Search NASA⌕ Search

SEARCH · Search NASA

Results for “task programming”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40

Modeling and evaluating 239 Pu and 235 U PFNS and average prompt-neutron multiplicity [Slides]

The following are currently in VIII.1 LANL and IAEA test files: 239 Pu nu-bar including CGMF modeling and CEA data, 239 Pu PFNS including Chi-Nu & CEA data, 239 Pu(n,f) cross section including fission TPC data. The following are currently being tested: 235 U nu-bar including CGMF modeling and 235 U PFNS including Chi-Nu data. Upcoming tasks include the correction of 235 U Chi-Nu PFNS at higher E inc , benchmarking 235 U PFNS and nu-bar evaluations, and getting 238 U nu-bar.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. A-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. In conclusion, we find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Antarctica Radiological Source Removals (Final Report)

On 18 May 2015, the research vessel Akademik Fedorov of the Russian Antarctic Expedition (RAE) delivered four radioisotope thermoelectric generators (RTGs) from Antarctica to the port of St. Petersburg, Russia. The RTGs had been previously used in the work program of the former Soviet Antarctic Expedition (SAE). From the mid-1970s, RTGs have been used to provide autonomous power at geophysical and meteorological stations located in remote regions of Antarctica. The research was completed in the early 1990s, but not all the generators had been removed when the stations were decommissioned. The remaining four unsecured RTGs, with their strontium-90 ( 90 Sr) radiological material and a combined total activity of 80,000 curies (Ci), still posed a potential risk as they could be used for nefarious purposes. Therefore, a plan of action for the removal of the RTGs from Antarctica was developed. This was done within the framework of the international program for the disposal of Russian RTGs. Financial support and technical oversight was provided by the U.S. Department of Energy (DOE), while logistical support was provided by the Russian Antarctic Expedition (RAE) of the Arctic and Antarctic Research Institute (AARI) and by subsidiaries of the Russian Federation’s Rosatom State Corporation. Recovering and repatriating the RTGs from the continent of Antarctica to the Russian Federation proved to be a very difficult task, including satisfying all the legal aspects within a very tight timeframe. This involved indemnification issues, adhering to new laws enacted after the Soviet Union era, and obtaining all the necessary permits and certifications for transport of the RTGs by air, sea, and land. It was an international, collaborative effort involving the cooperation of multiple organizations to complete the legal work and preparation of necessary documents. In addition, AARI needed to create a separate structure within its organization to manage the process, including the provision of special medical screenings, training, and certifications of its institute personnel. This was done so AARI could fulfill its obligation for the safe handling of radioactive materials while in the field. Recovery of the RTGs required overcoming several logistical hurdles. The Akademik Fedorov, which transported the RTGs back to the Russian Federation, required retrofits to accommodate the RTGs for safe and secure transport of radioactive material. The United States Project Team (USPT) inspected the vessel while it was in dry dock in Turku, Finland, being retrofitted with their design. Special sleds had to be designed to transport RTGs, as well as equipment for identifying crevasses in the glaciers, over the Antarctic packed snow and ice. Provision of adequate fuel supplies for the long trek to the interior of Antarctica to recover one of the RTGs also had to be addressed. With the successful recovery, repatriation, and disassembly of the RTGs, the Radioactive Heat Sources (RHSs) were extracted and dispositioned, thus eliminating any risk of these radioactive sources being used for terrorist purposes. Successful completion of the project also fulfilled the requirements of the Protocol on Environmental Protection to the Antarctic Treaty concerning the need to remove unused radioactive materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Transport Modeling of As-Run ATR Cycles to Support U-10Mo Research Reactor Fuel Qualification Experiment

