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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

{beta}- and {gamma}-spectroscopy study of {sup 119}Pd and {sup 119}Ag

Neutron-rich Pd-119 nuclei were produced in fission of natural uranium, induced by 25-MeV protons. Fission fragments swiftly extracted with the Ion Guide Isotope Separation On-Line method were mass separated using a dipole magnet and a Penning trap, providing mono-isotopic samples of Pd-119. Their beta(-) decay was measured with gamma gamma- and beta gamma-spectroscopy methods using low-energy germanium detectors and a thin plastic scintillator. Two distinct nuclear-level structures were observed in Ag-119, based on the 1/2(-) and 7/2(+) isomers reported previously. The beta(-)decay work was complemented by a prompt-gamma study of levels in Ag-119 populated in spontaneous fission of (252)cf, performed using the Gammasphere array of germanium detectors. Contrary to previous suggestions, our data show that the 1/2(-) isomer is located below the 7/2(+) isomer and is proposed as a new ground state of Ag-119 with the 7/2(+) isomer excitation energy determined to be 33.4 keV. Our data indicate that there are two beta unstable isomers in Pd-119, a proposed ground state of Pd-119 with tentative spin 1/2(-) or 3/2(+) and a half-life of 0.88 s and the other one about 350 keV above, having spin (11/2(-)) and a half-life of 0.85 s. The higher-energy isomer probably decays to the 1/2(-) or 3/2(+) ground state via a gamma cascade comprising 18.7-219.8-X-keV transitions. The unobserved isomeric transition with energy X approximate to 100 keV probably has an E3 multipolarity. Its hindrance factor is significantly lower than for analogous E3 isomeric transitions in lighter Pd isotopes, suggesting an oblate deformation of levels in Pd-119. Oblate configurations in Ag-119 are discussed also.

Kurpeta, J↗
DOE OSTI2020

Hierarchical morphologies in co-sputter deposited thin films

Co-depositions of immiscible alloy films at specific processing conditions have yielded hierarchical microstructures which consist of distinct features at multiple length scales, often agglomerates and concentration modulations on the sub-micrometer-scale and fine nanoprecipitates in a matrix on the nanoscale. The present work examined a series of immiscible alloy systems: Cu-Mo, Cu-Ag, Cu-Fe, Cu-Ta, Mo-Ag, Cu-Mo-Ag, to determine the kinetic conditions favorable for hierarchical organization and the formation mechanism of such structures. Thin films of six immiscible systems were sputter co-deposited over a range of deposition rates from 0.12 to 2 nm/s and various temperatures from 400 to 800°C. The resulting microstructures indicate that hierarchical structures form with sufficient disparity in kinetic energy between the constituent atoms, one species being highly mobile (A) and the other relatively immobile (B). This condition arises typically at elevated deposition temperatures and reduced deposition rates but is also alloy dependent. The hierarchical structures form during deposition via phase separation and self-organization processes across the multiple length scales. The adatoms diffuse on the film surface with the highly mobile species swiftly agglomerating into A-rich domains within which B-rich nanoprecipitates form, often self-organizing into periodic arrays. The smallest B-rich nanoprecipitates in the A-rich domains are found to be coherent and in a metastable crystal structure (B taking the structure of A), but coarser precipitates that exhibit the equilibrium structure of B element. The A-rich domains are surrounded by a B-rich matrix that phase-separates into a concentration modulated structure. In conclusion, the observations are interpreted via a model incorporating material properties and process parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Influence of Nb alloying on Nb recrystallization and the upper critical field of Nb 3 ⁢Sn

