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At least 595 records · Page 33

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DOE OSTI2020

Initial Results From the Newly Upgraded LTX- β

LTX-β, the upgraded Lithium Tokamak Experiment, recently began its first campaign with a goal to study the transport properties of gradient-free electron temperature profile equilibria with increased toroidal field and neutral beam injection. The temperature-gradient-free equilibria in LTX were enabled by the lithium plasma-facing surface. To make the lithium surface conditions more consistent for LTX-β, a new, faster lithium evaporator system has been developed. The toroidal magnetic field has been nearly doubled to ≥0.3 T. Auxiliary heating and fueling with a high flux (35 A, 20 kV) neutral beam has begun. The beam provides core fueling without cold edge neutrals, to test whether flat temperature profiles can be sustained, and makes possible core ion temperature measurements via charge exchange recombination spectroscopy. Neutral beam fueling has been demonstrated with increases in line-integrated density. The coupling of the beam matches well with NUBEAM predictions for shine through. Improved performance has been observed following lithium evaporative coatings and an increase in field strength.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Protection Against Graph-Based False Data Injection Attacks on Power Systems

Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. By representing power system voltages as smooth graph signals, recent research has focused on developing GSP-based methods for state estimation, attack detection, and topology identification. Included, efficient methods have been developed for detecting false data injection (FDI) attacks, which until now were perceived as non-smooth with respect to the graph Laplacian matrix. Consequently, these methods may not be effective against smooth FDI attacks. In this paper, we propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph total variation (TV) under practical constraints. In addition, we develop a low-complexity algorithm that solves the non-convex GDFI attack optimization problem using ell_1-norm relaxation, the projected gradient descent (PGD) algorithm, and the alternating direction method of multipliers (ADMM). We then propose a protection scheme that identifies the minimal set of measurements necessary to constrain the GFDI output to high graph TV, thereby enabling its detection by existing GSP-based detectors. Our numerical simulations on the IEEE-57 bus test case reveal the potential threat posed by well-designed GSP-based FDI attacks. Moreover, we demonstrate that integrating the proposed protection design with GSP-based detection can lead to significant hardware cost savings compared to previous designs of protection methods against FDI attacks.

Morgenstern, Gal↗

DOE OSTI2026

ARC disruption physics and strategy

Commonwealth Fusion Systems (CFS) plans to operate a tokamak power plant called ARC in the early 2030s. Tokamak plasmas have stability limits that, if crossed, lead to a rapid termination of the plasma, referred to as a disruption. Disruptions pose a melt risk to the first wall resulting from thermal and non-thermal particle heat fluxes, and an electromagnetic loading risk on all metal components within the equilibrium coils. A comprehensive set of models is used herein to provide an assessment of both mitigated and unmitigated ARC disruption loads. A preliminary massive gas injection system is baselined and a runaway electron mitigation coil option is proposed to close possible gaps in the baseline. It is predicted that all ARC disruption loads are within a factor of 2 of the disruption loads in SPARC, a tokamak presently under construction by CFS, and therefore SPARC provides an opportunity to calibrate models, test solutions and inform the design of ARC. The goal for ARC is disruption-free operation, however, the pragmatic design target is to withstand one mitigated disruption per day, and to restart the plasma following mitigation in tens of seconds without interrupting the power output. Unmitigated disruptions must be rare, and experience with unmitigated disruption impacts in SPARC will better define what rare means. The implications of this strategy for plasma disruptivity and disruption prediction are discussed, and operating the ARC scenario on SPARC is expected to refine the ARC final design and operational plan.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Influence of living grass Roots and endophytic fungal hyphae on soil hydraulic properties

