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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Framework for Neural Network Inference on FPGA-Centric SmartNICs

FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication, thereby mitigating the von Neumann bottleneck. Existing work, however, has been generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs, but most control tasks are left to the CPUs. This leads to frequent waiting, inferior performance, and scaling challenges. In this work, we propose a new distributive data-centric computing framework, named FCsN, for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture, FCsN allows the complete detaching of kernel execution, control logic, system scheduling, and network communication to the SmarNICs. This boosts performance by: (i) avoiding the control dependency with CPUs and (ii) supporting streaming kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network applications including graph neural networks; as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with the support of FCsN can achieve, on average, 144 speedups over the MPI-based standard CPU baselines.

Guo, Anqi↗
DOE OSTI2024

Model Formulations: Market clearing models, market design specifications, and dispatch simulation

This document provides various model formulations that will be implemented for the Energy Storage Participation Algorithm pilot competition (ESPA-Comp). This competition will assess the performance of different storage offer algorithms in terms of their ability to maximize the value of storage resources participating under various market designs that vary in the range of complexity of possible storage offers. The model formulations include a general market clearing optimization model and specification to implement it in various market designs. We provide specifications for three market designs to be tested in ESPA-Comp, which we call the two-settlement, multi-settlement, and rolling horizon forward markets. The market designs include different trading frequencies and offer formats. Storage resources participating in the market are provided with primitive values for the resource’s physical capabilities, and they are provided with a storage offer format that their algorithms will be tasked with populating. A detailed physical model is used to assess each storage resource’s ability to maintain its scheduled dispatch according to its state-of-charge, operating temperature, and power conversion efficiency.

25 ENERGY STORAGE↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Automation and Use of Robotics for Decommissioning Tasks - 18322

Decommissioning across the globe is being executed largely by manual labor. The Organization for Economic Co-operation and Development (OECD) has concluded that the nuclear industry has not fully exploited or implemented current technological capabilities, and relies on sometimes outdated technology to perform decommissioning tasks [1]. To address this issue, EPRI recently published the report Evaluation of System Automation and Robotics for Decommissioning Applications (EPRI 3002010599) [2]. To facilitate wide usage of the guidance by technology developers, the report is publicly available at no cost. The use of more efficient and effective decommissioning technologies and processes could reduce the decommissioning project schedule and therefore reduce the period-dependent costs incurred throughout the decommissioning project. Such systems may also offer a substantial benefit with respect to worker safety and radiation exposure reduction. However, tasks for which use of such systems is beneficial must be carefully considered, being robust and generally trouble free to operate in the demanding environment of commercial power plant decommissioning. A review of the major tasks involved in the decommissioning of a nuclear power plant is presented, with a critical assessment of the benefits for the use of robotics and/or automation. Recommendations for areas of future research and development are identified. The results of this project will be beneficial to global organizations working on development of engineered systems for decommissioning applications by providing a focus on priority tasks. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Potential Research and Development Opportunities for Light Water Reactor Spent Nuclear Fuel at INL