The Department of Energy’s (DOE) Office of Materials Management and Minimization has been tasked with converting the five remaining United States High Performance Research Reactors (U.S. HPRR) from highly enriched uranium to low-enriched uranium. The Nuclear Regulatory Commission (NRC) regulates Massachusetts Institute of Technology Reactor (MITR), Missouri University Research Reactor (MURR), and National Bureau of Standards Reactor (NBSR); and DOE regulates the High Flux Isotope Reactor and Advanced Test Reactor (ATR). To meet the high demands of these reactors, the U.S. HPRR program has chosen to use 90% uranium - 10% molybdenum (U-10Mo) monolithic fuel. This plate-type fuel will undergo multiple irradiation experiment campaigns in ATR, from mini-plates to full element tests, over a large range of operating conditions. This will provide data in support of the fuel qualification of each reactor. This summary focuses on the as-run neutronic analysis of the first series of mini-plate (MP-1) experiments, which have been irradiated in the ATR. MP-1 experiment’s main goal is to demonstrate the fabrication process and meet the fuel irradiation performance requirements with primary focus on the fuel plates associated with the three NRC reactors, with a small focus on the low power requirements of the ATR fuel. The MP-1 experiments are planned to be the basis of the monolithic U-10Mo fuel for qualification through the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Transport Modeling of As-Run ATR Cycles for U-10Mo Fuel Qualification Experiment

The Department of Energy’s (DOE) Office of Materials Management and Minimization has been tasked with converting the five remaining United States High Performance Research Reactors (U.S. HPRR) from highly enriched uranium to low-enriched uranium. The Nuclear Regulatory Commission (NRC) regulates Massachusetts Institute of Technology Reactor (MITR), Missouri University Research Reactor (MURR), and National Bureau of Standards Reactor (NBSR); and DOE regulates the High Flux Isotope Reactor and Advanced Test Reactor (ATR). To meet the high demands of these reactors, the U.S. HPRR program has chosen to use 90% uranium - 10% molybdenum (U-10Mo) monolithic fuel. This plate-type fuel will undergo multiple irradiation experiment campaigns in ATR, from mini-plates to full element tests, over a large range of operating conditions. This will provide data in support of the fuel qualification of each reactor. This summary focuses on the as-run neutronic analysis of the first series of mini-plate (MP-1) experiments, which have been irradiated in the ATR. MP-1 experiment’s main goal is to demonstrate the fabrication process and meet the fuel irradiation performance requirements with primary focus on the fuel plates associated with the three NRC reactors, with a small focus on the low power requirements of the ATR fuel. The MP-1 experiments are planned to be the basis of the monolithic U-10Mo fuel for qualification through the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

California CO 2 Storage Assurance Facility Enterprise (C2SAFE): Final Technical Report