Nb 3 Sn conductors are important candidates for high-field magnets for particle accelerators, and they continue to be widely used for many laboratory and NMR magnets. However, the critical current density, J c , of present Nb 3 Sn conductors declines swiftly above 12-15 T. State-of-the-art Ta- and Ti-doped strands exhibit upper critical field, H c2 , values of ~ 24-26.5 T (4.2 K) and do not reach the FCC target J c , which serves as the present stretch target for Nb 3 Sn development. As recently demonstrated, to meet this goal requires enhanced vortex pinning but an independent and supplementary approach is to significantly enhance H c2 . In this study, we have arc-melted multiple Nb alloys with added Hf, Zr, Ta and Ti and drawn them successfully into monofilament wires to investigate the possibilities of H c2 enhancement through alloying. H c2 (T) was measured for all samples in fields up to 16 T and some up to 31 T. We have found that all alloys show good agreement with the standard Werthamer, Helfand, and Hohenberg (WHH) fitting procedure without the need to adjust the paramagnetic limitation parameter (α) and spin-orbit scattering parameter (λ so ). The evaluation of dH c2 /dT near T c , which is proportional to the electronic specific heat coefficient γ and the normal state resistivity ρn, allows a better understanding of the induced disorder introduced by alloying in the A15 phase. So far, we have observed that Hf alloying of pure Nb can enhance H c2 (0) by 3-4 T to ~28 T, while adding just 1 at. %Hf or Zr into a Nb4Ta base alloy can raise H c2 (0) to ~31 T. Very importantly we find that Hf and Zr raise the alloy recrystallization temperature above the usual A15 reaction temperature range of 650°C – 750°C, thus ensuring denser A15 phase nucleation in the Nb alloy grain boundaries, possibly leading to a more homogeneous A15 phase Sn content and refined A15 grain size. Furthermore, the potential for further advancements in Nb 3 Sn properties is explored in relation to the recrystallization of the Nb alloy and the factors controlling the upper critical field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Observation of high-pressure polymorphs in bulk silicon formed at relativistic laser intensities

Silicon polymorphs with exotic electronic and optical properties have recently attracted significant attention due to their wide range of useful band gap characteristics. They are typically formed by static high-pressure techniques, which limits the crystal structures that can be made. This constitutes a major obstacle to study these polymorphs and their incorporation into existing technology. Approaches have attempted to address this shortcoming through using dynamic conditions and chemical precursor materials. Here, we report on an approach to create unusual crystal structures deep in the bulk of a silicon crystal by irradiating it with a laser pulse at ultrarelativistic intensity of up to 7.5 × 10 19 W/ c m 2 . Laser-generated electrons with MeV energy swiftly penetrate the target with speed close to the speed of light and deposit their energy into a large volume across the whole thickness of the sample. The relativistic electron current creates, via branching propagation and ionization, high-energy-density conditions for thermodynamically nonequilibrium phase transformation paths into new crystal polymorphs. X-ray microdiffraction and synchrotron x-ray diffraction analyses indicate, along with conventional dc-Si, the presence of exotic silicon structures in the bulk of the laser intact target volume. These structures are identified as body-centered bc8-Si, rhombohedral r8-Si, hexagonal-diamond hd-Si, and the tetragonal Si-VIII, all phases of Si that have previously been made through static techniques. Additionally, simple-tetragonal st12-Si and body-centered tetragonal bt8-Si were observed along with signatures of not yet identified diffraction spots. Both st12-Si and bt8-Si have only been observed in ultrafast laser microexplosion conditions at much lower laser intensity ∼ 10 14 W/ c m 2 and within a micron-thin surface layer. The findings here are supported by direct observation of nanoparticles with high-resolution transmission electron microscopy and corresponding fast Fourier transform analysis of their interatomic distances. The presented analyses of absorbed laser energy, generation of the MeV electron current, and deposition of energy across the whole target thickness provide a solid basis for drawing the conclusion that the observed silicon polymorphs were produced because of laser-generated high-energy electrons fast-penetrating deeply into the bulk of silicon. In contrast to solid-solid transformations, the plasma-solid transitions offer a paradigm for the creation of exotic, high-energy density materials inside the bulk of the sample by using laser pulses at relativistic intensities. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗
DOE OSTI2025

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗
DOE OSTI2025

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

Frequency Nadir Constrained Unit Commitment for High Renewable Penetration Island Power Systems

