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Various embodiments described in the disclosure relate to wireless sensor systems. Some embodiments specifically relate to unitary modules for sensing, power, processing, communication, and data storage; and that may be connected by a common interface to form a wireless sensor system.
Plug and process loads (PPLs) account for a large portion of U.S. commercial building energy use. There is a huge potential to reduce whole building consumption by targeting PPLs for energy savings measures or implementing some form of plug load management (PLM). Despite this potential, there has yet to be a widely adopted commercial PLM technology. This paper describes the Automatic Type and Location Identification System (ATLIS), a PLM system framework with automatic and dynamic load detection (ADLD). ADLD gives PLM systems the ability to automatically identify devices as they are plugged into the outlets of a building. The ATLIS framework takes advantage of smart, connected devices to identify device locations in a building, meter and control their power, and communicate this information to a central database. ATLIS includes five primary capabilities: location identification, communication, control, energy metering, and data storage. A laboratory proof of concept (PoC) demonstrated all but the energy metering capability, and these capabilities were validated using a series of system tests. The PoC was able to identify when a device was plugged into an outlet and the location of the device in the building. When a device was moved, the PoC's dashboard and database were automatically updated with the new location. The PoC implemented controls to devices from the system dashboard so that devices maintained correct schedules regardless of where they were plugged in within the building. ATLIS's primary technology application is improved PLM, but other applications include asset management, energy audits, and interoperability for grid-interactive efficient buildings. An ATLIS-based system could also be used to direct power to critical devices, such as ventilators, during a brownout or blackout. Such a framework is an opportunity to make PLM more widespread and reduce the amount of energy consumed by PPLs in current and future commercial buildings.
The Nuclear Science User Facilities (NSUF) has been facilitating nuclear materials research since 2007 by providing access to national laboratory and other partner capabilities at no cost to the researchers. With funding from the Department of Energy – Office of Nuclear Energy, approximately 400 projects have been awarded to support research into radiation effects on fuels and materials. During these ten years, the NSUF has created tools to help researchers write better proposals and perform better research projects. In 2018, several of those tools have been combined to create a single point of access to NSUF information management resources. The Combined Materials Experiment Toolkit or CoMET, combines the Nuclear Energy Infrastructure Database (NEID), the Nuclear Fuels and Materials Library (NFML), and the NSUF proposal system, with a new subject matter expert database and an experiment design tool. The NEID contains information on 500 nuclear R&D facilities around the world, including 85 NSUF partner facilities. The NFML has information on nearly 10,000 material specimens, mostly irradiated materials, which can be used for research in NSUF projects. The new SME database includes contact information for NSUF researchers and scientists. The experiment design tool is an expert system that can guide a researcher through the process of designing an NSUF project by interacting with the other resources. Once a researcher has worked through this process, they will be able to save their experiment plan and input it into the NSUF proposal system. These tools are intended to aid both new and veteran researchers build solid research projects and take full advantage of NSUF resources. They also help the NSUF administer the hundreds of proposals that it receives each year. In the future, NSUF plans to add integrated data storage and retrieval to CoMET.
We present a novel data format design that obviates the need for data tiers by storing individual event data products in column objects. The objects are stored and retrieved through Ceph S3 technology, with a layout designed to minimize metadata volume and maximize data processing parallelism. Performance benchmarks of data storage and retrieval are presented.
