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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Novel Simulation-Based Framework for Sensor Error Impact Analysis in Smart Building Systems: A Case Study for a Demand-Controlled Ventilation System

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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Smart Refractory Sensors Development for Corrosion and Erosion Monitoring in High Temperature Systems

To optimize the operation and functioning of high temperature systems such as slagging gasifiers, coal boilers and glass/steel melters, it is important to monitor corrosion and erosion of refractory used in such systems. Corrosion test strategies are generally based on continuous gravimetric and chemical reactivity monitoring at operational temperatures (750°-1500°C). Both thermocouples and failure sensors and arrays would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance demonstration of smart refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The main feature of this technology is that electroceramic based sensors are embedded into smart refractory without significantly impact to the intrinsic properties of the refractory. This technology circumvents the need to insert an isolated monolithic, stand-alone sensor into the refractory via an access port. This technological approach guarantees the integrity and the chemical stability of the materials used in the sensor fabrication within the harsh environment and does not introduce molten material (such as slag) penetration pathways within the refractory. One interesting and important aspect of this innovation is that these embedded sensors can be used to in situ monitoring processes such as chemical reactions and at the same time give information and a deeper understanding of the corrosion and erosion process of the refractory within the system. As stated above, the objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). Prior to the development of the high-temperature sensors, the oxides composites developed in this study were sintered up to 1600°C under oxidizing atmosphere in order to investigate densification, microstructural evolution, phase development, and their thermoelectrical performance as a function of the composition. The 4-point DC conductivity measurements were performed between 100°-1500°C. The sensors were fabricated from the composite materials by 3D-printing or screen-printing methods into the refractory brick during the consolidation process. An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV around 1400 °C. Corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory brick an in-house glass composition was prepared and pressed into pellets and delivered into a pre-cut cavity in the brick. Corrosion experiments results showed the glass penetrated the brick over a 90 h period, and the penetration of the glass through the brick could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

Potyrailo, Radislav A.↗
DOE OSTI2020

Demonstration of Embedded Sensors in Ceramic Structures

The Transformational Challenge Reactor (TCR) program seeks to demonstrate an advanced 3D printed nuclear reactor core. A binder jet additive manufacturing process is used to fabricate the complex SiC ceramic components of the TCR core. After printing, the components are densified via chemical vapor infiltration (CVI). This process allows complex cavities to be created in which sensors can be placed at strategic locations. Inserting the sensors during the CVI process allows the sensors to be embedded within the component as it is infiltrated. The primary challenge for embedding the sensors is identifying the sensors and sensor sheath materials that can survive the temperatures (>1,000°C) and chemical exposure to H 2 and HCl during CVI. The embedded sensors that are being investigated for use during TCR operation include thermocouples, self-powered neutron detectors, and spatially distributed fiber-optic temperature sensors. This report describes the CVI materials compatibility tests and initial trials for embedding functional sensors. Mo is identified as the most suitable sensor sheath material based on its availability in small diameter tubing, relatively low neutron absorption cross section, and compatibility with H 2 , HCl, and the surrounding SiC matrix at the process temperatures. Amorphous SiO2 fiber-optic samples with Au and Cu metal coatings were successfully embedded in SiC along with bare fibers, although the metal coatings showed some evidence of localized melting during CVI. Initial experiments attempting to embed functional sensors showed that the sensors can be embedded in 3D printed SiC parts, but there are still challenges that must be overcome to ensure that the sensors and sensor sheaths do not fail during CVI. In particular, Nb-sheathed Mo-Nb thermocouples failed at temperatures far lower than expected despite thermodynamic calculations and materials compatibility tests that show the Nb sheath to be compatible with the CVI environment. One potential explanation for the large expansion of the Nb sheath could be related to hydride formation in the lower temperature region of the sheath. Future work will focus on Mo-sheathed thermocouples instead of Nb. The fiberoptic sensors tested during the instrumented experiment broke due to the lack of mechanical protection after their polymer-based coatings were vaporized. Future instrumented experiments will use metal-coated optical fibers for strain sensing to provide additional mechanical protection. However, custom coatings with higher melting points than commercially available Au and Cu might be required. Regardless, fiber-optic sensors are still candidates for spatially distributed temperature measurements since they can be inserted into an embedded metal sheath after CVI.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2008