Soil hydraulic properties are often estimated based on laboratory data or pedotransfer functions dependent on soil physical properties, which often do not consider potential impacts of soil roots or fungal hyphae. Here, we first review current knowledge of how these soil biotic components affect hydraulic properties, then we conducted laboratory experiments to specifically test if the presence of roots and mycorrhizal fungi had a significant effect on the hydraulic properties of two soils with contrasting textures: Flint sand and Hamblen silt loam. Soil cores were seeded with (Panicum virgatum) and grown in a greenhouse over three separate growth periods. The endophytic fungus Serendipita indica was injected as liquid inoculant into designated mycorrhizal cores. Saturated hydraulic conductivity (K sat ) measurements were made with a constant head permeameter, and soil water retention curves were obtained by the evaporation method, supplemented at the dry end for Hamblen silt loam with water activity meter data. Retention curve parameters were obtained by fitting the van Genuchten equation to the resulting measurements. Mean root volume ratios were higher in the mycorrhizal inoculated treatment than in the uninoculated treatment for both soils. For Flint sand, analysis of variance revealed that K sat was reduced by the presence of roots as compared to bare soil. This was likely due to roots clogging soil pores. Results also indicated the presence of roots changed the shape of the water retention curve for Flint sand by increasing water content at saturation and by reducing the slope of the curve. These changes suggested roots created additional porosity and broadened the pore-size distribution. The presence of mycorrhizal fungi accentuated the root effects. The influence of roots and mycorrhizal fungi on hydraulic properties was less obvious for the Hamblen silt loam, as none of the treatments differed from each other at p < 0.05. The results highlight the necessity to consider the impact of root and fungal structures on models of soil hydraulic properties.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Erosion-Corrosion Detection in Carbon Steel Pipe Loops using SRNLs Thickness and Mass Loss Measurement Coupons - 20464

Structural health of waste transfer infrastructure is of utmost importance to DOE and communities surrounding their active sites. A Fitness-for-Service (FFS) program for the Waste Transfer System has been implemented by the US Department of Energy (DOE) for the purposes of evaluating and quantifying the degradation of critical components. Currently, the method of approach is to section and evaluate pipelines that have been removed at end service. In order to augment data collection methods applied to making lifespan estimates of waste transfer system components, Florida International University (FIU) has been investigating new-technology based strategies for quantifying the rate of degradation. Present research considers the option of a coupon system installed on a section of pipe at points of interest that will provide easy access to high resolution measurements of erosion/corrosion rates during operation. Researchers at Savannah River National Laboratory (SRNL) have developed this novel solution for in situ erosion and corrosion measurements in many applicable locations such as tanks, pipes, ducts, and injection points. The coupons are approximately 12.7 mm (1/2 inch) in diameter and 25.4 mm (1 inch) in length and can be placed in the pipe sections using Swagelok compression fittings. The coupon design allows for monitoring of the total degradation resulting from corrosion and erosion. High-resolution Ultrasonic Thickness (UT) testing allows in-situ monitoring of both degradation mechanisms. When removed from the system mass loss resulting from corrosion and erosion may also be evaluated. Experimental testing at FIU implemented the SRNL coupon system in a 6.10 meter (20 foot) pipe loop system comprised of 50.8 mm (2 inch) and 76.2 mm (3 inch) pipe sections in order to replicate expected use cases at the sites. Operational conditions were emulated by pumping abrasive materials through the pipeline at full capacity and collecting measurements from the SRNL coupons. Initial testing was conducted by circulating water followed by a mixture of sand and water for uniform wear rates. Thickness changes and mass loss were measured, and the surface exposed to the process fluid was observed for visual evidence of characteristic wear patterns resulting from an erosive environment. The test matrix consisted of several hours of operation and using fine, medium and coarse grain sands. Test results were analyzed to check for all three parameters (mass loss/gain, thickness change and surface patters) which could provide estimates of the remaining useful life of the pipe sections. Thus, the research presents the findings of this evaluation of the SRNL coupons as a means for conducting high accuracy, cost effective in-situ analysis of pipeline and waste transfer systems for erosion and corrosion susceptibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Paragenetic controls on CO 2 -fluid-rock interaction and weakening in a macroporous-dominated sandstone

The injection and storage of anthropogenic CO 2 in the subsurface is being deployed as a climate change mitigation tool; however, diagenetic-paragenetic heterogeneity in sandstone reservoirs often contributes to interval specific chemomechanical changes that affect injection and can increase leakage risk.In this paper we address reservoir heterogeneities’ impact on chemomechanical changes in a macroporous-dominated lithofacies of Morrow B sandstone, a formation containing several diagenetically-distinct hydraulic facies while undergoing enhanced oil recovery (EOR) and carbon dioxide (CO 2 ) sequestration. We performed three flow-through experiments using a CO 2 -charged or uncharged formation water combined with four indirect tensile strength tests per post-test sample. We then used the microstructure and paragenetic sequence to understand chemomechanical weakening with key observations as follows: dissolution of carbonates and feldspars changed porosity; increased permeability led to reclassifying each sample in a different hydraulic flow unit; decreased ultrasonic velocity; and did not lead to a loss of tensile strength. Tensile strength maintenance occurred due to the low abundance and minor dissolution of siderite, the stability of quartz, and the relative position of diagenetic ankerite within feldspar. This macroporous-dominated lithofacies is the primary reservoir for the Morrow B Sandstone, and is analogous to other porous sandstone reservoirs. It represents an end-member of a chemomechanically low-risk siliceous CO 2 sequestration and CO 2 -EOR reservoir.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning

Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Trap-assisted Auger-Meitner recombination in GaN p-i-n diodes

Most properties of semiconductor devices are dominated by shallow impurities. However, deep defects often play an important role, for instance, in recombination processes or high field transport. While a variety of techniques are available to assess the density and energy levels of impurities, other properties, such as the recombination mechanisms of the defects, escape observation. We report on the direct measurement of hot electrons generated by trap-assisted Auger-Meitner recombination (TAAR) in GaN p-i-n diodes. By performing electron emission spectroscopy (EES) on diodes with surfaces activated to negative electron affinity by cesium, we observe the expected overflow electrons of p-i-n diodes under low current injection. However, when operating the devices at higher current densities, as low as ∼25 A/c⁢m 2 , we measure the emission of high-energy electrons. At variance with the observed hot electrons in light-emitting diodes (LEDs) using EES, the hot electrons generated in p-i-n diodes at our tested currents cannot be from eeh Auger-Meitner recombination due to the diodes' significantly lower carrier densities compared to those in LEDs. During our measurements, we observe the emission of accumulated electrons with energies ∼0.42 eV, ∼0.99 eV, ∼1.43 eV, and ∼2.32 eV above the conduction-band minimum (CBM) at various bias conditions, suggesting the existence of conduction-band features in GaN at these energies where electrons can be long-lived, such as satellite-valley minima and inflection points. We also measure incompletely relaxed hot electrons approaching energies 1.97 ± 0.13 eV and 2.94 ± 0.13 eV above the CBM, as the diodes are biased to high currents, suggesting at least some of the TAAR partaking defects have an energy level ≳1.97 eV and ≳2.94 eV away from either the conduction or valence band edges. Additionally, at our highest operating currents, we measure hot electrons with energies 3.28 ± 0.13 eV above the CBM, providing direct evidence of TAAR processes involving shallow impurities. This unexpected observation of TAAR in GaN p-i-n diodes spotlights the importance of further studies of defects in GaN and the necessity to incorporate the multi-phonon emission, radiative, and TAAR capture steps of defect-assisted recombination cycles into device modeling. Furthermore, this experiment demonstrates the applicability of the simplest semiconductor structures, p-i-n diodes, as a test bed to study the rich recombination physics of semiconductor materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

A comprehensive guide to CAN IDS data and introduction of the ROAD dataset

Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Producing vehicular CAN data with a variety of intrusions is a difficult task for most researchers as it requires expensive assets and deep expertise. To illuminate this task, we introduce the first comprehensive guide to the existing open CAN intrusion detection system (IDS) datasets. We categorize attacks on CANs including fabrication (adding frames, e.g., flooding or targeting and ID), suspension (removing an ID’s frames), and masquerade attacks (spoofed frames sent in lieu of suspended ones). We provide a quality analysis of each dataset; an enumeration of each datasets’ attacks, benefits, and drawbacks; categorization as real vs. simulated CAN data and real vs. simulated attacks; whether the data is raw CAN data or signal-translated; number of vehicles/CANs; quantity in terms of time; and finally a suggested use case of each dataset. State-of-the-art public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, lacking fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but is missing a corresponding “raw” binary version. This issue pigeon-holes CAN IDS research into testing on limited and often inappropriate data (usually with attacks that are too easily detectable to truly test the method). The scarcity of appropriate data has stymied comparability and reproducibility of results for researchers. As our primary contribution, we present the Real ORNL Automotive Dynamometer (ROAD) CAN IDS dataset, consisting of over 3.5 hours of one vehicle’s CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real (i.e. non-simulated) fuzzing, fabrication, unique advanced attacks, and simulated masquerade attacks. To facilitate a benchmark for CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS research field.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Effects of Inlet Area Ratio on Operability of an Axial Air Inlet Rotating Detonation Combustor

