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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 73 records · Page 4

American WAKE experimeNt (AWAKEN)

The national laboratories, directed by the U.S. Department of Energy Wind Energy Technologies Office, will organize, design, and execute a landmark international wake observation and validation campaign known as the American WAKE experimeNt or AWAKEN. This document describes the vision and purpose for AWAKEN and describes some of the organizational activities that have taken place to date. The driving need for this experimental campaign is that wake interactions are among the least understood physical phenomena in wind plants today, leading to unexpected power and financial losses. New observation data gathered will be used to further validate wind plant models and lead to both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs. This campaign will occur in the U.S. Midwest, where the largest concentration of wind farms in the U.S. is located and where significant future growth is most likely. The field campaign will provide a data set that is unique among wake studies, based on location, scope, and observational fidelity: most wake studies have largely focused on offshore wind farms, which are not currently critical to the U.S. wind energy supply, and with a limited number of observations. While AWAKEN is focused on land-based wind farms in flat terrain, many of the observations will help researchers understand fundamental wake behavior applicable in both offshore and complex terrain environments.

17 WIND ENERGY↗

Characterizing in-stream turbulent flow for tidal energy converter siting in Cook Inlet, Alaska

Cook Inlet in Alaska is the most promising location for tidal energy development in the U.S. due to its significant tidal range of approximately 10 meters and high volume flux. The inlet's unique geometry and flow characteristics make it the most energetic tidal stream in the nation, with GW-scale potential energy capacity. With the growing interest in tidal energy converter (TEC) deployment in this area, we implemented a regional-scale, 3D hydrodynamic modeling framework to predict tidal current and turbulence characteristics that can assist TEC designers and project managers. We validated the model results extensively using various datasets collected with bottom-mounted acoustic Doppler current profilers and velocimeters. The comparison between the model outputs and observational data highlighted the effectiveness of the 3D FVCOM model and the Mellor-Yamada Level 2.5 Turbulence Model in accurately assessing macro-scale kinetic energy, turbulence intensity, and the production and dissipation rates at a prospective TEC site. Using two months of model simulation data, we examined the channel cross-section for TEC deployment, focusing on undisturbed power density and macro-scale turbulent properties. Further, our findings indicate that understanding the turbulence characteristics and flow properties can enhance Stage I/II resource characterization by identifying optimal locations for TECs and their layouts within the channel. Furthermore, we demonstrated that TEC designers can utilize macro-scale turbulence data from 3D coastal models as boundary conditions for other turbulence models, allowing for a more detailed resolution of the turbulence structure at TEC siting locations. Ultimately, this work emphasizes the importance of estimating flow and turbulence conditions in energetic systems to understand turbulent sites better and improve resource characterization.

16 TIDAL AND WAVE POWER↗

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

Glprof: A Gprof inspired, Callgraph-oriented Per-Object Disseminating Memory Access Multi-Cache Profiler

Application analysis is facilitated through a number of program profiling tools. The tools vary in their complexity, ease of deployment, design, and profiling detail. Specifically, understand- ing, analyzing, and optimizing is of particular importance for scientific applications where minor changes in code paths and data-structure layout can have profound effects. Understanding how intricate data-structures are accessed and how a given memory system responds is a complex task. In this paper we describe a trace profiling tool, Glprof, specifically aimed to lessen the burden of the programmer to pin-point heavily involved data-structures during an application's run-time, and understand data-structure run-time usage. Moreover, we showcase the tool's modularity using additional cache simulation components. We elaborate on the tool's design, and features. Finally we demonstrate the application of our tool in the context of Spec bench- marks using the Glprof profiler and two concurrently running cache simulators, PPC440 and AMD Interlagos.

Janjusic, Tommy↗

Meteorological Controls on Wind Turbine Wakes

The primary control on the magnitude of the power losses induced by wind turbine wakes in large wind farms is the hub-height wind speed via its link to the turbine thrust coefficient. Hence, at low to moderate wind speeds (between cut-in and rated turbine wind speeds) when the thrust coefficient is high, wake losses are proportionally larger and decrease to be virtually undetectable at wind speeds above rated wind speeds. Wind direction is also critical. Not only does it determine the effective spacing between turbines but also the wind speed distribution is primarily determined by synoptic forcing and typically has a predominant direction from which wind speeds tend to be higher (from southwest for much of the central United States and northern Europe). Two other interlinked variables, turbulence intensity (TI), and atmospheric stability also dictate wake losses. Quantifying, understanding, modeling, and predicting this complex and interdependent system is therefore critical to understanding and modeling wind farm power losses due to wakes, and to optimizing wind farm layout. This paper quantifies the impact of these variables on the power loss due to wakes using data from the large offshore wind farms located at Horns Rev and Nysted in Denmark.