Sensor Selection and Data Validation for Reliable Integrated System Health Management

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Garg, Sanjay↗
NASA NTRS2009

Smart and Intelligent Sensors

John C. Stennis Space Center (SSC) provides rocket engine propulsion testing for NASA's space programs. Since the development of the Space Shuttle, every Space Shuttle Main Engine (SSME) has undergone acceptance testing at SSC before going to Kennedy Space Center (KSC) for integration into the Space Shuttle. The SSME is a large cryogenic rocket engine that uses Liquid Hydrogen (LH2) as the fuel. As NASA moves to the new ARES V launch system, the main engines on the new vehicle, as well as the upper stage engine, are currently base lined to be cryogenic rocket engines that will also use LH2. The main rocket engines for the ARES V will be larger than the SSME, while the upper stage engine will be approximately half that size. As a result, significant quantities of hydrogen will be required during the development, testing, and operation of these rocket engines.Better approaches are needed to simplify sensor integration and help reduce life-cycle costs. 1.Smarter sensors. Sensor integration should be a matter of "plug-and-play" making sensors easier to add to a system. Sensors that implement new standards can help address this problem; for example, IEEE STD 1451.4 defines transducer electronic data sheet (TEDS) templates for commonly used sensors such as bridge elements and thermocouples. When a 1451.4 compliant smart sensor is connected to a system that can read the TEDS memory, all information needed to configure the data acquisition system can be uploaded. This reduces the amount of labor required and helps minimize configuration errors. 2.Intelligent sensors. Data received from a sensor be scaled, linearized; and converted to engineering units. Methods to reduce sensor processing overhead at the application node are needed. Smart sensors using low-cost microprocessors with integral data acquisition and communication support offer the means to add these capabilities. Once a processor is embedded, other features can be added; for example, intelligent sensors can make a health assessment to inform the data acquisition client when sensor performance is suspect. 3.Distributed sample synchronization. Networks of sensors require new ways for synchronizing samples. Standards that address the distributed timing problem (for example, IEEE STD 1588) provide the means to aggregate samples from many distributed smart sensors with sub-microsecond accuracy. 4. Reduction in interconnect. Alternative means are needed to reduce the frequent problems associated with cabling and connectors. Wireless technologies offer the promise of reducing interconnects and simultaneously making it easy to quickly add a sensor to a system.

