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208 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2024

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

42 ENGINEERING↗
DOE OSTI2021

High-Temperature Multi-Process Sensor Development and Demonstration at a Full-scale PC Combustion System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
NASA NTRS2004

Research Capabilities for Oil-Free Turbomachinery Expanded by New Rotordynamic Simulator Facility

A new test rig has been developed for simulating high-speed turbomachinery shafting using Oil-Free foil air bearing technology. Foil air journal bearings are self-acting hydrodynamic bearings with a flexible inner sleeve surface using air as the lubricant. These bearings have been used in turbomachinery, primarily air cycle machines, for the past four decades to eliminate the need for oil lubrication. More recently, interest has been growing in applying foil bearings to aircraft gas turbine engines. They offer potential improvements in efficiency and power density, decreased maintenance costs, and other secondary benefits. The goal of applying foil air bearings to aircraft gas turbine engines prompted the fabrication of this test rig. The facility enables bearing designers to test potential bearing designs with shafts that simulate the rotating components of a target engine without the high cost of building actual flight hardware. The data collected from this rig can be used to make changes to the shaft and bearings in subsequent design iterations. The rest of this article describes the new test rig and demonstrates some of its capabilities with an initial simulated shaft system. The test rig has two support structures, each housing a foil air journal bearing. The structures are designed to accept any size foil journal bearing smaller than 63 mm (2.5 in.) in diameter. The bearing support structures are mounted to a 91- by 152-cm (3- by 5-ft) table and can be separated by as much as 122 cm (4 ft) and as little as 20 cm (8 in.) to accommodate a wide range of shaft sizes. In the initial configuration, a 9.5-cm (3.75-in.) impulse air turbine drives the test shaft. The impulse turbine, as well as virtually any number of "dummy" compressor and turbine disks, can be mounted on the shaft inboard or outboard of the bearings. This flexibility allows researchers to simulate various engine shaft configurations. The bearing support structures include a unique bearing mounting fixture that rotates to accommodate a laserbased alignment system. This can measure the misalignment of the bearing centers in each of 2 translational degrees of freedom and 2 rotational degrees of freedom. In the initial configuration, with roughly a 30.5-cm- (12-in.-) long shaft, two simulated aerocomponent disks, and two 50.8-cm (2-in.) foil journal bearings, the rig can operate at 65,000 rpm at room temperature. The test facility can measure shaft displacements in both the vertical and horizontal directions at each bearing location. Horizontal and vertical structural vibrations are monitored using accelerometers mounted on the bearing support structures. This information is used to determine system rotordynamic response, including critical speeds, mode shapes, orbit size and shape, and potentially the onset of instabilities. Bearing torque can be monitored as well to predict the power loss in the foil bearings. All of this information is fed back and forth between NASA and the foil bearing designers in an iterative fashion to converge on a final bearing and shaft design for a given engine application. In addition to its application development capabilities, the test rig offers several unique capabilities for basic bearing research. Using the laser alignment system mentioned earlier, the facility will be used to map foil air journal bearing performance. A known misalignment of increasing severity will be induced to determine the sensitivity of foil bearings to misalignment. Other future plans include oil-free integral starter generator testing and development, and dynamic load testing of foil journal bearings.

Howard, Samuel A.↗
DOE OSTI2025

Results and lessons learned from accelerating radio frequency modeling using machine learning [slides]

The “advanced tokamak” reactor concept is a leading candidate for a steady state fusion pilot plant. An advanced tokamak (AT) sustains a majority of the required plasma current with effects resulting from maintenance of the peaked pressure at the device center. This current is augmented by auxiliary current drive sources. These auxiliary actuators may consist of neutral particle beams and/or radio frequency (RF) systems such as lower hybrid current drive (LHCD) and high harmonic fast wave (HHFW) current drive using radio and microwaves from antennas. The primary focus of this work is to develop models of RF current profile control suitable for use in integrated modeling frameworks and for real-time control in experiments. Direct physics models of RF current drive can be computationally intensive. In order to achieve predictive times appropriate for the thousands of calls needed in real-time control of experiments and for use in integrated models, we will apply modern machine learning (ML) techniques to accelerate these models and interpolate their results. To generate the fast and accurate models for use in control level algorithms and integrated modeling we need to replace present models with high dimensional interpolation of their results. We will perform additional simulations across a broader parameter range for EAST and other tokamaks in different physics regimes (Alcator C-Mod, DIII-D, WEST, CFETR, ARC, ITER) and combine them into a larger database for training and testing of the ML models. Further testing of the control level models with experimental current profile data from EAST and C-Mod tokamaks will provide additional confirmation of the control level model before integration in a tokamak control system or integrated modeling suite. ML will be used to optimize the selection of training data consisting of RF current driven at different values of density profile, temperature profile, plasma current, and wavenumber. ML will also be used to facilitate classification of current drive from these input data. The output of this effort will be a validated classifier capable of determining the current drive profiles for HHFW CD and LHCD on a mille-second timescale. This will provide a breakthrough capability enabling real-time control of RF driven current profiles in experiments including ITER ICRF and use integrated modeling frameworks requiring thousands of current profile calculations in discharge simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1999

