Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

The International Atomic Energy Agency (IAEA) has established a designation for an International Centre based on Research Reactors (ICERR). The intention of this designation is to provide a vehicle for IAEA member states to access international research-reactor and ancillary nuclear research and development infrastructure. The U.S. Department of Energy (DOE) has made a commitment to world leadership in the development of advanced nuclear energy, science, and technology. To this end, DOE has established programs and initiatives to enhance this leadership role. ICERR designation constitutes an important step in achieving the DOE vision. DOE, represented by Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL), is submitting this application for re-designation as an IAEA ICERR. Both INL and ORNL have a decades-long and storied history that supports nuclear research, development, and deployment both nationally and internationally. Both have a history of safe and efficient nuclear operations and have demonstrated a track record of international collaboration and cooperation. INL and ORNL are home to two primary and two secondary research reactors. The primary reactors are • Advanced Test Reactor at INL • High-Flux Isotope Reactor at ORNL. Secondary reactors include: • Neutron Radiography Reactor at INL • Transient Reactor Test (TREAT) Facility at INL • INL Advanced Test Reactor: Critical Facility (ATRC). ICERR designation will also include numerous ancillary facilities at both laboratories, including the following: • Multiple post-irradiation examination facilities at both INL and ORNL • Radiochemistry hot-cell facilities at ORNL • INL nuclear-fuel-fabrication and materials-science facilities • INL and ORNL radiological and nuclear analytical capabilities.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesus Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Post-irradiation examination (PIE) is being performed in support of tristructural isotropic (TRISO) coated particle fuel development and qualification for High-Temperature Gas-cooled Reactors (HTGRs). AGR-1 was the first in a series of TRISO fuel irradiation experiments initiated in 2006 under the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program; this work continues to be funded by the Department of Energy's Office of Nuclear Energy as part of the Advanced Reactor Technologies (ART) initiative. AGR-1 fuel compacts were fabricated at Oak Ridge National Laboratory (ORNL) in 2006 and irradiated for three years in the Idaho National Laboratory (INL) Advanced Test Reactor (ATR) to demonstrate and evaluate fuel performance under HTGR irradiation conditions. PIE is being performed at INL and ORNL to study how the fuel behaved during irradiation, and to examine fuel performance during exposure to elevated temperatures at or above temperatures that could occur during a depressurized conduction cooldown event. This report summarizes safety testing of irradiated AGR-1 Compact 5-1-1 in the ORNL Core Conduction Cooldown Test Facility (CCCTF) and post-safety testing PIE.

The development of information technology encourages the government to digitize business processes. It is generated with a large and varied volume from various data sources so that advanced data analytics (DA) is required to overcome this to support organization's data driven decision making. It's necessary to prepare DA based on DA readiness model so that the implementation of DA can run successfully. Whereas currently, there is limited study and no standard model for DA readiness. The focus of this study is to propose model readiness of implementing data analytics that is suitable in Indonesian government. The model refers to DA readiness model based on literature review on 15 papers relevant to DA readiness. Then it's verified by 7 experts. Furthermore, online survey was conducted to test the model that affects the readiness of implementing data analytics in Indonesian government. The survey results were analyzed using factor analysis. As a result, DA readiness model contains 4 dimensions, 11 factors, and 78 indicators where its dimensions consist of information system, organizational and cultural, organization structure and resource readiness. This model can describe 85% of the data analysis readiness requirements in the Indonesian government. In order to implement data analytics successfully, the government needs to improve the readiness of information systems, organizational and cultural, organizational structures, and resources.

