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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Initial Temperature Testing of Advanced Manufactured Melt Wire Package

As a part of the In-Pile Instrumentation Program, The Idaho National Laboratory (INL) has recently established in-house capabilities to fabricate and test new advanced manufactured sensors for measuring the peak irradiation temperature inside a nuclear test reactor. Although nuclear test reactors can use standard methods of real-time temperature monitoring, such as thermocouples, the complexity of feedthroughs and attachment complications to collect real-time measurements greatly increases the cost of the experiment. Passive monitoring techniques may be utilized that exploit the melting point of well characterized material (standard melt wires), however, the limited space available for instrumentation during experiments is also a challenge. To accommodate the challenge, INL has expanded its temperature detection instrumentation capabilities paired with the advancement of in-house advanced manufacturing options to include printed melt wires for peak irradiation temperature measurement. The advanced manufactured melt wire package can determine peak temperatures in experiments that can fit the challenging space available during irradiation. Recent results and knowledge from this ongoing work have made it possible to develop unique temperature sensors that can accomplish user specific temperature ranges inside the harsh environments experienced during radiation testing. This report summarizes the initial temperature testing using fabricated melt wires with two different melting temperatures, silver (melting temperature of ~960 °C) and solder (melting temperature of ~160 °C), that were initially tested for their relative melting points using non-standard method of furnace and hot plate testing under normal air conditions. Future work will include more controlled way of testing printed melt wires under inert gas with multiple temperature measurements followed by comparison of standard melt wire package performance for baseline characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Fugitive methane detection using open-path stand-off chirped laser dispersion spectroscopy

We report an open-path chirped laser dispersion spectrometer capable of detecting the atmospheric methane concentration above the background using both specular and diffusive reflective surfaces via two distinct operation modes in a stand-off detection configuration. The system is integrated with simultaneous ranging functionality, which enables average concentration measurements for varying optical pathlengths. The system was first tested for accuracy and characterized to achieve sensitivity of 2.9 p p m - m / H z 1 / 2 and pathlength precision of 0.2 m / H z 1 / 2 with a controlled release of methane outside the laboratory. The instrument was subsequently field-deployed in the proximity of a natural gas compressor station for fugitive methane detection. The instrument successfully detected methane plumes and narrowed down the location of the plume through multi-path measurement. The field measurements were verified by a co-located reference mobile methane sensor.

Chen, Yifeng↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

HyPerPy (Hydrogen Extraction and Parabolic Trough Plant Performance Models using Python) [SWR-21-53]

The HyPerPy package consists of three scripts: 1) Model for Hydrogen Tracking in Parabolic Trough Power Plants, 2) Model for Receiver Performance, and 3) Model for Hydrogen Extraction Process. The power plant model (1) tracks hydrogen generation and transport within the circulating heat transfer fluid (HTF) of the power plant. This script is a transient, initial value simulation, in which the hydrogen concentration in the circulating HTF is 0 moles per cubic meter everywhere at time 0 seconds. Hydrogen concentration is calculated at discrete locations within the circulating HTF with 4-second time resolution. During each time step, the change in hydrogen concentration due to hydrogen generation and permeation is calculated for each location according to the local HTF temperature, vessel or piping properties, hydrogen concentration and partial pressure. This model predicts hourly hydrogen concentrations for typical operating days in the spring, summer, fall, and winter seasons. Data is used to create hourly mappings of hydrogen concentrations for a typical operating year. The receiver model (2) uses a 1-hour time step to estimate getter loading and annulus pressure. For each time step, the model uses HTF and ambient temperature data to estimate absorber tube, bellows, and getter temperatures for the time step. In addition, the model uses hourly HTF hydrogen concentrations that are generated by the power plant model (1), and annulus hydrogen pressure from the previous time step. With these data, the model calculates the moles of hydrogen permeating across the absorber tube and bellows during the time step. The net change in moles hydrogen is added or subtracted from the getter loading for the previous time step, and the hydrogen pressure is re-calculated based on getter loading and temperature. The model uses this algorithm to simulate hydrogen permeation and loading 24 hours per day, 365 days per year using seasonal temperature data. The model repeats these calculations for 25 years to create a mapping of receiver getter loading and annulus hydrogen pressure for four seasons of each year. The model for hydrogen extraction (3) estimates hydrogen extraction rates for a specific separation module configuration. The rate depends primarily on membrane area, vacuum pump performance, headspace gas flowrate to the membrane, and headspace gas hydrogen partial pressure. This model has two versions. The steady-state version predicts hydrogen extraction rates when the module is operating in separation mode. The dynamic version predicts hydrogen transfer through the membrane when the module is operating in sensor mode. The steady-state version is used with the plant model (1) to predict hydrogen partial pressures in the power plant when the extraction process is operating.