This report documents the commercial spent nuclear fuel currently in storage at Idaho National Laboratory with a specific focus on the potential research and development (R&D) opportunities for this material. Commercial spent nuclear fuel is stored in dry casks on the INL site at the Idaho Nuclear Technology and Engineering Center (INTEC, formerly the Idaho Chemical Processing Plant or ICPP). This spent fuel was part of the DOE-industry cooperative dry cask demonstration project that began in 1983 and from historical fuel testing and treatment activities that had been performed at the Test Area North (TAN)-607 facility. Most of the fuel is “low burnup,” but given that it was removed from the respective reactors between 1970 and 1985, it was representative for its time. The majority of the fuel is of the 15×15 Westinghouse PWR variety, though additional fuel types (e.g., TN-BRP) and configurations are also stored. Potential R&D opportunities identified are (1) the REA-2023 cask, (2) the TN-BRP cask, and (3) the Castor V/21 cask. Fuel from the TN-24P may be an alternative to the intact assemblies in the Castor V/21. Specifically, the REA-2023 cask contains 9 rods that have been exposed to air since 2005. These rods have a detailed pre-loading characterization and thus any changes in corrosion or geometry can most likely be attributed to the storage environment. In addition, similar rods to the nine in REA-2023 reside in the Castor V/21 cask whose environment has been maintained with inert nitrogen. The Castor V/21 cask also contains intact PWR assemblies that could be used to perform limited confirmation of the pyroprocessing approach. An alternative to the use of the Castor assemblies for pyroprocessing is the consolidated fuel that is stored in the TN-24P, which would not require removal from the assembly grid since it has already been packaged in accessible two-piece shell strongback containers. The TN-BRP cask contains a number of Gd-doped, low burnup assemblies that could be of use to validate code packages used to justify burnup-up credit for boiling water reactors (BWRs). The INL dry casks are stored on the CPP-2707 pad at INTEC, adjacent to the CPP-603 Irradiated Fuel Storage Facility, which incorporates a dry hot cell that has the potential to receive a full scale commercial cask of the types identified. Several evaluations have been made regarding this task, including full scale mockup testing of the physical clearances required to remove and handle fuel assemblies and components. A proposed schedule of 14 months and cost of $5.3 million was the outcome of analysis performed by Fluor Idaho and is included as Appendix A & B. In addition to the spent fuel at INL, there is also a full-scale mockup assembly that was fabricated for the Protypical Consolidation Demonstration Project to validate fuel rod consolidation approaches. The assembly was fabricated by Westinghouse as a 15 x 15 PWR, using Zircaloy-4 tubing and steel end fittings in accordance with product specifications. The only departure from specification is the use of copper metal in lieu of uranium oxide pellets to achieve a weight that is within 10% of the standard.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
DOE OSTI2022

Development and Demonstration of a Fuel-Efficient, Class 8 Tractor & Trailer Engine System (SuperTruck II)

Navistar presents the SuperTruck II (ST II) Final Report to the Unites States Department of Energy (US DOE), which covers the five Budget Periods (BPs) from 10-1-2016 through 6-30-2022. For ST II, Navistar built on the achievements of the SuperTruck I (ST I) Program as a catalyst to continue critical research, design and development, testing, and operations to reach the ambitious goals of the ST II project. This approach allowed Navistar to continue contributing to the essential needs of our nation for safe, efficient, and cost-effective delivery of goods and services, as we reduced negative environmental effects and improved operational productivity. This document contains information specified in DOE F 4600.2, Final Scientific/Technical Report DOE F 241.3, B. SCIENTIFIC/TECHNICAL REPORTS, explaining how we met and exceeded program requirements. Throughout this Final Report, Navistar extracted information from documents prepared during the project that represent our management, design and development, building, and testing efforts to meet and exceed SuperTruck II project goals. Navistar followed Plan requirements to achieve / exceed Project Objectives: a) >100% improvement in vehicle freight efficiency (FE) (on ton-MPG basis) relative to 2009 baseline with stretch goal of 140% improvement [actual: 170%); b) >55% engine brake thermal efficiency (BTE) demonstrated in operational engine at a 65-mph cruise point on a dynamometer – ≥31% increase from 2009 baseline [actual: 55.20% of combined BTE) ; and c) development and implementation of commercially cost effective technologies (in terms of a simple payback). Technology selection / development path focused on developing technologies applicable for production within 3-year approach, while ensuring technology readiness and cost of ownership for end users. The Program was organized into five budget periods: Requirements / Technology Assessment and Initial Hardware Testing; Technology Development and Concept Readiness Demonstration; Technology Finalization and Validation Tractor / Trailer Fabrication, Integration and Commissioning Demonstration; and Fuel Economy (FE) and Brake Thermal Efficiency (BTE) and Program Completion. Leadership was provided by DOE, with tasks performed by laboratories (Argonne National Laboratory, Lawrence Livermore National Laboratory); partners at Bosch, TPI, Dana, and J.B. Hunt; , and support from University of Michigan and Clemson University. Navistar lead this team with Principal Investigator / Contracting Officer; Project Manager (PM); Vehicle, Engine, and Aftertreatment Engineers; Finance Manager, Technical Program Leads, and Legal/IP; and other key personnel. Work also included personnel in risk management; funding / budget / finance. Work involved analysis, development, testing, and down selection of individual/system engine, aftertreatment, and vehicle technologies, with integration of selected technologies into a prototype vehicle for demonstration of fuel-efficiency gain. Work also included component/integrated system level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. As ST II progressed, Navistar performed computer-based modeling / simulations of technologies focused on the primary operational areas: Engine, Aftertreatment, and Vehicle. During the ST II Program, the COVID Virus outbreak unexpectedly challenged by the effects of, which affected staffing, scheduling, design, supplies, availability of materials, production procedures, and testing. The DOE responded by extending the program by three quarters to ensure that project tasks were completed for this vital project. Focus continued on analyzing, developing, testing, and down selecting individual-/system-level engine and vehicle technologies for integration of the final selected technologies into a prototype vehicle that would demonstrate fuel-efficiency gains made possible through these technologies. This included component/integrated system-level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, solar power, distributed and intelligent vehicle power, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. Throughout the program, function, reliability, and performance at all levels were ensured through testing. Proof of this approach was demonstrated in multiple, on-road demonstrations: Scenario A (Flatland) Fuel Economy, Scenario B (Hilly) Fuel Economy, and City Cycle Tests. Other benefits derived from ST II included new/improved products, publications, patents, and next-step capabilities related to electric/hydrogen vehicles and autonomous driving.