The California CO 2 Storage Assurance Facility Enterprise (C2SAFE) project conducted a preliminary study into the feasibility of a commercial-scale carbon dioxide (CO 2 ) storage complex located in California’s Southern San Joaquin Valley (SSJV). The work represents the first phase of a multi-phased development approach outlined by the United States Department of Energy’s (DOE’s) Carbon Storage Assurance and Facility Enterprise (CarbonSAFE) program meant to develop carbon storage sites capable of permanently storing 50 million metric tonnes of CO 2 , or greater, injected over a typical facility life of 20 to 30 years. This report represents the culmination of work performed under C2SAFE and outlines a phased implementation plan for large-scale, industrial carbon capture and permanent storage in California’s SSJV. The report is meant to be a standalone document and as such provides high-level summaries of all tasks of the project, but is also meant to complement the other three topical reports issued by the project – reports referenced herein. The study considered multiple scenarios including an east-side storage facility, a west-side storage facility, and potential multi-site storage facilities. The east-side storage facility is to be located at Clean Energy Systems, Inc.’s Kimberlina Power Plant, or nearby, and the west-side facility will be located at the California Resources Corporation’s (CRC’s) Elk Hills oil field. The majority of carbon emissions are planned to be captured from existing sources and transported to the selected and verified storage site(s). Capture from the single closest source to the eastern storage facility and the sources within the Elk Hills field would meet the CarbonSAFE storage goal of 50 million tonnes. The study has shown there is sufficient storage capacity to meet this requirement and more; thus, capture from any of the numerous additional large nearby sources would substantially leverage the CarbonSAFE investment. A comparison of preliminary CO 2 capture, transportation, and storage costs to economic value and incentives in the state shows current market conditions make it economically feasible to develop large-scale industrial storage sites in California’s SSJV. The business case for Carbon Capture and Storage (CCS) in California’s SSJV appears economically viable today for large CO 2 point sources involved in the production of transportation fuels, such as oilfield cogeneration units and steam boilers. The overall levelized CCS cost is estimated at roughly 125 per tonne of CO 2 , which includes CO 2 amine-solvent post-combustion capture, transportation, and storage. The levelized CO 2 capture cost is based on today’s technology, with no future cost reduction factored in for technology improvement. The sum of the value of incentives and revenue from California Low Carbon Fuel Standard (LCFS) credits (discounted roughly 20% from their current transaction value), sale of California cap-and-trade emission allowances, and Section 45Q tax credits—either for saline storage or the Enhanced Oil Recovery (EOR) credit plus CO 2 sales revenue—is about 165/tonne-CO 2 . The economic viability of CCS for Natural Gas Combined Cycle (NGCC) power plants in the SSJV is more site-specific. A plant providing power solely to the electric grid, if it added CCS, should be eligible for Section 45Q tax credits and could sell its excess cap-and-trade emission allowances, but it would not generate LCFS credits. Using the cost and incentive values detailed in this report, it would not be economical for such a plant to add CCS today, barring other considerations. An NGCC plant within an oilfield (e.g., CRC Elk Hills and NRG Sunrise) that provides power to the oilfield as well as to the grid should receive “partial credit” in terms of generating LCFS credits if it added CCS (pro-rated for the fraction of power going to the oilfield). Such a plant should also be eligible for Section 45Q tax credits and garner revenue from selling its excess cap-and-trade emission allowances. Using the cost and incentive values described in this report, the economic viability of adding CCS today would depend on the relative fractions of electricity supplied to the oilfield and the grid. As the value of cap-and-trade emission allowances grows over time (either by market forces or the escalating floor price), the relative fraction of power needing to remain in the oilfield to achieve “breakeven” would diminish. At some point, even plants providing all their power to the grid would be candidates for economical CCS application. The C2SAFE team has proposed to expand upon the knowledge gained during this study by conducting a detailed feasibility assessment under a follow-on phase of the program, Phase II – Feasibility. This would include detailed data collection at the two sites, analysis, reporting, stakeholder outreach, and risk mitigation activities to support the development of a commercial carbon storage complex in the SSJV.

58 GEOSCIENCES↗

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-On Criticality Safety Training [Abstract]

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP) has conducted two-week Nuclear Criticality Safety (NCS) Practitioner courses since 2011 to support the training and qualification of new NCS staff. The course was developed in accordance with the American National Standard Institute/American Nuclear Society (ANSI/ANS) standard for NCS training and qualifications (ANSI/ANS-8.26-2007). In 2013, an NCS Manager’s course was developed for process supervisors, managers, regulators, and other professionals with NCS-related responsibilities. This course was revised in 2019 for Criticality Safety Officers (CSOs) based on an NCSP Criticality Safety Support Group tasking (2018-01). This course was piloted at the Nevada Field Office and the National Criticality Experiments Research Center (NCERC) in June 2021. These courses consist of the following training components: classroom education, facility training, and hands-on subcritical and critical experiments training. The two-week Practitioner course offers a week of classroom training, with practical workshops and exercises focused on teaching students how to perform an NCS evaluation. The second week of training involves hands-on critical and subcritical experiments and measurements. The first week is offered in Las Vegas, Nevada, at the DOE Nevada Field Office or the National Atomic Testing Museum. Depending on the student’s clearance level, the second week is offered at Sandia National Laboratory (SNL) (uncleared and L-cleared students) or at the National Criticality Experiments Research Center (Q-cleared students). The one-week Manager’s course is offered at SNL or NCERC, depending on clearance or interest, and includes classroom and hands-on critical and subcritical experiments and measurements. This paper provides an overview and status report for the DOE/NNSA NCSP training courses in NCS and to provide information about future course offerings. This paper will also discuss the challenges associated with executing the training courses during the COVID-19 pandemic. The 2-week Practitioner and 1-week manager courses are currently offered twice per year and adjustments are made based upon demand.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-On Criticality Safety Training