Barthelmie, RJ↗

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) { on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.

irregular applications, energy opttimization, many↗

Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automated and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.

active canvas↗

Hydrodynamic Analysis and Optimization of Aquantis Marine Turbine: Cooperative Research and Development (Final Report)

The primary aim of this proposal is to improve the accurate prediction of hydrodynamic performance and dynamic load responses of the AQ10 floating axial-flow tidal turbine with a tri-cat mooring configuration. The validation of reduced-order modeling approaches with high-fidelity model will be implemented. Additionally, the frequency response domain, Response Amplitude Floating Wind (RAFT) toolbox plus an optimizer expanded for marine hydrokinetic turbines under the Submarine Hydrokinetic And Riverine Kilo-megawatt. Systems (SHARKS) program will be used for designing and exploring different key design parameters (platform dimension, mooring layout and its parameters) of next marine hydrokinetic (MHK) turbine generation.

16 TIDAL AND WAVE POWER↗

Demonstration of Algorithm for Sensor Placement Optimization using Simulation Data

This deliverable reports FY26 progress in advancing a neural-network-based Green’s-function framework for reconstructing neutron-flux distributions from ex-core measurements and for translating reconstruction requirements into a practical detector-layout strategy. Building on the FY25 formulation, the present work had two main objectives: (1) refine and re-evaluate the reconstruction methodology on an updated Purdue University Reactor Number One (PUR-1) model, and (2) develop a systematic, Green’s-function-guided procedure for boundary detector placement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗

Pre-Project Planning for a Flameless Pressurized Oxy-Combustion Pilot Plant (Final Technical Report)

The team of Southwest Research Institute® (SwRI®), ITEA, Electric Power Research Institute, Inc. (EPRI), GE Global Research (GE), Jacobs Engineering (Jacobs), and Peter Reineck Associates (PRA) are advancing Flameless Pressurized Oxy-Combustion (FPO), a novel coal technology. This effort seeks to complete the preliminary engineering and planning of a 50-MWth Pilot Plant that produces electricity and steam for district heating. The technology, pioneered by ITEA at the 5-MWth scale, must be brought to a higher technology readiness level (TRL) to be viable at the commercial scale. This project builds upon extensive evaluation of the techno-economic performance of the FPO technology already performed by ITEA. This includes system improvements, such as the addition of a turbo-expander to the flue gas stream. Some of the performance-enhancing components are not as well defined as others. The primary goal of this project was to bring all of the core cycle components to the same level of design maturity. Extensive cycle modeling and evaluation explored various configurations of the process. Different coals, operating conditions, and heat recovery devices were considered and modeled using Aspen Plus. The results of the cycle modeling informed the pilot plant configuration and the planned configuration at the commercial scale. The system pressurization is maintained in the flue gas clean-up and water removal. This allows for efficient and cost-effective carbon dioxide compression and sequestration package. Once the cycle was optimized, preliminary drawings, layouts, and plans defined the pilot facility. Process flow diagrams (PFD) were used to describe the pilot configuration in greater detail. A heat and mass balance with stream data was created. A master equipment list specified the operating conditions for major pieces of equipment within the pilot using this heat and mass balance. A detailed cost estimate for the planned configuration was completed with an Association for the Advancement of Cost Engineering (AACE) Class IV level of cost analysis. A site selection effort evaluated a variety of potential candidate sites. The selection effort focused on three sites for an in-depth evaluation of the merits and viability of hosting a pilot demonstration. The ability of the host site to support the project with cost sharing was also assessed. The selection process chose the University of Wyoming (UW) as the host site, and cost estimates for the pilot capital estimate were made using local data. A preliminary test plan and operation plan, which creates figures of merit, control, and instrumentation was produced. An effort that outlines methods of pilot cost reduction was completed near the end of the project. A techno-economic assessment (TEA) evaluated the FPO technology at the commercial scale in comparison to the NETL baseline cases. The reference plants were used in combination with proprietary equipment estimates to build a plant capital cost and cost of electricity evaluation. FPO performed better than the sub-bituminous post-combustion capture cases. Further preliminary estimates of improvements to the FPO cycle show even further gains when compared to conventional carbon capture methods.

01 COAL, LIGNITE, AND PEAT↗

Design and performance of a novel low energy multispecies beamline for an antihydrogen experiment

The ALPHA Collaboration, based at the CERN Antiproton Decelerator, has recently implemented a novel beamline for low energy (≲100 eV) positron and antiproton transport between cylindrical Penning traps that have strong axial magnetic fields. Here, we describe how a combination of semianalytical and numerical calculations was used to optimize the layout and design of this beamline. Using experimental measurements taken during the initial commissioning of the instrument, we evaluate its performance and validate the models used for its development. By combining data from a range of sources, we show that the beamline has a high transfer efficiency and estimate that the percentage of particles captured in the experiments from each bunch is (78 ± 3)% for up to 10 5 antiprotons and (71 ± 5)% for bunches of up to 10 7 positrons.

