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DOE OSTI2021

Fiber optic system for direct detection of CO2 leakage in carbon storage wells

25 ENERGY STORAGE↗

DOE OSTI2021

Fiber optic system for direct detection of CO 2 leakage in carbon storage wells (Abbreviated Final Report)

The feasibility study focused on the development of new fiber for distributed chemical sensing that will allow direct detection of CO2 leakages in the environment. This is particularly important for monitoring well integrity for carbon capture and storage (CSS), which can provide early warning for an incoming well failure and potential CO2 leaking through it. We proposed Raman or IR interrogation within gas-filled Holey Fibers (HoFs) interleaved with Fiber Bragg Grating (FBG) sections, so that the location and concentration of the gases would be provided simultaneously. The methodology would overcome current roadblocks to using fiber optics for CO2 (and other gases) detection in wells with direct in-situ measurements of concentration along with other important parameters such as temperature and pressure. We were able to assess commercially available IR/Raman hollow core fiber and demonstrated detection of CO2 through them in our controlled environment setups at various pressure conditions. We have also established the ability of drilling precisely with fs-lases side holes to enable penetration of CO2 into the hollow core fiber and reduce diffusion rates. Open joint collars were also explored with double functionality: to obtain splice to solid core fiber critical for field deployment and create gas ingress locations. Both diffusion-only and pressurized fiber system have been constructed following COMSOL based semi-hybrid optical /fluidodynamics models. FBGs have been identified and procured and characterized. Along the work we leveraged internal modeling/design, photonics/laser characterization, optical fiber fabrication, and AM lab capabilities to design, develop and test in-house components or assemblies. Our results indicate the potential of HoF for CO2 downhole direct detection.

25 ENERGY STORAGE↗

DOE OSTI2021

Feasibility of using distribute chemical sensing for CO2 leakage monitoring

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Feasibility of using distributed chemical sensing for CO2 leakage monitoring

The feasibility study focused on the development of new fiber for distributed chemical sensing (DCS) that will allow direct detection of CO 2 leakages in the environment. This is particularly important for monitoring well integrity for carbon capture and storage (CCS), to provide early warning for an incoming well failure and potential CO 2 leaking through it. We proposed using optical spectroscopy in optical fiber for direct detection of CO 2 . The main approach is based on Raman interrogation within gas-filled Holey Fibers (HoFs), so that the location and concentration of the gases would be provided simultaneously via backscattering. Additionally, Infrared (IR) Absorption Spectroscopy could also be used, and the architecture would be more discrete since interleaving with standard solid core fibers and Fiber Bragg Grating (FBG) sections is required to enable reflection to the I/O controls. The possible Raman length or the IR numbers of sections would be defined based on signal to noise ratio. The optical spectroscopy methodology would overcome current roadblocks to CCS, as fiber optics will allow for CO 2 (and other gases) detection in wells with direct in-situ measurements of concentration along with other important parameters such as temperature and pressure as the baseline of environment background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

The Role of Nuclear-Conventional Intermingling on State Decision-making and the Risk of Inadvertent Escalation

The questions answered by this report are: What are the implications of nuclear and conventional intermingling on crisis stability and the potential risk of miscalculation?, and Specifically, how might entanglement influence US and competitor decision making during crisis and conflict? In practice, there are three main forms of nuclear-conventional intermingling. First, intermingling between nuclear and non-nuclear weapon systems can occur via the fielding of dual capable delivery systems like missiles or aircraft. Second, intermingling can happen due to the co-location of nuclear and non-nuclear forces and their support structures—for example, the co-location of strategic bombers and general-purpose aircraft, or the co-location of strategic submarines and general-purpose vessels. Third, intermingling can occur via convolving nuclear and conventional military command and control systems, to include ballistic missile early warning and potentially space surveillance systems as well. All three forms of nuclear-conventional intermingling have significantly increased since the end of the Cold War, driven by both technological and doctrinal changes. However, there are important differences in the rationale behind, and also the risks associated with these three different forms of intermingling. The mere existence of dual capable systems is not new— deploying such systems can increase the effectiveness of forces, and it can also provide more flexibility. The major powers have both employed and threatened with dual capable systems for decades, and they have done so without nuclear escalation. Similarly, the major powers co-located nuclear and conventional systems in the Cold War, and they did so for variety of reasons that had nothing to do with complicating the adversary’s risk calculus. The Soviet Union, for example, decided to co-locate its nuclear and conventional forces for economic and administrative reasons. Although today it might be recognized as a useful deterrent tool, it was not their primary intention. Major powers want to convince rivals that the co-location of forces creates a high bar for targeting and raises the risk of nuclear escalation, but they also want to have the flexibility of this not being true in an actual crisis or conflict.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗

DOE OSTI2022

AOI.1 Application of Artificial Intelligence techniques enabling coal fired power plants the ability to achieve higher efficiency, improved availability, and increased reliability of their operations (Final Report)

During this effort, SparkCognition with support from the Electric Power Research Institute (EPRI) was tasked with applying artificial intelligence (AI) to improve the reliability, efficiency, and safety of operations at a coal-fired plant. By implementing AI techniques, like machine learning (ML), it is believed that operators can leverage existing data sources to gain more insights such as advanced warning of machine degradation. With enough lead time, a reliability engineer can take action to minimize, or even avoid, impact to production. To complete this work effort, SparkCognition developed and refined an ML-based model using sensor data for a Steam Turbine unit at a host site. The models were deployed in an online, web-based solution that allows users to visualize model outputs and supporting data. The final solution, based on SparkCognition’s proprietary software platform called SparkPredict®, was shared with EPRI who completed an online evaluation of results to determine the solution’s ability to detect actionable events.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Integrated Pest Management (IPM) for Early Detection Algal Crop Protection. Final Report

During large-scale, outdoor algal biomass growth, production strain(s) are subject to attack by pathogens, predators, and non-productive competitors, compromising the biomass yield. The project team at UCSD developed and demonstrated the effectiveness of Volatile Organic Compound, VOC, analysis via mass spectrometry (MS) as an early detection system for the presence of production algal ponds infection. The MS detection can then trigger high-resolution melt analysis (HRMA) and enhanced quantitative PCR (qPCR) to identify predatory or pathogenic species precisely. This study gathered the foundational data informing how wholly automated systems monitor the health of production ponds. By detecting the presence of low molecular weight VOCs in the air space above ponds, the instrument was able to mimic “sniffing” the health of the algae pond. This early warning system will be free growers from time-consuming and costly regular surveillance of ponds with the current techniques, such as qPCR and flow-cam assays, which are slower, less sensitive, and not easily fully automated, resulting in increased production efficiency through the reduction of crop loss. This project demonstrated that Chemical Ionization Mass Spectrometry, CIMS, could serve as a real-time monitor of ponds health tracking up to 100 different VOCs simultaneously for continuous periods up to 60 days without interruption, signaling infection within an algal crop 24 to 36 hours prior to techniques such as qPCR and microscopy. While mass spectrometry is an expensive tool, this simple VOC sampling system proved not only to be sensitive and robust but could to multiplexed to monitor several ponds, lowering the overall cost serially. This technology demonstrated a reduction in biomass production cost by approximately 50% compared to growers not employing any detection methodology.

09 BIOMASS FUELS↗

DOE OSTI2022

Repurposing Fossil-Fueled Assets For Energy Storage

The annual retirement of U.S. coal-fired electricity generation units (CF-EGU) is at an all-time high and is expected to continue. This loss of reliable baseload generation, combined with predicted growth of variable renewable energy (VRE) generation, is expected to stress grid reliability as the number of load-following resources drops below the experienced load variability. Many regions are already experiencing challenges, and fossil retirement-related warnings by the North American Electric Reliability Corp. are becoming more dire. All CF-EGU retirements pose significant challenges to asset owners, local workforces, and their communities. Repurposing a retiring CF-EGU as a long-duration energy storage plant can address these challenges and offer a suite of additional benefits to asset owners, the grid, and society. This project performed a techno-economic evaluation and assessment of repurposing a Duke Energy fossil-fueled asset (in particular, a coal plant) into an energy storage system by integrating the retiring asset with a Malta long duration Pumped Heat Energy Storage (PHES) system. The project validated the technoeconomic benefits of repurposing retiring coal plants into long-duration energy storage using Malta’s PHES. Key findings for this project are summarized below: (1) Technical: (a) Retiring coal plants (and other steam turbine fossil generation) can be repurposed to enable the clean energy transition using Malta’s technology. (b) For older retiring coal plants, repurposing the site and electrical interconnection for a standalone PHES plant is the most economically favorable option. (c) For newer coal plants where there is also a local peaking capacity need, repowering the steam cycle into a hybrid integration with PHES is attractive. (d) A process was developed to assist fossil generation owners in choosing the best path for each plant’s circumstances. (2) Economic (a) Communities facing economic challenges caused by the retirement of fossil generation would benefit from repurposing the plant as long-duration energy storage using Malta’s PHES. (b) On a $/MW basis, repowering retiring coal units into Malta PHES plants can maintain the same number and types of jobs and economic activity. (c) For a 70% carbon reduction scenario, a 10-hour Malta PHES plant is more economic for the asset owner than similar-power 4-hour batteries. This project showed that repurposing a retiring coal unit into thermal energy storage, by integrating it with a Malta PHES system, makes techno-economic sense. At least two integration options are available, with the optimal solution depending on the coal plant and its location. Repurposing retiring coal plant into energy storage results in economic benefits for the plant owner and local communities.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Conflicting Information and Compliance With COVID-19 Behavioral Recommendations