The current phase-out decision of the German Federal Government and the 'Energiewende' - resulting from the Fukushima Daiichi Accident in 2011 - changed the nuclear market significantly. Together with the change in political and social attitude towards nuclear energy since 2002 it has contributed to a decline in technical expertise. This as well as the generation change in the nuclear technology lead to the risk of losing accumulated knowledge and experience which still are essential for the future - not only in Germany but in other countries - even though the focus in Germany will shift from operating to decommissioning, dismantling and radioactive waste management. A long-term strategy in order to preserve technical expertise to solve the forthcoming tasks in Germany was required. This strategy should include e.g. the implementation of technical programs not only for internal training of the staff but also for authorities. Together with authorities TUV NORD developed individual training concepts for a broad range of customers to preserve a transfer of know-how with the state-of-the-art in science and technology and even support international projects, especially in countries with less experience in the nuclear fields. Additionally, special data bases were developed in order to ensure the accessibility of long-term nuclear data storage. Furthermore technical conferences in different fields like decommissioning and radioactive waste disposal were established as an opportunity for knowledge exchange. Based on given examples it is being demonstrated how technical expertise can be preserved even though the field of nuclear energy in Germany has changed. (authors)
In December 2013, Cabrera Services (Cabrera) executed transition of the FUSRAP Maywood Superfund Site remediation project operations, including an on-site accredited environmental lab, from a large business that had established technology infrastructure in place for over a decade which required updating to support the expected remediation scope and client needs. As of November 2015, Cabrera has shipped over 116,000 yd{sup 3} of contaminated soil to disposal facilities. During the transition, the USACE FUSRAP Maywood Laboratory (UFML) continued to support project operations, providing the expected timely and reliable results and maintaining their New Jersey Department of Environmental Protection certification for all parameters of concern, while having to cope with new challenges and additional tasks having the potential to disrupt the laboratory's ability to support project operations. This case study examines the challenges with maintaining a certified radiochemistry laboratory and an aggressive remediation schedule during a period of prime contractor transition which included a complete IT infrastructure overhaul, and conversion of the existing Microsoft Access laboratory database and project Electronic Data Management System (EDMS) to a customized commercial solution. Challenges: I. Procurement of a new database subcontractor and working closely with them to achieve the following: a. Migration of all environmental (including radiological, chemical, and quality control), health and safety, and spatial data as well as reports, queries, and analytical instrument software processes from a MS Access data management system to a SQL server system; and b. Verification that the functionality and data integrity of the databases remained intact. II. The onsite laboratory was changed over to a new onsite network and the following tasks were completed with little or no interruption to site operations by January 10, 2014: i. Installation of new instrument personal computers (PCs) and making them fully operational; ii. Configuration of the lab domain and server to host the LIMS (replaced all servers/domain/workstations with Windows 7 and Server 2012 machines). iii. Networking of the laboratory building into the rest of the project site via fiber optic cabling across the site. iv. Upgrade of the wide area network (WAN) data connection to high-speed Fiber Optic service; v. Upgrade of the telephones into voice over internet protocol (VOIP) phones. vi. Splitting of the database into front ends and a centralized back-end hosted on the new server to allow simultaneous access for fixes and improvements. III. Phone service interruption; phone systems were operational by January 10, 2014. Additional Tasks: I. Revision of the USACE FUSRAP Maywood Laboratory (UFML) Quality Systems Manual Rev. 1 Submitted 01/16/14; II. Writing of the Uniform Federal Policy Quality Assurance Project Plan Rev. 1 (UFP-QAPP) submitted 1/21/14. This document replaced the prior project data quality plan, the Chemical Data Quality Management Plan. III. Support of USACE communications to the New Jersey Department of Environmental Protection (NJDEP) on lab management and personnel transition to Cabrera. This work was important since it ensured a smooth transition and shows how critical project records infrastructure such as databases, can be upgraded with minimal interruption of project support. Such work may provide benefit to other similar projects, especially long term projects where database infrastructure upgrades may be beneficial in terms of improved efficiencies in data recording and transfer, and improved security in data storage. (authors)