This report documents calculations performed to predict failure probability of TRISO-coated fuel particles and diffusion of fission products through these particles during safety tests following the second irradiation test of the Advanced Gas Reactor program (AGR-2). The calculations include the modeling of the AGR-2 irradiation that occurred from June 2010 to October 2013 in the Advanced Test Reactor (ATR) and the modeling of a safety testing phase to support safety tests planned at Oak Ridge National Laboratory and at Idaho National Laboratory (INL) for a selection of AGR-2 compacts. The heat-up of AGR-2 compacts is a critical component of the AGR-2 fuel performance evaluation, and its objectives are to identify the effect of accident test temperature, burnup, and irradiation temperature on the performance of the fuel at elevated temperature. Safety testing of compacts will be followed by detailed examinations of the fuel particles to further evaluate fission product retention and behavior of the kernel and coatings. The modeling was performed using the particle fuel model computer code PARFUME developed at INL. PARFUME is an advanced gas-cooled reactor fuel performance modeling and analysis code (Miller 2009). It has been developed as an integrated mechanistic code that evaluates the thermal, mechanical, and physico-chemical behavior of fuel particles during irradiation to determine the failure probability of a population of fuel particles given the particle-to-particle statistical variations in physical dimensions and material properties that arise from the fuel fabrication process, accounting for all viable mechanisms that can lead to particle failure. The code also determines the diffusion of fission products from the fuel through the particle coating layers, and through the fuel matrix to the coolant boundary. The subsequent release of fission products is calculated at the compact level (release of fission products from the compact). PARFUME calculates the release fraction as the ratio of the number of atoms released from the compact to the amount produced in the compact fuel kernels and through uranium contamination. These safety test predictions aim at giving guidance for the upcoming safety tests to be conducted at ORNL and INL. The AGR-2 compacts modeled in this study belong to the four U.S. capsules of the AGR-2 irradiation test train. For each capsule, the compacts with the lowest and highest burnups were selected to provide a prediction envelope. The objective of this document is to present results of calculations and analyses made by PARFUME on these selected AGR-2 compacts. These results include: • Fuel failure probability • Palladium penetration • Fractional release of fission products Details associated with completion of these calculations are provided in the remainder of this document: the AGR-2 Safety Testing plan is briefly introduced in Section 2, PARFUME modeling is outlined in Section 3, results are described in Section 4, conclusions are given in Section 5, and references are listed in Section 6.

DOE has initiated the Uranium Soils Integrated Demonstration (USID) project. The objective of the USID project is to develop a remediation strategy that can be adopted for use at other DOE sites requiring remediation. Four major task groups within the USID project were formed, namely the Characterization Task Group (CTG), the Treatability Task Group (TTG), the Secondary Waste Treatment and Disposal Task Group (SWTDTG), and the Risk and Performance Assessment Task Group (RPATG). The CTG is responsible for determining the nature of the uranium contamination in both untreated and treated soil. The TTG is responsible for the selective removal of uranium from these soils in such a manner that the leaching does not seriously degrade the soil`s physicochemical characteristics or generate a secondary waste form that is difficult to manage and/or dispose. The SWTDTG is responsible for developing strategies for the removal of uranium from all wastewaters generated by the TTGs. Finally the RPATG is responsible for developing the human health and environmental risk assessment of the untreated and treated soils. Because of the enormity of the work required to successfully remediate uranium-contaminated soils, an integrated approach was designed to avoid needless repetition of activities among the various participants in the USID project. Researchers from Oak Ridge National Laboratory (ORNL), Los Alamos National Laboratory (LANL), Argonne National Laboratory (ANL), and Idaho National Engineering Laboratory (INEL) were assigned characterization and/or treatability duties in their areas of specialization. All tasks groups are involved in the integrated approach; however, the thrust of this report concentrates on the utility of the integrated approach among the various members of the CTG. This report illustrates the use of the integrated approach for the overall CTG and to provide the results generated specifically by the CTG or ORNL from FY1993 to the present.

The importance of data has been gradually acknowledged by fashion professionals to improve sales and margins because fashion brands and retailers need to develop, manufacture, and sell styles that resonate with consumers. Lately, advancements in data analytics, machine learning, and computing power, the value of utilizing artificial intelligence (AI)-based software or applications has been well acknowledged by fashion brans and retailers who want to apply a data-driven decision-making approach to develop more efficient fashion design, merchandising, and marketing strategies. In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that are currently available in the fashion industry are also introduced. Finally, a need for developing courses or programs focusing on fashion- specific data analytics in higher education is addressed as more and more fashion brands and retailers are trying to hire fashion data analysts. Collaboration with technology partners who are providing AI-powered data analytics services to fashion brands and retailers is needed to educate fashion students with practical knowledge and skills

