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DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Electrochemical CO 2 Utilization: Scalable System Operation for Formic Acid Production

In recent years, research into the electrochemical reduction of CO 2 has grown exponentially. Formic acid (FA) has become a particular value product from CO 2 due to its versatility [1], optimal atom economy [2], and thermodynamic favorable [3]. FA can be produced electrochemically via a direct two-electron transfer process involving CO 2 with a proton source, requiring less energy input and fewer reaction steps than the conventional Kemira process. However, product selectivity, electrode stability, and low faradic efficiency due to increased resistance under constant current, remain key issues for CO 2 reduction to FA [4]. University of Kentucky’s Center for Applied Energy Research (UK CAER) is currently investigating reactor designs to address the challenges associated with electrochemical CO 2 conversion to FA [5]. The current reactor system employs: (1) an organic-based charge carrier which shuttles charge directly to the catalyst to enable CO 2 reduction to FA; (2) novel electrode materials to mitigate large voltages and improve conductivity; and (3) a flow system which not only allows for the volumetric scale-up of both charge carrier and catalyst, but also decouples the charger carrier re-energized and FA production processes to protect the catalyst’s stability due to overpotential. Through careful adjustment of electrode, charge carrier, and electrolyte selection, cell resistances have decreased by over 15%, facilitating conduction necessary for effective CO 2 reduction to FA. The system uses a highly specific engineered catalyst, which has produced over 100 mM FA at a rate of over 10 mM FA/hour following the aforementioned electrode/charge carrier changes. System design considerations (flow rate, bulk volume) and modeling of the system will also be discussed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Community-Centric Microgrid Feasibility Analysis Framework to Accelerate Resilience of Island Communities

Marine energy offers a reliable energy solution for island and coastal communities, which often lack traditional local generation, to support their transition to energy independence and reduce reliance on externally imported fuels. Successful deployment of new technologies in these isolated locations requires community acceptance and approval from the outset, as these communities typically lack the financial and technical resources to operate and maintain new systems. This report presents a community-centric microgrid planning framework for remote coastal and island communities. Community engagement is integrated as the first step in the planning process, incorporating community profiles and visions into energy development scenarios. A case study was conducted in St. George, Pribilof Islands, Alaska, which relies entirely on diesel yet has significant wind and wave energy potential. Community engagement revealed a unique history and current economic status, with an interest in adopting advanced energy technologies despite past failures. Various microgrid configurations were optimized, considering different technologies to meet current and future energy needs while balancing cost and energy resilience. Wave energy converters (WECs) were a key component, integrated with other energy sources using the Xendee optimization tool. The Marine Energy Microgrid Toolkit, developed as part of this work, uses commercial power system analysis tools to optimize and analyze microgrid scenarios. The developed framework and toolkit can be applied to island and coastal communities to enhance resilience and support microgrid deployments. Future enhancements will include incorporating new marine resources, developing dynamic models, and automating the integration of Xendee and PowerFactory simulations.

02 PETROLEUM↗

DOE OSTI2021

Characterizing Impacts of Dry Coal Feeding in High Pressure Oxy-Coal Combustion Systems

