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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Experimentally Validated Computational Modeling of Creep and Creep-Cracking for Nuclear Concrete Structures

In a Nuclear Power Plant, one of the most important components is the concrete nuclear reactor cavity, which serves both a structural and protective function as the biological radiation shield. Given that creep has been identified as a major knowledge gap in the assessment of nuclear structures (NUREG/CR-7153), this work helps to further the understanding of creep behavior of massive concrete containment structures for decades to enable safe and long-term operation of these facilities. This project has developed a robust, experimentally validated model to predict creep in nuclear concrete structures for up 60 years using short-term creep data thereby enabling a longer service life of critical facilities and early detection of structural failure. The work presented in this report is a pairing of computational and experimental methods. For the first time, the time temperature superposition (TTS) principle was successfully used to generate a uniaxial creep compliance master curve to predict mortar creep response for up to 22,500 days (nearly 60 years) at a reference temperature of 20°C. These data were used as input into finite element analysis (FEA) codes that use highly realistic random, 3D concrete microstructures from reconstructed coarse limestone aggregates. Finite element analysis performed provides the ability to quickly upscale mortar viscoelastic behavior to long-term concrete creep/relaxation data. A master creep compliance curve, constructed from the TTS principle, spanning 27 years, was used to validate two and a half decades of simulated concrete creep. Concurrently, three different simulated wall specimens were designed to mimic the behavior of post-tensioned concrete nuclear containment facility vessel walls over time as a result of concrete creep. The specimens were designed with different thicknesses, transverse and longitudinal reinforcement ratios, and level of post-tensioning stress. Each specimen contained various instrumentation to measure internal concrete temperature, concrete strain, and post-tensioning strain hourly for over 3 years. The concrete creep model developed in this project, based on the FEA concrete simulations, was applied to simulate the structural-scale experiments of prestressed concrete walls conducted in this project using the Grizzly code. These models can represent the effects of reinforcing and prestressing. Although there are some discrepancies with the experimental data, the model can predict the overall trends of the creep response in these experiments. One of these experimental models was also applied to an extended time to demonstrate how the findings from this study can be used to predict the behavior of actual structures of interest that have been in service for extended periods of time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

ATF-2 Ramp Conceptual Design Report

The following report documents the conceptual design for the ATF-2 Ramp experiment. The experiment aims to fill the in-pile irradiation testing gap to conduct integral ramp testing, which was created by the closure of R2, Osiris, and Halden test reactors. The concept involves the simultaneous ramping of three fuel pins using a power axial locator mechanism in the Loop-2A testing facility in the center flux trap of the advanced test reactor (ATR). The three fuel pins are ramped in individual coolant channels containing a prototypic PWR environment. Rod failure will be detected using a fuel rod elongation sensor attached to the fuel pin upper end cap. An axial stack of concentric hafnium and zirconium shrouds will be used to shape the flux around the test pins to create different power levels in each pin and to ensure the peak power location of each pin remains in the center of the test rod. Monte Carlo simulations are used to demonstrate the viability of this design concept.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
DOE OSTI2025

A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems

Wind energy generation proliferated over the past decades, introducing unique challenges and opportunities for failure prediction, operation and maintenance. Decision-makers are continuously looking into new methods to infer failure mechanisms and behaviors of wind turbine components to detect and intervene in the failures before they happen. Evidently, degradation modeling and prognosis become engaging topics for researchers and practitioners to prevent catastrophic failures. Prognostics-driven approaches predict the time of failure for the components (e.g., predicting remaining useful life), which provides significant insights for scheduling of operations and maintenance activities. Integrating these prognostics-driven insights into wind farm operations and maintenance presents a substantial challenge, demanding careful consideration of numerous factors such as accessibility, crew routing, and spare part logistics. This study provides state-of-the-art review for degradation modeling, prognosis, and prognostics-driven maintenance techniques for wind energy systems. The discussed techniques align with the United Nations' sustainable development goals, in particular Goal 7 (Affordable and Clean Energy), by enhancing effectiveness and sustainability of wind energy operations. This work also showcases open research questions related to degradation modeling, prognosis, and prognostics-driven maintenance.

Altinpulluk, Nur Banu↗
DOE OSTI2022

Presence Detection in Connected Lighting Systems

Despite established technology and decades of experience, presence detection still faces challenges, including resistance to its use. This report provides context for current presence detection in lighting, considers various failure modes and their causes, characterizes various approaches to implementing presence detection, and describes performance evaluation as conducted in classrooms and offices at the Living Lab.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Field Experience Detecting PV Underperformance in Real Time Using Existing Instrumentation

Maintenance at large-scale photovoltaic plants employs a mix of preventative and corrective maintenance practices. Large outages, such as an inverter tripping offline, are often easy to detect. More subtle sub-inverter faults and failures can accumulate and go unnoticed for months or years. A software-based fault detection method has been developed to analyze commonly measured data from large-scale PV plants for more timely detection of subtle underperformance. The method has been demonstrated on eight datasets from large-scale plants with high accuracy of detection. Results are validated using aerial infrared scanning. String outages are detected with a true positive rate of 73 percent and tracker issues are detected with a true positive rate of 88 percent. The developed method can be uniformly applied to photovoltaic plants across a range of scales and configurations to assess performance, quickly detect underperformance, and determine the source and location of failures. The results inform and improve operations and maintenance at PV plants, ultimately aiding in improved affordability, reliability, availability, and resiliency of solar electricity.

