepts and useful tips in treatment planning. To learn about the imaging guidance procedures and the quality assurance program in SRS/SBRT. National Institutes of Health, Varian Medical System; L. Ren, The presenter is funded by National Institutes of Health and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-C-BRC-02: A Review of Emerging Technologies in Robotic SRS/SBRT Delivery

The delivery techniques for SRS/SBRT have been under rapid developments in recent years, which pose new challenges to medical physicists ranging from planning and quality assurance to imaging and motion management. This educational course will provide a general overview of the latest delivery techniques in SRS/SBRT, and discuss the clinical processes to address the challenges of each technique with special emphasis on dedicated gamma-ray based device, robotic x-band linac-based system and conventional C-arm s-band linac-based SRS systems. (1). Gamma-ray based SRS/SRT: This is the gold standard of intracranial SRS. With the advent of precision imaging guidance and frameless patient positioning capabilities, novel stereoscopic CBCT and automatic dose adaption solution are introduced to the Gamma-ray based SRS for the first time. The first North American system has been approved by the US regulatory for patient treatments in the spring of 2016. (2). Robotic SRS/SBRT system: A number of technological milestones have been developed in the past few years, including variable aperture collimator, sequential optimization technique, and the time reduction technique. Recently, a new robotic model allows the option of a multi-leaf collimator. These technological advances have reduced the treatment time and improved dose conformity significantly and could potentially expand the application of radiosurgery for the treatment of targets not previously suitable for robotic SRS/SBRT or fractionated stereotactic radiotherapy. These technological advances have created new demanding mandates on hardware and patient quality assurance (QA) tasks, as well as the need for updating/educating the physicists in the community on these requirements. (3). Conventional Linac based treatments: Modulated arc therapy (MAT) has gained wide popularities in Linac-based treatments in recent years due to its high delivery efficiency and excellent dose conformities. Recently, MAT has been introduced to deliver highly conformal radiosurgery treatments to multiple targets simultaneously via a single isocenter to replace the conventional multi-iso multi-plan treatments. It becomes important to understand the advantages and limitations of this technique, and the pitfalls for implementing this technique in clinical practice. The planning process of single-iso multi-target MAT will be described, and its plan quality and delivery efficiency will be compared with multi-iso plans. The QA process for verifying such complex plans will be illustrated, and pitfalls in imaging and patient set up will be discussed. Overall, this session will focus on the following areas: 1) Update on the emerging technology in current SRS/SBRT delivery. 2) New developments in treatment planning and Quality Assurance program. 3) Imaging guidance and motion management. Learning Objectives: To understand the SRS/SBRT principles and its clinical applications, and gain knowledge on the emerging technologies in SRS/SBRT. To review planning concepts and useful tips in treatment planning. To learn about the imaging guidance procedures and the quality assurance program in SRS/SBRT. National Institutes of Health, Varian Medical System; L. Ren, The presenter is funded by National Institutes of Health and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-C-BRC-03: Emerging Linac Based SRS/SBRT Technologies with Modulated Arc Delivery

The delivery techniques for SRS/SBRT have been under rapid developments in recent years, which pose new challenges to medical physicists ranging from planning and quality assurance to imaging and motion management. This educational course will provide a general overview of the latest delivery techniques in SRS/SBRT, and discuss the clinical processes to address the challenges of each technique with special emphasis on dedicated gamma-ray based device, robotic x-band linac-based system and conventional C-arm s-band linac-based SRS systems. (1). Gamma-ray based SRS/SRT: This is the gold standard of intracranial SRS. With the advent of precision imaging guidance and frameless patient positioning capabilities, novel stereoscopic CBCT and automatic dose adaption solution are introduced to the Gamma-ray based SRS for the first time. The first North American system has been approved by the US regulatory for patient treatments in the spring of 2016. (2). Robotic SRS/SBRT system: A number of technological milestones have been developed in the past few years, including variable aperture collimator, sequential optimization technique, and the time reduction technique. Recently, a new robotic model allows the option of a multi-leaf collimator. These technological advances have reduced the treatment time and improved dose conformity significantly and could potentially expand the application of radiosurgery for the treatment of targets not previously suitable for robotic SRS/SBRT or fractionated stereotactic radiotherapy. These technological advances have created new demanding mandates on hardware and patient quality assurance (QA) tasks, as well as the need for updating/educating the physicists in the community on these requirements. (3). Conventional Linac based treatments: Modulated arc therapy (MAT) has gained wide popularities in Linac-based treatments in recent years due to its high delivery efficiency and excellent dose conformities. Recently, MAT has been introduced to deliver highly conformal radiosurgery treatments to multiple targets simultaneously via a single isocenter to replace the conventional multi-iso multi-plan treatments. It becomes important to understand the advantages and limitations of this technique, and the pitfalls for implementing this technique in clinical practice. The planning process of single-iso multi-target MAT will be described, and its plan quality and delivery efficiency will be compared with multi-iso plans. The QA process for verifying such complex plans will be illustrated, and pitfalls in imaging and patient set up will be discussed. Overall, this session will focus on the following areas: 1) Update on the emerging technology in current SRS/SBRT delivery. 2) New developments in treatment planning and Quality Assurance program. 3) Imaging guidance and motion management. Learning Objectives: To understand the SRS/SBRT principles and its clinical applications, and gain knowledge on the emerging technologies in SRS/SBRT. To review planning concepts and useful tips in treatment planning. To learn about the imaging guidance procedures and the quality assurance program in SRS/SBRT. National Institutes of Health, Varian Medical System; L. Ren, The presenter is funded by National Institutes of Health and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Ground Motion Inputs for the Seismic Shake Table Test

