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This presentation will be presented at the NNSS FY 2020 SDRD Annual (Webex) Meeting scheduled for September 23–24, 2020. Phenomenology and Node-Level Processing of MASINT Sensor Data across Distributed Sensor Networks STL-016-19, Year 2
Graphical abstract: A new color-tunable luminescent Ln{sup 3+} (Ln = Eu, Tb or Eu and Tb in different molar ratio) composite was prepared through a simple method, which can be applied as self-referencing and ratiometric sensor for detecting low-level water content (0.01-0.5 vol%) in ethanol. - Highlights: • A novel Ln{sup 3+} (Ln = Eu, Tb or Eu and Tb in different molar ratio) complex with tunable luminescence from red to green was prepared through a simple procedure. • The luminescence color of the Eu/Tb co-doped complex can be tuned by water through the luminescence quenching mechanism as well as the variation of energy transfer efficiency from Tb{sup 3+} to Eu{sup 3+}. • The complex with Eu{sup 3+}:Tb{sup 3+} = 1:1 can be applied as self-referencing and ratiometric luminescent sensor for detecting low-content water (0.01–0.5 vol%) in ethanol. - Abstract: Developing simple, fast and sensitive detection of low-level water in organic solvents is in high demand for academic and industrial applications. In this paper, a novel color-tunable lanthanide composite has been prepared by simply mixing 2,2':6',2”-terpyridine-4'-carboxylic acid (Hctpy) and Ln{sup 3+} with a molar ratio of 3:1 in absolute ethanol at room temperature, in which the Ln{sup 3+} ions are mainly coordinated with carboxyl moieties rather than with terpyridine moieties of Hctpy. In addition, the emission colors of the composite can also be tuned by water, ascribed to the luminescence quenching mechanism and the variation of Tb-to-Eu energy transfer efficiency. Based on these properties, a self-referencing and ratiometric luminescent water detector with the Eu/Tb molar ratio of 1:1 has been developed, showing proportional relationship between the intensity ratio of {sup 5}D{sub 0} → {sup 7}F{sub 2} transition of Eu{sup 3+} to {sup 5}D{sub 4} → {sup 7}F{sub 5} transition of Tb{sup 3+} (I{sub Eu}/I{sub Tb}) and the water concentration (0.01–0.5 vol%) in ethanol.
Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.
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There are a number of applications in nuclear energy and hazardous waste disposal that require monitoring of fluids under extreme environments, including high levels of temperature, pressure, toxicity and radioactivity. Many of these applications will benefit from a monitoring technique that is external and non-invasive. Currently the sensors used are invasive, must reside inside the pressurized vessels and must penetrate the vessel walls, which can create a weakness in the vessel. Additionally, instruments that are used inside such containers must be exceptionally hardened to the environment. Information Systems Laboratories (ISL) has developed an external mass (gravimetric) measuring technique for monitoring nuclear coolant in Small Modular Reactors (SMRs), which will also work for measuring fluid levels in waste tanks, that avoids the problems inherent in invasive sensors. It utilizes a COTS gravitational sensor of unprecedented accuracy, leveraged via proper sensor placement geometry, to detect fluid changes of small amplitude from an outside position, obviating the need to penetrate the vessel. The technique is called Gravisense{sup TM}. ISL has proven via simulation and experiment that this concept can be usefully applied to monitoring fluid levels in both nuclear reactors and large waste tanks. Numerical simulation algorithms were developed to calculate the gravity effect of small changes in water level, which were verified by experiments at the NIST Physical Simulator facility at the Oregon State University. The measured ultralow noise levels of the superconducting gravimeter type which utilizes a Niobium sphere suspended in a magnetic field to attain its phenomenal accuracy, demonstrated that fluid levels in SMRs can be measured at least to within 3 cm. Furthermore, the method can distinguish between a contained leak (from reactor to containment vessel) from an external leak (from reactor to outside of containment). Additionally, simulations of waste canisters that hold spent fuel rods show that the fluid level measuring accuracy can potentially do better than 1 cm accuracy by measuring from below the vessel, and judicious placement of sensors on top of large waste tanks can potentially achieve a very impressive 2 mm measurement accuracy. These encouraging results prove that the Gravisense{sup TM} technique for fluid determination can be very useful in nuclear energy generation, testing, and research, as well as in waste monitoring situations that are difficult to monitor via traditional sensing technology. We believe that the next step should be to test the technique on canisters of the type that are currently storing waste in various DOE locations. (authors)
This research effort enabled the development of new classes of small-scale sensing systems that use mass and/or electrochemical sensors to detect the presence of carbon dioxide (CO 2 ). The concentration of this gas within the indoor environment is an important metric, as it can help enable the use of variable speed ventilation fans in commercial and governmental buildings, thus saving a significant amount of energy. Furthermore, there is a pressing need for enhanced CO 2 sensing to improve the comfort and productivity of people in commercial and government buildings, including academic spaces.
