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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗
DOE OSTI2012

Scalable Node Monitoring

Project description is: (1) Build a high performance computer; and (2) Create a tool to monitor node applications in Component Based Tool Framework (CBTF) using code from Lightweight Data Metric Service (LDMS). The importance of this project is that: (1) there is a need a scalable, parallel tool to monitor nodes on clusters; and (2) New LDMS plugins need to be able to be easily added to tool. CBTF stands for Component Based Tool Framework. It's scalable and adjusts to different topologies automatically. It uses MRNet (Multicast/Reduction Network) mechanism for information transport. CBTF is flexible and general enough to be used for any tool that needs to do a task on many nodes. Its components are reusable and 'EASILY' added to a new tool. There are three levels of CBTF: (1) frontend node - interacts with users; (2) filter nodes - filters or concatenates information from backend nodes; and (3) backend nodes - where the actual work of the tool is done. LDMS stands for lightweight data metric servies. It's a tool used for monitoring nodes. Ltool is the name of the tool we derived from LDMS. It's dynamically linked and includes the following components: Vmstat, Meminfo, Procinterrupts and more. It works by: Ltool command is run on the frontend node; Ltool collects information from the backend nodes; backend nodes send information to the filter nodes; and filter nodes concatenate information and send to a database on the front end node. Ltool is a useful tool when it comes to monitoring nodes on a cluster because the overhead involved with running the tool is not particularly high and it will automatically scale to any size cluster.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

FPGA-based prototype of portable environmental radiation monitor

This new portable radiological environmental monitor consists of 2 main components, Gamma ionization chamber and a FPGA-based electronic enclosure linked to convivial software for treatment and analyzing. The HPIC ion chamber is the heart of this radiation measurement system and is running in range from 0 to 100 mR/h, so that the sensitivity at the output is 20 mV/μR/h, with a nearly flat energy response from 0,07 to 10 MEV. This paper presents a contribution for developing a new nuclear measurement data acquisition system based on Cyclone III FPGA Starter Kit ALTERA, and a user-friendly software to run real-time control and data processing. It was developed to substitute the older radiation monitor RSS-112 PIC installed in CNESTEN's Laboratory in order to improve some of its functionalities related to acquisition time and data memory capacity. As for the associated acquisition software, it was conceived under the virtual LabView platform from National Instrument, and offers a variety of system setup for radiation environmental monitoring. It gives choice to display both the statistical data and the dose rate. Statistical data shows a summary of current data, current time/date and dose integrator values, and the dose rate displays the current dose rate in large numbers for viewing from a distance as well as the date and time. The prototype version of this new instrument and its data processing software has been successfully tested and validated for viewing and monitoring the environmental radiation of Moroccan nuclear center. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

The compressed state Kalman filter for nonlinear state estimation: Application to large-scale reservoir monitoring

Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their prod-ucts. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covari-ance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N << m. Here in this paper we present a generalized CSKF algorithm for nonlin-ear state estimation problems such as CO 2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO 2 monitoring, we show that the CSKF can reproduce the Kal-man gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Methylxanthine Drug Monitoring with Wearable Sweat Sensors

Drug monitoring plays crucial roles in doping control and precision medicine. It helps physicians tailor drug dosage for optimal benefits, track patients' compliance to prescriptions, and understand the complex pharmacokinetics of drugs. Conventional drug tests rely on invasive blood draws. While urine and sweat are attractive alternative biofluids, the state-of-the-art methods require separate sample collection and processing steps and fail to provide real-time information. In this paper, a wearable platform equipped with an electrochemical differential pulse voltammetry sensing module for drug monitoring is presented. A methylxanthine drug, caffeine, is selected to demonstrate the platform's functionalities. Sweat caffeine levels are monitored under various conditions, such as drug doses and measurement time after drug intake. Elevated sweat caffeine levels upon increasing dosage and confirmable caffeine physiological trends are observed. This work leverages a wearable sweat sensing platform toward noninvasive and continuous point-of-care drug monitoring and management.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Method of Determining the Reliability of Real-Time In-reactor Monitoring of VVER

A methodological approach and the basic tenets for evaluating the reliability of in-reactor monitoring of VVER are presented. The method is based on the statistical analysis of indications in real time and includes methods for evaluating the admissible maximum error of the measurement channels used for monitoring as well as the average value and admissible deviation of indications in groups of parallel monitoring channels, as well as a method of evaluating reliability by comparing the values of a parameter with the regime values. In comparing the indications of a monitoring channel with the indications of other parallel channels, the median value is proposed as the baseline value. The developed method is implemented in the Diagnostika SVRK special software, which has been successfully tested during the commissioning of the No. 1 unit at the Novovoronezh NPP-2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