Glatzmaier, Gregory↗
DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗
DOE OSTI2020

3D Broadband, Noncontact, and Nonlinear Phased Array

Researchers at Los Alamos have developed a 3D broadband, noncontact, and nonlinear ultrasonic phased array technology that enables businesses to characterize and identify cracks and defects using laserbased sensing at ultra-high resolution. It can be applied to a wide range of commercial and industrial markets, e.g., aerospace, aeronautical, nuclear energy, oil and gas, and civil and material engineering and more broadly to those interested in the non-destructive evaluation (NDE) of materials and structures. Traditional phased-array technology for subsurface imaging operate over a narrow frequency range, with a limited number of sensors, using one type of ultrasonic waves which may decrease the likelihood of detecting a structural material crack. This technology fills the information gap by utilizing the potential of laser-based sensing to remove all these limitations. We are seeking a commercialization partner to license the technology or fund us through a Cooperative Research and Development Agreement (CRADA) to further develop a usable product for commercial purposes.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Range Hood Use and Effectiveness in Reducing Indoor Air Pollution During Gas and Induction Cooking

The Cooking Energy and Ventilation Impacts on Children's Asthma (CEVICA) study measured cooking frequency, range hood use, indoor air quality and respiratory health indicators of children with asthma living in homes with gas stoves in California's San Joaquin Valley. The study installed electric induction stoves and repeated measurements over three 2-week intensive periods, at baseline and at the end of two consecutive 3-month study phases. Stove replacements occurred at the start of Phase 1 or Phase 2 by random assignment. There were 4184 cooking events identified by automated analysis of time-series data from temperature sensors mounted above the cooktops and 1038 related range hood usage events detected from data recorded by anemometers, smart plugs, or motor loggers. Analysis of 1-minute resolved PM2.5 and NO 2 data identified and quantified 2685 PM 2.5 events and 2606 NO 2 events. Range hood use was characterized as a binary variable (>3 min vs. <3 min use). Range hood use was more common during cooking events associated with particle emissions and longer cooking durations. PM 2.5 concentrations during events with range hood use were comparable to those without use, which could result from limited effectiveness or if range hoods were preferentially used during higher-emission cooking scenarios. In homes with gas cooking, integrated NO 2 concentrations were about 45 percent higher during cooking events with no range hood use compared to those range hood use. The lowest pollutant levels were observed when the range hood operated for more than half of the cooking duration. These findings show that operation of venting range hood during cooking can substantially reduce short-term indoor exposure NO 2 in homes with gas cooking.

Fang, Yi↗
DOE OSTI2021

Mid Infra-Red Laser Sensor for Continuous Sulfur Trioxide Monitoring to Improve Coal-Fired Power Plant Performance during Flexible Operations