Recent advances in Additive Manufacturing (AM), particularly in production scenarios, have been largely driven by insights achieved through data analytics. AM has greatly benefited from the increasingly large amounts of data generated during the design to product transformation. Despite the large amounts of data that can be generated from each build, the variations that can occur between builds create challenges in data reuse. Advances in data analytics are coming in the form of advanced machine learning algorithms, and are often associated with deep learning, where large amounts of data are analyzed, and interpretations are made. While such algorithms are increasingly adept at solving complex problems, solutions are highly dependent on the data used to train the algorithms and thus often subject to unwanted bias. Contextualization of data can help limit unintended bias and improve the probability of attaining viable results. Data contextualization often comes through domain context. This work describes the development of an AM ontology to support context-aware data analytics. The Additive Manufacturing Data Analytics Ontology, or AMDA Ontology, is developed to facilitate the contextualization of AM data through the representation of explicit AM concepts throughout the design to product transformation and the encoding of inhering relationships within the concepts. The AM concepts are accompanied by a suite of concepts representative of the necessary modeling, simulation, and analytics terms to create links between AM data, AM data analytics opportunities, and appropriate machine learning algorithms. The early results indicate that AMDA ontology has the ability to facilitate key correlations between AM data and the analytic opportunities to enhance the design to product transformation of AM parts.

Data analytics is a key factor to make fully informed and data-driven decisions in modern manufacturing companies. With increasing international competition, classical approaches to data analytics have to be enhanced toward advanced data analytics to create smarter products, production processes and customer services. This leverages the full potential of the data to quickly gain insights and make decisions. Application areas include predictive analytics, augmented analytics and real-time analytics. In this paper an advanced data analytics platform (ADAP) for manufacturing companies is presented and verified on a real-world production line. ADAP combines process data obtained in a physical environment with domain knowledge from experts to create data-driven models in a digital environment. In addition, ADAP is well scalable and provides a comprehensible data structure throughout the entire data workflow. ADAP is verified on two different advanced data analytics applications on an electrocoating plant. In order to relate the applications to real production requirements, the needs of local manufactures from the region Ostwestfalen-Lippe (OWL) are taken into account.

The U.S. commercial nuclear industry is facing increasing economic pressure due to low-cost alternatives such as natural gas combined-cycle combustion power plants and renewable energy technologies such as wind and solar. More efficient and intelligent operations and maintenance strategies can yield substantial economic gains through a proposed risk-informed predictive maintenance strategy. The strategy leverages plant process, maintenance, and vibration data with advanced data analytics to monitor and predict condition degradations in support of preventative maintenance. This paper documents the operating experience review of the existing monitoring and diagnostic process. A semi-structured series of interviews assessed current practices, tools, shortcomings, and anlaysts’ needs to support the development of a human system interface (HSI) for the advanced data analytics approach. The results of the interviews are reported as feature requirements for the proposed HSI system which is actively under development as part of this ongoing project.

The Space Exploration Initiative (SEI) has revitalized interest in adapting nuclear energy for power and propulsion. Prior to the selection of a nuclear thermal propulsion (NTP) system, extensive testing of the various proposed concepts will be required. In today’s environmental, safety and health culture, full size rocket engine tests as were done under the Rover/NERVA program will be extremely difficult and expensive to perform and meet NASA’s schedules. A different test strategy uses a hydrogen loop in an existing reactor to test a wide variety of single elements or clusters of elements for fuel qualification. This approach is expected to reduce operating and capital costs and expedite the testing schedule. This paper examines the potential of performing subscale tests in a hydrogen loop in an existing reactor such as the Advanced Test Reactor (ATR) at the Idaho National Engineering Laboratory or the High Flux Isotope Reactor (HFIR) at the Oak Ridge National Laboratory. The HFIR is expected to achieve power densities comparable to those achieved in ATR because of the 85 MWt power level and the high thermal and fast flux levels. The available length and diameter of the test region of FHIR are 60 cm and 10 cm whereas the available length and diameter of the test region of ATR are 120 cm and 12 cm respectively.