Zukouski, Russ↗
DOE OSTI2020

Analysis of Respirator Cartridge Performance Testing on Hanford Tank AX-101

Washington River Protection Solutions (WRPS) conducted tests using two types of chemical cartridges for use in air purifying respirators to determine the period of time that the cartridges would provide adequate protection to worker when exposed to a mixture of Chemicals of Potential Concern (COPCs) from vapors emanating from the headspace of tank AX-101 on the Hanford Site. Pacific Northwest National Laboratory (PNNL) was tasked with conducting an independent analysis of the analytical results and making recommendations based on the results for respiratory cartridge performance and change schedules.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Analysis of Respirator Cartridge Performance Testing on Hanford Tank BY-108 (Rev.1)

Washington River Protection Solutions (WRPS) conducted tests using two types of chemical cartridges for use in air purifying respirators to determine the period of time that the cartridges would provide adequate protection to worker when exposed to a mixture of Chemicals of Potential Concern (COPCs) from vapors emanating from the headspace of tank BY-108 on the Hanford Site. Pacific Northwest National Laboratory (PNNL) was tasked with conducting an independent analysis of the analytical results and making recommendations based on the results for respiratory cartridge performance and change schedules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Summary of US DOE R&D Activities on Graphite Oxidation (2006–2021)

The objective of the international collaboration between United States Department of Energy (U.S.-DOE) and Generation IV International Forum (GIF) is the development of the next generation of nuclear energy systems. The current GIF Project Arrangement (PA) on Materials (2018-2022) was revised in 2019 and extended for another 10 years (2020-2030). The Work Package 1 (“Graphite”) of the extended Project Plan (PP) on Materials specifies technical tasks and High Level Deliverables for research and development (R&D) activities related to using graphite in fuel elements, reflectors, and support structures of Very High Temperature Reactors (VHTR). The graphite tasks include specification and acquisition, qualification and development of new grades, characterization of properties, and development of behavior models. Specifically, Task 1.4 (“Graphite Oxidation Behavior”) outlines planned activities related to acute oxidation by air and chronic oxidation by impurities in the helium coolant. A final report on experimental data regarding graphite oxidation behavior is scheduled for 2022 (deliverable 3.1.1.4.a). In preparation of this deliverable, this document summarizes the R&D activities funded by U.S.-DOE from 2006 (the inception of the VHTR system arrangement) through present (2021). This report is being submitted to the GIF Graphite Working Group (GWG) to serve as input for the GWG high-level deliverable to the Project Management Board (PMB) of PA on Materials. Besides U.S.-DOE, other organizations participating to Task 1.4 of the current PA on Materials are: European Commission’s Joint Research Center (JRC), Korea Atomic Energy Research Institute (KAERI), and Japan Atomic Energy Agency (JAEA). U.S.-DOE is the main contributor on graphite oxidation R&D, with 85 % commitment of total funding during 2018-2022.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Status Report on ASME Code Development for Nonmetallic Core Components in 2020