The Energy Systems Integration Facility (ESIF), located at the National Renewable Energy Laboratory (NREL) South Table Mountain campus, is a world-renowned user facility for research and development of modern, advanced, and clean energy technologies. ESIF is distinguished by its continuously evolving, highly integrated systems that span throughout the building, connecting research capabilities across multiple laboratories and test areas. The primary ESIF research systems include: [1] data, cyber, and control networks, [2] research electrical distribution buses (REDB), [3] thermal integration infrastructure, and [4] hydrogen systems. The data, cyber, and control networks provide monitoring, control, communication, automation, visualization, and time series data storage and tagging capabilities for research projects and ESIF systems, including facility safety functions. The REDB system consists of four dedicated AC and DC electrical power networks that can connect devices located across the facility through versatile, automatic circuit configuration to support complex power electronics experiments up to the megawatt-scale. The thermal integration infrastructure consists of three temperature-conditioned water loops that provide heating and cooling interfaces and capabilities for thermal energy research. The hydrogen systems provide megawatt-scale hydrogen production, drying, compression, high-pressure storage, and delivery to laboratory end uses, including hydrogen fuel cell vehicle fueling. The ESIF research systems interconnect and extend throughout the various lab areas of the facility to create elaborate networks composed of diverse technologies for cutting-edge research. The ESIF capabilities are operated and stewarded by the ESIF Research Operations group, who also actively upgrade and advance the systems to ensure they remain ahead of anticipated research - enabling the success of many pioneering energy integration projects. The poster, created by members of the ESIF Research Operations team, highlights and summarizes the four core integrated systems at ESIF. The poster was first presented at the internal NREL Energize Forum on May 13th, 2024, and received the "Best Poster" award.
Scientific workflows are increasingly used in High Performance Computing (HPC) environments to manage complex simulation and analyses, often consuming and generating large amounts of data. However, workflow tools have limited support for managing the input, output and intermediate data. The data elements of a workflow are often managed by the user through scripts or other ad-hoc mechanisms. Technology advances for future HPC systems is redefining the memory and storage subsystem by introducing additional tiers to improve the I/O performance of data-intensive applications. These architectural changes introduce additional complexities to managing data for scientific workflows. Thus, we need to manage the scientific workflow data across the tiered storage system on HPC machines. In this paper, we present the design and implementation of MaDaTS (Managing Data on Tiered Storage for Scientific Workflows), a software architecture that manages data for scientific workflows. We introduce Virtual Data Space (VDS), an abstraction of the data in a workflow that hides the complexities of the underlying storage system while allowing users to control data management strategies. We evaluate the data management strategies with real scientific and synthetic workflows, and demonstrate the capabilities of MaDaTS. Our experiments demonstrate the flexibility, performance and scalability gains of MaDaTS as compared to the traditional approach of managing data in scientific workflows.
Economic assessments of battery energy storage systems (BESSs) rely on optimal dispatch methods to capture temporal interdependency of BESS operations and coupling among different grid applications. Many existing BESS economic assessment studies are based on a simplified first-order scalar model using BESS nameplate numbers. Such a method cannot accurately capture varying capabilities and nonlinear dynamics of a BESS, leading to inaccurate assessment results. This paper presents a practical implementation of performance data into the economic assessment of the BESSs recently developed within the Snohomish Public Utility District system in Washington State. Extensive testing conducted over multiple seasons is used to assess the technical performance and develop high-fidelity models of the BESSs. A dynamic programming algorithm is proposed to optimally dispatch the BESSs and assess the potential benefits from stacked value streams. The proposed method can be adapted for the economic assessment of other BESSs with different applications.
Urban Air Mobility (UAM) defines an environment for managing operations of vertical takeoff and landing (VTOL) and short takeoff and landing (STOL) vehicles in an urban environment. Within a UAM environment, UAM operators manage fleets of vehicles, relying on Providers of Services for UAM (PSUs) for managing flights in a region of airspace. Flight plan deconfliction is primarily performed by the Discovery and Synchronization Service (DSS), and the Federal Aviation Administration (FAA) maintains control over the UAM space via the FAA-Industry Exchange Protocol (FIDXP). UAM is a federated environment with many different entities owning and operating vehicles, PSUs, and other services. These entities often need to interoperate or access data generated by other organizations. This paper demonstrates the feasibility of using blockchain to facilitate a secure data exchange and storage for this flight information in a UAM environment. In particular, this paper is focused on flight plans and telemetry data. A blockchain network was developed with a set of smart contracts for managing relevant flight data. Hyperledger Fabric was chosen as it is performent, scalable, and allows organizations to reuse existing public key infrastructure (PKI) for identity management. A set of simulated UAM services were also developed. These services propose flight plans and negotiate with other UAM services for airspace access. All interactions between UAM services, as well as vehicle telemetry data, is recorded onto the blockchain. Vehicle telemetry data is generated by a vehicle flight simulation service. This paper successfully demonstrates the feasibility of using blockchain as a secure data exchange and storage mechanism in a UAM environment.