Enhanced Boiling on Micro-Configured Composite Surfaces Under Microgravity Conditions

In order to accommodate the growing thermal management needs of future space platforms, several two-phase active thermal control systems (ATCSs) have evolved and were included in the designs of space stations. Compared to the pumped single-phase liquid loops used in the conventional Space Transportation System and Spacelab, ATCSs offer significant benefits that may be realized by adopting a two-phase fluid-loop system. Alternately, dynamic power systems (DPSs), based on the Rankine cycle, seem inevitably to be required to supply the electrical power requirements of expanding space activities. Boiling heat transfer is one of the key technologies for both ATCSs and DPSs. Nucleate boiling near critical heat flux (CHF) can transport very large thermal loads with much smaller device size and much lower pumping power. However, boiling performance deteriorates in a reduced gravity environment and operation in the CHF regime is precarious because any slight overload will cause the heat transfer to suddenly move to the film boiling regime, which in turn, will result in burnout of the heat transfer surfaces. New materials, such as micro-configured metal-graphite composites, can provide a solution for boiling enhancement. It has been shown experimentally that this type of material manifests outstanding boiling heat transfer performance and their CHF is also extended to higher values. Due to the high thermal conductivity of graphite fiber (up to 1,200 W/m-K in the fiber direction), the composite surfaces are non-isothermal during the boiling process. The composite surfaces are believed to have a much wider safe operating region (a more uniform boiling curve in the CHF regime) because non-isothermal surfaces have been found to be less sensitive to variations of wall superheat in the CHF regime. The thermocapillary forces formed by the temperature difference between the fiber tips and the metal matrix play a more important role than the buoyancy in the bubble detachment, for the bubble detachment manifests itself by a necking process which should not be weakened by reduced gravity. In addition, the composite surfaces introduce no extra pressure drop, no fouling and do not impose significant primary or maintenance costs. All of these suggest that this type of composite is an ideal material for the challenge of accounting for both reliability and economy of the relevant components applied in the ATCSs, the DPSs and other devices in future space missions. The aim of the proposed work is to experimentally investigate high nucleate pool boiling performance on a micro-configured metal-graphite composite surface and to determine the mechanisms of the nucleate boiling heat transfer both experimentally and theoretically. Freon-113 and water will be used as the test liquids to investigate wettability effects on boiling characteristics. The Cu-Gr and Al-Gr composites with various volume fractions of graphite fibers will be tested to obtain the heat transfer characteristic data in the nucleate boiling region and in the CHF regime. In the experiments, the bubble emission and coalescence processes will be recorded by a video camera with a magnifying borescope probe immersed in the working fluid. The temperature profile in the thermal boundary layer on the composite surfaces will be measured by a group of micro thermocouples consisting of four ultra fine micro thermocouples. This instrument was developed and successfully used to measure the temperature profile of evaporating liquid thin layers by the proposers in a study performed at the NASA/Lewis Research Center. A two tier model to explain the nucleate boiling process and the performance enhancement on the composite surfaces has been suggested by the authors. According to the model, the thicknesses of the microlayer and the macrolayer underneath the bubbles and mushrooms, can be estimated by the geometry of the composite surface. The experimental results will be compared to the predictions from the model, and in turn, to revise and improve it.

