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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Laser Driven Hydrothermal Processing (LDHP) for Rapid Dissolution of Glassy Nuclear Debris

Discovered in 2009 on the SLAB laser system at LLNL, Laser-Driven Hydrothermal Processing (LDHP) both spalls and transiently dissolves a wetted surface (e.g., rock, concrete, paint) when that surface is struck by a laser beam pulse under specific conditions. Previous projects have explored the application of LDHP to expediting sample dissolution for post-detonation nuclear forensics. Early results were very promising, indicating that this method could decrease material dissolution time by a factor of 10 or more. However, the large and complicated lasers previously used are impractical for this application. Under the R^3 venture we aimed to benchmark the LDHP capabilities of turn-key commercial off-the-shelf (COTS) lasers, and to establish how this process could be integrated into an analytical workflow for nuclear forensic analysis. We demonstrated that geological rock standards could be quickly processed into fine powder by LDHP followed by complete acid digestion in 15 minutes, without elemental fractionation that would skew analytical results. We determined that the COTS laser can be used for LDHP that could potentially reduce dissolution time for solid nuclear debris samples in post detonation nuclear forensics. One downside to this approach, however, is that the laser interactions are different with every material, so extensive method validation would be required for all likely material compositions. The LDHP processing rates of several materials were tested and determined to be favorable in the beginning with significant slowdown due to particulate scattering. This could be mitigated either by stopping and centrifuging the sample at specific intervals, or through design of a continuous solvent flow through mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting Failure Using Deep Learning SAND Report

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

FRMAC Assessment Manual Volume 3 - Pre-Assessed Default Scenarios

This volume of the Federal Radiological Monitoring and Assessment Center (FRMAC) Assessment Manual was developed to serve as a quick-start guide to expedite response for pre-assessed scenarios before event-specific information becomes available. The objective of Volume 3 is to provide dose assessors with the scenario-specific protective action guidance questions likely to be asked at the start of a response, along with supporting technical information and assumptions that differ from the default assumptions defined in Volume 2 of the Assessment Manual.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Ion Exchange Processing of AN-107 Hanford Tank Waste through Crystalline Silicotitanate in a Staged 2- then 3-Column System

The Hanford Site stores an estimated 56 million gallons of mixed radioactive and chemically hazardous waste in large underground tanks. In support of the Direct Feed Low-Activity Waste (DFLAW) Program for expediting Hanford tank waste supernate treatment, laboratory-scale ion exchange processing using prototypic unit operations was conducted on AN-107 tank waste at the Pacific Northwest National Laboratory Radiochemical Processing Laboratory. This report describes the small-scale ion exchange testing with 13.7 L of diluted and filtered supernate from Tank 241-AN-107 (hereafter referred to as AN-107) at 16 °C (62 °F). One of the waste acceptance criteria (WAC) for the Waste Treatment Plant (WTP) Low-Activity Waste Facility is that the waste must contain less than 3.18×10 -5 Ci 137 Cs per mole of Na. For the AN-107 tank waste to meet this criterion, only 0.147% of the influent 137 Cs concentration may be delivered to the WTP; this requires a Cs decontamination factor of 678. Testing with AN-107 matched current Tank Side Cesium Removal (TSCR) facility prototypic operations where a lead-lag configuration was used until the lag column reached the WAC limit, then a polish column was brought online for continued processing in a lead-lag-polish column configuration. Feed was processed at 1.9 bed volumes (BVs) per hour; the flowrate, in terms of contact time with the crystalline silicotitanate (CST) bed, matched the expected flowrate at TSCR. The Cs-decontaminated product was retained for vitrification testing (to be reported separately). The lead column reached 40% Cs breakthrough after processing ~1700 BVs of feed; the 50% Cs breakthrough was extrapolated from the breakthrough data to occur at 1873 BVs. Testing compared to previous AP-101 and AP-107 testing at 16 °C showed ~300 BV increases in volume processed to reach the WAC limit for both lead and lag columns. The increase in capacity was determined to be due to the significantly lower K concentration in the AN-107 compared to the other tank waste matrices. A comparison in breakthrough curves for the three tests indicated slightly slower kinetic behavior in the AN-107, with variations in feed matrices (high organic complexants) likely responsible for the deviation. The Cs effluent from the lag column reached the WAC limit after processing 1097 BVs. Anticipating this breakthrough point, the polish column was preemptively installed around 900 BVs. Cs breakthrough from the lag column began at 500 BVs, reaching 3.06×10 0 µCi/mL, or 2.6 % Cs breakthrough, after processing all 1700 BVs of feed. Table S.1 and Figure S.1 summarize the observed column performance and relevant Cs loading characteristics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Accelerated Irradiation and Qualification of Ceramic Nuclear Fuels

