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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Cybersecurity for a Modern Grid

By understanding how to secure today’s grid, researchers at NREL are working to design and secure the grid of the future. Watch this video to hear from NREL’s Juan Torres on his vision for cybersecurity research at NREL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Business Case Analysis for Digital Safety-Related Instrumentation & Control System Modernizations

This LWRS research seeks to assist in breaking the impasse which has precluded digital safety system upgrades by generating and demonstrating a process and related business case tool to enable a Business Case Analysis (BCA). The purpose of a BCA is to show such upgrades can be economically justified. This BCA methodology first systematically establishes a forecast of expected lifecycle costs for I&C identified for upgrade by: • Definitively bounding the scope of current I&C systems envisioned for upgrade. • Collecting historical labor and material usage data that bound cost contributors related to the systems to be upgraded. • Synthesizing and analyzing the data to establish lifecycle cost forecasts for the current systems. In collaboration with engineers familiar with the attributes of the digital equipment to be used in the upgrade and how it is envisioned to be applied, cost savings categories and expected savings in those categories are then identified and applied using the analysis tools developed for this purpose. The result is an estimate NPV of savings enabled by the upgrade. This includes both direct cost savings (e.g., surveillance labor costs) as well as cost avoidance items (e.g., inventory carrying costs). Finally, when utility-provided digital upgrade costs estimates are included, the resultant BCA provides an NPV for the upgrade project. Development of a useful BCA methodology requires a real-world basis. With the cooperation of Exelon Generation (“Owner”), an Exelon-owned 2-Unit BWR Station (“Station”) was used as the foundation for this research. Exelon is pursuing a digital upgrade of current, first-echelon, safety-related I&C systems at these units, including the following: • Reactor Protection System (RPS) • Nuclear Steam Supply Shutoff System (N4S) • Emergency Core Cooling Systems (ECCS) • Anticipated Transient Without Scram (ATWS) Mitigation System

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Tool for Performing Link Analysis, Operational Sequence Analysis, and Workload Analysis to Support Nuclear Power Plant Control Room Modernization

Nuclear power continues to have an imperative role for the U.S.’s electricity generation. However, for these nuclear power plants (NPPs) to remain economically viable, new strategies for reducing operations and maintenance costs must be explored. The U.S. Department of Energy Light Water Reactor Sustainability Program is researching how to digitally transform existing analog and hybrid main control rooms into a fully integrated control room that addresses this challenge. The transformation will fundamentally change the conduct of operations for these U.S. NPPs. Human factors engineering has a vital role in this effort, where traditional task analysis methods are important in informing the design of advanced human-system interface displays. This work describes a preliminary tool to support task analysis for the development of these advanced displays. Details on the use of this tool, including the specific task analysis methods offered, are presented in this paper.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Non-LWR Regulatory Framework Modernization

This report provides an end-of-year summary that reflects the progress and status of Idaho National Laboratory’s (INL) activities concerning the development of advanced reactor (AR) regulatory framework and its implementation in the United States (U.S.). The report also provides recommendations for work to be performed in Fiscal Year 2025 (FY-25) and beyond. This work was completed in Fiscal Year 2024 (FY-24) and was supported by the U.S. Department of Energy (DOE) Regulatory Development sub-program. These activities are managed by INL on behalf of DOE.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2017

Standard grids for high-precision integration of modern density functionals: SG-2 and SG-3

Density-functional approximations developed in the past decade necessitate the use of quadrature grids that are far more dense than those required to integrate older generations of functionals. This category of difficult-to-integrate functionals includes meta-generalized gradient approximations, which depend on orbital gradients and/or the Laplacian of the density, as well as functionals based on B97 and the popular “Minnesota” class of functionals, each of which contain complicated and/or oscillatory expressions for the exchange inhomogeneity factor. Following a strategy introduced previously by Gill and co-workers to develop the relatively sparse “SG-0” and “SG-1” standard quadrature grids, we introduce two higher-quality grids that we designate SG-2 and SG-3, obtained by systematically “pruning” medium- and high-quality atom-centered grids. The pruning procedure affords computational speedups approaching a factor of two for hybrid functionals applied to systems of ~100 atoms, without significant loss of accuracy. The grid dependence of several popular density functionals is characterized for various properties.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Safety Design of Water Infrastructures in A Modern Era