A new method of relative radiometric calibration for hyperspectral imaging based on skylight monitor

A new method of relative radiometric calibration based on the skylight monitor is presented for airborne hyperspectral remote sensing to eliminate the negative effect on data resulting from the time-varying skylight during a long flight. In terms of large area operation of mapping or spectral imaging, a static radiation model hardly reveals actual cases as external environment keeps changing. Compared with common considerable factors, fluctuation of skylight is the primary one that may observably affect spectral imaging continuously leading to radiation changes upon flight lines. To get rid of the influence of skylight on spectral imagery and monitoring the variation during the flight, we assemble a set of skylight monitor fixed to the aircraft cabin for the sake of recording the semi-spherical skylight keeping synchronization with hyperspectral imaging system and the position and orientation system. The skylight monitor consists of a cosine corrector on the top receiving the skylight, a stable optical fiber and a visible and near-infrared linear array modular spectrometer. The corresponding matched data of skylight with each frame of images is used to correct related digital number after stripe non-uniformity correction, achieving mean relative standard deviation (coefficient of variation or CV) of under 6% for all 256 bands. The final result shows a satisfactory calibrated image of an appropriate evaluation criteria with contrast of the original image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach

Additive manufacturing (AM) has attracted considerable attention in recent years. This technology overcomes the geometrical limits of workpieces produced with the traditional subtractive methods and so gives the opportunity to manufacture highly complex shapes. Unfortunately, the repeatability of the manufacturing process and the monitoring of quality are not reliable enough to be utilized in mass production. The quality monitoring of AM processes in commercial equipment has been largely based on temperature measurements of the process zone or high-resolution imaging of the layers. However, both techniques lack information about the physical phenomena taking place in the depth of the materials medium and this limits their reliability in real-life applications. To overcome those restrictions, we propose to combine acoustic emission and reinforcement learning. The former captures the information about the subsurface dynamics of the process. The latter is a branch of machine learning that allows interpreting the received data in terms of quality. The combination of both is an original method for in situ and real-time quality monitoring. Acoustic data were collected during a real process using a commercial AM machine. The process parameters were selected to achieve three levels of quality in terms of porosity concentration while manufacturing a stainless steel 316L cuboid shape. Using our method, we demonstrated that each level of quality produced unique acoustic signatures during the build that were recognized by the classifier. The classification accuracy reached in this work proves that the proposed method has high potential to be used as in situ and real-time monitoring of AM quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Review of on-line and near real-time spectroscopic monitoring of processes relevant to nuclear material management

Spectroscopic chemometric based on-line monitoring of used nuclear fuel (UNF) reprocessing solutions and characterization of legacy nuclear waste (LNW) stored at Hanford is discussed in this manuscript. Using on-line and near real-time monitoring with spectroscopy, as opposed to traditional off-line monitoring, can significantly reduce the cost, risk and improve the efficiency of characterizing UNF reprocessing streams and LNW since the data can be collected non-destructively and analyzed nearly instantly. It can accurately quantify and qualify nuclear material in near-real time which can help to know the immediate UNF reprocessing stream and LNW conditions and detect the diversion of materials. The ability to be applied in a real reprocessing plant and in an actual Hanford waste tank/transfer pipe has been demonstrated by applying this technique to accurately quantify analytes in real UNF streams and LNW samples. Furthermore, the future development of spectroscopy based on-line monitoring is also discussed in this manuscript.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring

Unmanned aerial system (UAS)-based remote sensing can provide high spatial- and temporal-resolution crop monitoring for precision management. Existing crop monitoring UAS primarily use conventional techniques such as multi-spectral broadband vegetation indices (VIs) to track canopy structure/biomass. Recently developed lightweight hyperspectral sensors can track crop physiology and performance via complementary signals such as solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) that can be derived from hyperspectral reflectance. However, few existing UAS can acquire high-quality SIF and hyperspectral reflectance. Here, we designed a novel UAS that simultaneously captures far-red SIF and hyperspectral reflectance, using a single bifurcated fiber and motorized arm to measure downwelling and upwelling irradiance. The UAS was tested over (1) a heterogeneous corn field with spatially varying crop yield potential and (2) a set of soybean and corn plots under differing nutrient treatments. Our UAS can maintain stability under low-to-moderate winds (2–3 m/s) with <0.4 m geolocation accuracy, <0.1 m altitude drift, and <1.5° error of the fiber optic from nadir position during scanning. Weekly seasonal campaigns reveal that SIF outperforms conventional VIs such as the normalized difference vegetation index (NDVI) for distinguishing plots with different crop yield potential, especially after canopy closure when NDVI tends to saturate. The UAS can capture the diurnal dynamics of SIF and PRI for both corn and soybean. At the seasonal scale, SIF acquired from the UAS correlates well with that from a fixed SIF tower system ( R 2 = 0.81), which measured the same target albeit with different footprints, demonstrating the capability of UAS in characterizing the seasonal progression of crop activity. In conclusion, our newly developed UAS provides high-quality SIF and hyperspectral reflectance, facilitating mechanistic understanding of the physiological control on photosynthesis dynamics. Our findings also imply that this UAS could enable extension of fixed tower-based systems to monitor multiple targets from diurnal to seasonal scales and improve monitoring of heterogeneous fields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Confronting barriers and recognizing opportunities: Developing effective community-based environmental monitoring programs to meet the needs of Aboriginal communities