This research presents the first results from a new Rotating Detonation Combustor (RDC) design with an axial air inlet and rear-facing angled fuel injection. This design features a variable air inlet that allows for the study of the effect of inlet area ratio on operation and to directly study the coupling between the air plenum and channel of the RDC while minimizing inlet pressure losses. This device maintains the same downstream channel configuration as those previously tested in our laboratory, which includes a 154 mm outer channel diameter and a constant area channel with a width of 7.6 mm. All tests were completed with a downstream hydrogen-air afterburner capable of flashing back in the channel to initiate detonation operation. Initial tests demonstrated successful operation at air mass flow rates from 160 to 770 g/s and equivalence ratios from 0.53 to 1.05 at multiple air inlet to channel area ratios, indicated that sensors installed within the air feed plenum are able to characterize the dynamic characteristics of waves within the detonation channel, and verified that the design goals for this configuration were achieved.

Shepard, Joshua↗

DOE OSTI2024

An Electrical Resistance Diagnostic for Conductivity Monitoring in Laser Powder Bed Fusion

With the growing interest in metal additive manufacturing using laser powder bed fusion (LPBF), there is a need for advanced in-situ nondestructive evaluation (NDE) methods that can dynamically monitor manufacturing process-related variations, that can be used as a feedback mechanism to further improve the manufacturing process, leading to parts with improved microstructural properties and mechanical properties. Current NDE techniques either lack sensitivity beyond build layer, are costly or time-consuming, or are not compatible for in-situ integration. In this research, we develop an electrical resistance diagnostic for in-situ monitoring of powder fused regions during laser powder bed fusion printing. The technique relies on injecting current into the build plate and detecting voltage differences from conductive variations during printing using a simple, cheap four-point electrode array directly connected to the build plate. A computational model will be utilized to determine sensitivities of the approach, and preliminary experiments will be performed during the printing process to test the overall approach.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations

The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. Finally, these results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Density wavenumber spectrum measurements, synthetic diagnostic development, and tests of quasilinear turbulence modeling in the core of electron-heated DIII-D H-mode plasmas

Abstract Measurements of the turbulent density wavenumber spectrum, δ n ˆ e ( k ⊥ ) , using the Doppler Back-Scattering (DBS) diagnostic are reported from DIII-D H-mode plasmas with electron cyclotron heating as the only auxiliary heating method. These electron-heated plasmas have low collisionality, ν e ∗ < 1 , T e / T i > 1 , and zero injected torque—a regime expected to be relevant for future fusion devices. We probe density fluctuations in the core ( ρ ≈ 0.7) over a broad wavenumber range, 0.5 ⩽ k ⊥ ⩽ 16 cm −1 ( 0.1 ⩽ k ⊥ ρ s ⩽ 5 ), to characterize plasma instabilities and compare with theoretical predictions. We present a novel synthetic DBS diagnostic to relate the back-scattered power spectrum, P s ( k ⊥ ) —which is directly measured by DBS—to the underlying electron density fluctuation spectrum, δ n ˆ e ( k ⊥ ) . The synthetic DBS P s ( k ⊥ ) spectrum is calculated by combining the SCOTTY beam-tracing code with a model δ n ˆ e ( k ⊥ ) predicted either analytically or numerically. In this work we use the quasi-linear code Trapped Gyro-Landau Fluid (TGLF) to approximate the δ n ˆ e ( k ⊥ ) spectrum. We find that TGLF, using the experimental profiles, is capable of closely reproducing the DBS measurements. Both the DBS measurements and the TGLF-DBS synthetic diagnostic show a wavenumber spectrum with variable decay. The measurements show weak decay ( k −0.6 ) for k < 3.5 cm −1 , with k −2.6 at intermediate- k ( 3.5 ⩽ k ⩽ 8.5 cm −1 ), and rapid decay ( k −9.4 ) for k > 8.5 cm −1 . Scans of physics parameters using TGLF suggest that the normalized ∇ T e scale-length, R / L T e , is an important factor for distinguishing microturbulence regimes in these plasmas. A combination of DBS observations and TGLF simulations indicate that fluctuations remain peaked at ITG-scales (low k ) while R / L T e -driven TEM/ETG-type modes (intermediate/high k ) are marginally sub-dominant.

