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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Information technology equipment cooling method

According to one embodiment, a system for removing heat from a rack of information technology equipment may include a sidecar indoor air to liquid heat exchanger that cools air utilized by the rack of information technology equipment to cool the rack of information technology equipment. The system may also include a liquid to liquid heat exchanger and an outdoor heat exchanger. The system may further include configurable pathways to connect and control fluid flow through the sidecar heat exchanger, the liquid to liquid heat exchanger, the rack of information technology equipment, and the outdoor heat exchanger based upon ambient temperature and/or ambient humidity to remove heat generated by the rack of information technology equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

On Information Transfer in Dynamical Systems with applications in Control of Non-equilibrium Dynamics

In this paper we develop the concept of information transfer between the Borel-measurable sets for a dynamical system described by a measurable space and a non-singular transformation. The concept is based on how Shannon entropy is transferred between the measurable sets, as the dynamical system evolves. We show that the proposed definition of information transfer satisfies the usual notions of information transfer and causality, namely, zero transfer and transfer asymmetry. Furthermore, we show how the information transfer measure can be used to characterize ergodicity and mixing in dynamical systems. We also develop the computational methods for information transfer computation and apply the framework for optimal placements of actuators and sensors for control of non-equilibrium dynamics.

Sinha, Subhrajit↗
DOE OSTI2013

Waste Information Management System with 2012-13 Waste Streams - 13095

The Waste Information Management System (WIMS) 2012-13 was updated to support the Department of Energy (DOE) accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to waste treatment and disposal were potential critical path issues under the accelerated schedule. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of radioactive waste that would be generated by DOE sites over the next 40 years. Each local DOE site historically collected, organized, and displayed waste forecast information in separate and unique systems. In order for interested parties to understand and view the complete DOE complex-wide picture, the radioactive waste and shipment information of each DOE site needed to be entered into a common application. The WIMS application was therefore created to serve as a common application to improve stakeholder comprehension and improve DOE radioactive waste treatment and disposal planning and scheduling. WIMS allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and inter-modal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, developed and deployed the web-based forecast and transportation system and is responsible for updating the radioactive waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Investing in International Information Exchange Activities to Improve the Safety, Cost Effectiveness and Schedule of Cleanup - 13281

With decreasing budgets and increasing pressure on completing cleanup missions as quickly, safely and cost-effectively as possible, there is significant benefit to be gained from collaboration and joint efforts between organizations facing similar issues. With this in mind, the US Department of Energy (DOE) and the UK Nuclear Decommissioning Authority (NDA) have formally agreed to share information on lessons learned on the development and application of new technologies and approaches to improve the safety, cost effectiveness and schedule of the cleanup legacy wastes. To facilitate information exchange a range of tools and methodologies were established. These included tacit knowledge exchange through facilitated meetings, conference calls and Site visits as well as explicit knowledge exchange through document sharing and newsletters. A DOE web-based portal has been established to capture these exchanges and add to them via discussion boards. The information exchange is operating at the Government-to-Government strategic level as well as at the Site Contractor level to address both technical and managerial topic areas. This effort has resulted in opening a dialogue and building working relationships. In some areas joint programs of work have been initiated thus saving resource and enabling the parties to leverage off one another activities. The potential benefits of high quality information exchange are significant, ranging from cost avoidance through identification of an approach to a problem that has been proven elsewhere to cost sharing and joint development of a new technology to address a common problem. The benefits in outcomes significantly outweigh the costs of the process. The applicability of the tools and methods along with the lessons learned regarding some key issues is of use to any organization that wants to improve value for money. In the waste management marketplace, there are a multitude of challenges being addressed by multiple organizations and the effective pooling and exchange of knowledge and experience can only be of benefit to all participants to help complete the cleanup mission more quickly and more cost effectively. This paper examines in detail the tools and processes used to promote information exchange and the progress made to date. It also discusses the challenges and issues involved and proposes recommendations to others who are involved in similar activities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Waste Information Management System with 2016-17 Waste Streams - 17246