Aboriginal communities can be negatively affected by resource development, but often they do not have a full opportunity to participate in project review and the resulting monitoring and mitigation activities. Cumulative impacts of resource development are also typically neglected in monitoring protocols that focus on a limited number of environmental values, rather than adopting a long-term, holistic view of development over time and space. Community-based environmental monitoring (CBEM) is emerging as a way to meaningfully include local Aboriginal citizens in the decision-making process as well as the assessment of the long-term impacts of the development of natural resources. We explored opportunities and barriers for developing CBEM programs that meet the needs of small and rural Aboriginal communities that are faced with the rapid and wide-spread development of natural resources. We conducted interviews with a local Aboriginal community, and natural resource management practitioners who could provide perspectives on the application of CBEM to resource management in north-central British Columbia, Canada. Results demonstrate that CBEM offers a locally adapted and culturally appropriate approach to facilitate the participation of Aboriginal communities in natural resource decision making and management. The interpretation of the specific role and purpose of CBEM differed among participants, depending on their objectives for and concerns about natural resource development. However, all parties were consistent in viewing CBEM as an effective method for engaging in dialogue, cooperation, and tracking environmental change. The development or improvement of CBEM programs should consider the efficacy of monitoring protocols, social cohesion and relationships, ability to inform decision-making, and effectiveness of CBEM for the members of the community. - Highlights: • We explored how to develop effective CBEM with a focus on Aboriginal communities. • We identified opportunities and barriers for developing CBEM programs. • CBEM can facilitate Aboriginal community participation in natural resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Feasibility of FDEM cross-dipole and TDEM loop sources for monitoring CO 2 at the Kemper CarbonSAFE site

Electromagnetics has been shown to be a viable tool to monitor CO 2 plumes embedded in saline reservoirs. However, the majority of studies focus on measuring the electric field, which requires cumbersome equipment in the field and it is difficult to install permanent measurement stations. Magnetic field receivers offer an opportunity to reduce the form factor of the survey and increase the mobility by utilizing upcoming technologies, such as drones. We explore the use of frequency-domain electric dipole sources, and timedomain loops with a focus on measuring the secondary magnetic field at the surface for a conceptual injection scenario based on the Kemper CarbonSAFE site. We find that electric dipole sources give a response above the sensitivity of current sensor technology and, therefore, be a viable tool for CO 2 monitoring. The time domain loop source does provide fields that are useful for determining the location of the CO 2 plume, however the field magnitude is below the sensitivity of the current generation of instruments. To explore the use of a potential borehole receiver we generate a map of the magnetic field at depth to explore potential borehole placement for monitoring efforts. Finally, we limit the spatial extent of the electric dipole survey to a single parcel of land to help understand how the fields change with survey geometry. We find that the shape of the secondary fields change slightly with the small transmitter, but are still measurable provided that the cultural noise at the site is low. Thus, we conclude that at the Kemper site a frequency-domain cross-dipole source with magnetometer receivers is suitable to monitor the expansion of the CO 2 plume in the saline reservoir, even with a limited transmitter footprint on the surface.