Reaction Engineering International (REI) has managed a team of experts from the University of Utah, Southeast University (SEU) in Nanjing, China, Electric Power Research Institute (EPRI), Corrosion Management Ltd. (C-M), Praxair, and Brigham Young University (BYU) to investigate dry pulverized coal feeding for pressurized oxy-coal combustion. Dry feed firing systems for entrained flow, pressurized, oxy-coal combustors have not been well developed, although related technologies have been used in the Shell Gasification Process and for pressurized fluid bed combustion. DOE-funded research recently completed at REI and the University of Utah focused on characterizing impacts of high temperatures and pressures in oxy-coal combustion systems. For high pressure combustion, that research used a coal slurry feed into a 17 bar pressurized combustor. As a consequence of that research, it was identified that fuel feeding and firing system flexibility are challenges that require attention. Based on that experience, the approach of using a coal slurry feed system leads to challenges in producing consistent atomization of the slurry, which causes burnout problems, especially at high pressures. In addition, slurry atomization processes may be difficult to scale to sizes appropriate for practical commercial use. Dry pressurized coal burner systems, on the other hand, have the potential to yield efficiency gains, improve flexibility and facilitate applications at larger scales. Experimental work was conducted at the University of Utah Industrial Combustion and Gasification Research Facility as well as the 100 kW pressurized oxy-coal combustor (POC) facility at Brigham Young University. Mechanism development and CFD-based combustion and dense-phase flow modeling were performed at REI. Successful completion of the project objectives has resulted in the following key deliverables: 1) Design and prototype of a pressurized pulverized coal feeding and oxy-firing system 2) Data from a 100kW, pressurized (15 bar) entrained flow reactor with a dry feeding delivery and burner system that describes flame characteristics, radiative heat flux profiles, carbon burnout, along with characteristics of ash aerosols, fouling, and slagging. 3) Validated and transportable models that describe the relevant conditions in pressurized oxy-combustion systems and that can be used for scale-up and optimization. 4) Principles to guide design of high pressure, pilot-scale and full-scale coal oxy-firing systems. 5) Assessment of pressurized oxy-combustion impacts on key parameters relevant to oxy-coal fired utility boilers such as coal devolatilization, char oxidation, mineral matter transformation, deposition, and corrosion. The experimental data, pressurized oxy-firing system principles, and process mechanisms provided by this work can be used by electric utilities, boiler OEMs, equipment suppliers, design firms, software vendors, consultants and government agencies to assess the use of high temperature and high pressure oxy-combustion in current research and to guide development of new oxy-coal boiler designs.

pressurized oxy-coal combustion, sub-micron ash ae↗

DOE OSTI2024

Solid State Quantum Refrigeration Superconducting, Absorption and Measurement Based (Final Technical Report)

During this DOE grant, DE-SC0017890, in place for the past six years, all proposed research was carried out and published in peer-reviewed papers, as well as other projects that emerged during the research. In that effort the research team accomplished all proposed research, as well as many closely related research projects discovered and conceived of during the grant. These works included “Efficient Quantum Measurement Engines”, a work published in Physical Review Letters, giving a theory of quantum measurement-based engines, which uses quantum measurement as a resource. These engines are designed to efficiently convert energy from the stochastic quantum measurement process into useful work. Further publications include “Experimental Realization of a Quantum Dot Energy Harvester”, a joint theory and experimental work in collaboration with the group of Charles Smith in Cambridge, UK, as well as long time theoretical collaborators, Rafael Sánchez and Björn Sothmann. This work, featured as an Editor’s Suggestion in Physical Review Letters, realized an earlier theoretical proposal of ours, whereby two resonant tunneling quantum dots are connected to a central electronic cavity that is heated by a hot energy source. We also published “Superconducting Quantum Refrigerator: Breaking and Rejoining Cooper Pairs with Magnetic Field Cycles” a work done in collaboration with experimentalist Francesco Giazotto from ENS Pisa, Italy, which also resulted in a patent. This paper, published in Phys. Rev. Applied, advanced the concept of a cyclic fridge based on the normal/superconducting phase transition together with layered materials separated by tunnel junctions. We also completed the proposed research on a heat transistor, publishing “Thermal transistor and thermometer based on Coulomb-coupled conductors”, carried out as a collaboration between my group and theorists Splettstoesser (Lund U., Sweden), Sothmann (U. Duisburg-Essen, Germany), and Sánchez (U. Autónoma de Madrid, Spain). We carried out an analysis of a quantum coupled to a quantum point contact as a sensitive thermometer and heat transistor. We found the optimal statistical estimator for the temperature and compared it with experiments on the same type of devices. We also investigated autonomous quantum absorption refrigerators using quantum dots to cool by using a very hot thermal reservoir to drive heat between two other reservoirs. In the article “Quantifying the quantum heat contribution from a driven superconducting circuit”, we demonstrated that for a driven superconducting circuit, we showed heat flow provided by a hot source to the qubit can be switched on and off by varying external parameters, the frequency and the intensity of the driving. In the work “Stochastic thermodynamic cycles of a mesoscopic thermoelectric engine”, we reconsidered the autonomous thermoelectric heat engine in terms of underlying cycles. Rather than periodic behavior, the cycles were stochastic in nature. Nevertheless, by undertaking a graph theoretical analysis of the elementary transport processed, great quantitative and qualitative insight could be found. We also considered the quantum measurement process and showed that a quantum version of Maxwell’s demon could be related to the work extraction of a quantum system, closely related to arrow-of-time measures for quantum measurement, as described in our article “Thermodynamics of quantum measurement and Maxwell's demon's arrow of time”. This work was selected in Phys. Rev. A as an Editor’s Suggestion. A recent preprint titled “Cyclic Superconducting Quantum Refrigerators Using Guided Fluxon Propagation” accomplished an important piece of this grant: to propose a new kind of quantum refrigerator using the dynamics of fluxons in a type II superconductor. This invention envisioned a race-track type geometry where fluxons are confined. By applying a gradient of magnetic field together with electric current in a Corbino geometry, the circulating fluxons can actively cool a cold reservoir, realizing a new type of cyclic superconducting refrigerator. We also investigated the possibility of thermal control from different points of view. The application of quantum measurement to the system gives a new kind of control on the system of interest – we have pioneered this approach and shown that measurement can boost the thermal power of quantum engines as described in “Continuous measurement boosted adiabatic quantum thermal machines”. The ability to have heat flows on demand is an outstanding challenge, and we have provided new solutions to this problem in Thermal control across a chain of electronic nanocavities” for a chain of electron cavities using gating voltage control. The control methods using qubit/qubit coupling to create absorption fridges at their most fundamental level have also been developed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Modeling of Reactor Design and Optimization for Scale-Up of the Catalyxx Process for Ethanol Conversion to Higher Alcohol Biofuels

