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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
NASA NTRS1978

Development of automated power system management techniques

The basic approach in the automated power system management (APSM) implementation is to use one central microprocessor for the overall power system supervision and several local microprocessors dedicated to one or more major subassemblies to perform simple monitoring and control functions. Communication between the central and each local processor is through a dedicated two-wire network employing serial data transfer. The block diagrams of the processors, the data bus characteristics, and the software functions and organization are presented.

Imamura, M. S.↗
NASA NTRS1994

Space Active Modular Materials Experiments (SAMMES): Low Earth Orbital mission aboard the Space Test Experiments STEP-3 platform

The requirement of satellite systems to survive in the space environment for 5 to 10 years to achieve greater cost effectiveness is discussed. Characterization of the orbital space environment and its effects on spacecraft systems have received considerable research attention. Instrumentation for long term measurement of key physical parameters characterizing the low Earth orbit (LEO) environment and its effects on degradation of spacecraft materials and solar arrays is reported. These measurements enable active, real time monitoring of the health of spacecraft and payload systems. SAMMES is designed to be autonomous, compact, low power, and lightweight.

Joshi, P.↗
NASA NTRS1995

Orbiting meteoroid and debris counting experiment

The Orbiting Meteoroid and Debris Counting Experiment (OMDC) flew for approximately 90 days in a highly elliptical earth orbit onboard the Clementine Interstage Adapter (ISA) Spacecraft. This experiment obtained data on the impact flux of natural micrometeoroids and it provided limited information on the population of small mass man-made debris as a function of altitude in near earth space. The flight of the OMDC experiment on the ISA spacecraft also demonstrated that the ultra-lightweight, low-power, particle impact detector system that was used is a viable system for flights on future spacecraft to monitor the population of small mass man-made debris particles and to map the cosmic dust environment encountered on interplanetary missions. An overview of the ISA spacecraft mission, the approach to the OMDC experiment, and the data obtained by the experiment are presented.

Kinard, William H.↗
NASA NTRS1995

A Microinstrumentation System for Remote Environmental Monitoring

This paper reports on a hybrid micro-instrumentation system that includes a embedded micro-controller, transducers for monitoring environmental parameters, interface/readout electronics for linking the controller and the transducers, and custom circuitry for system power management. Sensors for measuring temperature, pressure, humidity, and acceleration are included in the initial system, which operates for more than 180 days and dissipates less than 700 microW from a 6V battery supply. The sensor scan rate is adaptive and can be event triggered. The system communicates internally over a 1 MHz, nine-line intramodule sensor bus and outputs data over a hard-wired serial interface or a 315MHz wireless link. The use of folding platform packaging allows an internal system volume as small as 5 cc.

Mason, Andrew↗
NASA NTRS2002

Instrumentation and Controls Division Overview: Sensors Development for Harsh Environments at Glenn Research Center

The Instrumentation and Controls Division is responsible for planning, conducting and directing basic and applied research on advanced instrumentation and controls technologies for aerospace propulsion and power applications. The Division's advanced research in harsh environment sensors, high temperature high power electronics, MEMS (microelectromechanical systems), nanotechnology, high data rate optical instrumentation, active and intelligent controls, and health monitoring and management will enable self-feeling, self-thinking, self-reconfiguring and self-healing Aerospace Propulsion Systems. These research areas address Agency challenges to deliver aerospace systems with reduced size and weight, and increased functionality and intelligence for future NASA missions in advanced aeronautics, economical space transportation, and pioneering space exploration. The Division also actively supports educational and technology transfer activities aimed at benefiting all humankind.

Zeller, Mary V.↗
NASA NTRS2010

Stirling Convertor Extended Operation Testing and Data Analysis at Glenn Research Center

Extended operation of Stirling convertors is essential to the development of radioisotope power systems and their potential use for longduration missions. To document the reliability of the convertors, regular monitoring and analysis of the extended operation data is particularly valuable, allowing us to better understand and quantify long-life characteristics of the convertors. Furthermore, investigation and comparison of the extended operation data to baseline performance data provides an opportunity to understand system behavior should any off-nominal performance occur. Glenn Research Center (GRC) has tested 16 Stirling convertors under 24-hr unattended extended operation, including four that have operated in a thermal vacuum environment and two that are operating in the Advanced Stirling Radioisotope Generator Engineering Unit. Ten of the sixteen convertors are the Advanced Stirling Convertors (ASC) developed by Sunpower, Inc. with GRC. These are highly efficient (conversion efficiency of up to 38 percent for the ASC-1), low-mass convertors that have evolved through technologically progressive convertor builds. Six convertors at GRC are Technology Demonstration Convertors from Infinia Corporation. They have achieved greater than 27 percent conversion efficiency and have accumulated over 185,000 of the total 265,000 hr of extended operation at GRC. This paper presents the extended operation testing and data analysis of free-piston Stirling convertors at NASA GRC as well as how these tests have contributed to the Stirling convertor s progression toward flight.

