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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Three-dimensional migration behavior of juvenile salmonids in reservoirs and near dams

To acquire 3-D tracking data on juvenile salmonids, Juvenile Salmon Acoustic Telemetry System (JSATS) cabled hydrophone arrays were deployed in the forebays of two dams on the Snake River and at a mid-reach reservoir between the dams. The depth distributions of fish were estimated by statistical analyses performed on large 3-D tracking data sets from ~33,500 individual acoustic tagged yearling and subyearling Chinook salmon and juvenile steelhead at the two dams in 2012 and subyearling Chinook salmon at the two dams and the mid-reach reservoir in 2013. This research investigated the correlation between vertical migration behavior and passage routes. The depth distributions of fish within the forebays of the dams were significantly different from fish passing the mid-reach reservoir. Fish residing deeper in the forebay tended to pass the dam using deeper powerhouse routes. This difference in depth distributions indicated that the depth distribution of fish at the mid-reach reservoir was not related to behaviors of fish passing through certain routes of the adjacent dams. For fish that were detected deeper than 17.5 m in the forebays, the probability of powerhouse passage (i.e., turbine) increased significantly. Another important finding was the variation in depth distributions during dam passage associated with the diel period, especially the crepuscular periods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Design and implementation of a real-time underwater acoustic telemetry system for fish behavior study and environmental sensing

Underwater acoustic telemetry is an important tool for studying the behavior of aquatic animals and assessing the environmental impact of man-made structures such as water power turbines. However, current design of the acoustic telemetry system lacks the ability to transmit collected data continuously in real-time and requires manual retrieval of the recorded measurements, leading to a long delay of data retrieval, high operational cost associated with manpower as well as safety risk for the operators. In addition, there is no efficient way to monitor the status of the acoustic telemetry system, including acoustic transmitters and receivers. For example, if multiple receivers in an array fail, the detection efficiency could be significantly reduced. Herein, we developed a cloud-based, real-time underwater acoustic telemetry system that can be used to estimate behavior or survival of tagged aquatic animals and monitor environmental parameters in near-real-time (< 2 hours). The system incorporates microcontrollers and integrated circuits to create real-time data communication and processing units with a cloud-based service that processes the transmitted data stream to derive behavior and survival information of the tagged animals.

Yang, Yang↗
DOE OSTI2024

Extraction of Vibration Data with Imaging

To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Novel Temperature Sensors and Wireless Telemetry for Active Condition Monitoring of Advanced Gas Turbines

The objective of the program is to develop and engine test hardware and software technologies that will enable active condition monitoring to be implemented on hot gas path turbine blades in large industrial gas turbines. The specific objectives are (1) to fabricate and install Smart Turbine Blades with thermally sprayed sensors and high temperature wireless telemetry systems in a gas turbine engine, (2) to integrate the component engine test data with remaining useful life (RUL) models and develop an approach for networking the component RUL data with Siemens' Power Diagnostics® engine monitoring system. These significant advances carried out in Phase 1 in temperature wide bandgap telemetry, along with new induced power driver and receiver geometry combined with an innovative approach to transmit digital data wirelessly will enable the opportunity to proceed with more technical innovation. The Phase 2 program focused on validation testing of sensor-wireless telemetry package in spin rig and advanced operation-based assessment (OBA) model utilizing artificial intelligence. Significant efforts were dedicated on the download of the technology onto components to be tested an actual gas turbine engine for full realization of active condition monitoring for Smart Turbine Blades.

03 NATURAL GAS↗
DOE OSTI2019

Improving Operational Efficiency, Compliance and Community Perception with Real-time Data - 19260