synthetic diagnostics↗

DOE OSTI2021

A learning-augmented approach for AC optimal power flow

Because of the high nonlinearity of AC optimal power flow (OPF), numerous efforts have been made in recent decades to find efficient methods. Machine learning (ML) has proven to significantly reduce the computational costs in many real-world problems. Thus, this paper develops a learning-augmented method for solving AC OPF, which integrates both power network equations and ML to yield near-optimal solutions. More specifically, ML models are developed to first predict bus voltage magnitudes and angles. Then, physics-based network equations are employed to calculate the power injection at different buses. Three ML algorithms, i.e., random forest, multi-target decision tree, and extreme learning machine, are explored and compared. To evaluate the efficiency of the proposed learning-augmented AC OPF solver, the MATPOWER Interior Point Solver is adopted as a baseline. Case studies on both 500-bus and 4918-bus test networks show that the proposed learning-augmented method has reduced the computational time by 15–100 times depending on the network size with a minimal loss in optimality.

42 ENGINEERING↗

DOE OSTI2023

The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations

As the climate system warms, megafires have become more frequent with devastating effects. A byproduct of these events is the creation of smoke plumes that can rise into the stratosphere and spread across the globe where they reside for many months. To gain a deeper understanding of the plume dynamics, global climate simulations of a megafire were performed at a wide range of grid spacings from 2.0° down to 7 km, including a 7 km nonhydrostatic experiment. The analysis focuses on how the resolved dynamics affects the specification of the plume characteristics such as injection height and black carbon (BC) mass. Prior studies initialize the smoke plume at one or a few grid points and this is shown here to produce severely dissipative dynamics. In order to validate such simulations with observations, enhancements of the plume characteristics to offset the dissipation is necessary. Using a numerically converged simulation, sensitivity tests show that to approximate the observed stratospheric lifetime, a reduction in BC fraction by 50% is necessary for external mixtures. The vorticity dynamics of the plume is also analyzed with a Lagrangian budget to understand the mechanisms responsible for the evolution of a collocated anticyclonic vortex. The results can be distilled down into a simple conceptual model. As the plume rises, the air diverges at the top of the updraft where the largest concentrations of smoke are found. This divergence induces a dilution of the background cyclonic absolute vorticity producing an anticyclonic vortex. Vortex decay occurs from opposite arguments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Using Controlled-Source Electromagnetic (CSEM) for CO 2 Storage Monitoring in the North Dakota CarbonSAFE Project

Conference presentation for International Meeting for Applied Geophysics & Energy (IMAGE), September 26 – October 1, 2021, Denver, CO. Geophysical methods are key for characterizing the geologic formations to store CO 2 and monitor the injected CO 2 over time to ensure containment. In the integrated multimeasurement geophysical approach considered for this project, it is expected that the controlled-source electromagnetic (CSEM) method is a strong contributor to mapping the CO 2 movement. A feasibility study of the CSEM method, including 1D and 3D modeling and a field noise test, was conducted to determine its effectiveness in monitoring CO 2 in the Broom Creek and Deadwood Formations. The study results demonstrate that the CSEM method can be used for CO 2 storage monitoring in the study area.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗

DOE OSTI2021

A novel framework for hosting capacity analysis with spatio-temporal probabilistic voltage sensitivity analysis

Smart grids are envisioned to accommodate high penetration of distributed photovoltaic (PV) generation, which may cause adverse grid impacts in terms of voltage violations. Therefore, PV Hosting capacity is being used as a planning tool to determine the maximum PV installation capacity that causes the first voltage violation and above which would require infrastructure upgrades. Additionally, traditional methods of Hosting capacity analysis are scenario based and computationally complex as they rely on iterative load flow algorithms that require investigating a large number of scenarios for accurate assessment of PV impacts. Therefore, this paper presents a computationally efficient analytical approach to compute the probability distribution of voltage change due to random behavior of randomly located multiple distributed PVs. The proposed approach is based on Spatio-temporal probabilistic voltage sensitivity analysis that exploits both spatial and temporal uncertainties associated with PV injections. Thereafter, the derived distribution is used to quantify voltage violations for various PV penetration levels and subsequently determine the hosting capacity of the system without the need to examine large number of scenarios. Results of the proposed framework are validated via conventional load flow based simulation approach on the IEEE 37 and IEEE 123 node test systems.

42 ENGINEERING↗