This report summarizes the results of a collaborative efforts between Oak Ridge National Laboratory (ORNL) and Catalyxx Inc. to investigate scale-up of Catalyxx’s Ethanol upgrading to higher alcohols process. The study was funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) under CRADA (Cooperative Research and Development Agreement) No: NFE-20-08396. The project is part of the Direct Funding Opportunity (DFO) for Computational Science to Enable Bioenergy program which utilized computational toolsets developed by the Consortium for Computational Physics and Chemistry, a multi-laboratory consortium in BETO. The report here summarizes a packed-bed reactor modeling effort spanning the range from lab to industrial scale (from 4 gram to 5-ton catalyst beds), and examining reactor design, process optimization strategies, and suggested design and operating conditions for Catalyxx’s ethanol upgrading plants. The results in this report have been shared in monthly steering meetings and presentations are available in the shared data house owned by Catalyxx. The modeling effort helped to define optimum operation conditions for maximum alcohol selectivity and yield: i.e., temperature control scenarios ranging from adiabatic to isothermal, feed rate, pressure, and inlet H 2 /Ethanol ratio. The modeling results were verified at lab-(4 gram) and pre-pilot (4 kg) scales and has been used to evaluate a 5-ton packed-bed reactor and identify operating conditions to maximize the butanol yield. Special focus was given to understanding mass-transfer effects in the pre-pilot and pilot-scale reactors, over the domain of flow rate, pressure, feed composition, pellet size, shape, porosity, bed voidage, and reactor dimensions (i.e., length/diameter). Modeling was also used to evaluate innovative reactor design concepts such as water removal to improve alcohol selectivity and yield, and a reactor with an additional side inlet to facilitate quenching. These concepts were thoroughly explored, and potential benefits were disclosed. The results in this report are summarized and described qualitatively to protect the IP rights of Catalyxx. The details have been shared with the Catalyxx team in the regular steering meetings. At the end of the project, Catalyxx Inc. announced a successful demonstration of pilot scale operation in Seville, Spain.