The nuclear and medical radiation industry has always faced a significant burden in effectively balancing exposure and productivity. Maintenance operations and development of new techniques require significant staffing resources to monitor and review personnel exposures and changes in operational radiation levels. Currently utilised monitoring technology and review techniques remain unchanged since the 1950's, relying upon mostly analogue instrumentation and paper reporting. After utilising isolated-area monitoring during a reactor decommissioning project it became apparent that current systems created false assurance as the units continually failed without warning due to 'dirty' power. These instrument failures could have easily led to an unnecessary exposure while operators continued to assume they were being safely monitored. This paper is a philosophical discussion on how current shortcomings could be addressed, security increased, and exposure risks minimised, by utilising real-time monitoring and automated reporting. The community and media expectations of reported data and nuclear monitoring systems continue to be coloured by sensational technology seen in movies and emerging technology available through smart homes and smart cities initiatives. For the general public to be more engaged with real radiation levels and effects, the nuclear industry needs to more effectively communicate safe levels. Organisations can have regain control of exposure levels discussions through the controlled release of background and expected exposure level data. An increase in real world monitoring data communicated in a controlled and more effective way will aid in the general public accepting, and being educated about, the minimal increase above natural background levels of the nuclear industry and other radiation facilities. This is particularly as Australia is in the process of implementing its Nuclear Waste Facility, with community acceptance the number one inhibitor of being able to move forward. Storage, mobile assets, and processing all present unique management, security, exposure, and telemetry challenges for a facility operations manager. Active monitoring and automated review of the monitoring data can alert operators to any unexpected events before any resultant increases in radiation levels cause an unplanned exposure to operators or the environment. Integration of radiation monitoring data with global positioning telemetry enables better organisational awareness of mobile asset location and early detection of any potential shifting and reduction in shielding integrity of sources and encapsulated waste during transport. Additionally, the real-time monitoring can be set-up to enhance an organisations' or nation's security capabilities. For instance, the real time monitor will send an automated alert in the case of an unexpected movement of a source through a monitored facility or for an unscheduled movement of a monitored vehicle. There are further benefits to be gained in control, ALARA principles and effluent management by increasing real-time monitoring capabilities through the stages of the waste cycle. Increasing operational effectiveness while delivering operational cost savings sounds like an attractive proposition. With increased access to operational area radiation data comes drastically improved real-time management of exposures for new, and experimental practices. Increased live data can be utilised to identify optimum practices for reducing operational and environmental exposures or releases. This increased operational data effectively comes with the added benefit of a reduction in the required health physics personnel resources for general operations. By reducing exposure and the detachment of required health physics resources from routine operations, there is greater availability for these resources to support maintenance operations and project-based work. The review of this increased data will create internal capability to identify low-- and high-risk activities, resulting in the ability to reduce the number of highly skilled workers in an industry where the availability of this talent is declining. During my time as the Radiation Safety and Nuclear Security Manager at a research facility, I implemented a real-time monitoring system. The system identified a secondary risk that had previously been unidentified and posed a higher exposure and security risk than the radiation practice being monitored. What stops industry from implementing technology that encourages agile work practices, while minimising security risks and personnel exposure? (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

SPE3 Far-field Quicklook

The purpose of this report is to provide an overview of the seismic data collected during the SPE3 chemical shot. Farfield seismic data includes data collected at distances of more than 100 m from the shot and instruments deployed for the experiments. This report does not address infrasound, remote sensing, or seismic data collected by regional permanent stations. The primary goal is a fast data check before extensive analysis. The SPE3 shot was conducted on July 24, 2012 (DOY 206) at the SPE test site and in the same borehole as SPE1 and SPE2 (table 1). Data processing at LLNL primarily used mini-­seed data volumes downloaded from the UNR server along with associated metadata. This is in contrast to earlier shots, where LLNL data was based on reformatted Reftek data. Sensor deployment was similar to previous shots, with a few changes. Parts of lines 1 (13, 14, 15, 16), 3 (23,26,36), and 5 (1-­12) were recorded by telemetry as well as to disk, which provided immediate access to the data. A small array (6 station) geophone array was deployed southeast of the shot near AF9 to measure particle motions. A new rotational sensor (ATA ARS-­16) was deployed at station L3-­20.

58 GEOSCIENCES↗
DOE OSTI2017

Behavior-based approach to misuse detection of a simulated SCADA system - 339

This paper presents the initial findings in applying a behavior-based approach for detection of unauthorized activities in a simulated Supervisory Control and Data Acquisition (SCADA) system. Misuse detection of this type utilizes fault-free system telemetry to develop empirical models that learn normal system behavior. Future monitored telemetry sources that show statistically significant deviations from this learned behavior may indicate an attack or other unwanted actions. The experimental test bed consists of a set of Linux based enterprise servers that were isolated from a larger university research cluster. All servers are connected to a private network and simulate several components and tasks seen in a typical SCADA system. Telemetry sources included kernel statistics, resource usages and internal system hardware measurements. For this study, the Auto Associative Kernel Regression (AAKR) and Auto Associative Multivariate State Estimation Technique (AAMSET) are employed to develop empirical models. Prognostic efficacy of these methods for computer security used several groups of signals taken from available telemetry classes. The Sequential Probability Ratio Test (SPRT) is used along with these models for intrusion detection purposes. The different intrusion types shown include host/network discovery, DoS, brute force login, privilege escalation and malicious exfiltration actions. For this study, all intrusion types tested displayed alterations in the residuals of much of the monitored telemetry and were able to be detected in all signal groups used by both model types. The methods presented can be extended and implemented to industries besides nuclear that use SCADA or business-critical networks. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