The Waste Information Management System (WIMS) 2016-17 was updated to support the Department of Energy (DOE) accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to waste treatment and disposal were potential critical path issues under the accelerated schedule. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of radioactive waste that would be generated by DOE sites over the next 35 years. Each local DOE site historically collected, organized, and displayed waste forecast information in separate and unique systems. In order for interested parties to understand and view the complete DOE complex-wide picture, the radioactive waste and shipment information of each DOE site needed to be entered into a common application. The WIMS application was therefore created to serve as a common application to improve stakeholder comprehension and improve DOE radioactive waste treatment and disposal planning and scheduling. WIMS allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and inter-modal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, developed and deployed the web-based forecast and transportation system and is responsible for updating the radioactive waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Robot Navigation in a Rich Information Map for Inspection in Nuclear Environments - 18596

This paper discusses our approach of using an unmanned ground vehicle (UGV) to perform inspection tasks in nuclear environments. To reduce people's exposure to the environment with elevated radiation levels, our goal is enabling UGV to navigate and perform inspections fully autonomous with no human getting involved. To achieve the full automation, the UGV can take use of a rich information map. In traditional robot navigation, the 2D global cost map used for planning path consists only obstacle locations, while a rich information map also includes location information of interested objects to robot. The UGV can then use a rich information map to perform routine inspection tasks with known object locations. In this paper, we will present our method of generating a rich information map and the usage of this map for International Atomic Energy Agency (IAEA) Robotics Challenge. (authors)

42 ENGINEERING↗
DOE OSTI2019

Waste Information Management System with 2018-19 Waste Streams - 19106

The Waste Information Management System (WIMS) 2018-19 was updated to support the Department of Energy (DOE) accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to waste treatment and disposal were potential critical path issues under the accelerated schedule. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of radioactive waste that would be generated by DOE sites over the next 35 years. Each local DOE site historically collected, organized, and displayed waste forecast information in separate and unique systems. In order for interested parties to understand and view the complete DOE complex-wide picture, the radioactive waste and shipment information of each DOE site needed to be entered into a common application. The WIMS application was therefore created to serve as a common application to improve stakeholder comprehension and improve DOE radioactive waste treatment and disposal planning and scheduling. WIMS allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and intermodal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, developed and deployed the web-based forecast and transportation system and is responsible for updating the radioactive waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Informative Path Planning for Mapping Radiation

This project involves making measurements of contaminated areas using portable radiation detectors and small robots. Tasks involve operating detectors and small robots, combining data acquisition for the two systems, and automating the systems. The end result is a radiation map of contamination areas. Localization of radioactive sources and mapping of contamination distribution is essential when responding to emergencies, or when dealing with cleanup efforts. Radiation maps are used for situational awareness and for establishing a safe perimeter. However, it may be hazardous or impossible for humans to safely enter the site and survey the area. Informative Path Planning (IPP) is used to design a path for a robotic sensor platform to gather the most information about the radiation distribution while operating under the set of constraints given by the dynamics of the robot and other requirements such as minimizing the overall mission time. The key challenge for IPP is the tight coupling in multiple layers of decisions: Selection of locations to visit; Allocation of sensors to those locations; Processing of information gathered. Given a robot equipped with a basic gamma detector and Lidar sensor, how can we efficiently explore an unknown environment in such a way that we can satisfy the robot's constraints while simultaneously maximizing the accuracy of the map and minimizing the total runtime? This work presents a systematic procedure for IPP and environmental mapping that: Selects the best locations at which to sample; Finds the best path for robots; Estimates the measurement value at unknown locations using a Gaussian Process regression framework. Given a set of points where radiation measurements were taken, we are utilizing a Gaussian Process Regression with a Matern kernel to reconstruct the shape of the underlying ground truth. Mapping is done via the Robot Operating System's OpenSlam Gmapping package, which creates a 2D occupancy grid map. Simulated trials were run with a radiation source placed at random locations, and allowing the robot to select and move towards 20 samples at separate locations, as well as sampling along the path while moving. The simulated sensor model assumed an efficiency of 0.3, collection time of 1 second. Various weighting schemes were tested and compared. Quantitative evaluations often favored exploitation-heavy weighting schemes where μ was emphasized, with the Root Mean Squared Error (RMSE) quickly tending towards 2, whereas qualitative comparisons would favor exploration-biased schemes with greater weight on σ. The difference comes from operators often favoring knowing the general location of a source, rather than the exact properties of a source. Weighting of the path-length π often reduced the quality of the regression as less exploration occurred, however this may be deemed a worthwhile trade-off under certain circumstances, such as with sensitive robots. Successfully developed a methodology for informative path-planning for a robot to efficiently gather information and create a map of radiation. In the future, we would like to focus on: Hardware trials with integration of the robot platform and the IPP software; More refined sensor and environmental modeling; Multiple robot platforms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Waste Information Management System with 2019-20 Waste Streams - 20491