While phosphorus is a critical nutrient for all forms of life, runoff leads to phosphorus accumulation in surface water where it can cause a variety of health, environmental, and economic problems. The Department of Energy is interested in conducting long-term field experiments to monitor the impact of phosphorus in terrestrial and aquatic environments, and to better understand a cost vs. benefit analysis of reducing phosphorus pollution. Performing such long-term experiments requires affordable sensors capable of monitoring low-level phosphorus concentrations under challenging environmental conditions. However, such sensors are currently not available. The goal of this proposed project is to develop and demonstrate a low-cost portable phosphorus sensor suitable for in situ measurements. We will determine the technical requirements for a Raman-based sensor system, design and build a signal amplifier, integrate it with a Raman-based sensor, and demonstrate a proof-of-concept level sensor system. The expected accomplishments for Phase I include the identification of the technical requirements of a combined Raman laser/spectrometer and cavity enhancement system, building a proof-of-concept level sensor system capable of detecting orthophosphate compounds at the desired level of 0.25 mg P/L and organic P compounds, and verification of the sensor performance under laboratory and field conditions. In addition, we will identify initial hardening requirements that will need to be implemented during Phase II. While we plan to develop an integrated sensor system for monitoring phosphorus, the cavity-based enhancement system can also be used in combination with other (non-phosphorus) Raman sensors that we plan to develop or that are already commercially available. A recent market report highlights the increasing demand for such environmental monitoring applications, with the market for chemical detection anticipated to grow at a compound annual growth rate of 6.5% (2020-2025). Wide-spread monitoring of phosphorus could reduce the damage from eutrophication in freshwater, which is estimated to be $2.2 billion annually, thereby increasing biodiversity, reducing costs required for drinking water treatment, and decreasing economic losses related to recreation and angling and lake property values.
A mid-infrared ethane (C 2 H 6 ) sensor based on a wavelength modulation spectroscopy (WMS) technique was developed using a thermoelectrically cooled (TEC), continuous-wave (CW) interband cascade laser (ICL) emitting at 3.34 μm and a dense multi-pass gas cell (MPGC, 17 × 6.5 × 5.5 cm 3 ) with a 54.6 m optical path length. A compact optical sensor system with a physical size of 35.5 × 18 × 12.5 cm 3 was designed and constructed. An ICL was employed for targeting a strong C 2 H 6 line at 2996.88 cm -1 at <100 Torr gas pressure in the fundamental absorption band of C 2 H 6 . The sensor performance, including the minimum detection limit (MDL) and the stability were improved by reducing the effect of laser power drift by means of the 2f/1f-WMS technique. A MDL of ~1.2 parts per billion (ppbv) for 2f-WMS and ~1.0 ppbv for 2f/1f-WMS were achieved, respectively, with a measurement time of 4 s. The MDL was further improved from 299 pptv (@108 s for 2f-WMS) to 239 pptv (@208 s for 2f/1f-WMS), based on an Allan deviation analysis.The rise time (@0 → 100 ppbv) and fall time (@100 → 0 ppbv) were determined to be ~64 s and ~48 s,respectively, at a gas pressure of <100 Torr for the C 2 H 6 sensor operation.
Estimates of the power level of a nuclear reactor based on measurements from an independent monitoring sensor system can help in the compliance verification of its declared operations. We present a three-level fusion method to estimate the power level of a nuclear reactor using features derived from infrared, electromagnetic, and acoustic sensor measurements collected in proximity to the reactor. Based on a simplified analytical model of the secondary coolant system of the reactor, we identify partial regression functions of the power level in terms of the temperature difference between inlet and outlet of coolant pipes, and the activity levels of four fans and four pumps, which are estimated as features from the sensor measurements. The power level estimator employs a combination of aggregate and complementary fusion steps at three levels to incorporate the multi-modal features in a structure that reflects the secondary cooling system and its partial regression functions. Using the measurements from a test campaign at an operational reactor, we show that this estimator achieves 3.47% or lower root mean square error under 5-fold cross validation. More generally, these results illustrate a progressive reduction in estimation error as additional modalities are appropriately incorporated, and that the fuser outperforms single modality features and their sub-combinations.