Lansaw, John↗
NASA NTRS2013

Wireless Integrated Microelectronic Vacuum Sensor System

NASA Stennis Space Center's (SSC's) large rocket engine test facility requires the use of liquid propellants, including the use of cryogenic fluids like liquid hydrogen as fuel, and liquid oxygen as an oxidizer (gases which have been liquefied at very low temperatures). These fluids require special handling, storage, and transfer technology. The biggest problem associated with transferring cryogenic liquids is product loss due to heat transfer. Vacuum jacketed piping is specifically designed to maintain high thermal efficiency so that cryogenic liquids can be transferred with minimal heat transfer. A vacuum jacketed pipe is essentially two pipes in one. There is an inner carrier pipe, in which the cryogenic liquid is actually transferred, and an outer jacket pipe that supports and seals the vacuum insulation, forming the "vacuum jacket." The integrity of the vacuum jacketed transmission lines that transfer the cryogenic fluid from delivery barges to the test stand must be maintained prior to and during engine testing. To monitor the vacuum in these vacuum jacketed transmission lines, vacuum gauge readings are used. At SSC, vacuum gauge measurements are done on a manual rotation basis with two technicians, each using a handheld instrument. Manual collection of vacuum data is labor intensive and uses valuable personnel time. Additionally, there are times when personnel cannot collect the data in a timely fashion (i.e., when a leak is detected, measurements must be taken more often). Additionally, distribution of this data to all interested parties can be cumbersome. To simplify the vacuum-gauge data collection process, automate the data collection, and decrease the labor costs associated with acquiring these measurements, an automated system that monitors the existing gauges was developed by Invocon, Inc. For this project, Invocon developed a Wireless Integrated Microelectronic Vacuum Sensor System (WIMVSS) that provides the ability to gather vacuum-gauge measurements automatically and wirelessly, in near-real time - using a low-maintenance, lowpower sensor mesh network. The WIMVSS operates by using a self-configuring mesh network of wireless sensor units. Mesh networking is a type of networking where each sensor or node can capture and disseminate its own data, but also serve as a relay to receive and transmit data from other sensors. Each sensor node can synchronize with adjacent sensors, and propagate data from one sensor to the next, until the destination is reached. In this case, the destination is a Network Interface Unit (NIU). The WIMVSS sensors are mounted on the existing vacuum gauges. Information gathered by the sensors is sent to the NIU. Because of the mesh networking, if a sensor cannot directly send the data to the NIU, it can be propagated through the network of sensors. The NIU requires antenna access to the sensor units, AC power, and an Ethernet connection. The NIU bridges the sensor network to a WIMVSS server via an Ethernet connection. The server is configured with a database, a Web server, and proprietary interface software that makes it possible for the vacuum measurements from vacuum jacketed fluid lines to be saved, retrieved, and then displayed from any Web-enabled PC that has access to the Internet. Authorized users can then simply access the data from any PC with Internet connection. Commands can also be sent directly from the Web interface for control and maintenance of the sensor network. The technology enabled by the WIMVSS decreases labor required for gathering vacuum measurements, increases access to vacuum data by making it available on any computer with access to the Internet, increases the frequency with which data points can be acquired for evaluating the system, and decreases the recurring cost of the sensors by using off-the-shelf components and integrating these with heritage vacuum gauges.

Krug, Eric↗
NASA NTRS2019

Laboratory-Based Thermal Shock Investigation of Heat Flux Sensors for the Mars 2020 Backshell

In 2012 during the entry, descent, and landing of the Mars Science Laboratory (MSL), the MSL Entry, Descent, and Landing Instrumentation (MEDLI) sensor suite was collecting in-flight heatshield pressure and temperature data. The data collected by the MEDLI instruments has since been used for reconstruction of vehicle aerodynamics, atmospheric conditions, aerothermal heating, and Thermal Protection System (TPS) performance as well as material response model validation and refinement. The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite for the Mars 2020 heatshield and backshell is being designed to expand on the measurements and knowledge gained from MEDLI. Similar to MEDLI, MEDLI2 will measure the pressure and temperature of the heatshield. MEDLI2 will additionally measure the temperature, pressure, total heat flux, and radiative heat flux on the backshell.Since the backshell instrumentation is new to MEDLI2, Do No Harm (DNH) testing was conducted on instrumented backshell TPS (SLA-561V) panels. The panels consisted of four pressure port holes, one Mars Entry Atmospheric Data System (MEADS) pressure port plug, one MEDLI2 Integrated Sensor Plug (MISP) thermal plug, and one heat flux sensor. DNH testing was conducted to ensure the performance of the TPS was not degraded due to sensor integration and to characterize any TPS performance changes. The testing consisted of environmental testing— vibration, shock, thermal vacuum (TVAC) cycling— and bounding aerothermal (arc jet) testing. During arc jet testing, the heat flux sensors embedded in the SLA-561V panels exhibited an unexpected temporary reduction in the heat flux sensor temperature and response. After review of the test results, it was determined that this unexpected response was confined to the two heat flux sensors that experienced the greatest thermal shock condition. This condition consisted of a liquid nitrogen (LN2) bath that induced temperatures of approximately -190°C, and then a transition (thermal shock) to an arc jet test at a heat rate of approximately 21 W/cm2. Both heat flux sensors that were exposed to this thermal shock experienced a blister in the thermal coating during the arc jet test.Two heat flux sensor thermal shock test series were performed to investigate the cause of the blistering and subsequent energy release. In these tests, the heat flux sensor was first cold soaked in either a dry ice or LN2 bath to induce temperatures of approximately -78°C or -190°C, respectively. Then the sensors were thermally shocked using two propane torches with a heat rate of either approximately 8 W/cm2 or 21 W/cm2. The key findings indicated that there is a correlation between thermal shock and the blistering observed in the DNH test series, and that the cause appeared to be rooted in the heat flux sensor epoxy that encapsulates the sensor thermopile.Since the heat flux sensors are required to measure heat fluxes up to 15 W/cm2 during the Mars 2020 entry, a third test series was designed to determine if blistering is an issue at this maximum expected flight heat flux. Results from all three thermal shock test series and a discussion about whether or not blistering of the heat flux sensor thermal coating could be an issue for the Mars 2020 mission will be presented.

