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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 1,423 records · Page 79

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NASA NTRS2009

The Radio Frequency Health Node Wireless Sensor System

The Radio Frequency Health Node (RFHN) wireless sensor system differs from other wireless sensor systems in ways originally intended to enhance utility as an instrumentation system for a spacecraft. The RFHN can also be adapted to use in terrestrial applications in which there are requirements for operational flexibility and integrability into higher-level instrumentation and data acquisition systems. As shown in the figure, the heart of the system is the RFHN, which is a unit that passes commands and data between (1) one or more commercially available wireless sensor units (optionally, also including wired sensor units) and (2) command and data interfaces with a local control computer that may be part of the spacecraft or other engineering system in which the wireless sensor system is installed. In turn, the local control computer can be in radio or wire communication with a remote control computer that may be part of a higher-level system. The remote control computer, acting via the local control computer and the RFHN, cannot only monitor readout data from the sensor units but can also remotely configure (program or reprogram) the RFHN and the sensor units during operation. In a spacecraft application, the RFHN and the sensor units can also be configured more nearly directly, prior to launch, via a serial interface that includes an umbilical cable between the spacecraft and ground support equipment. In either case, the RFHN wireless sensor system has the flexibility to be configured, as required, with different numbers and types of sensors for different applications. The RFHN can be used to effect realtime transfer of data from, and commands to, the wireless sensor units. It can also store data for later retrieval by an external computer. The RFHN communicates with the wireless sensor units via a radio transceiver module. The modular design of the RFHN makes it possible to add radio transceiver modules as needed to accommodate additional sets of wireless sensor units. The RFHN includes a core module that performs generic computer functions, including management of power and input, output, processing, and storage of data. In a typical application, the processing capabilities in the RFHN are utilized to perform preprocessing, trending, and fusion of sensor data. The core module also serves as the unit through which the remote control computer configures the sensor units and the rest of the RFHN.

Valencia, J. Emilio↗

DOE OSTI2014

Region templates: Data representation and management for high-throughput image analysis

Compartment fires exhibit unique features associated with smoke accumulation and restricted air ventilation. The objective of the present study is to evaluate the ability of CFD-based fire models to simulate these features and in particular the effects of flame extinction that results from under-ventilated fire conditions. The study is performed using FireFOAM; FireFOAM is an advanced fire modeling software developed by FM Global and is based on a general-purpose open-source software called OpenFOAM. A new flame extinction model based on the concept of a critical value of the flame Damköhler number is incorporated into FireFOAM. The performance of the extinction model is evaluated via comparisons with a previously developed experimental database corresponding to a reduced-scale, heptane-fueled, compartment fire configuration. The numerical simulations also include a description of fuel evaporation driven by the computed gas-to-liquid heat feedback. Comparisons between experimental data and numerical results provide a suitable test bed to evaluate the ability of CFD-based fire models to describe the transition from over- to under-ventilated fire conditions, as well as the transition from extinction-free conditions to conditions in which the flame experiences partial or total quenching.

Energy & Fuels↗

DOE OSTI2024

DOE Cold Climate Heat Pump Challenge Field Validation: Data Collection and Analysis Plan

This document details the data collection, storage, and analysis plan and methodologies for conducting the field validation portion of DOE’s Cold Climate Heat Pump (CCHP) Challenge. The study is focused on validating the in-field heating performance of prototypical CCHPs that have successfully demonstrated that they meet or exceed the Challenge specification in a laboratory setting. The units that have passed laboratory testing will be installed in real homes along with sensors and loggers for monitoring performance over an entire heating season. Data collected through monitoring will be cleaned and stored in a secure database. The data will be analyzed for calculating the key metrics defined by the Challenge including heating capacities at low outdoor air temperatures (below 32 °F), efficiency in terms of the Coefficient of Performance (COP), switchover temperatures and auxiliary heat staging. Demand Response (DR) capabilities will also be tested using specially designed DR events. In most cases, shoulder season and cooling season performance will also be determined and reported.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1988

On-line object feature extraction for multispectral scene representation

A new on-line unsupervised object-feature extraction method is presented that reduces the complexity and costs associated with the analysis of the multispectral image data and data transmission, storage, archival and distribution. The ambiguity in the object detection process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the decision making process. The unity relation was defined that must exist among the pixels of an object. Automatic Multispectral Image Compaction Algorithm (AMICA) uses the within object pixel-feature gradient vector as a valuable contextual information to construct the object's features, which preserve the class separability information within the data. For on-line object extraction the path-hypothesis and the basic mathematical tools for its realization are introduced in terms of a specific similarity measure and adjacency relation. AMICA is applied to several sets of real image data, and the performance and reliability of features is evaluated.