Currently, spent nuclear fuel (SNF) is stored in on-site independent spent-fuel storage installations (ISFSIs) at seventythree (73) nuclear power plants (NPPs) in the US. Because a site for geologic repository for permanent disposal of SNF has not been constructed, the SNF will remain in dry storage significantly longer than planned. During this time, the ISFSIs, and potentially consolidated storage facilities, will experience earthquakes of different magnitudes. The dry storage systems are designed and licensed to withstand large seismic loads. When dry storage systems experience seismic loads, there are little data on the response of SNF assemblies contained within them. The Spent Fuel Waste Disposition (SFWD) program is planning to conduct a full-scale seismic shake table test to close the gap related to the seismic loads on the fuel assemblies in dry storage systems. This test will allow for quantifying the strains and accelerations on surrogate fuel assembly hardware and cladding during earthquakes of different magnitudes and frequency content. The main component of the test unit will be the full-scale NUHOMS 32 PTH2 dry storage canister. The canister will be loaded with three surrogate fuel assemblies and twenty-nine dummy assemblies. Two dry storage configurations will be tested – horizontal and vertical above-ground concrete overpacks. These configurations cover 91% of the current dry storage configurations. The major input into the shake table test are the seismic excitations or the earthquake ground motions – acceleration time histories in two horizontal and one vertical direction that will be applied to the shake table surface during the tests. The shake table surface represents the top of the concrete pad on which a dry storage system is placed. The goal of the ground motion task is to develop the ground motions that would be representative of the range of seismotectonic and other conditions that any site in the Western US (WUS) or Central Eastern US (CEUS) might entail. This task is challenging because of the large number of the ISFSI sites, variety of seismotectonic and site conditions, and effects that soil amplification, soil-structure interaction, and pad flexibility may have on the ground motions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Chemistry and Reactivity of Oxide Films on Aluminum Clad Test Reactor Fuels

This paper describes recent work performed to assess the degradation of aluminum spent nuclear fuel (ASNF) as part of an ongoing Department of Energy project. As part of a multi-task effort, this paper specifically describes efforts initiated in 2018 to better understand chemical phenomena and degradation in ASNF). In this paper experimental and modeling activities are described. The modeling effort presents initial results in two areas potentially influencing stability. Using an established electrolyte model based on experimental data, the solubility of aluminum as a function of temperature and pH was examined. This work describes interesting results for the minimum pH providing relevant data that was not available from the literature. It was found that solubility increases with temperature and that the minimum pH shifts to low pH with increase in temperature. The second area involves understanding the metallurgy of aluminum alloys during fuel drying. In this study, the temperature range from 100°C to 300°C was analyzed. Thermodynamic models predict that AA1100 will not experience significant phase changes while the more highly alloyed AA6061 and AA5052 may experience significant phase changes. These thermodynamic measurements establish a potential for phase changes during drying, however they do not predict the kinetics for such processes. Phase 1 of the experimental program has initiated and results are forthcoming. The test program will examine aluminum oxidation under anticipated dry storage conditions (temperature and humidity). Examination of localized corrosion due to tight crevices and galvanic contact (with stainless steel) will be included. Finally, water deliquescence will be assessed using thermogravimetric analysis with dynamic humidity profiles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

