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This dataset consists of an Excel spreadsheet detailing the fracture picks from a reinterpretation of the formation micro-imaging (FMI) log from Utah FORGE well 16A(78)-32. The provided information details fracture location, geometry, and type. Also included here is a link to the original raw and processed FMI logs, as well as other data from the 2021 well logging.
The Functional Mock-up Interface - Machine Learning Center is a tool to interface the Python-Tensorflow library with building simulation models, to develop and train machine learning-based controls. In particular, this framework allows the use of reinforcement learning through the standardized Functional Mock-up Interface, an open industry standard for co-simulation. First application was the development of a controller for dynamic façade and heating ventilation and air conditioning (HVAC) systems.
An in-memory coupling between the sub-channel thermal hydraulics code COBRA-TF (CTF), which is included in the Virtual Environment for Reactor Applications (VERA), and the systems code Transient Simulation Framework of Reconfigurable Models (TRANSFORM) was developed in this work. Data exchange is accomplished by using the Functional Mock-Up Interface (FMI), an open standard for coupling dynamic systems models together. The FMI-based coupling necessitated the development of a novel FORTRAN wrapper for communicating with Functional Mock-Up Units. This wrapper will facilitate future FMI-based code couplings with other FORTRAN-based programs. CTF-TRANSFORM is exercised on a simplified Molten Salt Reactor Experiment (MSRE) model in steady-state and transient configurations. The coupled CTF-TRANSFORM model is shown to predict core temperature deltas similar to those available in historical MSRE operational data and is shown to be robust to fast power transients.The coupling paves the way to performing sensitivity and design studies impossible with VERA/CTF alone, in which secondary and tertiary loop operating conditions and design parameters may be perturbed and their impact on the core operating conditions studied.
This dataset contains drilling core data from well 16B(78)-32, including PDF documents with flattened core images annotated by feature type and core interval, as well as spreadsheets detailing feature morphologies by depth, planar feature measurements, and planar feature orientations rotated to in situ conditions. Core was recovered from three intervals, one per stimulation stage, in the crystalline rocks affected by the stimulation of well 16A(78)-32. Seven core runs were conducted, yielding 135.8 feet of recovered core. Features in the core were categorized into planar fractures, semi-planar fractures, unbroken mineralized fractures, rough fractures, curviplanar fractures, concave-convex surfaces, and planar compositional features such as mylonite or dike-like structures. Planar features were measured while the core was positioned horizontally, with the core axis aligned to a downhole azimuth of 42 degrees. Planar core measurements from stimulations 2 and 3 that could be confidently correlated with FMI data were rotated to in situ orientations. This was done by rotating the planes along vertical and horizontal axes to match the azimuth and inclination data recorded in the directional survey of well 16B(78)-32, as well as applying an axial rotation to resemble the fracture orientations observed in the FMI log at corresponding depths. Coherent sets of planar fracture measurements were made by aligning the core within each 3-foot section of the dissected core barrel, and between adjacent 3-foot sections within a core run by matching rock fabrics, saw cuts and/or tool marks. Where coherent fracture measurements could not be made within a core run, data sets are denoted by a subscript (i.e. 2-Ta and 2-Tb both come from tangent core run number 2).
Ferromagnetic insulating (FMI) double perovskite oxides (DPOs) A2BB′O6 with near-room-temperature Curie temperatures are promising candidates for ambient-temperature spintronics applications. To realize their potential, epitaxial stabilization of DPO films and understanding the effect of multiple broken symmetries across the film/substrate interface are crucial. This study investigates ultrathin films of the FMI Nd2NiMnO6 grown on SrTiO3 (STO) and NdGaO3 (NGO) substrates. By comparing growth on these substrates, we examine the influence of polarity and structural symmetry mismatches, which are absent in the NGO system. The interface exhibits immeasurable resistance in both cases. Using synchrotron x-ray diffraction, we show that films have three octahedral rotational domains because of the structural symmetry mismatch with the STO substrate. Furthermore, our coherent Bragg rod analysis of specular x-ray diffraction reveals a significant modification of the out-of-plane lattice parameter within a few unit cells at the film/substrate interface and the surface. This arises from polarity compensation and surface symmetry breaking, respectively. These structural alterations influence the Mn orbital symmetry, a dependence that we further confirm through x-ray linear dichroism measurements. Since the ferromagnetism in insulating DPOs is mediated by orbital-dependent superexchange interactions [Phys. Rev. Lett. 100, 186402 (2008)0031-900710.1103/PhysRevLett.100.186402], our study provides a framework for understanding the evolution of magnetism in ultrathin geometry.