The Waste Information Management System (WIMS) 2019-20 was updated to support the Department of Energy (DOE) accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to waste treatment and disposal were potential critical path issues under the accelerated schedule. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of radioactive waste that would be generated by DOE sites over the next 35 years. Each local DOE site historically collected, organized, and displayed waste forecast information in separate and unique systems. In order for interested parties to understand and view the complete DOE complex-wide picture, the radioactive waste and shipment information of each DOE site needed to be entered into a common application. The WIMS application was therefore created to serve as a common application to improve stakeholder comprehension and improve DOE radioactive waste treatment and disposal planning and scheduling. WIMS allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and intermodal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, developed and deployed the web-based forecast and transportation system and is responsible for updating the radioactive waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Nuclear power plant risk-informed surveillance frequency control program implementation at the Duke Catawba nuclear plant with a focus on instrumentation and control systems - 333

This paper summarizes an updated process for risk-informed surveillance frequency control program (SFCP) implementation at the Catawba Nuclear Power Plant in the past year. This program includes selection and prioritization of specific target surveillance test interval extensions; and development, review, and implementation of surveillance test risk informed documented evaluation (STRIDE) packages designed to support extension of conventional surveillance requirement test intervals, in accordance with 'Risk-Informed Technical Specifications Initiative 5b, Risk-Informed Method for Control of Surveillance Frequencies, Industry Guideline,' NEI 04-10, Revision 1 . The scope of work associated with STRIDE development includes probabilistic risk assessment (PRA) case studies, deterministic assessment (DA) evaluations, and, where required, instrument drift evaluation (IDE). The STRIDE implementation efforts have also included support of independent decision-making panel (IDP) meetings at the implementing power stations and IDP member training. The purpose of this paper is to provide a presentation of a refined process for STRIDE development with a focus on instrumentation and control systems based on author experience, which includes support for the development of multiple STRIDE packages for the Catawba Nuclear Plant. This paper outlines a framework for practical implementation of an SFCP within the context of an integrated risk-informed performance-based regulation application program with emphasis on instrumentation and control systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

This paper describes an original approach to generating scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a cpability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used in creating scenarios for testing of algorithms for SNM detection. The ontology-driven scenario generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users' domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. Furthermore, the approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks

Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work, we demonstrate that the failure of plain physics-informed neural networks arises from the significant discrepancy in the convergence rate of residuals at different training points, where the slowest convergence rate dominates the overall solution convergence. Based on these observations, we propose a pointwise adaptive weighting method that balances the residual decay rate across different training points. The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics-informed neural networks and physics-informed deep operator networks. In conclusion, through extensive numerical results we demonstrate that our proposed approach of balanced residual decay rates offers several advantages, including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.

Balanced convergence rate↗
DOE OSTI2025

First-of-a-Kind Risk-Informed Digital Twin for Operational Decision Making

A digital twin (DT) is a digital model or a collection of models of a physical entity. DTs in the nuclear arena can be used from plant design through decommissioning. Decisions are typically a priori or made offline. Risk-informed decision making is identifying what can go wrong, its frequency, and the consequences of its failure. Ideally risk-informed decision making reflects the current state of the plant and provides a decision in real time. Traditionally, probabilistic risk assessments (PRAs) evaluate the failures of safety systems, the risk of core damage, and the offsite dose as the consequence. However, this DT evaluates the decisions on the control side rather than the protection side. It uses the same risk methods to probabilistically inform the decision-making process but in a different way. Rather than evaluating the risk of core damage, this DT evaluates the likelihood of avoiding a trip set point while maintaining plant safety. Performance-based assessments are identified via its probabilistic evaluation of operational alternatives based on system status. Because the purpose of the control system is to maintain system variables within prescribed operating ranges, upsets or challenges that can exceed a trip set point resulting in a plant transient and a challenge to plant mitigating systems based on actual plant conditions, are evaluated to safely maintain the plant within the operating ranges. The probabilistic portion of the model is autonomously and automatically adjusted, and the metric of interest (i.e. likelihood of avoiding a trip set point) is recalculated. The digital representation of the physical system (i.e. the DT) performs a deterministic performance–based assessment of the probabilistically identified alternatives identified to validate the probabilistic assessment. A decision-making algorithm selects the appropriate option based on the probabilistic and deterministic assessments and transmits a control signal to a component(s) to initiate a corrective action or informs an operator of its decision.