This report presents cost data and recommends cost estimation methods for costs associated with CO 2 injection and surface facility management, including well equipment, onsite booster compression. Where water is produced for pressure management in saline aquifers, costs for water production, water injection, and associated surface treatment and wellhead equipment (along with associated piping and facilities) is provided. Comparable cost estimation recommendations are provided for CO 2 -EOR operations, including the costs associated with oil and water management and production, and CO 2 recycling.
The Transportation Storage Logistics (TSL) Model Data Management Manual describes the SQL Server database that is used by the TSL model to house reference data as well as the scenario data. The manual is a reference document, subject to change as the model evolves.
Scientific discoveries rely heavily on efficient access, search, and management of massive data sets. Data management technologies have, for decades, provided foundational capabilities for scientific computing. Just as storage, input/output (I/O), and data management have been fundamental to simulation-based science for many years, so too are capable data-management technologies key to the success of today’s scientific workflows utilizing data intensive and machine learning (ML) techniques. The Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program has invested broadly in data-management research focused on high-performance computing (HPC) systems, from parallel file systems that store data to application software that makes these systems more productive. Still, advances in technology combined with growing diversity of supported science strongly motivate continued investment in this area. In January 2022, ASCR convened a workshop to identify priority research directions in the area of data management for high-performance and scientific computing. Attendees were challenged to identify promising approaches that would support the breadth of the DOE mission, including the explosion of artificial intelligence (AI) uses and the growing needs of experimental and observational science. Technological and science drivers were identified and considered as they relate to key aspects of data management such as interfaces, architectural design, and FAIR principles (Findable, Accessible, Interoperable, and Reusable). The thoughts of the workshop participants were distilled into a set of four priority research directions with the potential for high impact on DOE science. These research directions are summarized in the following pages.
Data mining is the automatic discovery of patterns, associations, and anomalies in data sets. Data mining requires numerically and statistically intensive queries. Our assumption is that data mining requires a specialized data management infrastructure to support the aforementioned intensive queries, but because of the sizes of data involved, this infrastructure is layered over a hierarchical storage system. In this paper, we discuss the architecture of a system which is layered for modularity, but exploits specialized lightweight services to maintain efficiency. Rather than use a full functioned database for example, we use light weight object services specialized for data mining. We propose using information repositories between layers so that components on either side of the layer can access information in the repositories to assist in making decisions about data layout, the caching and migration of data, the scheduling of queries, and related matters.
The Urban Air Mobility (UAM) environment is derived from the Unmanned Traffic Management (UTM) concept of operations. Within the environment, UAM operators work independently to manage aerial vehicles in the urban environment. Providers of Services (PSU), UAM operators, and Supplemental Data Service Providers provide services to support flight operations within the UAM environment. The intent of this work is to leverage a permissioned blockchain approach, to, simulate secure data exchange and storage for UAM environments. Blockchain technologies can be used for identity management of vehicles, people, and systems.
Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. To address these challenges, this research focuses on the secure data exchange and storage of this decentralized UAM environment. The intent of this research is to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.
Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. To address these challenges, this research focuses on the secure data exchange and storage of this decentralized UAM environment. The intent of this research is to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.
Improved technique was developed for transferring and storing data at faster than real time speeds on hybrid computer. Predominant advantage is combined use of electronic relays, track and store units, and analog-to-digital and digital-to-analog conversion units of hybrid computer.