Ghassemian, Hassan↗

DOE OSTI2017

Matrix approach to land carbon cycle modeling: A case study with the Community Land Model

The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically-resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial-temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO 2 fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data assimilation, and facilitate tracking and benchmarking of model behaviors. Altogether, the matrix approach can make a broad range of future modeling activities more efficient and effective.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1991

Integrated Vertical Bloch Line (VBL) memory

Vertical Bloch Line (VBL) Memory is a recently conceived, integrated, solid state, block access, VLSI memory which offers the potential of 1 Gbit/sq cm areal storage density, data rates of hundreds of megabits/sec, and submillisecond average access time simultaneously at relatively low mass, volume, and power values when compared to alternative technologies. VBLs are micromagnetic structures within magnetic domain walls which can be manipulated using magnetic fields from integrated conductors. The presence or absence of BVL pairs are used to store binary information. At present, efforts are being directed at developing a single chip memory using 25 Mbit/sq cm technology in magnetic garnet material which integrates, at a single operating point, the writing, storage, reading, and amplification functions needed in a memory. The current design architecture, functional elements, and supercomputer simulation results are described which are used to assist the design process.

Katti, R. R.↗

DOE OSTI2015

Online technique for detecting state of onboard fiber optic gyroscope

Although angle random walk (ARW) of fiber optic gyroscope (FOG) has been well modeled and identified before being integrated into the high-accuracy attitude control system of satellite, aging and unexpected failures can affect the performance of FOG after launch, resulting in the variation of ARW coefficient. Therefore, the ARW coefficient can be regarded as an indicator of “state of health” for FOG diagnosis in some sense. The Allan variance method can be used to estimate ARW coefficient of FOG, however, it requires a large amount of data to be stored. Moreover, the procedure of drawing slope lines for estimation is painful. To overcome the barriers, a weighted state-space model that directly models the ARW to obtain a nonlinear state-space model was established for FOG. Then, a neural extended-Kalman filter algorithm was implemented to estimate and track the variation of ARW in real time. The results of experiment show that the proposed approach is valid to detect the state of FOG. Moreover, the proposed technique effectively avoids the storage of data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1984

NASA contributions to the global habitability program

As a result of developments occurring over the last two decades, the data acquisition, storage, analysis, and transmission facilities are now available for a concerted long-term interdisciplinary and international study of the global environmental system. Such a study is the essence of the 'Global Habitability' concept introduced in 1982. The aims of Global Habitability research are considered, taking into account an understanding of the vital global processes of the earth's energy balance, the global hydrological cycle, and the biogeochemical cycling of carbon, nitrogen, phosphorus, and sulfur. Details of NASA planning for Global Habitability are discussed along with international data exchange arrangements. Attention is given to the possible contributions of satellite data and associated techniques to Global Habitability, examples of specific research conducted by NASA in support of the Global Habitability and the international sharing of data and results for Global Habitability.

Mcconnell, D. G.↗

NASA NTRS1987

Loran-C monitoring

The Loran-C monitor developed by Ohio University will collect Loran signal data for storage on magnetic tape. Stationed at the Ohio University Airport, Athens, Ohio, the monitor will provide valuable information concerning the daily and seasonal variation of the Loran-C signals for use in non-precision approach studies. With the aid of a second monitor, located in Gallion, Ohio, it can be determined if the errors found at a particular geographic location correlate with those found at another location. This will give some indication as to how far apart monitors can be positioned to obtain accurate non-precision approach data for various airports.