Accelerated neutron irradiation testing is an component of accelerated qualification of new nuclear fuels for light water reactor (LWRs), microreactors, and other special purpose reactors. The qualification and licensing of nuclear fuel is a lengthy process that can take 20-25 years to bring a new fuel into service. Accelerated fuel qualification combines both experimental and modeling work to expedite the total qualification time to 5-10 years timeframe. The experimental aspect of this is accelerated irradiation aims to reduce the total time needed for neutron irradiation to achieve targeted burnup, which can take years using conventional irradiation profiles. The data that results from this irradiation testing can then be entered into BISON models to develop robust and reliable performance simulations to ensure safe operation under normal and off normal conditions. This milestone focused on the fabrication of test articles for accelerated irradiation testing at the Advance Test Reactor (ATR).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application

This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Automated AI-driven Molecular Design for Therapeutic Discovery

In recent years, artificial intelligence and machine learning (AI/ML) approaches have revolutionized the process of designing new therapeutics, enabling scientists to rapidly respond to emerging threats from various pathogens. A prime example is the SARS-CoV-2 main protease, a key target for the development of antiviral inhibitors. In this study, we employed a novel, integrated approach that combines AI-driven iterative design of inhibitor candidates, screening based on physio-chemical properties and toxicity, physics-based computational modeling of protein-inhibitor interactions, and AI-assisted analysis of Native MS biophysical assay and characterization of designed candidates. Our deep learning 3D-scaffold model, which uses an input scaffold as a starting point, generated tens of thousands of compounds while preserving the key scaffold. To optimize these candidates, we calculated a comprehensive set of 136 descriptors, including both 2D and 3D molecular features, for compounds targeting the SARS-CoV-2 Main protease (Mpro) and a neurodegenerative disease-associated protein, cyclophilin (Cyp). The generated compounds were initially filtered based on their properties and then ranked according to their predicted binding affinity using our automated modeling and ML methods. Experimental validation of the Mpro candidates showing inhibitory activity demonstrates that our workflow can expedite the therapeutic discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Solar Energy Zone Market Analysis for the Afton Region of New Mexico

This report examines the commercial viability of the Afton solar energy zone (SEZ) in southern New Mexico based on market conditions in 2022. The Bureau of Land Management may designate a SEZ on federal land to expedite environmental review and permitting for solar energy projects. The purpose of this analysis is to understand whether current market conditions support solar development in the SEZ.

14 SOLAR ENERGY↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Net Zero World 2024 Achievements

Net Zero World, launched at COP26, aims to expedite the transition to clean energy systems worldwide. This report details Net Zero World's achievements in 2024. Leveraging expertise from ten U.S. Department of Energy national laboratories and nine federal agencies, Net Zero World has provided specialized technical support and energy modeling to eight partner countries: Argentina, Chile, Egypt, Indonesia, Nigeria, Singapore, Thailand, and Ukraine.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Clean Energy Cybersecurity Accelerator: Cohort 2 - Asimily Public Report