This dataset is a collection of well logs provided by Schlumberger Technologies from the Utah FORGE well 16B(78)-32 drilling project. Information here includes critical borehole information collected by an ultrasonic borehole imager (UBI) and a fullbore formation microimager (FMI). Well 16B(78)-32 serves as the production well for reservoir creation, fluid circulation, and demonstration of heat extraction for the FORGE project. It has been drilled as a doublet approximately 300 feet parallel to and above the injection well 16A(78)-32. The total depth measured 10,947 feet and the vertical depth measured 8,357 feet.
This dataset consists of drilling data (Pason data spreadsheets, daily reports, days v. depth, mud logs), Schlumberger logs (FMI, shear anisotropy analysis, memory, sonic, array induction/spectral density/dual spaced neutron/gamma ray/caliper, spectral GR/temperature, and Gardner density correlation), and an end of well report (EOWR) for Utah FORGE well 56-32. This is a vertical well that will be used for seismic monitoring. It was drilled between February 7th and February 21st 2021 to a depth of 9,145 feet. More information about this well can be found at: https://utahforge.com/2021/02/09/drilling-progress-of-well-56-32/ (linked below)
This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.
This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.
Climate change and anthropogenically-forced shift of weather in the future will impact energy use and resilience of both the built environment and the electric grid. The aim of this analysis is to understand how future climate scenarios will impact electricity and natural gas use of commercial buildings in the United States. Here, this study analyzes this impact for 2030, 2045, and 2100 using Representative Concentration Pathways (RCP) scenarios defined in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5. The large, gridded simulation of meteorological variables for RCPs 2.6, 4.5, 6.0, and 8.5 are selected and downscaled to make available hourly Future Meteorological Year (FMY) weather files for use and improvement in subsequent studies. High performance computing resources use these FMYs to simulate commercial prototype buildings in every American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) climate zone of the United States (US), and results are scaled to nation-wide energy use using conditioned floor area multipliers. The analysis is conducted without speculating the physical and performance traits of future buildings or the grid characteristics. This analysis quantifies the impact of climate change on source electrical and natural gas usage for commercial buildings in the United States over the next 80 years. If US commercial floorspace remained constant, total energy use by 2100 is predicted between an 1.75% decrease under the greatest emission scenario (8.5) and a 1.76% increase under the lowest emission scenario (2.6). When adjusted for anticipated urban growth by 2100, the predicted range is 65% increase (8.5) and 71% increase (2.6). Under a global temperature rise climate scenario, the warmest US climate zones will see a large increases in electricity use derived from space cooling while the coldest US climate zones will see significant decreases in natural gas use caused by the decrease in heating necessary. While climate change may ultimately require adaptations of the built environment to withstand its effects and because the United States is a country that requires more heating than cooling, from a building energy perspective, climate change (average temperature rise) is a net energy saver for the United States.
Quantum materials that combine magnetism with topological order are emerging as key platforms for next-generation spintronics and low-energy electronics. They enable the realization of emergent quantum phenomena, such as the quantum anomalous Hall effect and axion insulator states. The ferromagnetic insulator (FMI)/topological insulator (TI)/FMI sandwich structure of a single-septuple layer (1SL) MnBi 2 Te 4 /four-quintuple layer (4QL) Bi 2 Te 3 /1SL MnBi 2 Te 4 holds great potential to achieve such desirable quantum phenomena at an elevated temperature, owing to its large Dirac point band gap and high Curie temperature. Here, for this work, spin- and angle-resolved photoemission spectroscopy (spin-ARPES) is employed to directly verify that the band gap arises from broken time-reversal symmetry via proximity-driven magnetization. This study demonstrates direct control of the spin state via external magnetic fields and unambiguously confirms the exchange interaction as the gap-opening mechanism. The robust magnetic gap and controllable spin texture make this heterostructure a suitable candidate for spintronic applications and magnetic topological quantum phases.