digital twin↗
DOE OSTI2025

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One such innovation is the Kolmogorov–Arnold Network (KAN), which has demonstrated benefits over traditional MLPs, including faster neural scaling and better interpretability. The application of KANs to physics-informed learning has led to the development of Physics-Informed KANs (PIKANs), enabling the use of KANs to solve PDEs. However, despite their advantages, KANs often suffer from slower training speeds, particularly in higher-dimensional problems where the number of collocation points grows exponentially with the dimensionality of the system. To address this challenge, we introduce Separable Physics-Informed Kolmogorov–Arnold Networks (SPIKANs). This novel architecture applies the principle of separation of variables to PIKANs, decomposing the problem such that each dimension is handled by an individual KAN. This approach drastically reduces the computational complexity of training without sacrificing accuracy, facilitating their application to higher-dimensional PDEs. Through a series of benchmark problems, we demonstrate the effectiveness of SPIKANs, showcasing their superior scalability and performance compared to PIKANs and highlighting their potential for solving complex, high-dimensional PDEs in scientific computing.

Kolmogorov-Arnold networks↗
DOE OSTI2019

Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information

We consider the k-user successive refinement problem with causal decoder side information and derive an exponential strong converse theorem. The rate-distortion region for the problem can be derived as a straightforward extension of the two-user case by Maor and Merhav (2008). We show that for any rate-distortion tuple outside the rate-distortion region of the k-user successive refinement problem with causal decoder side information, the joint excess-distortion probability approaches one exponentially fast. Our proof follows by judiciously adapting the recently proposed strong converse technique by Oohama using the information spectrum method, the variational form of the rate-distortion region and Hölder’s inequality. The lossy source coding problem with causal decoder side information considered by El Gamal and Weissman is a special case (k = 1) of the current problem. Therefore, the exponential strong converse theorem for the El Gamal and Weissman problem follows as a corollary of our result.

42 ENGINEERING↗
DOE OSTI2023

Using Cyber-Informed Engineering for Cyber Defense Workbook

This workbook was prepared for a detailed workshop in Cyber-Informed Engineering. It is designed to provide an audience of electrical cooperative engineering staff with an opportunity to practice leveraging the principles of Cyber-Informed Engineering for a hypothetical system upgrade. This workbook contains material describing the fictional project, information about Cyber-Informed Engineering, hands-on exercises, and a copy of the slides presented during the workshop. It can stand alone as a CIE resource.

42 ENGINEERING↗
DOE OSTI2018

An (R, S)-norm fuzzy information measure with its applications in multiple-attribute decision-making

In this paper, we introduce a quantity measure which is called (R, S)-norm entropy and discuss some of its major properties with Shannon’s and other entropies in the literature. Based on this (R, S)-norm entropy, we have proposed a new (R, S)-norm fuzzy information measure and discussed its validity and properties. Further, we have given its comparison with other fuzzy information measures to prove its effectiveness. Attribute weights play an important role in multiple-attribute decision-making problems. In the present communication, two methods of determining the attribute weights are introduced. First is the case when the information regarding attribute weights is incompletely known or completely unknown and second is when we have partial information about attribute weights. For the first case, the extension of ordinary entropy weight method is used to calculate attribute weights and minimum entropy principle method based on solving a linear programming model is used in the second case. Finally, two methods are explained through numerical examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Information storage capacity of discrete spin systems

Understanding the limits imposed on information storage capacity of physical systems is a problem of fundamental and practical importance which bridges physics and information science. There is a well-known upper bound on the amount of information that can be stored reliably in a given volume of discrete spin systems which are supported by gapped local Hamiltonians. However, all the previously known systems were far below this theoretical bound, and it remained open whether there exists a gapped spin system that saturates this bound. Here, we present a construction of spin systems which saturate this theoretical limit asymptotically by borrowing an idea from fractal properties arising in the Sierpinski triangle. Our construction provides not only the best classical error-correcting code which is physically realizable as the energy ground space of gapped frustration-free Hamiltonians, but also a new research avenue for correlated spin phases with fractal spin configurations. -- Highlights: •We propose a spin model with fractal ground states and study its coding properties. •We show that the model asymptotically saturates a theoretical limit on information storage capacity. •We discuss its relations to various theoretical physics problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