The prevalence of COVID-19 is shaped by behavioral responses to recommendations and warnings. Available information on the disease determines the population’s perception of danger and thus its behavior; this information changes dynamically, and different sources may report conflicting information. We study the feedback between disease, information, and stay-at-home behavior using a hybrid agent-based-system dynamics model that incorporates evolving trust in sources of information. We use this model to investigate how divergent reporting and conflicting information can alter the trajectory of a public health crisis. The model shows that divergent reporting not only alters disease prevalence over time, but also increases polarization of the population’s behaviors and trust in different sources of information.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

The ASC-V&V Program [Slides]

The fundamental goal of the ASC-V&V Program is to obtain a quantitative understanding of our customers’ needs and priorities, and use it to assess applicability of numerical models of physical phenomena to physics questions, sensitivity of physics questions to physical phenomena, fidelity of a numerical implementation to the theory, the bounds of validity of theories, fidelity and coverage of experimental data relative to regimes of interest for physics questions, in order to warn users of IC codes and PEM data when simulations may lead to unsupported conclusions, and provide estimates of uncertainties on figures of merit calculated by the simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Domestic Extremism: Countering the Threat Posed to Critical Assets

Domestic extremism has been a growing concern in the United States in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the threat of insider attack and violence, creating a new threat to be countered in the Office of Radiological Security’s radiological source security mission. In this effort, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions and next steps. Twenty-two subject matter experts participated in a series of five focus group sessions. Questions focused on definitions of domestic extremism, potential changes in the threat, best practices in securing facilities, assets, and personnel, and any perceived gaps. Upon completion of the focus groups, notes were analyzed thematically to identify any recurring patterns in the results. In addition, a review of academic, industry, and government literature was conducted to understand the threat, describe the process of radicalization to extremism, and to identify empirically informed practices in prevention and response. Results of this project demonstrated that further work is needed to define domestic extremism in law, regulation, and policy, to help the U.S. develop a consistent response to the threat within organizations. This is especially important, as SMEs emphasized the need for early intervention in prevention efforts, noting that organizations need clear guidance on when and how to intervene. In addition, the need for social media monitoring was discussed, although challenges remain to do so with appropriate respect for privacy and civil liberties concerns.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

Domestic Extremism (Executive Summary)

Domestic extremism (DE) has been a growing concern in the U.S. in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the danger of insider attack and violence, creating a new threat to be countered. In this effort, therefore, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions for the Office of Radiological Security (ORS) to address DE with its partners.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

Technical Specification Surveillance Interval Extension Using Self-Diagnostics

As part of the Light Water Reactor Sustainability program, an ongoing research effort is being conducted on technical specifications surveillance interval extension of digital equipment in nuclear power plants. The research team is led by Idaho National Laboratory and includes Pacific Northwest National Laboratory, Technology Resources, and Oak Ridge National Laboratory. This research focuses on developing methods for applying the U.S. Nuclear Regulatory Commission (NRC)–approved guidance to implement a licensee-controlled, risk-informed surveillance frequency change program in digital instrumentation and control (I&C) systems that include self-diagnostics and online monitoring (OLM) capabilities. Although approved methods exist for extending technical specifications (TS) surveillance test intervals (STIs) for general equipment, including analog I&C equipment, gaps remain in technology and guidance on crediting newer digital equipment’s internal self-diagnostics and OLM characteristics. Previous research described a general methodology for crediting internal self-diagnostics for extending surveillance test intervals. The methodology used self-diagnostics to detect—and credited recovery from—failure. Self-diagnostics were also applied for performance monitoring during the extended surveillance interval. This report discusses the status of recent activities to evaluate the previously developed methodology using a pilot study. Although both a utility partner for a pilot study and a specific digital asset were identified in FY2020, delays in obtaining proprietary information resulted in a limited ability to fully evaluate the methodology, and further interactions were complicated by the COVID pandemic. Therefore, at that time, the use of public-domain information—along with current processes for surveillance interval extension through a surveillance frequency control program—identified the need to fully assess diagnostic coverage as part of the pilot study. Furthermore, self-diagnostics were also identified as a potential option to replace the drift analyses conducted as part of current STI extension procedures. In FY2022, the project was reconstituted with the industry partner, and information and data were made available by the industry partner to the research team for review. The shared information included failure event descriptions and data for a digital I&C system since its implementation, as well as recent STI extension interval reports developed by the utility partner on that digital I&C system. This report presents an evaluation of this information and data and describes an application of the proposed methodology cited above. The methodology seeks to take advantage of the self-diagnostics and OLM capabilities to reduce risk or reduce the level of qualitative monitoring assessment needed to perform a risk-informed STI extension using existing NRC approved guidance or both. Addressing these issues of STI extension by crediting self-diagnostics is likely to result in benefits for current and future nuclear power plant (NPP) operations, including lowering the barriers to adoption of digital I&C systems and increasing cost savings by deferring or eliminating unneeded preventive maintenance (tasks or checks or activities). Specifically, self-diagnostic and OLM capabilities of newer digital equipment being installed in non-safety and safety applications are designed to detect failures, provide early warning of potential failures, and notify plant operators to take appropriate action to reduce out of service (OOS) time thus protecting safety margins. Moreover, the equipment is expected to provide information that time-related operational degradation is identified early to ensure timely and planned corrective actions instead of a reactive and unplanned approach ahead of an extended-surveillance interval.