ABSTRACT The Cambrian Mount Simon Sandstone has been the subject of extensive study and multiple industrial-scale carbon storage demonstrations at Decatur, Illinois, USA. The development of a reliable paleoenvironmental model is critical to successful large-scale carbon dioxide (CO2) storage, but is complicated by the need to interpret pre-vegetation sedimentation processes. The present study presents a paleoenvironmental model of the Lower Mount Simon Sandstone, based on analysis of primary sedimentary structures in two cores and four complete high-resolution resistivity logs (FMI). The Lower Mount Simon Sandstone represents a vertical “drying-up” sequence composed of three associated depositional units: a north–south oriented coastal system at the base, an eastward-directed fluvial unit in the middle, and a westward-directed eolian system at the top that recycled medium- and fine-grained sand in the basin. Quantitative analysis of fluvial cross-strata indicates that the perennial river system was shallow (c. 1 m deep) with relatively narrow channel belts (c. 1 km). Adjacent sandy eolian-floodplain deposits contain abundant thin, crinkly planar laminae that are enriched in fines and are interpreted as cementation surfaces, likely of biological origin. Deflation lags and wind-ripple strata are commonly interbedded with the crinkly strata, suggesting that the recurrence of erosion and deposition that controlled sedimentary preservation on the floodplain were dominated by eolian transport, re-wetting, and (bio-) cementation. Such a prominent role of exposure to the wind, basin-scale sediment recycling, and eolian removal of fine-grained sediment would have ceased to exist for most climates after the development of vegetation on land, yet, may well be key to understanding the environmental context for early life on Earth.
During phase-I, the project team led by Echogen Power Systems (EPS) had two primary objectives based on investigating the application of gas turbines with supercritical carbon dioxide (sCO 2 ) power cycles. The first objective was to improve the overall efficiency and performance of a hybrid gas turbine/sCO 2 power system through a joint optimization of the two subsystems (gas turbine and sCO 2 power cycle) using non-linear optimization techniques that simultaneously evaluate thermal performance of the combined cycle. The hybrid power system included several points of interaction, including (but not limited to) gas turbine exhaust, fuel heating, inlet chilling and turbine cooling. The second objective was to establish a baseline transient response model of the hybrid power system and a notional microgrid and begin steps to integrate the control systems of the three major elements (gas turbine, sCO 2 cycle and grid controller). The project team established a baseline performance for a combined cycle power plant using a production gas turbine and scaled sCO 2 power cycle only utilizing exhaust heat recovery. Echogen’s non-linear techno-economic optimization code was extended by adding gas turbine component models derived from a in-house developed gas turbine design code. With the two cycles coupled by the gas turbine exhaust, design parameters of both cycles were allowed to vary simultaneously to determine performance opportunity versus isolated designs. Returning to the baseline gas turbine/sCO 2 power cycle transient models: Echogen had in-house developed sCO 2 cycle transient model in GT-Suite system simulation software, and had partnered with Siemens Finspång for gas turbine transient model, and Siemens PTI group to provide micro-grid load profile as well as hybrid power cycle generated load (power and frequency) analysis. The transient model for the SGT-750 Siemens gas turbine was a “black-box” functional mock-up interface (FMI) model developed by Siemens Industrial Turbomachinery in Finspång, Sweden. The SGT-750 is a twin-shaft gas turbine that produces 40 MW electricity with an efficiency of about 40% at ISO conditions. At 100% gas turbine throttle (load), the SGT-750 has average exhaust conditions of 114.6 kg/s and 469.8°C. The transient model for sCO 2 power cycle was developed by Echogen in GT-SUITE 1D system simulation software platform. The basic CO 2 flow circuit has single-shaft turbomachinery with net 11.5 MW electrical power output at design conditions. The power turbine has a double-ended shaft with one end connected to synchronous generator through a fixed-ratio gearbox. The other end of power turbine is connected to the compressor through a continuously variable transmission. The major components of the sCO 2 power cycle modeled include air cooled condenser/cooler, CO 2 compressor, recuperator, two waste heat exchanger coils, power turbine, continuous variable transmission, gearbox and generator. Integration of SGT-750 transient model and sCO 2 power cycle transient model was done in Matlab Simulink. In the integrated model, the gas turbine and sCO 2 power cycle interacted at two points, first one being the gas turbine exhaust gas flow rate and temperature, which were inputs to sCO 2 power cycle model. The second point was the distribution of micro-grid load demand signal between the SGT-750 generator and sCO 2 cycle generator. For a given combined-cycle load demand, the gas turbine load demand was equal to the total demand minus the sCO 2 cycle power generated. In the present study the integrated model was simulated for two cases of grid load demand: (i) for a step change, both positive-step and negative-step, in grid load demand (ii) for a micro-grid load demand curve provided by Siemens PTI group. Finally, the time series plots representing load demand versus integrated system response were presented including the sCO 2 power cycle control system performance plots. The actual generated power and frequency of both the generators, gas turbine and sCO 2 power cycle, was supplied to Siemens PTI group for dynamic grid assessment, results of which are provided in appendices.