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP) has conducted two-week Nuclear Criticality Safety (NCS) Practitioner courses since 2011 to support the training and qualification of new NCS staff. The course was developed in accordance with the American National Standard Institute/American Nuclear Society (ANSI/ANS) standard for NCS training and qualifications (ANSI/ANS-8.26-2007). In 2013, an NCS Manager’s course was developed for process supervisors, managers, regulators, and other professionals with NCS-related responsibilities. This course was revised in 2019 for Criticality Safety Officers (CSOs) based on an NCSP Criticality Safety Support Group tasking (2018-01). This course was piloted at the Nevada Field Office and the National Criticality Experiments Research Center (NCERC) in June 2021. These courses consist of the following training components: classroom education, facility training, and hands-on subcritical and critical experiments training. The two-week Practitioner course offers a week of classroom training, with practical workshops and exercises focused on teaching students how to perform an NCS evaluation. The second week of training involves hands-on critical and subcritical experiments and measurements. The first week is offered in Las Vegas, Nevada, at the DOE Nevada Field Office or the National Atomic Testing Museum. Depending on the student’s clearance level, the second week is offered at Sandia National Laboratory (SNL) (uncleared and L-cleared students) or at the National Criticality Experiments Research Center (Q-cleared students). The one-week Manager’s course is offered at SNL or NCERC, depending on clearance or interest, and includes classroom and hands- on critical and subcritical experiments and measurements. This paper provides an overview and status report for the DOE/NNSA NCSP training courses in NCS and to provide information about future course offerings. This paper discusses the challenges associated with executing the training courses during the COVID-19 pandemic. The 2-week Practitioner and 1-week manager courses are currently offered twice per year and adjustments are made based upon demand.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Southeast Regional Carbon Sequestration Partnership Phase III Overview of Accomplishments (SECARB Phase III Final Report)

The Southeast Regional Carbon Sequestration Program (SECARB) is a carbon management program established in 2003 and managed by the Southern States Energy Board (SSEB). SECARB is one of seven regional carbon sequestration partnerships formed by the Department of Energy (DOE). The seven partnerships form a national network of more than 400 organizations covering 43 states and four Canadian provinces with expertise in the areas of carbon capture, transportation, and storage. The SECARB program was funded by DOE and cost-sharing partners. The primary goal of SECARB was to identify major sources of carbon emissions, characterize the geology of a 13-state region, determine the most promising options for commercial deployment of carbon dioxide (CO 2 ) sequestration technologies in the South, and validate the technology options through carefully executed field testing through 2017. This report summarizes significant Phase III accomplishments and is organized by task as established within the SECARB Phase III Statement of Project Objectives (SOPO). This document does not provide an exhaustive overview of individual tasks. Rather, this report provides an overview of programmatic accomplishments and a timeline of events. Where applicable, hyperlinks to specific project deliverables are provided.

54 ENVIRONMENTAL SCIENCES↗

Understanding How Organizations Handle Cybersecurity

If there is anything we can learn from the media, it is the frequency and severity of cyber-attacks is increasing and there are not enough qualified people to combat the risk organizations are facing. Current estimates say there are 3.5 million available cybersecurity related jobs globally and there has been a 350% growth in cybersecurity jobs since 2013 (Group, 2020). The Idaho Cyber Research Project (ICRP) is focused on finding an implementing solution to the problems in the workforce development pipeline. Our team consists of Cohort 2 of the ICRP, we are tasked with solving issues faced by organizations hiring new cyber personnel. To provide solutions to these issues we focused our research on four components of workforce availability and competency: resume and transcript analysis, apprenticeships, cyber incident response plan development, and adversarial mindset training. From this research we have produced the following focus areas and subsequent steps for each component of workforce capability: transcript and knowledge skills abilities (KSA) focused analysis, cybersecurity apprenticeships programs, the value of an adversarial mindset, and a guide to setting up cyber incident response plans for underprepared organizations. These solutions can be further developed and implemented to reduce the gap in workforce demand and talent.