Miller, R. A.↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal monitoring: a case study

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.

OAE↗
NASA NTRS2011

Intelligent Wireless Sensor Networks for System Health Monitoring

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of PAN configuration, providing the appropriate response for maintaining overall sensor system function, even when sensor modules fail or the WSN is reconfigured. The session will present the architecture and technical feasibility of creating fault-tolerant WSNs for aerospace applications based on our application of the technology to a Structural Health Monitoring testbed. The interim results of WSN development and testing including our software architecture for intelligent sensor management will be discussed in the context of the specific tradeoffs required for effective use. Initial certification measurement techniques and test results gauging WSN susceptibility to Radio Frequency interference are introduced as key challenges for technology adoption. A candidate Developmental and Flight Instrumentation implementation using intelligent sensor networks for wind tunnel and flight tests is developed as a guide to understanding key aspects of the aerospace vehicle design, test and operations life cycle.

networks↗
DOE OSTI2023

Technology Maturation of Wireless Harsh Environment Sensors For Improved Condition Based Monitoring Of Coal Fired Power Generation

The overall goal of this project was to demonstrate and develop the usage of high-temperature (HT) harsh-environment (HE) wireless surface acoustic wave resonator (SAWR) sensor technology to promote reliable maintenance through condition-based maintenance (CBM) for field applications in harsh service conditions associated with power plant environments. The project aimed to advance the HT HE wireless SAWR sensor technology from TRL 5 to TRL 7. In addition to HT HE wireless temperature sensing, efforts were dedicated during this project to investigate, develop and increase the TRL from 3 to 5 for the following technologies: (a) HT HE strain sensors to address additional CBM monitoring needs, such as boiler tube mechanical / thermal stresses, which can provide early indications for boiler tube cracking and failure; and (b) HT aluminum nitride (AlN) and scandium aluminum nitride (ScAlN) based piezoelectric thin film fabrication and implementation of SAW sensors, with the goal of releasing the need to use single crystal piezoelectric materials for SAWRs and thus broaden possible technology applications to non-planar and harder to modify surfaces. To achieve the goals mentioned above, UMaine and its partner, Environetix Technologies Corporation, established partnerships with the following power plants: Longview Power (Maidsville, WV), a coal-fired power plant; Penobscot Energy Recovery Corp (PERC, Orrington, ME), a waste-to-energy power plant; and the UMaine Steam Plant (Orono, ME), an oil / natural gas power plant. To realize wireless HT HE SAWR sensor systems in these harsh service conditions, the University of Maine research team worked with Environetix and these power plants to define, design, fabricate, test and validate a mature prototype wireless temperature SAWR sensor system for boiler tube applications within the HT HE of the reheater pass damper chamber to directly and wirelessly monitor the temperature at eighteen independent boiler tube locations. The system included three levels, or “tiers”, of wireless communication to enable remote monitoring: Tier 1, the wireless link in the reheater pass damper chamber directly accessing the sensors on the boilers; Tier 2, the wireless local area network link, transmitting processed sensor information within the power plant to the Tier 3, a commercial wireless signal carrier company for secure remote data monitoring outside of the power plant. Regarding the wireless sensor system installed at Longview Power, temperature information from the boilers was continuously transmitted from the Longview boilers at Maidsville, WV, to Environetix headquarters, Orono, ME, over a 34 month period, when the system was finally decommissioned. Strain sensors and piezoelectric ScAlN thin film sensors were successfully installed on the exhaust duct at the UMaine Steam Power plant. The advances in wireless strain sensors and thin film piezoelectric film fabrication and testing were performed mostly in UMaine laboratories and field tested at the UMaine Steam Plant, due to its close proximity to UMaine/Environetix, access to the plant facility, and due to difficulties in accessing the other power plants during the COVID shut-down period. The project accomplished the TRL level increase of the targeted CBM technologies through the successful fabrication, installation, test, and validation of dedicated and commercial wireless sensor systems, utilizing the three different power plants. The outcomes of this project, including the wireless sensor data capability, are expected to yield an advance for CBM in harsh power plant environments. The reduction of maintenance costs, improved safety during plant operation, and increased power plant efficiency will lead to increased revenues (i.e., fewer forced outages) due to better process monitoring enabled by the wireless HT HE SAWR temperature sensor technology.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1983