A HUMAN FACTORS ENGINEERING PROCESS TO SUPPORT HUMAN-SYSTEM INTERFACE DESIGN IN CONTROL ROOM MODERNIZATION

The primary objective of the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program is to sustain operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R&D). The Advanced Instrumentation, Information, and Control (II&C) System Technologies pathway conducts targeted R&D to address aging and reliability concerns with legacy instrumentation and control (I&C) and other information systems in existing U.S. NPPs. Control room modernization is an important part following this pathway, and human factors experts at Idaho National Laboratory (INL) have been involved in conducting R&D to support migration of new digital main control room (MCR) technologies from legacy analog and legacy digital I&C. This paper describes a human factors engineering (HFE) process that supports human-system interface (HSI) design in MCR modernization activities, particularly with migration of old digital to new digital I&C. The process described in this work is an expansion from the LWRS Report INL/EXT-16-38576, and is a requirements-driven approach that aligns with NUREG-0711 requirements. The work described builds upon the existing literature by adding more detail around key tasks and decisions to make when transitioning from HSI Design into Verification and Validation (V&V). The overall objective of this process is to inform HSI design and elicit specific, measurable, and achievable human factors criteria for new digital technologies. Upon following this process, utilities should have greater confidence with transitioning from HSI design into V&V.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

A Human Factors Engineering Process to Support Human-System Interface Design in Control Room Modernization

The primary objective of the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program is to sustain operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R&D). The Advanced Instrumentation, Information, and Control (II&C) System Technologies pathway conducts targeted R&D to address aging and reliability concerns with legacy instrumentation and control (I&C) and other information systems in existing U.S. NPPs. Control room modernization is an important part following this pathway, and human factors experts at Idaho National Laboratory (INL) have been involved in conducting R&D to support migration of new digital main control room (MCR) technologies from legacy analog and legacy digital I&C. This paper describes a human factors engineering (HFE) process that supports human-system interface (HSI) design in MCR modernization activities, particularly with migration of old digital to new digital I&C. The process described in this work is an expansion from the LWRS Report INL/EXT-16-38576, and is a requirements-driven approach that aligns with NUREG-0711 requirements. The work described builds upon the existing literature by adding more detail around key tasks and decisions to make when transitioning from HSI Design into Verification and Validation (V&V). The overall objective of this process is to inform HSI design and elicit specific, measurable, and achievable human factors criteria for new digital technologies. Upon following this process, utilities should have greater confidence with transitioning from HSI design into V&V.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Chemistry and Reactivity of Oxide Films on Aluminum Clad Test Reactor Fuels - 19294

This paper describes recent work performed to assess the degradation of aluminum spent nuclear fuel (ASNF) as part of an ongoing Department of Energy project. As part of a multi-task effort, this paper specifically describes efforts initiated in 2018 to better understand chemical phenomena and degradation in ASNF). In this paper experimental and modeling activities are described. The modeling effort presents initial results in two areas potentially influencing stability. Using an established electrolyte model based on experimental data, the solubility of aluminum as a function of temperature and pH was examined. This work describes interesting results for the minimum pH providing relevant data that was not available from the literature. It was found that solubility increases with temperature and that the minimum pH shifts to low pH with increase in temperature. The second area involves understanding the metallurgy of aluminum alloys during fuel drying. In this study, the temperature range from 100 deg. C to 300 deg. C was analyzed. Thermodynamic models predict that AA1100 will not experience significant phase changes while the more highly alloyed AA6061 and AA5052 may experience significant phase changes. These thermodynamic measurements establish a potential for phase changes during drying, however they do not predict the kinetics for such processes. Phase 1 of the experimental program has initiated and results are forthcoming. The test program will examine aluminum oxidation under anticipated dry storage conditions (temperature and humidity). Examination of localized corrosion due to tight crevices and galvanic contact (with stainless steel) will be included. Finally, water deliquescence will be assessed using thermogravimetric analysis with dynamic humidity profiles. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Climatology of Aerosol Optical Depth at Mid-Continental US Site: Ground-based Observations