Zhang, Nengli↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Enhancement of Optical Efficiency of CSP Mirrors for Reducing O&M Cost via Near-Continuous Operation of Self-Cleaning Electrodynamic Screens (EDS). Final Report

Over the past decade, techno-economical advancements in solar energy systems, particularly the improvement in the conversion efficiency of PV modules made with mono-crystalline silicon solar cells from 12% to 20%, as well as the cost reduction in manufacturing by a factor of about 10%, have made it possible to achieve a levelized cost of electricity (LCOE) in PV plants that is comparable to, or less than, the cost of deriving electricity from fossil fuels. Operating PV plants in mid-latitude sun-belt regions, where the solar irradiance level is highest, provides a high annual energy-yield (kWh/kWp) due to two factors: (1) the availability of predictable high solar irradiance throughout the year with the fewest interruptions in solar flux from clouds and rain, and (2) the increased conversion efficiency of crystalline solar cells, as recombination loss has decreased with increased intensity of the sunlight that illuminates the silicon solar cells. Semi-arid and desert regions, however, are plagued by high atmospheric dust concentrations and frequent sand storms. The deposition of a layer of dust on the optical surfaces of solar collectors such as PV modules and concentrating mirrors reduces the transmission efficiency of sunlight that actually reaches the solar cells or receivers, resulting in high energy-yield soiling loss. There are two major cost components to operating a solar plant: (1) installation costs, and (2) operation-and-maintenance (O&M) costs. There is no fuel cost; hence operating a solar plant in a semi-arid or desert region provides high returns on investment if soiling losses are mitigated via efficient cleaning methods and optimized cleaning frequency. If solar collectors are not cleaned, the accumulation of dust layers on solar collectors may cause the operation of such plants in arid regions to become economically unviable. Washing solar collectors with water and detergent, as is most commonly done now, is an efficient method for cleaning. The conventional approach in utility-scale solar plants is to use a large truck with a water tank and pump system for spraying deionized water on the surface of the solar collectors. Robotic cleaning with brushes, used for many solar plants, requires lesser water for cleaning. The water consumed using semi-automated cleaning of PV modules in utility-scale solar plants is approximately 2 liters/m 2 per cleaning cycle. The total optical surface area of the solar collectors in 1 TW-scale solar installation will be more than 3 × 109 m 2 ; hence an enormous amount of water be needed for cleaning. There simply is not enough fresh water in the sun-belt areas of the world for predicted cleaning needs. In solar power plants, the estimated cost of cleaning solar collectors includes expenses related to the equipment used, labor, cost of transportation of water, the energy required for cleaning, plus ancillary costs whereas the cost of water is not considered. The water used is obtained from sources located close to plant sites, unmindful of the environmental and societal impact as the power plants are oftentimes located in regions that face severe drought. This practice is very similar to the cost calculations in deriving the levelized cost of electricity (LCOE) in conventional power plants based on burning fossil fuel such as coal and gas, while disregarding the cost of climate change and health effects. Unless a water-free or low-water cleaning method is established, the expansion of solar plants may lose public support in areas suffering long intervals of drought. The goal of this research project has been the development and application of the Electrodynamic Screen (EDS) as a means for a water-free, scalable cleaning process applicable to solar-power installations, including rooftop applications. We describe here the development of an EDS film-based cleaning process as an emerging method for use on PV modules, parabolic troughs, and heliostats. This report aims to show the feasibility of integrating or retrofitting EDS films onto the optical surfaces of solar collectors (both PV and CSP) while maintaining high transmission or reflection efficiency. The cleaning action provided by the EDS film is an active method to remove dust deposits by electrodynamic force. Current lab-scale prototype EDS films, retrofitted onto solar panels and mirrors, have shown to be capable of maintaining optical transmission or specular-reflection efficiencies higher than 90% of initial values under clean conditions. The optical surfaces of solar collectors laminated with EDS films can remove more than 90% of deposited dust when the EDS is activated for less than two minutes. As an electrodynamic dust removal process, the EDS film-based method is designed primarily for the removal of dust in solar installations located in semi-arid and desert areas, where the atmosphere is often dry and dusty and rainfall is infrequent. While EDS film application minimizes water consumption and facilitates cleaning as frequently as needed, it has limitations in removing contaminants such as soot, organic pollutants deposited as fine films on the surface, and bird droppings. The dust removal efficiency of the EDS is