For nearly fifty years, the US national laboratories, under the direction of the Department of Energy, have maintained a tradition of outstanding scientific research and innovative technological development. With the end of the Cold War, their roles have undergone profound changes. Although many of their original priorities remain--stewardship of the nation`s nuclear stockpile, for example--pressing budget constraints and new federal mandates have altered their focus. Promotion of energy efficiency, environmental restoration, human health, and technology partnerships with the goal of enhancing US economic and technological competitiveness are key new priorities. The multiprogram national laboratories offer unparalleled expertise in meeting the challenge of changing priorities. This volume aims to demonstrate each laboratory`s uniqueness in applying this expertise. It describes the laboratories` activities in eleven broad areas of research that most or all share in common. Each section of this volume is devoted to a single laboratory. Those included are: Argonne National Laboratory; Brookhaven National Laboratory; Idaho National Engineering Laboratory; Lawrence Berkeley Laboratory; Lawrence Livermore National Laboratory; Los Alamos National Laboratory; National Renewable Energy Laboratory; Oak Ridge National Laboratory; Pacific Northwest Laboratory; and Sandia National Laboratories. The information in this volume was provided by the multiprogram national laboratories and compiled at Lawrence Berkeley Laboratory.

The Department of Energy (DOE) has large quantities of sodium-nitrate based liquid wastes. Around 1 billion liters of high level waste tank supernatant are present at Hanford, Savannah River Plant, Rocky Flats Plant, Idaho National Engineering Laboratory, and Oak Ridge National Laboratory. The largest quantity of these wastes is in the 149 single shell tanks at Hanford which hold up to 1 million gallons each. These tank waste are typically 4 to 5 molar in nitrate and contain radionuclides, various salts, and heavy metals. INEL high-level waste tank supernatant contains about 0.7 and 0.6 grams per liter of chromium and mercury, respectively. SRP high-level waste tank supernatant contains about 0.2 g/L of chromium. Other heavy metals could well be present at lower levels in theses tank wastes. The major components present in these wastes are summarized in Appendix A. These wastes are currently regulated and managed by the DOE. Under the Federal Facility Compliance Act (FFCA) DOE is subject to RCRA, which would apply to these tank supernatants. Stabilization of this waste is difficult because nitrates are very mobile. Additionally, vitrification of these wastes produces large quantities of hard-to-manage NO{sub x} emissions. The conversion of sodium nitrate to ammonia is discussed.

Numerous statutes and common law doctrines conceive of a dividing line between work time and nonwork time and delineate the activities that must be compensated as work. While technological innovations and increasing desires for workplace flexibility have begun to erode this divide, it persists, in part, because of the ways in which the division protects employers and employees alike. Nonetheless, the explosion of data analytics programs that allow employers to monitor and rely upon a worker's off‐duty conduct will soon weaken the dividing line between work and nonwork in dramatically greater and more troubling ways than ever before. The emergence of programs allowing employers to track, predict, rely upon, and possibly control nonwork activities, views, preferences, and emotions represents a major blurring of the line between work and nonwork. This article contends that these advances in data analytics suggest a need to reexamine the notion of work versus nonwork time and to question whether existing protections adequately consider a world in which these lines are so significantly muddled. As a society, we need to acknowledge the implications of the availability of massive quantities of employees’ off‐duty data and to decide whether and how to regulate its use by employers. Whether we, as a society, decide to allow market forces to dictate acceptable employer behavior, choose to regulate and restrict the use of off‐duty data for adverse employment decisions, or find some middle ground that requires disclosure and consent, we should choose our own course rather than allowing the technology to be the guide.

Technological advances in data analytics, robotic process automation (RPA), and artificial intelligence (AI) are transforming businesses and redefining what skill sets are needed in the accounting profession. To prepare students, academic accounting departments must develop strategies to integrate data analytics and emerging technologies into the curriculum. This paper discusses the design process used in a graduate-level advanced data analytics course that incorporates RPA and AI at a public university in the northeastern region of the United States. The paper concludes with a statistical analysis of precourse/postcourse student reflections and feedback surveys as part of a quality assurance initiative. The results suggest strong evidence of student learning related to the course learning objectives.