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DOE OSTI2024

The Pixel Anomaly Detection Tool : a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗

DOE OSTI2022

AMTS update for STTR

Advanced nuclear reactor systems currently in development include designs that utilize higher energy neutron spectrums, higher temperatures, and corrosive environments that are quite different from the current fleet of nuclear power plants in use today. As a result, new structural materials must be developed and qualified for use to support these advanced systems. For example, research is currently being conducted on a new class of materials known as multi-principle-element-alloys. These alloys show some promise to be able to meet the structural needs of these challenging reactor systems. However, due to the vast available compositional space, and the limited amount of research conducted thus far, Idaho National Laboratory (INL)sought a way to advanced mechanical testing capability for highly irradiated structural materials to increase both throughput and higher fidelity data analysis through incorporation of digital image correlation. The Automated Mechanical Testing System accomplishes these tasks by automating the testing and incorporating digital image correlation to provide higher fidelity data. INL has now received this system and is currently performing functional testing and training. INL is also performing some modifications to the system to allow for installation into a remote shielded enclosure where testing of irradiated structural materials can be performed. After testing, training, and modifications of the system are complete, INL will install the system into a shielded enclosure and begin using the system to test irradiated structural materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

GeN-ROM—An OpenFOAM®-based multiphysics reduced-order modeling framework for the analysis of Molten Salt Reactors

This work presents a projection-based multiphysics Model Order Reduction (MOR) framework for the analysis of nuclear systems and its application to parametric simulations of Molten Salt Reactors (MSR). The framework, named GeN-ROM, is developed using OpenFOAM® and employs a Proper Orthogonal Decomposition aided Reduced-Basis technique (POD-RB). It can be used to reduce steady-state and transient multiphysics problems involving parametric fluid dynamics, heat exchange, and neutronics phenomena. For the treatment of structural elements in the hydraulic systems, a porous medium approach has been adopted. The reduction process is data-driven and snapshot information is extracted via POD to learn the solution manifold and to build global spatial basis functions. At the data collection phase, GeN-ROM makes use of the solvers available in GeN-Foam, a similarly OpenFOAM®-based multiphysics framework developed for the analysis of nuclear reactors. The global bases are used both to approximate the solution fields and to project the full-order equations onto lower-dimensional subspaces, thus considerably reducing the number of unknowns in a numerical system. This reduction leads to significant computational speedups, which is ideal for multi-query applications such as uncertainty quantification or design optimization. The developed tool has been tested using a 2D multiphysics model of the Molten Salt Fast Reactor (MSFR) with steady-state and transient scenarios, with speedups on the order of 10 – 10 5 .

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Heterointercalation in Chevrel-Phase Sulfides: A Model Periodic Solid for the Investigation of Chain Electron Transfer

Modulation of electron density localization on periodic crystal solids through electron transfer from interstitial cations can directly influence the bonding configurations of small-molecule intermediates at the catalyst binding site. This study presents the microwave-assisted solid-state synthesis of four heterointercalant Chevrel-phase (CP) sulfides with varying metal cation intercalants with compositional and electronic structure investigations of the electron density redistribution as a result of intercalation. The heterointercalant CP sulfides, with the general formula Cu x M y Mo 6 S 8 (where M = Cr, Mn, Fe, Ni; x, y = 1.5−2.5), are presented here for the probe reaction of electrochemical CO 2 reduction. A change in product selectivity is observed toward the production of methanol at low overpotentials of −0.5 V vs reversible hydrogen electrode (RHE), as a result of the intercalant combination present within the CP interstitial cavity. Structural confirmation of all materials was examined through Rietveld refinement of the powder X-ray diffraction (PXRD) data, high-resolution transmission electron microscopy (HR-TEM), and selected-area electron diffraction (SAED). Electron transfer from the intercalated metal cations to the Mo 6 S 8 cluster was investigated via X-ray photoelectron spectroscopy (XPS) of the intercalated metal cations and the chalcogenide cluster. Electron transfer was further confirmed through X-ray absorption analysis (XAS) of the K-edges of Mo and intercalants. Intermediate studies of electrochemical reduction of formaldehyde to methanol resulted in a faradaic efficiency of ∼78% methanol production on Cu x Ni y Mo 6 S 8 . The results presented herein identify distinct principles for materials design that can be utilized in other compositional spaces within the broad families of periodic crystal solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Deep-Learning-Enabled Crack Detection and Analysis in Commercial Lithium-Ion Battery Cathodes