The purpose of this report is to provide a status update on the progress and ongoing activities of the current ASME Boiler Pressure Vessel (BPV) Code Section III, Division 5, on nonmetallic core components and assemblies which includes the design rules for graphite and ceramic composite materials. Section III is concerned with the design and construction of nuclear reactor components and Division 5 focuses on high temperature reactors (HTR). The ASME Section III Committee’s function for nuclear items other than pressure-retaining components, is to establish rules of safety related to structural integrity. The term “construction” refers to the all-inclusive effort comprising of materials, design, fabrication, examination, inspection, testing, certification, and pressure relief. The Department of Energy (DOE) supports industry codes and standards development through focused research providing the technical basis for new or modified codes and standards and the participation of subject matter experts in codes and standards committees. The activities for the nonmetallic core components and assemblies committees are the pursuit of two working groups that meet quarterly during ASME BPV Code Week meetings. The two working groups are General Requirements for Graphite and Ceramic Composite Core Components and Assemblies (GR GCCCCA) and Nonmetallic Design and Materials (NDM). The rules on general requirements for nonmetallic core components are discussed in the articles of Subsection HA subpart B. Ceramic composites were recently incorporated within the rules, and the GR GCCCCA working group is now in the process of aligning the requirements with the subsections and articles of ASME BPV Code Section III NCA, the general requirements for Division 1 and 2 of the code. The NDM working group is concerned with the design rules for graphite and composite materials for core components and assemblies discussed in Subsection HH (Class SN Nonmetallic Core Components) subpart A and subpart B which reference graphite and composite materials respectively. The bulk of the progress and work is done within this working group. A significant undertaking, with discussion that started in 2015, is to gain endorsement of ASME Section III, Division 5, by the U.S. Nuclear Regulatory Commission (NRC). In June 2018, ASME formally recommended endorsement to the NRC. The NRC has since initiated the endorsement review process, which is to be concluded early in 2021. ASME task groups on metallic structures and components, as well as non-metallic core support structures, have been formed to define potential pathways and schedules for NRC endorsement of Division 5. NRC determined to perform a review on the 2017 edition of Section III Division 5 as a baseline. As a result, the use of graphite as nonmetallic core components formed part of the review, but it excluded composite materials (which were first introduced in the 2019 edition). This report documents progress in FY 2020 directed toward ASME’s efforts to develop design codes for graphite and composites. Progress includes a major code review by NRC, the first code enquiry, and its solution, an overhaul of the treatment of Weibull Statistics, and the addition of Nonmandatory Appendices to the composites code relating to carbon-carbon composite materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

On the adequacy of lightweight thread approaches for high-level parallel programming models