High Temperature 300°C Measurement While Drilling System

Many countries around the world, including the USA, have untapped geothermal energy potential. Enhanced Geothermal Systems (EGS) technology is needed to economically utilize this resource. Temperatures in some EGS reservoirs can exceed 300°C. To effectively utilize EGS resources, an array of injection and production wells must be accurately placed in the formation fracture network. Therefore, EGS can greatly benefit from a high temperature directional drilling system. Most commercial services for directional drilling systems are rated for 175°C while geothermal wells require operation at much higher temperatures. Two U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) projects have been initiated to develop a 300°C capable directional drilling system, the first developing a drill bit, directional motor, and drilling fluid (award number DE-EE0002782), and the second adding navigation and telemetry systems. This report is for the second project, “High Temperature 300°C Measurement While Drilling System”, award number DE-EE0005505. The “High Temperature 300°C Measurement While Drilling (MWD)” system complements the already developed drilling system with real time navigation capability. It is comprised of 175°C directional measurement electronics including the required sensors for measuring inclination and azimuth in 3 axes. It is embedded in a cooling and temperature isolation system to protect the electronics and sensors from the higher temperature outside the system, which can reach 300°C. Building the cooling and isolation system from non-magnetic materials and keeping a required distance to any magnetic component, such as alternator parts, ensures a high quality measurement of the earth’s magnetic field. This is required to achieve the specified navigation accuracy. The MWD system has a turbine driven alternator that is capable of delivering the required electrical energy to the electronic system at a 300°C borehole temperature. Measured data will be communicated to the surface by mud-pulse-telemetry. For this purpose the tool has a mud-pulse-valve that is controlled by the measurement electronics to establish a serial data communication via the mud stream. The mud-pulse-valve is also built to operate under 300°C borehole conditions. Three prototypes of the 300°C MWD were built and tested. These systems are available for a joint application with the previously developed 300°C drilling system, in a geothermal well.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Analysing a multi‐stage cyber threat and its impact on the power system

Abstract Electric power systems are composed of physical and cyber sub‐systems. The sub‐systems depend on each other. If the cyber sub‐system is compromised by a cyber threat, what is the impact on the physical system? This paper presents a case study that shows the steps of a multi‐stage cyber threat involving a database injection attack, and what happens to the power system if this threat is not detected in its early stages. The threat first affects one utility but it can spread to the balancing authority, which is responsible for keeping the voltage and frequency stable in the power grid. During the cyber threat, the authors also show defence tools, such as a cyber‐physical data fusion tool that displays and analyses power and cyber telemetry.

Al Homoud, Leen [Texas A&amp,M University College ↗
DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗
DOE OSTI2023

Analysis of a Runtime Data Sharing Architecture over LTE for a Heterogeneous CAV Fleet

This paper describes a lightweight runtime architecture for telemetry, communication, and control of cars deployed with advanced driver assistance systems where a human is in the loop with the car, via an LTE connection. The system architecture supports both local control decisions based on car sensors and safety algorithms as well as high-level input from external systems that may provide insight into traffic state ahead of sensor data. Implementation of the architecture is done in ROS and depends on open-source software packages for runtime decoding of information from the vehicle’s controller area network (CAN) and integration of GPS data from accompanying sensors. The contribution of the paper is to describe the overall architecture, the data it can communicate to other systems, performance of the system at runtime, and challenges faced when deploying the architecture across a heterogeneous fleet. Preliminary results from analysis of test data will provide insights into whether the use of high-latency communication can be effective for societal-scale intelligent transportation systems when applied in future scenarios

Richardson, Alex↗
DOE OSTI2019

Autonomous Radiological Survey Systems for Locating Fired Depleted Uranium Munitions - 19212