Investigation of the applicability of using the triple redundant hydrogen sensor for methane sensing

Application specifications for the methane sensor were assembled and design guidelines, development goals and evaluation criteria were formulated. This was done to provide a framework to evaluate sensor performance and any design adjustments to the preprototype sensor that could be required to provide methane sensitivity. Good response to hydrogen was experimentally established for four hydrogen sensor elements to be later evaluated for methane response. Prior results were assembled and analyzed for other prototype hydrogen sensor performance parameters to form a comparison base. The four sensor elements previously shown to have good hydrogen response were experimentally evaluated for methane response in 2.5% methane-in-air. No response was obtained for any of the elements, despite the high methane concentration used (50% of the Lower Flammability Limit). It was concluded that the preprototype sensing elements were insensitive to methane and were hydrogen specific. Alternative sensor operating conditions and hardware design changes were considered to provide methane sensitivity to the preprototype sensor, including a variety of different methane sensing techniques. Minor changes to the existing sensor elements, sensor geometry and operating conditions will not make the preprototype hydrogen sensor respond to methane. New sensor elements that will provide methane and hydrogen sensitivity require replacement of the existing thermistor type elements. Some hydrogen sensing characteristics of the modified sensor will be compromised (larger in situ calibration gas volume and H2 nonspecificity). The preprototype hydrogen sensor should be retained for hydrogen monitoring and a separate methane sensor should be developed.

Lantz, J. B.↗
NASA NTRS2003

Advanced Packaging Technology Used in Fabricating a High-Temperature Silicon Carbide Pressure Sensor

The development of new aircraft engines requires the measurement of pressures in hot areas such as the combustor and the final stages of the compressor. The needs of the aircraft engine industry are not fully met by commercially available high-temperature pressure sensors, which are fabricated using silicon. Kulite Semiconductor Products and the NASA Glenn Research Center have been working together to develop silicon carbide (SiC) pressure sensors for use at high temperatures. At temperatures above 850 F, silicon begins to lose its nearly ideal elastic properties, so the output of a silicon pressure sensor will drift. SiC, however, maintains its nearly ideal mechanical properties to extremely high temperatures. Given a suitable sensor material, a key to the development of a practical high-temperature pressure sensor is the package. A SiC pressure sensor capable of operating at 930 F was fabricated using a newly developed package. The durability of this sensor was demonstrated in an on-engine test. The SiC pressure sensor uses a SiC diaphragm, which is fabricated using deep reactive ion etching. SiC strain gauges on the surface of the diaphragm sense the pressure difference across the diaphragm. Conventionally, the SiC chip is mounted to the package with the strain gauges outward, which exposes the sensitive metal contacts on the chip to the hostile measurement environment. In the new Kulite leadless package, the SiC chip is flipped over so that the metal contacts are protected from oxidation by a hermetic seal around the perimeter of the chip. In the leadless package, a conductive glass provides the electrical connection between the pins of the package and the chip, which eliminates the fragile gold wires used previously. The durability of the leadless SiC pressure sensor was demonstrated when two 930 F sensors were tested in the combustor of a Pratt & Whitney PW4000 series engine. Since the gas temperatures in these locations reach 1200 to 1300 F, the sensors were installed in water-cooled jackets, as shown. This was a severe test because the pressure-sensing chips were exposed to the hot combustion gases. Prior to the installation of the SiC pressure sensors, two high-temperature silicon sensors, installed in the same locations, did not survive a single engine run. The durability of the leadless SiC pressure sensor was demonstrated when both SiC sensors operated properly throughout the two runs that were conducted.