Edwards, Jamie↗

DOE OSTI2019

numericalFunctions

The numericalFunction C library is designed to handle point-wise interpolative data representing a mathematical function. The library currently handles 1-d functions (i.e., functions of the format f(x)). The main object is the ptwXYPoints C struct that holds list of x,y pairs sorted by ascending x-values with an interpolation rule. That is, it contains the 1-d function f(x) = y(x). Functions are included that support common unitary and binary operations (e.g., negation, addition, multiplication). As example, if f(x) and g(x) are two ptwXYPoints instances, then routines to calculate f(x) + g(x) and f(x) * g(x) are included. The library support storage of data with lin-lin, lin-log, log-lin, log-log and flat (or histogram) interpolation. Functions to convert any of the supported interpolation to lin-lin are included. However, many of the binary functions only support lin-lin interpolation. The library also contains a C struct for storing a Legendre series representation of a 1-d function, and functions to convert Legendre series to and from a ptwXYPoints instance. A python interface to the library is included, with the ptwXYPoints C struct treated as a class. This class has many of the standard python math methods (e.g., __add__, __mul__).

Beck, Bret↗

NASA NTRS1991

Using semantic data modeling techniques to organize an object-oriented database for extending the mass storage model

A methodology for optimizing organization of data obtained by NASA earth and space missions is discussed. The methodology uses a concept based on semantic data modeling techniques implemented in a hierarchical storage model. The modeling is used to organize objects in mass storage devices, relational database systems, and object-oriented databases. The semantic data modeling at the metadata record level is examined, including the simulation of a knowledge base and semantic metadata storage issues. The semantic data model hierarchy and its application for efficient data storage is addressed, as is the mapping of the application structure to the mass storage.

Campbell, William J.↗

DOE OSTI2025

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗

DOE OSTI2021

SiC-Based Wireless Power Transformation for Data Centers & Medium Voltage Applications

Data centers have grown in physical size and their electrical power consumption has grown to levels of several 100kW and approaching 1 GW in large installations. The low voltage electrical distribution inside of Data Centers consists of several conversion stages and a lot of wiring to bring the power from medium voltage (MV) levels outside of the building to the low voltage levels that the servers and racks require. Energy losses during distribution and conversion and high incident arc flash energy levels at the point of use are significant. The electrical distribution system is complex and costly.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Direct-Write Printed Current Sensor for Load Monitoring Applications

We report on a flexible current sensor development employing direct-write printing and additive manufacturing techniques. The integrated current sensor unit incorporates sensing, data processing & storage, and in-field calibration capabilities. A combination of direct-write printing technique and low-temperature curable metallic ink is used to print Ag wires around a 12.5-μm -thick permalloy film. The overall thickness of the polyimide substrate and permalloy sheet remains well-below 500 μm enabling wrap-around sensor functionality. Fused deposition modeling (FDM) additive manufacturing is used to build custom parts for sensor mounting, encapsulation, and an in-field calibration unit using heat and chemical-resistant thermoplastic which responds well to mechanical stress. In the present configuration, the integrated current sensor unit can be powered through USB or 4 AAA batteries to allow data collection and sensor calibration in the field. The flexible current sensor employing a continuous sheet of the permalloy exhibited a well-behaved signal output response at peak current levels exceeding 15A and in the applied ac frequency range of 60 Hz–10 kHz. Overall, the combination of direct-write printing, thin permalloy material, and additive manufacturing technique shows promise for the development of a reconfigurable low-cost current sensor that can be deployed in large numbers at locations of interest to avail the cost and energy-saving opportunities in industrial/commercial and residential applications.

Yu, Yongchao↗

DOE OSTI2013

Overview of the United States department of energy's used fuel disposition research and development campaign

The United States Department of Energy (US DOE) is conducting research and development (R and D) activities within the Used Fuel Disposition Campaign (UFDC) to support storage, transportation, and disposal of used nuclear fuel (UNF) and wastes generated by existing and future nuclear fuel cycles. R and D activities are ongoing at nine national laboratories, and are divided into two major topical areas: (1) storage and transportation research, and (2) disposal research. Storage R and D focuses on closing technical gaps related to extended storage of UNF. For example, uncertainties remain regarding high-burnup nuclear fuel cladding performance following possible hydride reorientation and creep deformation, and also regarding long-term canister integrity. Transportation R and D focuses on ensuring transportability of UNF following extended storage, addressing data gaps regarding nuclear fuel integrity, retrievability, and demonstration of subcriticality. Disposal R and D focuses on identifying multiple viable geologic disposal options and addressing technical challenges for generic disposal concepts in various host media (e.g., mined repositories in salt, clay/shale, and granitic rocks, and deep borehole disposal in crystalline rock). R and D will transition to site-specific challenges as national policy advances. R and D goals at this stage are to increase confidence in the robustness of generic disposal concepts, to reduce generic sources of uncertainty that may impact the viability of disposal concepts, and to develop science and engineering tools that will support the selection, characterization, and ultimately licensing of a repository. The US DOE has also initiated activities that can be conducted within the constraints of the Nuclear Waste Policy Act to facilitate the development of an interim storage facility and supporting transportation infrastructure. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2023

How Have Hydrological Extremes Changed Over the Past 20 Years?

Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.

Bailing Li↗

DOE OSTI2024

Deep Learning-based Parameterization of Complex 3D CO2 Saturation Data in Large-scale Geological Carbon Storage

In deep learning (DL), dimension reduction plays a pivotal role in improving training efficiency and minimizing overfitting, especially when working with complex datasets like three-dimensional (3D) saturation data. In the context of geological carbon storage (GCS), 3D saturation data introduces unique challenges due to its sparse nature and sharp transitions at plume boundaries, known as shock fronts. To tackle these challenges, we developed a novel DL framework that combines dimension reduction with advanced 3D reconstruction techniques. Our approach utilizes latent variables derived from 2D average saturation fields to efficiently capture the essential features of high-dimensional data while reducing the number of variables. This enhances both the robustness and accuracy of DL models, making the framework more practical for real-world applications. By offering a tailored solution for modeling complex 3D saturation dynamics, this framework holds significant potential for environmental monitoring, energy storage, and other geological applications.

Wang, Hongsheng [University of Texas at Austin]↗

DOE OSTI2018

Development of a Technology Roadmap for Online Monitoring of Nuclear Power Plants

The nuclear power industry has recently recognized the potential cost saving that can be achieved from migrating the current manual and labor dependent surveillance and preventive maintenance activities to data-driven online monitoring methods. Since then, several efforts have been launched with various degrees of momentum to tackle specific surveillance or preventive maintenance activities. The light water reactor sustainability program (LWRS) program has anticipated this need and launched an effort that aimed to develop a technology roadmap for the nuclear power industry. Because the roadmap has to be focused towards the user or application’s value, and because nuclear power plants have different process efficiencies and deficiencies, the technology roadmap has to be plant independent. Achieving this objective required optimizing the extent of details described by a technology roadmap. The approach followed is for the roadmap to describe the sequence of activities needed to automate a process or equipment activity, but not describe the sequence of processes or equipment to target. For example, the steps required to migrate the inspection of a pump are plant independent. However, defining that the migration of feed water pumps inspections to online monitoring should be performed before inspections of feed water valves is dependent on multiple plant factors such as the plant requirements, labor work capacity, and process or equipment conditions. The technology roadmap was broken down into six elements, four of which have a sequential path of advancement, while the other two are supporting elements to the sequential elements. The sequential part of the roadmap analyzes data collection, analytics, visualization, and management. For these elements of the technology roadmap, the migration of an activity is defined in three states. The base state is defined as the most primitive, manual, and labor dependent process used by some nuclear power plants. The modern state is defined as the state that can be achieved if the activities are augmented or replaced by current technologies that are commercially available or are soon to be available. The state of the art is defined as using technologies of the future, i.e. concept and technologies that are being researched. The data collection part of the technology roadmap defines three states of every process performed in a nuclear power plant. However, the data analytics part is described based on equipment, because automating an activity performed on one equipment required considering multiple data sources. The visualization and data management elements are process or equipment independent. Visualization describes the means to use human factor science and technologies advancement in presenting the data collected and the data analytics results. The data management targets storage, communication, and computational power need, in addition to data integration and sharing. Since storage, communication, and computational power technologies and path for deployment are known to any information technology organization, this effort targeted the cost of data management. This has a direct impact on the feasibility of the automation effort. The supporting elements analyzes the value of automating the activity and the change challenges to tackle. For the support elements, the roadmap targeted describing activities that need to be performed as part of the roadmap development. The value element of the roadmap describes how to analyze the cost saving and the associated risk. The change management identifies multiple factors to consider such as regulation, cybersecurity, resource availability, supporting infrastructure availability, feasibility (power, fitness, environment tolerance, facilities), and cultural change.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