97 MATHEMATICS AND COMPUTING↗

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Intermediate-Term Thermal Aging Effect Evaluation for Grade 92 and 316L at The LWR Relevant Temperature

Life extension of the existing nuclear reactors imposes accumulated damages, such as higher fluences and longer period of corrosion, to structural materials, which would result in significant challenges to the traditional reactor materials such as type 304 and 316 stainless steels. Advanced alloys with superior radiation resistance will increase safety margins, design flexibility, and economics for not only the life extension of the existing fleet but also new builds with advanced reactor designs. The Electric Power Research Institute (EPRI) teamed up with Department of Energy (DOE) on the Advanced Radiation Resistant Materials (ARRM) program, aiming to develop and test degradation resistant alloys from current commercial alloy specifications by 2021 to a new advanced alloy with superior degradation resistance in light water reactor (LWR)-relevant environments by 2024. Based on a comprehensive microstructure and property screening performed in Phase-1 of the ARRM program, a total of five alloys, together with 316L and X-750 as references, were down-selected for Phase-2 neutron irradiation studies. Because thermal aging could exert a synergistic effect on neutron irradiation due to the low neutron damage rate on the order of 10 –7 displacements per atom per second (dpa/s), Grade 92 (one of the five down-selected alloys) and two heats of 316L were selected in this task to study the effect of aging at 350°C for 12.5–12.7 kh on microstructure and mechanical properties. The aging time is approximately corresponding to 5 dpa neutron irradiation, which can be served as a reference for the 5-dpa-irradiated samples of the alloys to help understand the independent neutron damage influence on microstructure and mechanical properties. Optical microscopy and scanning electron microscopy were used for microstructural characterization. Hardness, tensile, Charpy impact toughness, and fracture toughness in the ductile regime of the aged samples were examined. The aging of Grade 92 led to the formation of many Laves phase in sizes of ~100–200 nm and resulted in some reduction in hardness and yield/ultimate tensile strength with some increases in uniform and total plastic elongations, which may have helped the enhancement of Charpy impact toughness, e.g., ~4 J increase in upper-shelf energy and 20.5°C reduction in ductile-brittle transition temperature compared with the unaged condition. The fracture toughness of the aged Grade 92 showed decent toughness of ~303 MPa√m (K Jq ) with ~87 tearing modulus at 22°C, which reduced to ~241 MPa√m (~20% reduction) with ~75 tearing modulus at 300°C. The two heats of 316L are differentiated by their amounts of δ-ferrite, i.e., ~1 vol% in heat T1103 and ~4 vol% in heat N5B8. The two heats of 316L were subjected to 15% cold work (CW) as this condition is often used in nuclear reactors to help trapping radiation-induced defects and thus postpone the steady state swelling stage. The aging did not result in any noticeable microstructure changes, except for possible segregations at grain boundaries that need to be further investigated. The aging resulted in slight reduction in hardness of 316L-T1103 and slight increase in hardness of 316L-N5B8, which is consistent with their tensile testing results. The 15%CW might have introduced some inhomogeneity, resulting in large standard deviations in hardness, which were reduced after the aging. The aged 316L followed the same trend as the unaged condition for the strength and elongation results from the tensile testing. Minimum elongations appeared within ~300–600°C, above which the elongations seem to increase. Unlike the negligible or minor changes in microstructure, hardness and tensile results of the two heats of 316L, their fracture toughness showed noticeable difference. The aged 316L-T1103 showed good toughness of ~350 MPa√m with ~80 tearing modulus at 22°C, which reduced to ~271 MPa√m (~23% reduction) with ~83 tearing modulus at 300°C. In contrast, the aged 316L-N5B8 showed lower toughness of ~244 MPa√m with ~53 tearing modulus at 22°C, which reduced to ~180 MPa√m (~26% reduction) with ~31 tearing modulus at 300°C. The preliminary results indicate that the presence of high volume of δ-ferrite would noticeable impair fracture toughness although it may not influence hardness and tensile properties.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Gains in operational flexibility, safety margins, and cost efficiencies via integrated Plant Reload Optimization platform

The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Northwest Combined Heat and Power Technical Assistance Partnership: Final Scientific/Technical Report