The U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) sponsors the Clean Energy Cybersecurity Accelerator (TM) (CECA) to expedite the deployment of emerging security technologies that address the most urgent security concerns facing modern and future electric grids. CECA Cohort 2 assessed solutions focused on hidden risks due to incomplete system visibility and device security and configuration. Improving visibility can be achieved through operational technology (OT) asset identification solutions, including capabilities like automatic discovery, vulnerability reporting, and configuration monitoring. Solutions that monitor and identify assets in information technology (IT) networks in other domains are widely used; however, there is far less adoption of monitoring solutions for operational technology environments. Wider adoption may increase with increased confidence in the ability for these solutions to understand and respond to the specific requirements of OT environments. CECA Cohort 2 evaluated the active and passive asset discovery capabilities of market-ready solutions, documented and analyzed results, and identified gaps in functionality or capabilities. This report and describes how these results can help advance the adoption of these and similar solutions in the electric sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

PCAST: Report to the President on Accelerating Effective Reduction of Greenhouse Gas Emissions

The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On February 20, 2024, PCAST sent a report to the President with recommendations to expedite the reduction of greenhouse gas emissions through whole-of-country coordinated efforts and expanded assessment and verification systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

State Requirements for Electric Distribution System Planning

Utilities have conducted distribution planning since they first began building and operating electricity systems. But filing these plans for regulatory and stakeholder review is a relatively recent phenomenon. This report summarizes legislative and regulatory requirements for regulated electric utilities to file some type of distribution system plan in 20 U.S. jurisdictions. Some plans focus on expedited cost recovery for certain types of distribution system improvements; other plans focus on investments for grid modernization or distributed energy resources. Increasingly, states are adopting requirements for Integrated Distribution Plans. Such plans provide holistic grid investment strategies that address state and local policies and increasing complexity at the grid edge. The report covers the following topics for distribution system plans, highlighting advanced practices: -State goals and objectives -Procedural requirements -Forecasting loads and distributed energy resources -Hosting capacity analysis -Baseline information requirements -Grid modernization strategy -Grid needs assessment -Non-wires solutions -Reliability and resilience analyses -Stakeholder engagement -Equity -Pilots -Coordination with other planning processes The report includes links to legislation; regulatory requirements, proceedings, and orders; and filed utility plans. The U.S. Department of Energy’s Office of Electricity provided funding support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2025

EMPOWERED Distributed Energy Resources Permit Accelerator Pilot

This report summarizes the EMPOWERED Distributed Energy Resources (DER) Permit Accelerator Pilot project. Its primary objective was to streamline the design, installation, permitting, and inspections of targeted Distributed Energy Resource (DER) solutions. Streamlining these processes is critical to strengthening and accelerating the adoption of DER solutions nationwide. Moreover, streamlined processes help expedite the clean energy transition and expand energy resilience by improving understanding and capability among key stakeholders like property owners, code officials, and the general workforce. The project team accomplished this by developing and deploying a set of design and permitting guides for simple DER solutions, which they rolled out via a permitting pilot program. The project team developed nine permitting and inspection guides for simple DER solutions, as follows: 1. Single-Family and Duplex: Electric Vehicle Service Equipment (EVSE), and Storage, Solar + Storage (each in two code cycles) 2. Multi-family and Office: EVSE, Storage, Solar + Storage (each in two code cycles) 3. A permitting process guide to support the implementation of the permitting guides. The guides cover key code requirements via plan review and field inspection checklists for code officials and building owners to follow to streamline the permitting and inspections process for DER solutions. Following the development of the initial set of guides, the project team formed two cohorts, each made up of four jurisdictions, to participate in the Permit Accelerator Pilot. Each cohort received training and technical assistance to support them in incorporating the guides into use. The project successfully engaged jurisdictions in two disparate US regions: Chelsea, Somerville, Natick, and Norwood in metro Boston; and Pima County, Town of Gilbert, Flagstaff, and Sedona in Arizona. Feedback gathered from the project was valuable in refining the guides to better address jurisdictional needs. After the pilot period, the project team developed final versions of the guides based on extensive feedback from the cohorts and from external technical peer review. Feedback included needs and barriers related to implementation, as well as detailed technical improvements from external peer review. The final guides were presented in a series of webinars with a national reach, with 192 attendees from 35 states in the live session of the final webinar, and the final guides are posted online and available for use nationwide. Key findings: • The most receptive jurisdictions were those adjacent to other jurisdictions that had already adopted similar initiatives. The least receptive jurisdictions were those in the midst of adopting new code cycles. • The most common barrier to jurisdictional adoption of new guidelines were internal administrative and process delays, more so than technical barriers. • The main technical barrier to adoption proved to be the concern of fire hazard risks posed by energy storage systems. There is some apprehension in the code enforcement community about the safety of the batteries. Education coordinated with fire and safety services will help alleviate concerns and increase confidence in the acceptance of innovative technologies. • Opportunities to further leverage and advance the guides and related resources include outreach, education, and jurisdictional support; updates to the resources to align with code cycle changes and local requirements; and advancements supporting emerging technologies.