02 PETROLEUM↗

DOE OSTI2021

DEVELOPMENT OF A 25 MWTH FLAMELESS PRESSURIZED OXY-COMBUSTION PILOT

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.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

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

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Timeseries Unlabeled and Labeled Photos, Modeled Stream Elevation, and (Meta)Data of Variably Inundated Streams Across The Yakima River Basin, Washington, United States (v2)

This dataset is associated with the “River Monitoring Photos” (RMP) study and subsequent manuscript (Bao et al. 2025. Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence doi: 10.1016/j.envsoft.2025.106715). Game camera timeseries photos were collected to evaluate stream variable inundation via changes in width. A subset of photos was labeled for training the YOLOv8 and Mask2Former models and used to segment water surface fractions from all the game camera photos.This data package was originally published in March 2024. It was updated in October 2025 (v2) to add additional photos and files associated with the manuscript (i.e., processed data, labeled photos, and trained models). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.In addition to a readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; and (5) folders containing game camera photos and manuscript-associated files. Each Yakima River Basin site has a folder that contains subfolders for each month photos were collected. There is also a folder for files associated with the manuscript which has subfolders for labeled data, trained models, Yakima River Basin site water surface fractions, and USGS site water surface fractions. All files are .csv, .json, .txt, .yaml, .pth, .pt, or .pdf. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Development of a Comprehensive Two-Phase Flow Database for the Validation of NEK-2P

Three-dimensional (3-D) two-phase Computational Fluid Dynamics (CFD) codes are emerging as a powerful and potentially practical tool for applications in which detailed local flow information is needed. However, two-phase flow models and associated closure relations are not well established for CFD applications, which is partly due to the lack of high-quality validation data. The main objective of this work is to develop a comprehensive database of two-phase flows that can be used to validate two-phase CFD codes such as NEK-2P. In this project, four advanced local measurement systems, including Particle Image Velocimetry and Planar Laser-Induced Fluorescence (PIV-PLIF), high-speed imaging, x-ray densitometry, and multi-sensor conductivity probe are employed to measure the local two-phase flow parameters of both gas and liquid phases. By combining these techniques, the local void fraction, bubble velocity, interfacial area concentration, bubble frequency, liquid velocity, turbulence intensity, etc., in various two-phase flow regimes can be obtained. These local measurement techniques are first used in a 25.4 mm circular pipe test section. Seven air-water two-phase flow conditions spanning the bubbly, slug, churn-turbulent, and annular flow regimes are measured in this facility. The obtained database contains the radial profiles of both gas- and liquid-phase parameters at three axial locations along the test section. The second facility used in this work contains a 30 mm × 10 mm rectangular test section, in which three two-phase flow and two single-phase flow conditions are measured. Two-dimensional distributions of local two-phase flow parameters in the cross-sectional plane are measured in this facility at three axial locations as well. A facility featuring a 3×3 electrically heated rod bundle is also designed and being constructed in this project. This facility is specially designed for optical measurements and is expected to provide high-quality boiling data in the future. Preliminary analyses have been performed for the data taken in the 25.4 mm circular pipe. Both center-peaked and wall-peaked void fraction profiles have been observed in the data depending on the two-phase flow conditions and/or developing lengths. The 1-D drift-flux model was evaluated with the newly obtained datasets, in which both gas- and liquid-phases data were directly measured. The distribution parameter model has been optimized based on a new void-profile classification method proposed in this study. The optimized drift-flux model shows a significant improvement in predicting both gas velocity and void fraction. The measured liquid-phase turbulence was used to benchmark Sato’s turbulence model considering the bubble-induced shear stress for the three tested bubbly flows. The benchmark results showed good agreement between the PIV measurements and model predictions. In the bubbly flows tested that have low void fractions less than 3%, the effect of the bubble-induced turbulence was found not significant. However, the bubble-induced shear stress becomes important with the increase of the void fraction. The Conjugate Heat Transfer (CHT) model was developed and implemented in NEK-2P. This model allows the coupled simulation of the solid domain and two-phase fluid domain, allowing the specification of realistic boundary conditions. The CHT implementation was verified first with non-boiling simulations. The predicted temperatures in the fluid domain were shown to be identical in simulations with or without the CHT model. The CHT model was then validated through simulations of three Becker benchmark CHF tests performed under both Dryout (DO) and Departure from Nucleate Boiling (DNB) conditions. Reasonably good agreement was obtained between calculated wall temperatures and corresponding experimental data.