We report in Li-ion batteries, the mechanical degradation initiated by micro cracks is one of the bottlenecks for enhancing the performance. Quantifying the crack formation and evolution in complex composite electrodes can provide important insights into electrochemical behaviors under prolonged and/or aggressive cycling. However, observation and interpretation of the complicated crack patterns in battery electrodes through imaging experiments are often time-consuming, labor intensive, and subjective. Herein, a deep learning-based approach is developed to extract the crack patterns from nanoscale hard X-ray holo-tomography data of a commercial 18650-type battery cathode. Efficient and effective quantification of the damage heterogeneity with automation and statistical significance is demonstrated. The crack characteristics are further associated with the active particles’ packing densities and a potentially viable architectural design is discussed for suppressing the structural degradation in an industry-relevant battery configuration.

25 ENERGY STORAGE↗

DOE OSTI2021

Online Monitoring System for Concrete Structures Affected by Alkali-Silica Reaction

This report presents a comprehensive study of developing ultrasonic wave and acoustic emission (AE) techniques for long-term monitoring of alkali-silica reaction (ASR) development in concrete. Small, medium, and full-scale concrete specimens were cast, conditioned, and monitored for various periods, from at least one year to 2.4 years. The reactive concrete specimens contain coarse or fine reactive aggregates to study the effects of different types of reactive aggregates. Confinements were also designed to simulate the 2-dimensional confinement effects of reinforcement in the shield building of nuclear power plants. The concrete specimens were stored in a environmental chamber with high humidity and high temperature to accelerate the ASR development. The ultrasonic monitoring data shows high sensitivity to ASR development and could detect cracking initiation well before visible surface cracks occurred. However, the linear ultrasonic analysis based on wave velocity is strongly affected by temperature variation. Therefore, a nonlinear ultrasonic method was proposed to measure thermally induced nonlinear acoustic responses of concrete (thermal modulation of ultrasonic wave). The measured nonlinear acoustic parameter shows a high correlation with ASR expansion across specimens with different reactive aggregates and confinement conditions. The same conclusion was obtained from nonlinear resonance tests on small concrete prisms. Compared to the linear acoustic methods, the nonlinear tests show high sensitivities to ASR damage from internal microcracking initiation at the early stage to visible cracks at the late stage of ASR. The attributes of the thermal modulation of nonlinear ultrasonic method include high sensitivity, immunity to temperature effects, and strong correlation with ASR expansion, which demonstrate great potentials of the nonlinear ultrasonic method for diagnosis of ASR damage and prediction of concrete deterioration process. Acoustic emission is a passive sensing technique for damage assessment, and access to only one surface is needed even for thick and heavily reinforced elements such as the walls utilized for nuclear shield building. Additionally, relatively few sensors are required to monitor the progression of the damage process. Results indicate that the AE data can be related to the damage rating index, which is a petrography-based means of assessing damage due to ASR in reinforced concrete. Furthermore, AE is capable of detecting ASR damage long before surface cracking is visually noticeable. Boundary conditions play in important role in the progression of ASR damage and differences in boundary conditions are reflected in the AE data. Entropy based data assessment methods provide a means to assess the damage state, and convolutional neural network based data assessment provided a means to assess the damage state in real-time. Results indicate that artificial neural network models may be used as a means to predict volumetric expansion based on AE data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Dark Energy Survey Year 3 Results: Galaxy mock catalogs for BAO analysis

The calibration and validation of scientific analysis in simulations is a fundamental tool to ensure unbiased and robust results in observational cosmology. In particular, mock galaxy catalogs are a crucial resource to achieve these goals in the measurement of baryon acoustic oscillation (BAO) in the clustering of galaxies. Here we present a set of 1952 galaxy mock catalogs designed to mimic the Dark Energy Survey Year 3 BAO sample over its full photometric redshift range 0.6 < zphoto < 1.1. The mocks are based upon 488 ICE-COLA fast N-body simulations of full-sky light cones and were created by populating halos with galaxies, using a hybrid halo occupation distribution – halo abundance matching model. This model has ten free parameters, which were determined, for the first time, using an automatic likelihood minimization procedure. We also introduced a novel technique to assign photometric redshift for simulated galaxies, following a two-dimensional probability distribution with VIMOS Public Extragalactic Redshift Survey data. The calibration was designed to match the observed abundance of galaxies as a function of photometric redshift, the distribution of photometric redshift errors, and the clustering amplitude on scales smaller than those used for BAO measurements. An exhaustive analysis was done to ensure that the mocks reproduce the input properties. Finally, mocks were tested by comparing the angular correlation function w(θ), angular power spectrum Cℓ, and projected clustering ξp(r⊥) to theoretical predictions and data. The impact of volume replication in the estimate of the covariance is also investigated. The success in accurately reproducing the photometric redshift uncertainties and the galaxy clustering as a function of redshift render this mock creation pipeline as a benchmark for future analyses of photometric galaxy surveys.Key words: catalogs / large-scale structure of Universe / galaxies: distances and redshifts / Galaxy: halo / methods: numerical