During the course of this project, we performed exhaustive research and development of SO3/H2SO4 sensing technology for coal-fired power plant applications (Figure 1). The development culminated in a successful field campaign of a prototype continuous real-time H2SO4 monitor at a coal-fired power plant (TRL 6) accomplishing the primary goal of the project. The developed sensors utilize tunable laser absorption spectroscopy (TLAS) operating in the mid-infrared (Mid-IR) wavelength region, which is the so-called “molecular fingerprint” region. Systems operating in the Mid-IR have orders of magnitude more sensitivity than systems operating at shorter wavelengths, such as near-infrared (NIR). However, NIR systems are more widespread due to more mature supporting technology (e.g., fiber optics, optical components, etc.). In this project, we not only produced a specific Mid-IR sensor, we also advanced Mid-IR sensor technology in general through the development and demonstration of such supporting technology. In this project, we also developed proprietary broad tuning lasers enabling the ability to effectively measure SO3, H2SO4, H2O, and SO2. Different molecular species have unique spectral signatures that can be probed with lasers operating at different wavelengths. Standard TLAS uses relatively narrow wavelength tuning distributed feedback (DFB) lasers, which can typically only target a single species with narrow features, and are not appropriate for species with broad features, such as SO3 or H2SO4. In contrast, by developing unique, broad-tuning laser technology, we were able to measure these species, as well as SO2 and H2O simultaneously. Furthermore, to enable real-time analysis at a power plant, we modified a commercially available heated gas cell to operate in the Mid-IR wavelength range and fiber coupled the lasers to enable remote delivery of the beams. To generate reference data (library spectra), our collaborators at the University of California Irvine (UCI) developed a catalytic SO3 generation facility. It is worth mentioning that representative H2SO4 and SO3 Mid-IR spectra are not a part of any publicly available database and the data generated under this project is a valuable resource in and of itself. In addition, based on the UCI study we determined that detection of SO3 is complicated by the very strong SO2 absorption. For that reason, we concentrated on H2SO4 detection. Since SO3 and H2SO4 exist in a flue gas in a state of equilibrium, which depends on temperature and humidity, by measuring water concentration and controlling the temperature of the gas cell, we developed an approach to determine SO3 concentration from the H2SO4 measurement. During the development phase of the project, we performed three testing campaigns at our collaborator’s FERCo flue gas facility with conditions representative of the coal-fired power plant (~ 40ppm SO3, 1700ppm to 2800 ppm SO2, 10% water) with the exception of particulate matter. After three test campaigns at FERCo we performed field testing at Harrison Power Station. The final system was mounted on a duct and measured H2SO4, SO2 and water. The tests were highly successful with a demonstrated real-time H2SO4 precision of 1 ppm with a 1 second update. Our collaborators at EPRI conducted an industry survey and determined that there is a very high interest for the SO3/H2SO4 monitoring in the power generation industry as well as in heavy industries in general. Furthermore, work performed by OptoKnowledge beyond the scope of this project under a synergistic DOE SBIR determined another approach to SO3 detection. We applied for Phase II on this SBIR for development a of versatile SO3/H2SO4 sensor but were not selected. We are currently looking for another opportunity to leverage all the technological advancements produced by this project including but not limited to the flue gas facility at UCI, the hardware and software developed, and relationships with FERCo, EPRI CEMTEK, and Harrison Station.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Characterization of local parameters of inclined upward air–water bubbly two-phase flows in a round pipe

The present work seeks to develop a detailed experimental database and characterize the local effects of inclination on horizontal and upward bubbly air–water two-phase flows. Experimental conditions are selected to investigate bubbly flows at a variety of orientations while reducing the influence of flow regime transition. Detailed time-averaged data is collected using state-of-the-art four-sensor conductivity probes for measurements across the flow area for five flow conditions at five inclination orientations in a 25.4 mm inner diameter pipe. Using the newly established database, effects of inclination on bubbly flows are analyzed, as are changes in liquid and gas flow rate at different orientations. The observations include (1) bubble migration and agglomeration at the top pipe wall as orientation changes from vertical to horizontal; (2) reducing the angle from vertical reduces the local gas velocity, and correspondingly reduces the global slip from greater than unity at 90° to less than unity for 60° and lower; (3) changes in the bubble size distribution are primarily at the interface between the bubble cluster and the liquid region of the pipe, not coalescence within the bubble cluster. In conclusion, physical mechanisms for these observations are proposed, and the present trends at different orientations support previous experiments in horizontal two-phase flows.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during Geological Carbon Storage

Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.

Engineering↗
DOE OSTI2025

Fe Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environment

Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. Effective monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of Fe-coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscatterring reflectometer (OBR), corrosion was monitored based on the increase in backscattered intensity amplitude of the light being passed as Fe undergoes corrosion. The Fe-coated OFSs were prepared with a film thickness between 25-225 nm by an electroless plating approach and corroded by a CO2-saturated acidic electrolyte. The corrosion rate was approximately 2.5 mm/yr for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based corrosion rates were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during corrosion of Fe was established. Corrosion was measurable out to > 100 m which is a significant improvement over our previous work showing corrosion sensing at < 10 m. The corrosion sensor was also tested in soil at >1 ft depth. Fe-coated OFS was loaded into a Draka cable which provides mechanical support during fiber deployment and installation into either acidified soil, top-soil, or sandy soil.

aqueous environment↗
DOE OSTI2021

Effects of multiple simultaneous faults on characteristic fault detection features of a heat pump in cooling mode

Faults in air-cooled vapor compression air-conditioning systems are known to reduce performance, including efficiency, capacity, and lifespan. Their effects have been studied, and fault detection and diagnostic (FDD) methods have been developed as tools for field technicians to install or repair systems, or for monitoring to alert operators to the fault’s presence. Most of this work has focused on faults that occur singly. It is likely that in some systems, multiple faults occur simultaneously, but it is uncertain what effects this may have on diagnostics. Here, this paper describes a laboratory study of a split system air source heat pump in which combinations of two, three, and four simultaneous faults occur. The study includes all combinations of: improper evaporator airflow; overcharge or undercharge of refrigerant; liquid line restrictions; and non-condensable gas in the refrigerant, each at multiple fault intensities. Fault features – those characteristics that can be determined from measurements, for use in diagnostics – are analyzed, and the key fault features are presented. A robust existing method for determining refrigerant charge, the virtual refrigerant charge sensor (VRC) is tested using the multiple fault data, in order to understand how its performance is impacted by the combined faults. The VRC performs well, typically able to correctly determine whether a system is undercharged or overcharged, but the magnitude estimates are impacted. The results suggest that simple subcooling-based methods of charging a system are likely to provide unsatisfactory results when other faults are present.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Plant synthetic biology innovations for biofuels and bioproducts

Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. In conclusion, this review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.