42 ENGINEERING↗

DOE OSTI2024

High- and Mid-Fidelity Modeling Comparison for a Floating Marine Turbine System

There is a lack of suitable numerical tools, particularly open-source tools, that can be used for designing and optimizing marine turbine systems. The National Renewable Energy Laboratory has added features to their widely used mid-fidelity wind turbine modeling code, OpenFAST, to enable modeling of axial-flow marine turbines. This necessitated the addition of several physical effects relevant to marine turbines that are neglected for wind turbines. These include buoyancy, added mass and inertial loads, wave-current superposition, and changes to the coordinate systems. This updated version of OpenFAST allows for the modeling of both fixed and floating marine turbines at a speed comparable to real time. While efficient for large sets of load cases and design studies, mid-fidelity codes make simplifying assumptions that may impact their accuracy. High-fidelity computational fluid dynamics (CFD) simulations can capture more flow effects with fewer assumptions and provide detailed body pressure mapping and flow-field information. It is important to compare predictions between mid-fidelity and high-fidelity codes, both to verify the models and to understand the limitations. A floating marine turbine system was modeled both with OpenFAST and with the commercial CFD code STAR-CCM+. The CFD model used a three-dimensional unsteady Reynolds-averaged Navier-Stokes solver for a volume-of-fluid numerical wave and current tank. The blade-resolved simulations used the sliding-interface technique for the spinning rotor and an overset grid to accommodate the rigid-body motion of the floating system. The mooring system was modeled with a custom coupling of the CFD solver with the open-source code MoorDyn. This improves upon the existing quasi-static catenary solver in STAR-CCM+, which lacks seabed contact or line-to-line connections. Simulation results for a floating marine turbine are compared between OpenFAST and CFD, highlighting the capabilities of the mid-fidelity code and identifying the areas where a high-fidelity approach is needed.

CFD↗

DOE OSTI2024

High- and Mid-Fidelity Modeling Comparison for a Floating Marine Turbine System: Preprint

There is a lack of suitable numerical tools, particularly open-source tools, that can be used for designing and optimizing marine turbine systems. The National Renewable Energy Laboratory (NREL) has added features to their widely used mid-fidelity wind turbine modeling code, OpenFAST, to enable modeling of axial flow marine turbines. This necessitated the addition of several physical effects relevant to marine turbines that can be neglected for wind turbines. These include buoyant loads, added mass and inertia loads, wave-current superposition, and changes to the coordinate systems. This updated version of OpenFAST allows for the modeling of both fixed and floating marine turbine systems at a speed comparable to real time. While efficient for long simulations, large sets of load cases, and design studies, mid-fidelity codes cannot capture all of the potentially important physical phenomenon impacting marine turbine systems. High-fidelity computational fluid dynamics (CFD) simulations can capture more flow effects with fewer assumptions and provide detailed body pressure mapping and flow-field information. It is important to compare predictions between mid-fidelity and high-fidelity codes, both to verify the models and to understand the limitations. A floating marine turbine system designed by NREL was modeled both with OpenFAST and with the commercial CFD code, STARCCM+. The CFD model used a 3-D unsteady Reynolds-averaged Navier-Stokes solver for a volume-of-fluid numerical wave and current tank. The blade-resolved simulations used the sliding-interface technique for the spinning rotor and an overset grid to accommodate the rigid-body motion of the floating system. The mooring system was modelled with a custom coupling of the CFD solver with the open-source code, MoorDyn. This improves upon the existing quasi-static catenary solver in STARCCM+, which lacks seabed contact or line-to-line connections. Spatial and temporal convergence studies were conducted. The simulation results for a combined current and wave condition are compared between OpenFAST and CFD, highlighting the capabilities of the mid-fidelity code and identifying the areas where a high-fidelity approach is needed.