Beheim, Glenn M.↗
NASA NTRS2009

The Radio Frequency Health Node Wireless Sensor System

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

Valencia, J. Emilio↗
NASA NTRS2011

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This invention enables wide-area sensing and employs numerous globally distributed sensing devices that observe the physical world through the existing sensor network. This innovation enables distributed storage, distributed processing, distributed intelligence, and the availability of DiaK (Data, Information, and Knowledge) to any element as needed. It also enables the simultaneous execution of multiple processes, and represents models that contribute to the determination of the condition and health of each element in the system. The NCAP (intelligent process) can configure data-collection and filtering processes in reaction to sensed data, allowing it to decide when and how to adapt collection and processing with regard to sophisticated analysis of data derived from multiple sensors. The user will be able to view the sensing device network as a single unit that supports a high-level query language. Each query would be able to operate over data collected from across the global sensor network just as a search query encompasses millions of Web pages. The sensor web can preserve ubiquitous information access between the querier and the queried data. Pervasive monitoring of the physical world raises significant data and privacy concerns. This innovation enables different authorities to control portions of the sensing infrastructure, and sensor service authors may wish to compose services across authority boundaries.

Figueroa, Fernando↗
NASA NTRS2011

Virtual Sensor Test Instrumentation

Virtual Sensor Test Instrumentation is based on the concept of smart sensor technology for testing with intelligence needed to perform sell-diagnosis of health, and to participate in a hierarchy of health determination at sensor, process, and system levels. A virtual sensor test instrumentation consists of five elements: (1) a common sensor interface, (2) microprocessor, (3) wireless interface, (4) signal conditioning and ADC/DAC (analog-to-digital conversion/ digital-to-analog conversion), and (5) onboard EEPROM (electrically erasable programmable read-only memory) for metadata storage and executable software to create powerful, scalable, reconfigurable, and reliable embedded and distributed test instruments. In order to maximize the efficient data conversion through the smart sensor node, plug-and-play functionality is required to interface with traditional sensors to enhance their identity and capabilities for data processing and communications. Virtual sensor test instrumentation can be accessible wirelessly via a Network Capable Application Processor (NCAP) or a Smart Transducer Interlace Module (STIM) that may be managed under real-time rule engines for mission-critical applications. The transducer senses the physical quantity being measured and converts it into an electrical signal. The signal is fed to an A/D converter, and is ready for use by the processor to execute functional transformation based on the sensor characteristics stored in a Transducer Electronic Data Sheet (TEDS). Virtual sensor test instrumentation is built upon an open-system architecture with standardized protocol modules/stacks to interface with industry standards and commonly used software. One major benefit for deploying the virtual sensor test instrumentation is the ability, through a plug-and-play common interface, to convert raw sensor data in either analog or digital form, to an IEEE 1451 standard-based smart sensor, which has instructions to program sensors for a wide variety of functions. The sensor data is processed in a distributed fashion across the network, providing a large pool of resources in real time to meet stringent latency requirements.

Wang, Roy↗