During the years of 2018 through 2023, The Washington State University Energy Program (WSU EP) operated the Northwest Combined Heat and Power Technical Assistance Partnership under contract to the US Department of Energy’s Advanced Manufacturing Office (AMO), a part of DOE’s larger Office of Energy Efficiency and Renewable Energy. This contract provided direction and funding for providing technical outreach, information and technical/economic analysis services in support of expanding development of Combined Heat and Power (CHP) over a four state region in the Pacific Northwest. The states served included Alaska, Idaho, Oregon and Washington. During the duration of this contract, the WSU EP delivered performed specific assigned tasks and provided services of various generally prescribed types, with the overall goal of increasing adoption of CHP as an efficient heat and power supply in the region. Indications – though not formally evaluated to our knowledge – are that this goal was achieved. Over many years, the WSU team has provided support for CHP developments in these states under similar previous Department of Energy contracts, and many such projects have been built. While the early-phase outreach, education and techno-economic assessments that the NW CHP TAP provides rarely immediately result in project construction, there is no doubt that the WSU EP team has had substantial influence in the construction of a number of important CHP projects in the region. Examples of projects implemented directly under the current team’s guidance and services during this contract cycle include an 875 kilowatt biomass fueled CHP system installed at the University of Idaho, and 5 megawatt natural gas fueled CHP system at the University of Montana ( UM Breaks Ground on New Power Facility, Slashes Emissions 30% (umt.edu) ). Many other CHP systems recently installed are also either directly or at least partially the result of information and technical services provided by the team. Finally, though much more difficult to quantify and less glamorous, the team has helped countless organizations to understand CHP and determine whether and how CHP may be a fit for them, before they consider paying for engineering services. Whether the ultimate outcome has been to include CHP or not, the expert skills provided by the NW CHP TAP team have provided real value to these organizations considering this relatively complex technology. Over the duration of the contract our team provided 71 Initial Technical Assessments and 17 Advanced Technical Assessments; 53 End-User Engagements and 33 Stakeholder Engagements of various types; 42 Technical Profiles (Project Profiles, Program Profiles and Policy Profiles), and produced 14 technical articles for the Department of Energy. Unfortunately, these quantities provide no measure of the value that the team has provided. Only our clients can judge that, and we are confident that they are pleased with the benefits we have offered them.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

134 billion intersecting brane models

The landscape of string vacua is very large, but generally expected to be finite in size. Enumerating the number and properties of the vacua is an important task for both the landscape and the swampland, in part to gain a deeper understanding of what is possible and “generic”. We obtain an exact counting of distinct intersecting brane vacua of type IIA string theory on the T 6 /Z 2 × Z 2 orientifold. Care is taken to only count gauge-inequivalent brane configurations. Leveraging the recursive nature by which branes may be added together one-by-one, we use dynamic programming to efficiently count the number of solutions of the tadpole, K-theory and supersymmetry consistency conditions. The distributions of 4D gauge group rank and complex structure moduli for the entire ensemble of intersecting brane vacua are presented. The methods we developed here may be useful in obtaining sharp upper and lower bounds on other corners of the landscape.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗

Wootz: a compiler-based framework for fast CNN pruning via composability

Convolutional Neural Networks (CNN) are widely used for Deep Learning tasks. CNN pruning is an important method to adapt a large CNN model trained on general datasets to fit a more specialized task or a smaller device. The key challenge is on deciding which filters to remove in order to maximize the quality of the pruned networks while satisfying the constraints. It is time-consuming due to the enormous configuration space and the slowness of CNN training.The problem has drawn many efforts from the machine learning field, which try to reduce the set of network configurations to explore. This work tackles the problem distinctively from a programming systems perspective, trying to speed up the evaluations of the remaining configurations through computation reuse via a compiler-based framework. We empirically uncover the existence of composability in the training of a collection of pruned CNN models, and point out the opportunities for computation reuse. We then propose composability-based CNN pruning, and design a compression-based algorithm to efficiently identify the set of CNN layers to pre-train for maximizing their reuse benefits in CNN pruning. We further develop a compiler-based framework named Wootz, which, for an arbitrary CNN, automatically generates code that builds a Teacher-Student scheme to materialize composability-based pruning. Experiments show that network pruning enabled by Wootz shortens the state-of-art pruning process by up to 186X while producing significantly better pruning results.

Guan, Hui↗