CFD↗

DOE OSTI2022

Hydrogen Storage for Flexible Fossil Fuel Power Generation: Integration of Underground Hydrogen Storage with Gas Turbine (Final Report)

As the nation continues to encourage, through market structures and financial incentives, the proliferation of intermittent renewable electricity, how to optimize the ever-changing electric grid and identify means to retain and improve resilience, while ensuring continued reductions in GHG emissions, will be critical. According to Bloomberg, wind & solar generated 10.5% of US electricity in 2020 and that percentage continues to grow. In support of expanding renewable energy use, and to address its intermittent nature, this project will develop the Hydrogen Storage for Flexible Fossil Fuel Power Generation platform that is dispatchable, reliable, repeatable and have the ability to produce zero or negative carbon power while interfacing with geology capable of CO2 and hydrogen storage. GTI Energy (GTIE) and team members Illinois State Geological Survey (ISGS), Mitsubishi Heavy Industries America (MHIA), Ameren Illinois, Hexagon Purus, and the Low Carbon Resources Initiative (LCRI) completed a Phase I Conceptual Study under contract DE-FE0032012 for Hydrogen Storage for Flexible Fossil Fuel Power Generation: Integration of Underground Hydrogen Storage with Gas Turbine. The Hydrogen Storage for Flexible Fossil Fuel Power Generation platform addresses the intermittent nature of the expanding use of Variable Renewable Energy (VRE) generation. The low cost of the electricity (COE) generated results in greater dispatch and more operation at higher power levels (higher efficiency), fewer short intervals, and fewer start/stop cycles. The reliable, resilient system can produce zero carbon power and store hydrogen. It will demonstrate hydrogen storage in geologic formations like those used in natural gas underground storage thus enabling large scale storage of hydrogen in sedimentary strata across the United States rather than in geographically restricted salt caverns. The Phase I study confirmed the system is feasible and generates power at lower cost than other low carbon approaches. The demonstration defines the pathway for broad commercial application and will accelerate the development of larger systems suitable for centralized utility scale electricity production. The study advanced the maturity of the H 2 storage-based system with flexible power generation by completing a Pre-FEED study (Phase II). The Pre-FEED focused on the selected Energy Farm on the University of Illinois Urbana-Champaign (UIUC) site that includes above ground and underground hydrogen storage, low-carbon hydrogen production (GTI’s Compact Hydrogen Generator, CHG) with underground CO2 sequestration, and a 40-MW class gas turbine. The Pre-FEED addressed the entire system and its interconnection to the natural gas and electric grid and mitigation of key risks, such as storage behavior, load-following, and system operation. During Phase 1 of the project, the team completed key tasks, which moved the entire demonstration project, specific components and approaches closer to commercialization. These Phase I Accomplishments include: Completing System Requirements Review; Completing System Layout and Modeling - Heat & Mass Balance and Process Flow Diagram; Completing modelling of 9 turbine performance cases; Evaluating rock strata for underground storage of hydrogen and sequestration of carbon dioxide; Completing initial modelling of underground storage of hydrogen and withdrawal with evaluation of loss and water production; Identifying roadable storage for above ground hydrogen storage; Identifying existing electrical infrastructure for receiving/delivering electricity; Identifying existing gas supply infrastructure for receiving natural gas; Document concept design/development plans in required reports. Conclusions: The 12-month Feasibility study in Phase I study was completed and confirmed the system is feasible and generates power at lower cost than other low carbon approaches and even lower cost than the reference NGCC plant without carbon capture when taking advantage of 45Q carbon credits. The study enabled the fidelity of the concept to be improved and allowed identification of the requirements for the system. Defining the individual system and component requirements was performed via the system requirements review with the whole team. These requirements were then incorporated into and iterated with our Heat & Mass Balance process model and process flow diagrams were generated to reflect the overall system. This information was then used to complete the TEA and show economic feasibility. Large scale non-salt geologic storage of hydrogen is an enabling technology for a hydrogen-fired turbine that can be retrofitted into large-scale electric generating units (EGU). Our demonstration will include 428 MWh or ~4 hours full load of hydrogen storage (above and underground). Carbon capture inherent to the CHG process can capture 90% CO 2 (with upgrades to >98%). This system provides a COE of 23% savings relative to an NGCC with a post combustion amine system. Our proposed storage system decouples carbon capture and hydrogen production from power production; therefore, we expect our proposed system’s efficiency and variable COE to be superior resulting in overall higher dispatch and reduced deep cycling. Our demonstration will be full to multi-day hydrogen storage and has the potential for longer (seasonal) duration commercially. The demonstration defines the pathway for broad commercial application and will accelerate the development of larger systems suitable for centralized utility scale electricity production.