14 SOLAR ENERGY↗
DOE OSTI2022

Enhanced Component Performance Study: Emergency Diesel Generators 1998-2020

This report presents an enhanced performance evaluation of emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2020 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2020. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in NRC probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed are: subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: • HPCS EDG FTS failure probability • EPS and HPCS EDG frequency of start demands (demands per reactor year) • EPS and HPCS EDG frequency of FTLR demands. The following decreasing trends were identified for EDGs for the most recent 10-year period: • EPS EDG FTS failure probability • EPS EDG FTLR failure probability • EPS EDG unavailability • EPS and HPCS EDG frequency of FTLR events (failures per reactor year).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Enhanced Component Performance Study: Emergency Diesel Generators 1998–2022

This report presents an enhanced performance evaluation of the emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2022 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2022. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in Nuclear Regulatory Commission (NRC) probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time-period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed include subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: (1) HPCS EDG FTS failure probability, (2) EPS and HPCS EDG frequency of start demands (demands per reactor year), and (3) EPS and HPCS EDG frequency of FTLR demands. The following decreasing trends were identified for EDGs for the most recent 10-year period: (1) EPS EDG FTS failure probability, (2) EPS EDG FTLR failure probability, (3) EPS EDG unavailability, and (4) EPS and HPCS EDG frequency of FTLR events (failures per reactor year).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Abstract Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhanced Component Performance Study: Emergency Diesel Generators 1998-2024

This report presents an enhanced performance evaluation of the emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2024 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2024. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in Nuclear Regulatory Commission (NRC) probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time-period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed include subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: • EPS and HPCS EDG frequency of start demands (demands per reactor year) • EPS and HPCS EDG frequency of FTLR demands • EPS and HPCS EDG frequency of run>1H hours. The following decreasing trends were identified for EDGs for the most recent 10-year period: • EPS EDG FTR>1H failure rate • EPS EDG unreliability • EPS and HPCS EDG frequency of FTR>1H events (failures per reactor year).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2020

Enhanced Component Performance Study: Emergency Diesel Generators 1998–2018

This report presents an enhanced performance evaluation of emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Consolidated Events Database (ICES) data from 1998 through 2018 and (2) maintenance unavailability (UA) performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2018. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in NRC probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. Engineering analyses were performed with respect to time period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed are: sub-component, failure cause, detection method, recovery, manufacturer, and EDG rating. A statistically significant increasing trend was identified in the frequency of FTLR demands for emergency power system (EPS) and high pressure core spray (HPCS) EDGs and a statistically significant decreasing trend was identified in the frequency of run > 1H hours for EPS and HPCS EDGs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Ultrasonic Characterization of Lithium Ion Thermal Runaway Conditions for Real-time Ultrasonic Enabled BMS Integration

The demand for energy storage is growing, and lithium-ion batteries are a promising technology to meet this need due to their high power/energy density, high round-trip efficiency, rapid response time, and portability. However, recent catastrophic events caused by thermal runaway have slowed their adoption, highlighting the need for an early warning system for battery failure. In this work, ultrasound is used to detect physical changes in 950 mAh batteries by identifying material property changes independent of voltage and current. [Ultrasound signal features (e.g., time of flight, maximum frequency component) were extracted as the batteries were cycled and subjected to both constant current and constant voltage overcharge and were used to develop two metrics identifying failure: a warning to detect the start of overcharge and an emergency stop (E-stop) to immediately take the battery out of service. The identification method involved locating magnitude differences of several ultrasound features compared to baseline operation considering different currents and temperatures, and the warning/stopping metrics were consistent across all experiments. For an average overcharge time of 140 minutes, the average warning was issued 124 minutes before the failure and the average E-stop was triggered 94 minutes before failure. As a test of using ultrasound for early warning detection, a battery was forced into overcharge and returned to normal cycling conditions based on the previously determined warning metric. Both the voltage profile and the ultrasound measurements returned to their baseline behavior, indicating that ultrasonic detection can not only identify battery failure before a catastrophic event, but can also provide early enough warnings such that overcharges can be detected and corrected quickly enough so the battery does not need to be decommissioned.]

25 ENERGY STORAGE↗
DOE OSTI2022

Tolerating memory stack failures in multi-stack systems

Memory management circuitry and processes operate to improve reliability of a group of memory stacks, providing that if a memory stack or a portion thereof fails during the product's lifetime, the system may still recover with no errors or data loss. A front-end controller receives a block of data requested to be written to memory, divides the block into sub-blocks, and creates a new redundant reliability sub-block. The sub-blocks are then written to different memory stacks. When reading data from the memory stacks, the front-end controller detects errors indicating a failure within one of the memory stacks, and recovers corrected data using the reliability sub-block. The front-end controller may monitor errors for signs of a stack failure and disable the failed stack.