This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENviroment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report provides discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity is maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In addition to the investigative work being conducted on FMUs and FMIs, a series of updates to the hybrid repository has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and opensourcing the HYBRID repository with an integrated regression system.
This report discusses the advancements and status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and usage, which aims to ease the sharing and simulation of complex dynamic models. This report discusses the FMI/FMU development advancements, overall focusing on the deployment of methodologies for RAVEN to export and use FMI/FMU of both Python-based models and advanced AI-constructed algorithms.
This report demonstrates new functionality and applications stemming from the development of high fidelity to low fidelity (high-low) coupling for system simulations and to further explore the capabilities of RAVEN in the performance of uncertainty quantification in this kind of high-low coupled system models. The work builds from previous work on high-low coupling that utilized CTF, the high fidelity subchannel analysis code, with a low fidelity model built in TRANSFORM, the system analysis code, utilizing the Functional Mock-Up Interface (FMI)
The Wabash CarbonSAFE project drilled the Wabash #1 stratigraphic test well (ID# 168045) at the Wabash Valley Resources (WVR) IGCC facility in Vigo County, Indiana, to evaluate the feasibility of commercial-scale CO 2 storage near the site. The Mt. Simon Sandstone was the initial target for storage evaluation and was found to have generally poor reservoir qualities in the Wabash #1 well. Focus was then placed on the secondary target, the Potosi Dolomite. For storage and containment evaluation an extensive suite of geophysical logs was collected throughout the borehole, and whole cores were obtained from the Mt. Simon Sandstone, Eau Claire Formation, and Maquoketa Group. The Maquoketa Group is identified as a regionally extensive secondary sealing interval above the Mt. Simon Sandstone and as a primary seal for the Potosi Dolomite storage complex. Due to concerns over potential lost circulation zones in the Potosi Dolomite during drilling, no core samples or Formation Micro Imager (FMI) logs were acquired in the Potosi Dolomite. However, cased hole well testing was performed over a 20 feet (6 m) interval within the Potosi Dolomite during which a fluid swab sample was obtained. Approximately 35 miles of 2D seismic information was acquired in the project area near Wabash #1 to aid in reservoir and caprock characterization. The highly porous and permeable zones in the Potosi Dolomite and lower Oneota Dolomite observed within the Wabash #1 well are, for the purpose of this report, considered the same reservoir package and referred to as the Potosi reservoir interval. This report presents a regional depositional context for the Potosi reservoir interval, the overlying confining zones in the Knox Group, and Maquoketa Group primary seal. This context is developed through detailed information obtained from the Wabash #1 well such as lithologic data collected from cuttings and core, geophysical logging, geomechanical analysis of core samples, and well testing and fluid sampling within the Potosi reservoir interval. Information discussed in this report was compiled as a basis for the development of static and dynamic geologic models of the Potosi Dolomite storage complex. The static and dynamic modeling of CO 2 injection in the Potosi reservoir interval are discussed in a separate report (Dessenberger et al., 2022) under the Wabash CarbonSAFE project.
Abstract. Unmanned aerial systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, Colorado, USA, on 14–20 July 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably, resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way and, thus, demonstrate the power of UASs as new, promising tools in atmospheric and environmental research.