03 NATURAL GAS↗

DOE OSTI2022

Thermodynamic Model and Initial Experimental Investigation of Air Dehumidification through Electrically Charged Vapor Capturing Electrostatic Droplets

The removal of water vapor from the air to reduce relative humidity is a well known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation with the goal to reduce the energy consumption associated with dehumidification. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. In this thesis, a broad theoretical investigation to study the electrostatically enhanced condensation processes was carried out. These processes consist of the use of highly charged particles, preferably highly charged water droplets to attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. The electrical charge promotes the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation on curved surfaces and, consequently, the equilibrium between evaporation and condensation is shifted towards condensation. This investigation resulted in the development of a thermodynamics model, which was able to predict an effective size range of the charged droplets for optimal dehumidification, under ideal conditions. The range resulted in about 2 to 4 m in diameter, while the electrical charge was kept to the maximum limit predicted by the Rayleigh model. A sensitivity analysis on the variation of the size and the charge of the charged droplets was also performed. In terms of dehumidification rates, when six electrospray heads, i.e. the maximum number of heads tested in the experimental campaign, were considered, the model predicted very limited rates, way lower than the target of this research, i.e. a dehumidification rate in terms of relative humidity of 5 % with an air flow rate of 5 cfm. While ways to increase this rate existed, their implementation proved difficult for this initial investigation. According to the requirements predicted by the model, the use of electrosprays appeared the most suitable solution for the production of small but highly charged droplets. The electrospray features and operational modes were studied in detail and an electrospray assembly was designed to be tested in this initial experimental investigation. For this initial investigation, an experimental system was designed and built to validate the thermodynamics model and with the goal to achieve a dehumidification rate of 5 % in terms of relative humidity with an air flow rate of 5 cfm. The experimental system consisted of a wind tunnel test apparatus, dew point sensors, thermocouples for temperature measurements, pressure sensors and other devices. The experimental system was equipped with a computerized data acquisition and storage system. The system was suitable to evaluate the air water content differential before and after the test section where the electrospray heads were installed. The air water content differential was evaluated in terms of the ∆ω/ω_1 ratio, to eliminate the dependence on the dry bulb temperature along the test apparatus. This work presented initial experimental data for electrostatically enhanced dehumidification processes. Working parameters, such as air and water flow rates, high voltage potentials and polarity, deionized water types with different electrical conductivity, were varied to find the best combination for an improved dehumidification. With the set up used and the conditions considered, it was never possible to achieve a 5 % dehumidification rate with 5 cfm. In general, the dehumidification rate was always limited and lower than 1 % for all the air flow rates considered and mainly within the uncertainty of the dew point sensors. These results confirmed the already limited predictions of the thermodynamics model, for ideal conditions and the interesting and promising results obtained in some experimental investigations available in the open literature were not achievable in this thesis. The more probable reasons were the higher air flow rates considered and the complete absence of the use of cooling power, which was mainly implemented to facilitate the vapor condensation.

Morcelli, Stefano↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2024

The right conditions for high-precision dynamic temperature and heat capacity measurement via pyrometry and conductivity