Mappouras, Georgios↗
DOE OSTI2021

Optimizing the differential connection schemes for detecting 3D magnetic perturbations in DIII-D

Optimizing the differential pair connection scheme (i.e. the set of pairs) of a toroidal array of magnetic sensors dedicated to measuring slowly rotating asymmetric fields can enhance the mode number detection capability and failure-resilience. In this work the condition number obtained from singular value decomposition (SVD) of the design matrix is used as a metric to evaluate the quality of a connection scheme. A large number of possible pair connections are usually available, so evaluating all of them may require extensive use of computational resources and can be very time consuming. Alternative methods to reduce the number of pairs evaluated without losing the capabilities of toroidal mode detection are presented in this paper. Here, three examples of the applications of such analysis for the 3D magnetic diagnostic system of DIII-D are also presented: the addition of two new toroidal arrays with n > 3 detection capabilities, the modification of an existing toroidal array in the low field side of the machine to accommodate the addition of a helicon antenna, and the design of changes in several toroidal arrays in the high field side to accommodate the addition of a lower hybrid current drive antenna on the center post.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A Novel Architecture for Attack-Resilient Wide-Area Protection and Control System in Smart Grid

Wide-area protection and control (WAPAC) systems are widely applied in the energy management system (EMS) that rely on a wide-area communication network to maintain system stability, security, and reliability. As technology and grid infrastructure evolve to develop more advanced WAPAC applications, however, so do the attack surfaces in the grid infrastructure. This paper presents an attack-resilient system (ARS) for the WAPAC cybersecurity by seamlessly integrating the network intrusion detection system (NIDS) with intrusion mitigation and prevention system (IMPS). In particular, the proposed NIDS utilizes signature and behavior-based rules to detect attack reconnaissance, communication failure, and data integrity attacks. Further, the proposed IMPS applies state transition-based mitigation and prevention strategies to quickly restore the normal grid operation after cyberattacks. As a proof of concept, we validate the proposed generic architecture of ARS by performing experimental case study for wide-area protection scheme (WAPS), one of the critical WAPAC applications, and evaluate the proposed NIDS and IMPS components of ARS in a cyber-physical testbed environment. Our experimental results reveal a promising performance in detecting and mitigating different classes of cyberattacks while supporting an alert visualization dashboard to provide an accurate situational awareness in real-time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The future of subsurface monitoring: AEC’s breakthroughs in CCS technology

Carbon capture and storage (CCS) has emerged as a key solution in the fight against climate change. However, for CCS to succeed, it is crucial to ensure that the sequestered CO2 stays safely trapped underground. The U.S. Department of Energy (DOE) has emphasized the need for advancements in subsurface monitoring, measurement, reporting, and verification. Aside from caprock integrity failure, the other primary failure points usually involve defective cement in the casing annulus of wellbores or plugged and abandoned wells. In addition, many energy producers (e.g., oil and gas, geothermal) and storage and disposal operators (e.g., H2 and water) must deal with the same issue. Poorly placed or degraded cement can create pathways for gas or fluid to escape from casing annuli and in plugged and abandoned or orphan wells, posing environmental risks. Yet, a reliable and cost-effective way to monitor cement and well integrity over multiple decades is still unavailable. Traditional geophysical methods like 4D seismic imaging and surface-based electromagnetic monitoring lack the resolution and accuracy for detecting these types of failures (Vasco et al., 2022; Fawad and Mondol, 2021). Wireline logging is expensive to run continuously and is obtrusive to the operation. While fiber optics can potentially be a solution, its bulkiness can significantly compromise the cement's integrity. To address these challenges, the Advanced Energy Consortium (AEC) at The University of Texas at Austin’s Bureau of Economic Geology (the Bureau) has been pioneering research in subsurface monitoring using its portfolio of distributed autonomous microfabricated sensors for harsh subsurface environments since 2008. A class of these microsensors [System on a Chip (SoC)] can be mixed in cement and permanently placed without compromising the cement column; the sensors would then communicate with each other or a data acquisition (DAQ) master node. Another class of the AEC microsensors can be fully autonomous, with rechargeable micro-batteries capable of exceeding 100°C, flash memory, and, currently, a pressure and temperature sensor. They are designed to circulate in mud, geothermal fluids, U-loops, or pipelines. They can log data into memory and are unobtrusive to operations. Our team has been working on a multi-year DOE-funded project (DE-FE0031856)—supported by $2.95M in federal funding and $0.75M in cost-matching from the AEC—to demonstrate SoC sensor utility for CO2 leakage monitoring in CCS applications. This multi-institutional collaboration developed a novel sensing architecture utilizing radiofrequency (RF) microsensors embedded within the cement sheath. These sensors detect CO2 migration and are interrogated via a Smart Casing Collar (SCC).

58 GEOSCIENCES↗