14 SOLAR ENERGY↗
DOE OSTI2024

Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling

The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.

Saidi, Wissam↗
DOE OSTI2025

Recommendations to Improve Nuclear Licensing: Update to INL/RPT-23-72206, Recommendations to Improve the Nuclear Regulatory Commission Reactor Licensing and Approval Process

In 2023, various stakeholders had asked for BEA’s thoughts and recommendations to improve the U.S. Nuclear Regulatory Commission’s (NRC) licensing review and approval process. This included an April 14, 2023 request from the House Committee on Energy and Commerce on “information and recommendations to improve the licensing review and approval process, . . . as well as the siting, licensing, construction, and oversight of advanced nuclear reactor technologies.” In response to these requests, BEA prepared and published INL/RPT-23-72206, Recommendations to Improve the Nuclear Regulatory Commission Reactor Licensing and Approval Process (2023 Report). The 2023 Report included 13 recommendations related to streamlining NRC hearings, expediting NRC safety and environmental reviews, otherwise improving NRC licensing, and providing financial benefits to new reactor projects. Many of these earlier recommendations were addressed through various legislative actions or changes made by the NRC. Section 2 of this report addresses the current status of those earlier recommendations. BEA recently received a new request from the House Committee on Energy and Commerce seeking any suggestions for additional areas to examine or potential reforms “that may assist in modernizing the licensing and regulatory process that affects civil nuclear deployment.” Additionally, the new Secretary of Energy has identified initial DOE actions to support unleashing the golden era of American energy dominance, including “Unleash Commercial Nuclear Power in the United States” and “Streamline Permitting and Identify Undue Burdens on American Energy.” Given these developments, BEA has prepared a new set of updated recommendations in this report. The recommendations include updated versions of recommendations from the 2023 Report which have not been fully adopted, as well as entirely new recommendations. This set of recommendations has a slightly broader focus with some recommendations focused on DOE authorizations and some recommendations related to nuclear licensing beyond new reactors. Each recommendation below also identifies whether the recommendation would require legislative action or could be addressed directly by the respective agency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Field Validation of Electrocoagulation Treatment for Oily Wastewater at Cleveland-Cliffs Steel Mill in Cleveland, OH

The Industrial Technology Validation (ITV) program aims to identify and demonstrate the performance of new, emerging, and underutilized technologies in the industrial sector to help inform decisions towards accelerating commercialization and deployment. This initiative aims to identify, validate, and showcase new, emerging, and underutilized technologies in the industrial sector to expedite their commercialization and widespread deployment. By conducting thorough validations and demonstrating the efficacy of these technologies, the ITV program facilitates informed decision-making among industry stakeholders, contributing to the transition towards more sustainable and efficient industrial processes. This ITV demonstration involved the testing of an electrocoagulation (EC) treatment system at a steel plant in Ohio. The system was designed to treat oily wastewater generated from cold rolling operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