Yang, Yongil↗
DOE OSTI2023

Ordered porous RGO/SnO 2 thin films for ultrasensitive humidity detection

In this work, ordered porous thin films of reduced graphene oxide and tin oxide (rGO/SnO 2 ) were synthesized by a polystyrene sphere monolayer colloidal crystal template method, and their gas-sensing properties were systematically studied. The formed amorphous SnO 2 and partially reduced graphene oxide were analyzed using several complementary material characterization techniques. Further, the results show that the incorporation of rGO significantly improved the humidity sensitivity and the electrical conductivity of the sensor relative to the pristine SnO 2 thin film. Fast response time and excellent selectivity towards humidity were also achieved for the rGO/SnO 2 composite film. The long-term stability of the rGO/SnO 2 sensor was confirmed by comparing its performance to a commercial humidity sensor. The enhanced sensor performance is attributed to the synergistic effects of the incorporation of rGO and the ordered porous structure of the composite film.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

In-situ optical monitoring of gas turbine blade coatings under operational extreme environments

High temperature material systems consisting of thermal barrier coatings and the underlying superalloys are key to achieving higher gas temperatures which in turn control engine efficiencies and reduce emissions. With engine temperatures in excess of the limits that metallic blades and vanes can endure, advanced monitoring techniques that ensure the integrity and durability of these coatings are paramount to continuous and safe operation. Optical methods have the benefit of being non-invasive and are able to capture data under limited access available for monitoring the hot-section of turbines. Addressing Topic 5 on Advanced Instrumentation, the research and development effort for this project titled “In situ optical monitoring of gas turbine blade coatings under operational extreme environments” was completed by University of Central Florida researchers, including Raghavan (PI) and Ghosh (co-PI). In this project, the research team leveraged intrinsic properties of coatings and rare-earth dopants as active and intelligent “sensors” through their optical properties. The outcomes of this project pave the way for diagnostics of turbine blade coatings, under operating environments, through the development of the sensing thermal barrier coating configurations, calibration and associated instrumentation while ensuring coating integrity and durability goals are concurrently met.

42 ENGINEERING↗
DOE OSTI2023

First feedback-controlled divertor detachment in W7-X: Experience from TDU operation and prospects for operation with actively cooled divertor

In the last experimental campaign (OP1.2b) of the stellarator Wendelstein 7-X (W7-X), boronisation as a mean for first wall conditioning was applied for the first time which led to strongly reduced impurity fluxes from plasma-facing components. Thermal detachment at the uncooled target plates of the test divertor unit (TDU) was reached at higher plasma densities and was accompanied by high recycling of neutrals at the target plate. A feedback control system was established in W7-X to actively control the gas injection (actuator) for plasma fuelling and impurity seeding [3] through the divertors. It allowed very successful stabilisation of the detached plasma condition state as well as mitigation of thermal overloads to some baffle tiles. Different routinely available diagnostic signals were used as input parameters (sensors). We describe the setup of the feedback control system, its performance and provide some example results with the main focus on the development of the control scheme which led to the detachment stabilisation over the entire longest (30 s) high-power discharge at W7-X so far. In view of the achieved very successful detachment stabilisation and the necessity to include simultaneous optimisation of the core performance in the future, the feedback control system is being upgraded for the upcoming campaign (OP2.1) in which the water cooled and hereby inherently steady-state capable divertor has been currently installed. The prospects and some experiment ideas for active detachment control are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Molten salt environment creep testing extensometry system

Disclosed herein are systems, devices and methods for creep testing selected materials within a high-temperature molten salt environment. Exemplary creep testing systems include a load train for holding a test specimen under a load within a heated inert gas vessel. An extensometry system can be included to measure elongation of the test specimen while under load. The extensometry system can include fixed members and axially translating member that move along with the elongation of the test specimen, and the system can include a sensor to measure the relative axial motion between such components to measure elongation of the test specimen over time. The test specimen can include a cylindrical gage portion having an internal void filled with a molten salt during creep testing to simulate the corrosive effect of the molten salt on the specimen material during testing.

Ren, Wenlin↗