High-level parallel programming models (PMs) are becoming crucial in order to extract the computational power of current on-node multi-threaded parallelism. The most popular PMs, such as OpenMP or OmpSs, are directive-based: the complexity of the hardware is hidden by the underlying runtime system, improving coding productivity. The implementations of OpenMP usually rely on POSIX threads (pthreads), offering excellent performance for coarse-grained parallelism and a perfect match with the current hardware. OmpSs is a task oriented PM based on an ad hoc runtime solution called Nanos++; it is the precursor of the tasking parallelism in the OpenMP tasking specification. A recent trend in runtimes and applications points to leveraging massive on-node parallelism in conjunction with fine-grained and dynamic scheduling paradigms. In this paper we analyze the behavior of the OpenMP and OmpSs PMs on top of the recently emerged Generic Lightweight Threads (GLT) API. GLT exposses a common API for lightweight thread (LWT) libraries that offers the possibility of running the same application over different native LWT solutions. We describe the design details of those high-level PMs implemented on top of GLT and analyze different scenarios in order to assess where the use of LWTs may benefit application performance. Our work reveals those scenarios where LWTs overperform pthread-based solutions and compares the performance between an ad hoc solution and a generic implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Future of Work with Robotics - 18599

The DOE Environmental Management mission continues with remaining tasks that are high-hazards to human workers and high-consequences to the nation, resulting in potentially high risks to project costs and schedules. This is the mission pull. At the same time, robotic technologies are hitting a turning point with increased productivity and reliability within society, supporting a technology push. Enabling this push are better computer processing following Moore's Law, breakthroughs in biometric and learning algorithms,and riding a wave of venture funding for intended commercialization of mechatronic and Internet of Thing (IOT) projects. There has been overall improvements in material technologies, algorithms, and software standardization with tools/libraries such as the Robot Operating System (ROS). The internet has made knowledge ubiquitous and the cell phone sector has pushed the boundaries of systems-on-chip technology. This current growth is a race between large global technology companies, and between countries that understand that there is a prize with increased commerce based on being a leader in the field of robotics and automation. This technological advances have placed this technology solidly within the next 4. Industrial Revolution being tracked by the World Economic Council. Policies and law can slow this progression down, but technological changes in adjacent fields will put additional pressure on human workers by being more economical and in saving lives. A fundamental issue is will this change be gradual or disruptive such as with the forecasted singularity. There is also a realization with automation that the transition of human workers to other fields may be temporary, but in the long run, workers may run out of viable work and long-term occupations. The obvious future of human jobs are most assured by those who can embrace the change through the design, development, installment, and maintenance of the technology. Younger workers can be re-educated while older humans can be re-trained. The goal of this paper is to explore all the forces that are enabling this revolution, investigating its many impacts, and speculating on the future of human jobs during nuclear remediation. Automation has made an impact to the manufacturing sector, and has since expanded to the retail and services sectors. The question is will this same automation transition, including robotics and artificial intelligence, occur to the environmental management and cleanup sector, and how should we prepare ourselves for this change. (author)

42 ENGINEERING↗
DOE OSTI2024

Efficient Parallelization of Irregular Applications on GPU Architectures

With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with the computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to coarse-grained kernel and inefficient parallelism; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. Targeting these challenges, this dissertation proposes multiple optimizations to accelerate two real-world applications: many-body correlation functions to simulate nuclear physics in a large-scale scientific system; the other is the eALS-based matrix factorization recommendation system. To accelerate the calculations of many-body correlation functions, this dissertation presents three frameworks in GPU memory management and multi-GPU scheduling. Firstly, an optimized systematic GPU memory management framework, MemHC, utilizes a series of new memory reduction designs in GPU memory allocation, CPU/GPU communications, and GPU memory oversubscription. Secondly, an enhanced multi-GPU scheduling framework, MICCO, particularly by taking both data dimension (e.g., data reuse and data eviction) and computation dimension into account. MICCO designs a heuristic scheduling algorithm and a machine learning-based regression model to generate the optimal settings of a proposed new concept to manage the trade-off. Thirdly, a locality-aware multi-GPU scheduling framework. This scheduler leverages pipeline batch generation with a looking-ahead strategy by building local dependency graphs for memory transfer reduction and better data reuse, achieving up to 79.92% memory cost reduction and 1.67x speedup. To parallelize the eALS-based recommendation system, this dissertation proposes an efficient CPU/GPU heterogeneous recommendation system, HEALS. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. To mitigate the data dependence, HEALS presents a CPU/GPU collaboration model for both task parallelism and data parallelism with multiple kernel computation optimizations. In summary, this dissertation efficiently accelerates two typical irregular applications on GPUs by building four frameworks, including CPU/GPU collaboration, GPU memory management, and multi-GPU scheduling.