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensor systems were tested to measure the key parameters, such as hoop strain, pipe pressure, surrounding soil temperature, and acoustic vibrations. The underground product pipeline’s outer diameter is 30 inches, the wall thickness is 1.28 inches, and 3 feet deep from the surface. The fiber deployment strategies and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety. These findings from pilot-scale testing offer valuable insights into advancing pipeline monitoring technologies and improving the reliability of underground pipeline systems.

fiber optic sensors↗

DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Elucidating the geometric and electronic structure of a fully sulfided analog of an Anderson polyoxomolybdate cluster

The catalytic activity of transition metal sulfide (TMS) clusters in small molecule activation, redox transformations, and charge transfer has inspired the design of novel TMS-based materials for energy-related catalysis and chemical applications. Polyoxometalates (POMs), known for their structural diversity, can in principle be transformed into TMS clusters; however, fully sulfided analogs are rarely isolated, likely due to the strong tendency of uncapped TMS clusters to agglomerate. Here, we report the geometric and electronic structure of a capping ligand-free fully sulfided analog of heptamolybdate Anderson POM [Mo VI 7 O 24 ] 6− , synthesized through the sulfidation of a nanoconfined POM secured within a porous Zr-metal organic framework (NU-1000). A combined computational and experimental analysis indicates that the sulfided counterpart of the Anderson POM is geometrically and electronically more sophisticated than the parent POM. Comparison of experimental pair distribution function (PDF) data with computational simulations confirms that, unlike the oxygen-only [Mo VI 7 O 24 ] 6− cluster, the [Mo IV 7 (μ 3 -S) 6 (μ 2 -SH) 6 (S 2 ) 6 ] 2− polythiometalate (PTM) exhibits diverse sulfur anions (S 2− , HS − , S 2 2− ). DFT calculations indicate that H 2 S acts as a reducing agent, and together with terminal disulfide (S 2 2− ) ligands in the PTM structure, facilitates the complete reduction of all seven Mo VI centers in the parent POM to Mo IV . These findings are supported by X-ray photoelectron spectroscopy (XPS), which confirms exclusive Mo IV , and elemental analysis, which shows quantitative sulfur incorporation. Difference envelope density (DED) mapping further reveals that the PTM clusters are spatially confined within the MOF pores, preventing agglomeration and preserving molecular integrity.

Rabbani, S. M. Gulam [The Ohio State University, C↗

DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

The moderately defficient enzyme: Catalysis-related damage in vivo and its repair

Enzymes have in vivo lifespans. Analysis of lifespans – lifetime totals of catalytic turnovers – suggests that non-survivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme lifespan might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements is lacking: there are too few measurements of in vivo lifespan, and existing information on the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach to identify, predict, and validate instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability, and could change thinking about which enzymes die young in vivo, and why.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The moderately defficient enzyme: Catalysis-related damage in vivo and its repair

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Computed Tomography (CT) Analysis of 3D Printed Lattice Structures

LATTICE structures are increasingly used in product design across different industries, including the Department of Energy (DOE), due to their high strength and lightweight properties. These structures possess qualities such as high stiffness, energy absorption, large surface area, and structural support. To ensure lattice structures' quality and symmetry, it is crucial to have defect-free structures. However, in some cases, traditional non-destructive evaluation methods may not be practical due to encasing lattice structures in material. To analyze the lattice structures' quality, this project will employ CT imaging to gain access to the encased structures and perform a detailed analysis of each section. Three 3D printed complex lattice cubes were created, with two exposed and one encased in material, for this project. The CT X-ray cabinet will be used to scan all three cubes, and the data collected will be reconstructed and evaluated using complex interpretation software. This analysis will allow for a detailed examination of the structure's porosity, inclusions, cracks, wall thickness, and symmetry in each section.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The Moderately (D)efficient Enzyme: Catalysis-Related Damage In Vivo and Its Repair