58 GEOSCIENCES↗
DOE OSTI2022

Source Term Analysis of Xenon (STAX): An effort focused on differentiating man-made isotope production from nuclear explosions via stack monitoring

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. The final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Characterizing precipitate evolution of an Al–Zn–Mg–Cu-based commercial alloy during artificial aging and non-isothermal heat treatments by in situ electrical resistivity monitoring

In situ electrical resistivity monitoring technique was employed to continuously evaluate the precipitate evolution of an Al–Zn–Mg–Cu-based commercial alloy during typical artificial aging treatments. The effects of artificial aging on the precipitates stability during non-isothermal heat treatments were also explored. Conventional hardness test, transmission electron microscopy and differential scanning calorimetry were also adopted to verify the electrical resistivity results. The results indicated that both the precipitation process and its timely rate could be followed by the monitored electrical resistivity during artificial aging treatments. The electrical resistivity results gave overall information on continuous precipitation and dissolution processes, especially under high heating rates. Samples artificial aging heat treated at 120 °C for 24 h followed by aging at 150 °C for 24 h presented more stable state and coarser precipitates than the samples only artificial aging heat treated at 120 °C for 24 h or triple artificial aging heat treated at 120 °C/24 h + 195 °C/15 min + 120 °/24 h. While the incoherent η precipitates in the samples artificial aging heat treated at 120 °C for 24 h followed by aging at 150 °C for 24 h were more easiness to coarsening and dissolve during non-isothermal heat treatments as well. - Highlights: • In situ electrical resistivity monitoring technique was employed on an Al-Zn-Mg-Cu alloy. • The precipitate evolution during typical artificial aging treatments was studied. • The precipitate stability during non-isothermal heat treatments was explored. • The electrical resistivity wonderfully monitored continuous precipitation and dissolution. • The alloy submitted to a T7 treatment presents a more stable state during heating due to incoherent η precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Accuracy evaluation of the optical surface monitoring system on EDGE linear accelerator in a phantom study

Frameless stereotactic radiosurgery (SRS) requires dedicated systems to monitor the patient position during the treatment to avoid target underdosage due to involuntary shift. The optical surface monitoring system (OSMS) is here evaluated in a phantom-based study. The new EDGE linear accelerator from Varian (Varian, Palo Alto, CA) integrates, for cranial lesions, the common cone beam computed tomography (CBCT) and kV-MV portal images to the optical surface monitoring system (OSMS), a device able to detect real-time patient's face movements in all 6 couch axes (vertical, longitudinal, lateral, rotation along the vertical axis, pitch, and roll). We have evaluated the OSMS imaging capability in checking the phantoms' position and monitoring its motion. With this aim, a home-made cranial phantom was developed to evaluate the OSMS accuracy in 4 different experiments: (1) comparison with CBCT in isocenter location, (2) capability to recognize predefined shifts up to 2° or 3 cm, (3) evaluation at different couch angles, (4) ability to properly reconstruct the surface when the linac gantry visually block one of the cameras. The OSMS system showed, with a phantom, to be accurate for positioning in respect to the CBCT imaging system with differences of 0.6 ± 0.3 mm for linear vector displacement, with a maximum rotational inaccuracy of 0.3°. OSMS presented an accuracy of 0.3 mm for displacement up to 1 cm and 1°, and 0.5 mm for larger displacements. Different couch angles (45° and 90°) induced a mean vector uncertainty < 0.4 mm. Coverage of 1 camera produced an uncertainty < 0.5 mm. Translations and rotations of a phantom can be accurately detect with the optical surface detector system.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

A collision timing monitor for SuperKEKB

The analysis of beamstrahlung radiation, emitted from a beam of charged particles due to the electromagnetic interaction with a second beam of charged particles, provides a diagnostic tool that can be used to monitor beam–beam collisions in a e + e - storage ring. In this paper we show that the beamstrahlung time profile is related to the timing of the collisions and the length of the beams, and how its measurement can be used to monitor and optimize collisions at the interaction point of the SuperKEKB collider. The method has a unique passive monitor capability, since it allows to monitor the timing of the collision without disturbing (scanning) the beam–beam timing, which needs to be measured to unprecedented accuracy at SuperKEKB. To measure the time dependence of beamstrahlung, here we describe a method based on nonlinear frequency mixing in a nonlinear crystal of beamstrahlung radiation with photons from a pulsed laser. We demonstrate that the method allows to measure and optimize the relative timing and length of the colliding bunches with 3% accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Light monitoring system for the lead tungstate calorimeter in Hall D at Jefferson Lab

A new electromagnetic calorimeter composed of 1596 lead tungstate (PbWO 4 ) scintillating crystals has been constructed for the GlueX detector in Hall D at Jefferson Lab. The calorimeter is equipped with a light monitoring system that uses light-emitting diodes. The light monitoring system was fabricated, installed, and integrated into the GlueX trigger system. It was successfully operated during detector commissioning and data collection, providing monitoring of the detector response and verification of the calibration with a precision better than 1%. In conclusion, the paper describes the design, installation, and performance of the light monitoring system.

Lead tungstate calorimeter↗
DOE OSTI2024

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

ammonia↗