In this chapter, we review the current practice of engineering safety design of large water management infrastructures. Those large infrastructures are designed using probable maximum precipitation (PMP) to withstand extreme conditions, but climate change has been posing challenges on the conventional PMP estimation approach. We discuss multiple limitations of the current approaches, and propose a few possible solutions based on the advancement of atmospheric modeling and statistical analysis of extreme precipitation mechanisms.

Chen, Xiaodong↗
DOE OSTI2018

Neutronics Calculation of Fast Reactor Using Modern Computing Systems

The possibility of using the LUCKY-A three-dimensional multiprocessor transport code as a neutronics module for the COREMELT integrated non-stationary code for performing safety analysis, which is used for fast sodium reactor safety validation, is discussed. In this application, more stringent requirements are imposed on the computing speed of the LUCKY-A code. To test LUCKY-A possibilities, a typical computational problem is examined in a formulation characteristic for the proposed use of the code. The possibilities of the code for parallelized calculations are shown, and a comparison is made with calculations performed with the MMKKENO and TRIGEX codes. The efficacy of a parallel computational process is investigated as a function of the number of computing kernels used.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Dedekind η-Function in Modern Research

In this paper, we describe properties of the Dedekind η-function, constructions arising from it, and their applications in various topics of the number theory and algebra. We discuss connections with the group representation theory and the study of the structure of spaces of modular forms. Special attention is paid to the special class of modular forms, namely, η-functions with multiplicative coefficients.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Core-to-Specimen Energy Coupling Results of the First Modern Fueled Experiments in TREAT

For this study, a series of experiments was conducted in the Transient Reactor Test (TREAT) Facility following a decades-long operational hiatus. A series of physics tests were then performed emphasizing relevant transients for Light Water Reactor (LWR) fuels research. A series of fueled experiments were then conducted using LWR-type sub-length specimens in inert gas capsules with the primary objective to quantify the ratio of nuclear heat deposited in the core to that deposited in the specimens. This type of data can be difficult to measure but is highly important in the design and interpretation of transient tests. Five fueled capsules were irradiated and energy coupling factors (which express the ratio of nuclear heat deposited per mass in the test specimen to that deposited in the reactor core in units of J/gUO 2 MJ TREAT ) were observed ranging from 2.0 to 2.2 over a wide range of transient energies with good agreement between measurement methods.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Neutron thermalization in nuclear graphite: A modern story of a classic moderator

Graphite has been of great interest since the dawn of the nuclear era. In this work, we will focus on the thermal neutron scattering of two types of nuclear graphite that have different microstructures, namely, the historic PGA which has a very anisotropic medium-grained needle coke, and the new G347A which is a near-isotropic fine-grained graphite. We report the results of room temperature inelastic neutron scattering measurements for the scattering function, double differential scattering cross-section, and phonon density of states. The obtained results are discussed in conjunction with results extracted from first-principles lattice dynamics simulations of crystalline graphite, using the incoherent approximation and by also including the coherent-one phonon contribution. Excellent agreements have been found between the measured and calculated scattering functions and phonon densities of states. The inclusion of the coherent-one phonon scattering shows a significant improvement of the calculated scattering functions, double differentials, and inelastic scattering cross-sections. We also highlight the physical concerns about the current thermal neutron scattering ENDF/B-VIII of nuclear graphite library and its corresponding phonon density of states. This work aims to provide a new presentation of neutron thermalization by graphite and offers a new set of high quality experimental and computational data for consideration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