43 PARTICLE ACCELERATORS↗

Fusion PIC code performance analysis on the Cori KNL system

We study the attainable performance of Particle-In-Cell codes on the Cori KNL system by analyzing a miniature particle push application based on the fusion PIC code XGC1. We start from the most basic building blocks of a PIC code and build up the complexity to identify the kernels that cost the most in performance and focus optimization efforts there. Particle push kernels operate at high AI and are not likely to be memory bandwidth or even cache bandwidth bound on KNL. Therefore, we see only minor benefits from the high bandwidth memory available on KNL, and achieving good vectorization is shown to be the most beneficial optimization path with theoretical yield of up to 8x speedup on KNL. In practice we are able to obtain up to a 4x gain from vectorization due to limitations set by the data layout and memory latency.

97 MATHEMATICS AND COMPUTING↗

Preparation for a Measurement of Charge Asymmetry in the Bethe-Heitler Process

We have prepared a measurement of the energy asymmetry in wide- and medium-angle electron/positron pair production off protons and heavy targets. This asymmetry is caused by the interference between the first- and second-order Born diagrams and the Compton scattering diagram. It directly probes aspects of QED, as well as providing a direct measurement of the real part of the Compton amplitude. It will be conducted at the HI??S facility at Duke University, using a 60 MeV photon beam. This dissertation serves as documentation of the preparation stage of the Bethe-Heitler experiment. The major was the recommissioning of the vertical drift chambers previously used in the Q-weak experiment at the Jefferson Lab. Cosmic test runs were conducted, drift time data were collected and efficiency plateaus were measured. We made modifications to the JLAB Hall A analyzer to suit the geometry and drift characteristics of these wire chambers. The analyzer was used for the reconstruction of the trajectories of cosmic ray test runs with the results confirmed by direct measurement of trigger geometry. Spatial and angular resolution is estimated to ~300?? and 0.17° respectively. Geant 4 simulations with generated Bethe-Heitler pairs satisfying theoretical differential cross sections. It was used to check detector acceptance, optimize apparatus layout, and estimate measurable energy asymmetry. The measurable asymmetries from electron/positron pairs with polar angles around between approximately 5° and 8°, azimuthal angles differing by 180°, and energy differing by approximately 9 MeV to 15 MeV are predicted to be above 10%. The kinematics of primary vertices are reconstructed using the data from wire chambers in the simulation. The energy resolution is determined to be better than 1MeV.

Chen, Haoyu↗

MODELING AND TESTING OF A NOVEL ULTRA-LOW TEMPERATURE SCO2 OPPOSING PISTON EXPANDER

The Flameless Pressurized Oxy-Combustion (FPO) cycle, a novel flexible fuel technology, is being developed into a large pilot. This effort seeks to complete the preliminary engineering and planning of a 25 MWth Pilot Plant that will demonstrate the technology for scale-up to a commercial unit. The technology, pioneered by ITEA at the 5 MWth scale, must be brought to a higher technology readiness level (TRL) to be viable at the commercial scale. The 25 MWth pilot cycle was optimized for cost and technology development and demonstration. Preliminary drawings, layouts, and plans were defined. Process flow diagrams were used to describe the pilot configuration in greater detail. A heat and mass balance with stream data was created. A master equipment list specified the operating conditions for major pieces of equipment within the pilot using this heat and mass balance. The 25 MWth FPO pilot is assessed for environmental performance. A test campaign is developed to assess the type of test and number of hours required for pilot demonstration. The environmental performance is compared against projected performance at the commercial scale. This project builds upon extensive evaluation of the techno-economic performance of the FPO technology already performed by ITEA. This includes system improvements, such as the addition of a turbo-expander to the flue gas stream. Some of the performance-enhancing components are not as well defined as others. The primary goal of this effort was to bring all of the core cycle components to the same level of design maturity. A techno-economic assessment (TEA) evaluated the FPO technology at the commercial scale in comparison to the NETL baseline cases. The reference plants were used in combination with proprietary equipment estimates to build a plant capital cost and cost of electricity evaluation. FPO performed better than the sub-bituminous post-combustion capture cases. Further preliminary estimates of improvements to the FPO cycle show even further gains when compared to conventional carbon capture methods.

01 COAL, LIGNITE, AND PEAT↗