The pursuit of accurate bulk temperature T under extreme conditions has been a long-standing goal of the high pressure science community, complicated by a lack of data to inform models. To reach these extremely high-pressure, high-temperature (high P − T) conditions, a combination of dynamic and heated static experiments (e.g., diamond or gem anvil cel experiments) are used. For example, in a diamond anvil cell (DAC) experiment, a sample placed in the DAC is first pressurized. Following pressurization, the sample T is increased either by heating the entire DAC (usually using resistive heating, and limited to ∼1000K) or by applying intense laser power to the sample surfaces. In a dynamic experiment, the process of pressurizing the sample also heats it. In the case of shock physics experiments, such heating is substantial, easily reaching thousands of Kelvin; in our work we have seen T ∼17000K. Most methods of measuring temperature at ambient are not compatible with experiments under these high-pressure, high-temperature conditions: thermocouples break, melt, or have conductivity properties that differ from ambient where they are calibrated; thermometers would melt; both are too slow. As a result most methods are based on non-contact techniques such as x-ray diffraction broadening, neutron scattering, or optical methods. Of these, optical methods using the visible and near-infrared region of the spectrum are the most commonly used as the sources and detectors are readily available. In the case of optical methods the optical depth, and therefore the measurement location, is limited to the surface. When a window or anvil material is used, heat flows from the sample into the window/anvil. Likewise, if the sample undergoes a change in thermodynamic state, such as expansion upon release, different T may be expected. As a result, the surface or apparent temperature T app measurement will differ from the bulk or interior temperature that is desired. This surface measurement must be related to the bulk measurement using thermal transport models and material models. While it is tempting to conclude that one should just use x-ray methods that directly probe the interior, even these methods have been shown to depend on thermal transport and material models. Regardless of the method used to create the high P − T condition, therefore, we must understand the role of thermal transport and material models upon our interpretation of the T measurement, as well as the errors and uncertainties associated with the choice of models used in the analysis. This is a substantial area of research and this paper is by no means a complete survey of the relevant sources of uncertainty. For example, we have yet to begin to address alternate transport models in a detailed manner (e.g., Tan-Ahrens), or the many models that use additional layers to approximate melting, turbulence, or epitaxial phenomena). Likewise, we have not explored the impact upon uncertainty of thermal models that use temperature-dependent thermal transport coefficients, or the wide range of material models that can be applied. Instead, this paper focuses on using one simple model, the Urtiew-Grover model, to understand the sources of error in T measurement so that we may identify how best to focus future research efforts to return the best improvements and avoid working on over-optimizing a single type of measurement. To this end, we work through some of the best and worst case scenarios for T measurement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Enhancing Steam-Side Heat Transfer via Microdroplet Ejection using Inorganic Coatings

During condensation of water on a superhydrophobic nanostructured surfaces, droplets coalesce and jump (droplet ejection) due to the release of excess surface energy. Meticulously designed nanostructured surfaces or coatings capable of droplet ejection can enhance heat transfer performance by easy removal of droplets during condensation. In the present work, the capabilities of the Nelumbo droplet ejection coatings technology currently used in commercial heat exchangers are explored and optimized for materials and conditions relevant to steam condensers. Specifically, this includes (1) adapting and optimizing the procedures and chemistries to deposit droplet ejecting coatings on materials used in the steam condenser, (2) testing of the heat transfer, durability, and fouling of the fabricated coatings under a variety of steam condenser relevant conditions, and (3) modeling of the impact of droplet dynamics and interfacial properties on heat transfer in steam condensers. We experimentally demonstrated that at low supersaturation conditions (S~1.02), developed superhydrophobic nanostructured surface gives a 40% higher peak heat transfer flux and a 17% higher peak overall heat transfer coefficient (U) with droplet ejection mechanism compared to the dropwise condensation mechanism on the same nanostructured surface. Though the experimentally observed droplet ejection mechanism was short-lived (~3 min) due to the limitation of the chiller to maintain a constant supersaturation condition in fabricated steam condenser. And large variations in water inlet temperature (+ to - 2 degrees C) caused flooding of nanostructure (Wenzel state) at higher supersaturation (S > or = 1.12). This is an important finding because typical supersaturation levels in field operation are over 1.8 and as such, either the flooding potential for these materials should be improved or operation at lower supersaturation may be required to maximize heat transfer efficiency. If supersaturation conditions can be maintained reliably in the steam condenser in addition to droplet ejection mechanism during condensation, this could provide a potential 2% reduction in water flow rate which equates to a savings of over 3900 gallons of water per minute for a 500 MW steam turbine and over $0.3MM savings annually given a 0.02 cent marginal water withdrawal cost. It is also worth noting that these savings are based on improvement of droplet ejection coatings over coatings capable of performing consistent dropwise mechanism in in-field surface condensers. Savings due to the droplet ejections coatings when compared to bare substrates could be much higher. Additionally, the present work provides the importance of steam condenser operating conditions and insights into the challenges in modeling the condensing droplet dynamics on surfaces.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