Enzymes have in vivo life spans. Analysis of life spans, i.e., lifetime totals of catalytic turnovers, suggests that nonsurvivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme life span might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements are lacking: there are too few measurements of in vivo life span, and existing information about the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach for identifying, predicting, and validating instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability and could change our thinking about which enzymes die young in vivo, and why.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Technology Demonstration of a High-Pressure Swirl Oxy-Coal Combustor

This technical report presents the exploration of the design and prototyping of a High-Pressure Swirl Oxy-coal Combustor. Pressurized oxy-coal combustion systems have the potential to improve efficiency along with an increased carbon capture rate. Reduction of flue gas at higher pressure, smaller system size, and capital cost reductions render high-pressure oxy-coal systems particularly attractive as next-generation energy-producing systems. High-pressure oxy-coal combustion systems are a recent concept, and thus operability issues of combustor designs for such systems are not fully understood. Significant challenges exist to maintain oxy-coal combustion stability at elevated pressure and a high CO 2 diluent environment. Although a body of knowledge exists for high-pressure oxygen combustion in rocket engines (or similar applications), it is yet to be strategized how these fundamental concepts can be translated to low-temperature CO 2 diluent combustion regimes. The realization of the pressurized oxy-coal based systems requires combustor components to be designed and demonstrated for an operating pressure over 10 bar. However, pressurized oxy-coal combustor design information at this pressure range and scale relevant to validate those proposed systems is currently limited. Experimental data from MWth scale oxy-coal combustors are needed to identify the optimal trade-off between net efficiency and systems size. The proposed effort is aimed at demonstrating a 1 MWth down-fired swirl Oxy-Coal combustor and investigate the interrelation between combustor operating conditions (pressure; flame stability; flue gas recirculation ratio) and conversion efficiencies to minimize oxygen requirements. One of the key challenges is to configure burner design (i.e., swirl number and injector) and operating conditions for high-pressure oxy-coal combustion systems. These experiments differ from current systems partly due to the high theoretical flame temperature and related burner operability issues associated with oxy-combustion. An ASPEN PLUS® model study for 550 MWe TIPS and ENEL pressurized oxy-coal systems with CO 2 recirculation was performed to evaluate system design, subsystems sizing, and operating condition determination. The system analysis effort included TRL and technology gap determination of subsystems and critical components. This information was scaled to develop design requirements (design pressure and flue gas recirculation: RR Flue Gas = $\frac{m_{flue}}{m_{total}}$) for the 1 MWth combustor. The effects of a wide range of carbon dioxide recirculation ratios on the thermal efficiency of ENEL and TIPS cycles are studied. The pressure of 10 bar and 80 bar are used for ENEL and TIPS cycles, respectively. The thermal efficiency of ENEL is significantly higher than the efficiency of TIPS at a pressure of less than 10 bar. The insights from system analysis were then used to design a 1 MWth swirl oxy-coal combustor. Flame temperature analysis and material strength analysis was performed to determine the combustor thickness. The structural integrity of the combustor was validated by finite element analysis using Abacus® and Hypermesh®. Feasibility of igniters and secondary burners are investigated in successful high-pressure oxy-methane combustion. The secondary burners are designed in such a way that it can operate between 100 to 500 kW firing input. Three generations of the pintle injector were designed based on swirl numbers (S=0, 0.9, and 1.2). Key pintle injector parameters such as pintle size, pintle orifice size, spray pattern were investigated by cold flow tests. Information from these tests was used to modify injector design for smooth and successful operation. A 5 mm pintle orifice size was decided upon as the optimum size for oxy-coal operation for the combustor. Shadow sizing experiments were performed to identify the atomization rate of each injector. Different coal water slurry mixtures (30 – 50% coal by wt% in the mixture) at various total momentum ratios (TMR) were investigated for this purpose. These experiments provided decisive information to choose the best design of the injector. The injector with 1.2 swirl provided higher atomization in all cases than other designs. The mean equivalent droplet size of the jet was similar at different TMR and mixture ratios using this injector, thus making it suitable for use in most cases. Therefore, the 1.2 swirl-pintle injector was chosen for the shakedown test. The combustor and other sub-systems, including feed systems and control and data acquisition, have been manufactured, assembled, and integrated. The total system integration and installation began on July 1, 2020. The shake-down tests and initial operational capability demonstration are expected to be completed by September 30, 2020.

01 COAL, LIGNITE, AND PEAT↗