Here, we present a fast, accurate, and reliable method of obtaining cell dark current–voltage (I–V) curves from module electroluminescence (EL) images without requiring calibration or correction. For a pristine module, EL-derived dark I–V are compared to directly probed data for a variety of changing imaging parameters: camera sensor, lens, filter, aperture width, exposure time (level of sensor saturation), number of images used, and various combinations of these. Pristine modules and those experiencing different modes and degrees of degradation are examined. A recent study of modules using five different cell technologies demonstrates the practicality of our “EL sweep” technique for performance and degradation studies.
Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.
The performance of the ATLAS Inner Detector alignment has been studied using pp collision data at s√=13TeV collected by the ATLAS experiment during Run 2 (2015–2018) of the Large Hadron Collider (LHC). The goal of the detector alignment is to determine the detector geometry as accurately as possible and correct for time-dependent movements. The Inner Detector alignment is based on the minimization of track-hit residuals in a sequence of hierarchical levels, from global mechanical assembly structures to local sensors. Subsequent levels have increasing numbers of degrees of freedom; in total there are almost 750,000. The alignment determines detector geometry on both short and long timescales, where short timescales describe movements within an LHC fill. The performance and possible track parameter biases originating from systematic detector deformations are evaluated. Momentum biases are studied using resonances decaying to muons or to electrons. The residual sagitta bias and momentum scale bias after alignment are reduced to less than ~0.1 TeV -1 and 0.9×10 -3 , respectively. Impact parameter biases are also evaluated using tracks within jets.
Presentation to be presented during the Site-Directed Research and Development (SDRD) program FY 2021 review meeting (Webex), September 22–23, 2021.
This is a report for the FY2021 annual program overview.
Detecting helium leakage is important in many applications, such as in dry cask nuclear waste storage systems. This work develops a helium detection system based on the relative permittivity (dielectric constant) difference between air and helium. This difference changes the status of an electrostatic microelectromechanical system (MEMS) switch. The switch is a capacitive-based device and requires a very negligible amount of power. Exciting the switch’s electrical resonance enhances the MEMS switch sensitivity to detect low helium concentration. This work simulates two different MEMS switch configurations: a cantilever-based MEMS modeled as a single-degree-freedom model and a clamped-clamped beam MEMS molded using the COMSOL Multiphysics finite-element software. While both configurations demonstrate the switch’s simple operation concept, the clamped-clamped beam was selected for detailed parametric characterization due to its comprehensive modeling approach. The beam detects at least 5% helium concentration levels when excited at 3.8 MHz, near electrical resonance. The switch performance decreases at lower excitation frequencies or increases the circuit resistance. The MEMS sensor detection level was relatively immune to beam thickness and parasitic capacitance changes. However, higher parasitic capacitance increases the switch’s susceptibility to errors, fluctuations, and uncertainties.
Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.
Handheld multi-gas meters (MGMs) are equipped with sensors to monitor oxygen (O2) levels and additional sensors to detect the presence of combustible or toxic gases in the environment. This report is limited to operational response-type MGMs that include at least four different sensors. These sensors can vary by type and by the chemical monitored. In real time, the sensors report the concentration of monitored gases in the atmosphere near the MGM. To provide emergency responders with information on handheld multi-gas meters, the System Assessment and Validation for Emergency Responders (SAVER) Program conducted a market survey. This market survey report is based on information gathered between November 2015 and February 2016 from vendors, Internet research, industry publications, an emergency responder focus group, and a government issued Request for Information (RFI) that was posted on the Federal Business Opportunities website.
Handheld multi-gas meters (MGMs) are equipped with sensors to monitor oxygen (O2) levels and additional sensors to detect the presence of combustible or toxic gases in the environment. This report is limited to operational response-type MGMs that include at least four different sensors. These sensors can vary by type and by the monitored chemical. In real time, the sensors report the concentration of monitored gases in the atmosphere near the MGM. In April 2016 the System Assessment and Validation for Emergency Responders (SAVER) Program conducted an operationally-oriented assessment of MGMs. Five MGMs were assessed by emergency responders. The criteria and scenarios used in this assessment were derived from the results of a focus group of emergency responders with experience in using MGMs. The assessment addressed 16 evaluation criteria in four SAVER categories: Usability, Capability, Maintainability, and Deployability.