42 ENGINEERING↗

DOE OSTI2024

Distributed Strain Sensing based Reservoir Monitoring for Carbon Sequestration Reservoirs

Hydraulic or shear fractures propagation can significantly impact the integrity of the cap rock in carbon sequestration reservoirs. This presentation will evaluate distributed strain sensing (DSS) techniques to detect and provide early warnings for fracture propagation and cap rock integrity issues. We will highlight the advantages of DSS techniques over other methods for measuring cap rock integrity. We will then examine how the geometry of monitoring wells affects detection results and suggest optimal well designs for monitoring. Finally, we will present real-life field examples and propose future research directions.

Jin, Ge↗

DOE OSTI2024

Performance Assessment of Photovoltaic Panels Using Impedance Spectroscopy

The goal of this project was to develop a technique for measuring internal characteristics of a PV module using light modulation under a fixed voltage bias while measuring the resulting alternating current. This technique, light-intensity modulated impedance spectroscopy (LIMIS), has the promise of detecting early signs of panel aging and degradation that could be used for example by solar farm operators to have early warnings to repair or replace panels to maintain reliability of the overall PV array. LIMIS would be complementary with the previously developed electrochemical impedance spectroscopy (EIS), which uses an alternating voltage applied electrically to a solar cell with a similar alternating current measurement. EIS has mostly been developed for individual PV cells rather than whole modules. The project was intended to assess what different information could be revealed by LIMIS, which may in some cases be more scalable to larger modules and potentially more practical to apply in field measurements without needing to electrically disconnect PV modules. We began with small 10 W PV modules and built a testbed capable of oscillating the light intensity with frequencies up to 50 kHz. In parallel, we built a large testbed for testing large 250 W PV modules. Meanwhile, we developed procedures for established measurement techniques: current–voltage (I–V), EIS, and electroluminescence (EL) imaging, where panels are subjected to forward bias while their infrared emission is recorded using a camera modified to be sensitive to IR wavelengths. In addition, we developed protocols for accelerated aging of PV modules in two ways. Thermal cycling from –40°C to +90°C simulates the diurnal temperature cycles on a rooftop. Mechanical stress by dropping a 227 g ball from a height of 1 m simulates damage such as that due to hail impacts.

14 SOLAR ENERGY↗

DOE OSTI2025

ElementLIBS User's Guide: An operational aid for use and development

Laser-Induced Breakdown Spectroscopy or LIBS is a rapid, in-situ analytical technique where a laser of known energy is pulsed at the surface of an analyte. The laser pulse rapidly heats a localized area to many thousand degrees Kelvin, ablating part of the analyte and turning it into a plasma. As the plasma cools, excited atoms return to a ground state with known emission energies. This emitted energy is captured by various spectrometers and provides a spectrum of the emitted energies and intensities. This spectrum can be analyzed to provide the elemental composition of a sample by using known emission lines and relative abundance. ElementLIBS was developed for the SciAps hand-held LIBS model Z300 but will work with any model that provides a LIBS spectrum of similar resolution. The Z300 has an integrated resolution of 1/30 nm with a range of approximately 180nm – 960nm, providing an output spectrum of 23431 pixels across three spectrometers. These criteria are only provided as a reference, as the software was designed to work with any size spectrum, provided the input file is of the correct format and the models were developed using the same framework. If spectra of varying dimensions are used, the software will fail without warning and unusual events could occur.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