maximum at relative humidity RH is 40 to 50% and decreases at when RH > 65%. Many solar plant sites undergo diurnal and seasonal cycles of high ambient, early afternoon temperatures and an RH that reaches the dew point early in the morning. These variations in atmospheric conditions do not limit the operation of the EDS film. This report presents a brief review of the progress and the potential of EDS film technology for mitigating the impact of dust on solar collectors via water-free cleaning, as well as current technical challenges regarding efficiency and durability. Our experimental data on the performance of EDS films show that: (1) the dust-removal efficiency (DRE) can reach levels higher than 90%, (2) the specular reflectivity (SR) of EDS film-laminated second-surface mirrors reach levels in excess of 90%, (3) the specular reflectivity restoration (SRR) can exceed 90%, (4) the output-power restoration (OPR) of PV modules can exceed 95%, and (5) the optical transmission efficiency (TE) of the EDS films can be greater than 90%. Working with Sandia National Laboratories (NM), Corning Research and Development Corporation (NY), Eastman Kodak (NY), Tomark-Worthen Industries (NH), and EDS Chile SPA (Chile), we have produced EDS film-laminated PV modules and demonstrated their self-cleaning functions without requiring water. We have demonstrated that the operational range of EDS films will cover the expected ambient temperature of solar fields at RH cycling varying from 20 to 95% as long as the EDS films are activated in the RH range 20 to 50%. (Typical solar-field climates in deserts and semi-arid lands often reach near dew point in coastal areas.) Our experiments on the application of hydrophobic-fluorinated nanoparticle coatings on EDS film surfaces show that the EDS operational range can be extended to higher RH levels that approach the dew point. At Eastman Kodak, as one of our industrial partners, we were able to establish a process for manufacturing EDS films using flexographic printing of the electrodes onto transparent polymer films. This process utilizes an existing manufacturing line at Eastman Kodak that allows fabrication of medium-scale EDS films (26 cm × 30 cm). The manufacturing process has the capacity to produce EDS films at high production speeds. The medium-scale EDS films that have been printed at Kodak were evaluated in the lab at Boston University. The EDS films produced at Kodak were laminated at Tomark-Worthen using an industrial scale vacuum laminator to produce EDS film stacks, which can be affixed onto the optical surfaces of PV modules or concentrating mirrors. The EDS film stack consists of the EDS films that have Willow® Glass (WG) which has a thickness of 100 μm as the front surface. The WG sheets obtained from Corning Research and Development Corporation are customized in size and shape to cover the active area of the EDS films. The back surface of the EDS film stack is integrated onto the optical surface of the solar panel or mirror using optically clear adhesive (OCA) films or silicone adhesives. The OCA films (thickness 25 μm) are produced by 3M. The EDS film stacks have the architecture: WG/OCA/EDS Film/OCA/ over PV module or solar mirror. The power-supply units needed for activating the electrodes of the EDS film were designed and produced at Boston University. These power supply units provide three-phase, 1.2 kV voltage pulses at a very low current (micro-ampere) level and at a low frequency (≈ 5 Hz). The voltage pulses are applied to the electrodes in a sequence such that the train of pulses resembles a unidirectional traveling wave of electrical field on the surface of the EDS film. The dust particles on the surface become charged electrostatically and are levitated by the Coulomb force. The lateral sweeping action of the traveling electric field created by the three-phase voltages pulses then sweeps the dust off the surface. The energy consumed by the EDS electrodes is less than 0.2 Wh/m 2 /cleaning cycle, enabling energy-efficient restoration of output power (OPR) of PV module or specular reflectivity restoration (SRR) for solar mirrors. The EDS system consists of (1) an EDS film stack laminated onto the solar collectors, (2) connection of the EDS film to its power supply unit and (3) Interconnection of the power supply to PV modules or solar mirrors. Design, construction and assessment of field-testing units that have EDS stack laminated PV modules for evaluating the performance of the EDS films in solar fields is being carried out at BU. Our progress under this project, aimed at the advancement of EDS film technology, has reached DOE Technology Readiness Level (TRL) 6. Based on the extensive laboratory evaluations and limited field trials, as well as contacts with potential users, we believe that the technology has reached its commercial stage. We are conducting a cost analysis using the National Renewable Energy Laboratory (NREL) System Advisory Model (SAM) and are preparing for field trials of EDS films in different solar fields in the US, Chile, India and in the Middle East. A brief description of EDS film performance, construction and testing of the field-test unit, autonomous operation of the field-test unit for evaluating EDS performance in increasing energy yield, associated revenue savings, and water conservation is presented.

14 SOLAR ENERGY↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