The total aerosol burden in the atmosphere is typically represented by aerosol optical depth (AOD). To capture important and climate-relevant signatures of the aerosol burden, such as year-to-year and seasonal variability, continuous multi-year AOD observations are required. For more than two decades, these observations have been performed at the mid-continental Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (CF) using ground-based passive remote sensing. The partially overlapping and fragmentary AOD records at the ARM SGP CF have been provided by four individual instruments, namely two co-located (C1 and E13) Multifilter Rotating Shadowband Radiometers (MFRSRs), a Normal Incidence Multifilter Radiometer (NIMFR), and a Cimel Sunphotometer (CSPHOT). Since these individual records are sporadic with instrument- and time-dependent data quality, development of a continuous multi-year high-quality AOD dataset is a challenging task. In this work, an initial development of a continuous 20-year (1997-2017) high-quality AOD product is introduced. The development involves (1) incorporation of the available data quality information and delivery of the historical time series of AOD with high quality from four individual instruments, (2) comparison of multiple AOD retrievals to identify potential instrument-related issues and/or retrieval problems, and (3) merging these individual time series, generation of a two-decade continuous climatology of high-quality AOD and reporting of the uncertainty estimations of the merged product.

Ground based remote sensing, Atmospheric Radiation↗

DOE OSTI2017

Python and HPC for High Energy Physics Data Analyses

High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectors and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a “small” neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as “Dark Matter”. Here, using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

An introduction to optimal power flow: Theory, formulation, and examples

The set of optimization problems in electric power systems engineering known collectively as Optimal Power Flow (OPF) is one of the most practically important and well-researched subfields of constrained nonlinear optimization. OPF has enjoyed a rich history of research, innovation, and publication since its debut five decades ago. Nevertheless, entry into OPF research is a daunting task for the uninitiated--both due to the sheer volume of literature and because OPF's ubiquity within the electric power systems community has led authors to assume a great deal of prior knowledge that readers unfamiliar with electric power systems may not possess. This article provides an introduction to OPF from an operations research perspective; it describes a complete and concise basis of knowledge for beginning OPF research. The discussion is tailored for the operations researcher who has experience with nonlinear optimization but little knowledge of electrical engineering. Topics covered include power systems modeling, the power flow equations, typical OPF formulations, and common OPF extensions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Exploring the Psychometrics of Common Post-Scenario Human Factors Questionnaires of Workload, Situation Awareness, and Perceived Difficulty

Abstract: The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the primary mission of sustaining operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R&D). In this effort, the role of human factors engineering (HFE) has been an integral in ensuring new digital upgrades that are part of the overall main control room modernization strategy for various U.S. NPPs optimize human-system performance and do not introduce new human failure modes. In this process, testing and evaluation of concept (i.e., prototype) human machine interfaces (HMIs) are done in operator-in-the-loop studies at various stages of the HFE modification process, such as during design and verification and validation (V&V) [1]. During these studies, licensed operators go through select scenarios using a testbed, such as a full-scale simulator or at a NPP’s training simulator, to identify potential usability issues and provide general design feedback regarding the new digital HMIs. Human-system performance is measured and evaluated through a range of objective and self-report measures that capture aspects of plant performance, task performance, workload, situation awareness, and anthropometric and ergonomic concerns [2]. The use of post-scenario questionnaires is an acceptable method for gathering measures of workload and situation awareness. To this end, the NASA-Task Load Index (TLX) [e.g., 3, 4, & 5] and Situation Awareness Rating Technique (SART) [e.g., 6 & 7] are common standardized questionnaires used for evaluating workload and situation awareness, respectively. When combined, these questionnaires consist of a total of 15 questions, each using a different rating scale. Debriefs are also done through interviews with operators to gather design insight after each scenario. Since operator- and training simulator-time is limited, a concern during this post-scenario period of operator-in-the-loop studies is the limited time available to run scenarios, administer post-scenario questionnaires, and debrief within the allotted simulator time. To address this potential problem, this paper explores the psychometric characteristics of NASA-TLX, SART, and a common one-question post-scenario usability questionnaire (i.e., the single ease question – SEQ [8]) in effort to inform future development of a condensed questionnaire that captures aspects of workload and situation awareness. This exploratory work uses data from four different operator-in-the-loop studies completed under prior LWRS operator-in-the-loop studies. Findings from this paper should support future development of a consolidated questionnaire that may be used in operator-in-the-loop studies, when time is compressed, that conforms to the HFE scientific and regulatory requirements set for NPP control room modernization.

99 GENERAL AND MISCELLANEOUS↗