Cornell, Peggy A.↗
NASA NTRS2014

An Overview of the Development of High Temperature Wireless Smart Sensor Technology

The harsh environment inherent in propulsion systems is especially challenging for Smart Sensor Systems; this paper addresses technology development for such applications. A basic sensing system for high temperature wireless pressure monitoring composed of a sensor, electronics, and wireless communication with scavenged power developed for health monitoring of aircraft engines and other high temperature applications has been demonstrated at 475 C. Other efforts will be discussed including a brief overview of the status of high temperature electronics and sensors, as well as their use and applications.

health managment↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

Solar Air Sampler

Nation's first solar-cell-powered air monitoring station was installed at Liberty State Park, New Jersey. Jointly sponsored by state agencies and the Department of Energy, system includes display which describes its operation to park visitors. Unit samples air every sixth day for a period of 24 hours. Air is forced through a glass filter, then is removed each week for examination by the New Jersey Bureau of Air Pollution. During the day, solar cells provide total power for the sampling equipment. Excess energy is stored in a bank of lead-acid batteries for use when needed.

Source record↗
NASA NTRS1977

Preliminary demonstration of precision DSN clock synchronization by radio interferometry

A wideband digital data acquisition system has been developed to measure the clock offsets to the 10-ns accuracy required to facilitate three-way spacecraft ranging, to monitor the hydrogen maser frequency standard rates to 1 part in 10 to the forteenth power and to potentially reduce operational costs by replacing the current DSN operational time sync system.

Hurd, W. J.↗
DOE OSTI2025

Commercialization of the Transportation-Security, Tracking, and Reporting System (T-STAR)

The Transportation-Security, Tracking, and Reporting System (T-STAR) was developed by the National Nuclear Security Administration, NA-21, Office of Radiological Security (ORS) to provide a transportation security system for detection and tracking during transport of Category 1 and Category 2 radiological material. Few off-the-shelf systems for conveyance tracking offer detection of a cargo compartment breach or a removal of the cargo. Systems that do offer this capability often require permanent installation through modifying of the conveyance itself. This is not sustainable in many countries where ORS is building use, storage, and transport security capacity. The development of T-STAR has moved from fielding robust prototypes deployed in countries ranging from North America, Latin America and Central Asia to a commercially produced product that can now be deployed to provide enhanced security during transit. Each prototype deployment resulted in important lessons learned, which informed the requirements for the final commercial product. T-STAR uses both cellular and Iridium satellite modems to provide redundant communications to provide the configuration, status, and alerts to a server monitoring the shipment, which is accessible using a multilanguage browser-based user interface. A wireless security system employing using Z-wave sensors for intrusion detection located in the conveyance provide low cost but effective solution for a wide range of conveyance types. Additional capabilities include the ability to monitor a vehicles’ CANBUS (Controller Area Network) system, an ethernet port for high throughput sensor information such as video cameras, and the ability to power and use advanced external sensor payloads. These features make the T-STAR a capable and expandable security gateway that can be deployed on a variety of conveyances from box trucks to open trailers. The ability to provide tracking, monitoring, and detection provide a key component in overall best practices designed to protect shipments of radioactive material.

Schultze, Michael [ORNL] (ORCID:0000000283205671)↗
NASA NTRS1982

Civil helicopter propulsion system reliability and engine monitoring technology assessments

A study to reduce operating costs of helicopters, particularly directed at the maintenance of the propulsion subsystem, is presented. The tasks of the study consisted of problem definition refinement, technology solutions, diagnostic system concepts, and emergency power augmentation. Quantifiable benefits (reduced fuel consumption, on-condition engine maintenance, extended drive system overhaul periods, and longer oil change intervals) would increase the initial cost by $43,000, but the benefit of $24.46 per hour would result in breakeven at 1758 hours. Other benefits not capable of being quantified but perhaps more important include improved aircraft avilability due to reduced maintenance time, potential for increased operating limits due to continuous automatic monitoring of gages, and less time and fuel required to make engine power checks. The most important improvement is the on-condition maintenance program, which will require the development of algorithms, equipment, and procedures compatible with all operating environments.

Murphy, J. A.↗
NASA NTRS2007

Method and apparatus for multiple-projection, dual-energy x-ray absorptiometry scanning

Methods and apparatuses for advanced, multiple-projection, dual-energy X-ray absorptiometry scanning systems include combinations of a conical collimator; a high-resolution two-dimensional detector; a portable, power-capped, variable-exposure-time power supply; an exposure-time control element; calibration monitoring; a three-dimensional anti-scatter-grid; and a gantry-gantry base assembly that permits up to seven projection angles for overlapping beams. Such systems are capable of high precision bone structure measurements that can support three dimensional bone modeling and derivations of bone strength, risk of injury, and efficacy of countermeasures among other properties.

Charles, Jr., Harry K.↗
DOE OSTI2022

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification: Preprint

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs)at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases - distribution system state estimation (DSSE) and fault identification - are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system monitoring↗
DOE OSTI2023

Probing-Based Inertia Estimation Method Using Hybrid Power Plants

With the displacement of synchronous generation by inverter-based resources (IBRs), power systems could face the challenge of reduced inertia since IBRs do not inherently contribute to system inertia. Therefore, there is rising interest in monitoring system inertia in real-time applications for situational awareness. In addition, there is a growing number of IBRs that provide fast frequency responses (FFR) in the form of synthetic inertia and P-f droop. It is desirable to quantify the contribution of these FFR controls as equivalent inertia. This paper proposes a probing-based inertia estimation method using a PV-battery hybrid power plant in the Kauai island power system. The method is validated under different operating conditions.

hybrid power plant↗
DOE OSTI2020

Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.

distirbution systems↗
DOE OSTI2024

A data-driven sensor placement approach for detecting voltage violations in distribution systems

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. Here, in this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations.

24 POWER TRANSMISSION AND DISTRIBUTION↗