An ensemble of autonomous radiological survey platforms that utilize a suite of scintillation detectors that can be used to conduct radiological site characterization, assist with remediation efforts, or conduct site clearance in accordance with the Multi-Agency Radiologic Survey and Site Investigation Manual are described. The overall systems are composed of inorganic scintillators, light detection and ranging, global positioning, inertial guidance, wireless telemetry, and signal processing systems. Two primary designs are discussed: a three-meter wide platform and multiple half-meter-wide systems. The survey systems are used to conduct radiological surveys over large areas autonomously, without human involvement. The three-meter system has been designed to cover large, open areas free of obstacles. Smaller robotic platforms are designed to handle a large range of terrain types and to conduct surveys in areas that are plagued with obstacles, such as bushes and trees. The primary design criteria for these systems were accurate locating of fired depleted uranium (DU) munitions. Several U.S. Army facilities have remnant DU material on active firing ranges where DU rounds have been used in performance evaluations. Many ranges also have unexploded ordinance (UXO) challenges which are problematic for conducting walkover surveys. The combination of having active DU impacted firing ranges with UXO make conducting site characterization surveys very difficult and hazardous. The autonomous surveying systems allow radiological scanning data collection to be scheduled around range activities and reduces human exposure to ionizing radiation and UXO. All of the robotic platforms act independently, and are controlled by a centralized computer system via a wireless telemetry system. The central computer system is responsible for setting up survey units, survey paths, and scheduling which platform is to complete the scheduled survey. Unit selection and scheduling are completed by technicians, and the work is conducted autonomously by these survey platforms. The survey areas and survey paths are transmitted to each platform through the telemetry system. Radiological and positional data are collected by the systems and transmitted back to the central computer system in real-time. Collected data is saved to a database for post-survey analysis or can be analyzed in real-time. Analysis of the radiological data is used to construct radiation intensity and estimated DU concentration maps. Automatically generated reports for each survey unit will provide range managers with: survey coverage data maps, estimated uranium concentration maps, and a detailed list of areas that are above a user defined concentration. This paper describes the components of each of the surveying systems, their capabilities, and limitations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Progress In Developing An In-Pile Acoustically Telemetered Sensor Infrastructure

A salient grand challenge for a number of Department of Energy programs such as Fuels Cycle Research and Development ( includes Accident Tolerant Fuel research and the Transient Reactor Test Facility Restart experiments), Light Water Sustainability, and Advanced Reactor Technologies is to enhance our fundamental understanding of fuel and materials behavior under irradiation. Robust and accurate in-pile measurements will be instrumental to develop and validate a computationally predictive multi-scale understanding of nuclear fuel and materials. This sensing technology will enable the linking of fundamental micro-structural evolution mechanisms to the macroscopic degradation of fuels and materials. The in situ sensors and measurement systems will monitor local environmental parameters as well as characterize microstructure evolution during irradiation. One of the major road blocks in developing practical robust, and cost effective in-pile sensor systems, are instrument leads. If a wireless telemetry infrastructure can be developed for in-pile use, in-core measurements would become more attractive and effective. Thus to be successful in accomplishing effective in-pile sensing and microstructure characterization an interdisciplinary measurement infrastructure needs to be developed in parallel with key sensing technology. For the discussion in this research, infrastructure is defined as systems, technology, techniques, and algorithms that may be necessary in the delivery of beneficial and robust data from in-pile devices. The architecture of a system’s infrastructure determines how well it operates and how flexible it is to meet future requirements. The limiting path for the effective deployment of the salient sensing technology will not be the sensors themselves but the infrastructure that is necessary to communicate data from in-pile to the outside world in a non-intrusive and reliable manner. This article gives a high level overview of a promising telemetry infrastructure based on acoustic wireless transmission of data that is being developed and tested by the INL, Penn State and Westinghouse.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Evaluation of survival estimates generated from tracking downstream migrating juvenile sea lamprey ( Petromyzon marinus ) with a miniature acoustic telemetry tag

Fish that undertake juvenile migration pass through habitats that vary in mortality risk. The ability to identify regions of persistently low survival would improve fishery management. We conducted a field study combined with predictive modeling of survival in downstream migrating juvenile sea lamprey using a new micro-acoustic telemetry tag designed for implantation into small, slender-bodied fishes. Detection data were collected from eight receivers placed across a coastal riverine–wetland–lake complex. Juvenile sea lamprey initiated downstream movement near nautical twilight, stopped frequently, and were more likely to move during high river discharge. Estimated survival was highest in the riverine reach, declined through the river–wetland complex, and dropped precipitously in the drowned rivermouth lake. However, the high transmission rate and resulting short battery life of the microtransmitters (as configured) likely resulted in missed detections in the lower reaches. Simulation analyses suggested survival estimation could be improved by increasing the number of tagged lamprey and staggering release locations. We offer practical recommendations for the use of this new transmitter in field studies with small anguilliform fish.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

NetGraf: An End-to-End Learning NetworkMonitoring Service (NetGraf) v1

NetGraf is a novel end-to-end learning monitoring system that utilizes current monitoring tools, merges multiple data sources into one dashboard for easy use, and provides machine learning libraries to analyze the data and perform real-time anomaly findings. Using a database backend, NetGraf can learn performance trends and show users if network performance has degraded. We demonstrate how NetGraf can easily be deployed through automation services and linked to multiple monitoring sources to collect data. Via the machine learning innovation and merging various data sources, NetGraf aims to fulfill the need for holistic learning network telemetry monitoring. To the best of our knowledge, this is the first-ever end-to-end learning monitoring service. We demonstrate its use on two network setups to showcase its impact.

Mohammed, Bashir↗