Wang, Qihan↗
DOE OSTI2020

Mixed-Material Scintillators (MMSS Quarterly Report FY20Q2)

This project aims to invent, model, and prototype mixed-material scintillator systems (MMSSs), a new class of radiation detectors that use heterogenous internal structures of different scintillating materials to detect additional properties of radiation. These internal structures can be produced using additive manufacture (3D printing) of scintillator, a currently emerging application of additive manufacture technology. MMSSs combine the low cost and complexity of conventional scintillation detectors with capabilities currently only available in more expensive and complex detectors. By identifying promising MMSS designs, this project will enable a new class of detectors to meet DNN’s mission needs for SNM detection. This quarter, we delivered one the MMSS Particle-ID Modeling report, a major deliverable of this project. This report describes our results on simulations of the particle-ID (PID) class of detectors. We found that these detectors outperform competing approaches for neutron-gamma discrimination, neutron source pointing, and neutron spectroscopy. Further highlights of the report are described below. During this quarter, We alerted HQ to a change in schedule for the remaining simulations planned for this project: we plan to put off further simulation and reporting of the position-resolving (PR) detector class until July. This change in schedule has allowed us to go into additional detail in the PID work that is both very promising and motivates our upcoming proposal. The other active task in this project is prototyping 3D-printed scintillators with the aim of realizing the designs we’ve simulated. On that front, we had three focuses: measuring the light output, increasing the size of printed parts, and combining blue and green scintillators. We succeeded in measuring the light output, showing an output of 30% of commercial standard. This result leaves room for improvement but is within striking distance of where we need to be. We were on track to demonstrate a key requirement for increasing the size of parts and also to show combined green/blue prints before the laboratory moved to a Minimum Safe (MinSafe) Operations posture as a result of local Shelter In Place orders due to Covid-19.

42 ENGINEERING↗
DOE OSTI2023

Epithermal and Fast Neutron Radiography Facility HFIR Futures – Enhanced Capabilities Series (Vol. 8)

The Sustaining and Enhancing Nuclear Science Initiative at Oak Ridge National Laboratory (ORNL) was created to explore potential enhancements to scientific capabilities in the High Flux Isotope Reactor as part of a reactor pressure vessel replacement project. One proposed scientific enhancement included creation of an epithermal and fast neutron radiography station on the HB-3 beam tube with the capability to image highly radioactive specimens such as irradiated nuclear fuel rods, isotope production targets, or spallation neutron target materials. This document summarizes findings and recommendations from a working group of ORNL staff tasked with conceptualizing such a facility and includes a background of similar instruments at other research facilities, technical specifications, and an estimate of procurement cost and schedule.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-task and Inductive Transfer Learning

Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Status of the Full Circumference Examination of the Inner Container Closure Weld Region for Selected 3013 DE Container

One of the main focus areas of the 3013 Surveillance Program is a thorough evaluation of the inner container closure weld region (ICCWR) opened for destructive examination (DE). As part of the protocol to investigate the corrosion in the ICCWR a laser confocal microscope (LCM) is used to perform close visual examination of the surface and measurements of corrosion features on the surface. In FY17, DE containers from FY13 through FY16 were evaluated to select candidates for a full circumference analysis of the ICCWR. In FY18, the selected DE containers for full circumference evaluation (FCE) were processed according to the ICCWR protocol. However, due to the time intensive task for collecting high magnification images for a full circumference, only FY15 DE07 was selected for completion of LCM data collection in FY18 with the remaining DE containers scheduled to be completed in FY19.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