The process of energy decarbonization in island power systems is accelerated due to the swift integration of inverter-based renewable energy resources (IBRs). The unique features of such systems, including rapid frequency changes resulting from potential generation outages or imbalances due to the unpredictability of renewable power, pose a significant challenge in maintaining the frequency nadir without external support. This paper presents a unit commitment (UC) model with data-driven frequency nadir constraints, including either frequency nadir or minimum inertia requirements, helping to limit frequency deviations after significant generator outages. The constraints are formulated using a linear regression model that takes advantage of real-world, year-long generation scheduling and dynamic simulation data. The efficacy of the proposed UC model is verified through a year-long simulation in an actual island power system using historical weather data. The alternative minimum inertia constraint, derived from actual system operation assumptions, is also evaluated. Findings demonstrate that the proposed frequency nadir constraint notably improves the system's frequency nadir under high photovoltaic (PV) penetration levels, albeit with a slight increase in generation costs, when compared to the alternative minimum inertia constraint.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Volt/VAR Optimization (VVO) Application on GridAPPS-D Platform

There is a large increment in the distributed energy resources (DERs) installation and deployments of smart sensing devices and communication infrastructure; hence, the power distribution network is swiftly evolving from a passive network to an active network. This motivates the development of advanced applications to operate power distribution systems for higher efficiency and reliability. These advanced applications are model-based and data-driven. This requires an advanced distribution management system (ADMS) to provide required data for the optimal operation of the distribution systems by coordinating various grid controllable devices. In this paper a Volt-VAR optimization (VVO) application to coordinate the grid’s legacy and new voltage control devices for conservation voltage reduction (CVR) is deployed on the GridAPPS-D platform (an open-source platform ) for ADMS application development. The VVO application is validated for various operating conditions on using modified IEEE 8500-node distribution test feeders. Further, the application is successfully deployed on the GridAPPS-D platform.

Jha, Rahul↗
DOE OSTI2023

Black Start of Unbalanced Microgrids Harmonizing Single- and Three-Phase Grid-Forming Inverters

As power systems are transforming with increasing penetrations of inverter-based resources (IBRs), system restoration using IBRs has drawn attention. Using distributed grid-forming (GFM) assets located near critical loads, either three-phase or single-phase, to establish microgrid voltages in the absence of a bulk grid, a distribution system could obtain high system survivability. For swift and secure recovery of a critical load in a single-phase lateral, local single-phase GFM inverters can form a microgrid, and then it can be combined with a neighbouring grid with the inverters remaining in GFM mode for voltage and frequency regulation until the bulk grid comes online. It leads to dynamic interoperation of single-phase GFM inverters with three-phase ones in the black start process. This paper studies the novel approach with electromagnetic transient (EMT) simulations. To evaluate the potential and the technical challenges of the heterogeneous IBR-driven black start, three-phase and single-phase GFM inverter models are developed, including negative-sequence control for voltage balance and a phase-by-phase current limiter (three-phase) and current magnitude limiter (single-phase). To examine dynamic aspects of the black-start process, the EMT simulation also models transformer and motor dynamics emulating their inrush and startup behavior as well as network dynamics. Involvement of grid-following assets to facilitate the black-start process is also modeled. By allowing multiple GFM inverters to collectively black start without leader-follower coordination, regardless of phases, a system can achieve extreme resilience. An inverter-driven black start of a heavily unbalanced 2-MVA distribution feeder using 1 three-phase and 3 single-phase GFM inverters is demonstrated. The simulation shows the heterogeneous system can maintain stability with the single-phase GFM dynamics coupled with the three-phase one.

grid-forming inverter↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2021

The Gamow Explorer: a Gamma-Ray Burst Observatory to study the high redshift universe and enable multi-messenger astrophysics

The Gamow Explorer will use Gamma Ray Bursts (GRBs) to: 1) probe the high redshift universe (z < 6) when the first stars were born, galaxies formed and Hydrogen was reionized; and 2) enable multi-messenger astrophysics by rapidly identifying Electro-Magnetic (IR/Optical/X-ray) counterparts to Gravitational Wave (GW) events. GRBs have been detected out to z ~ 9 and their afterglows are a bright beacon lasting a few days that can be used to observe the spectral fingerprints of the host galaxy and intergalactic medium to map the period of reionization and early metal enrichment. Gamow Explorer is optimized to quickly identify high-z events to trigger follow-up observations with JWST and large ground-based telescopes. A wide field of view Lobster Eye X-ray Telescope (LEXT) will search for GRBs and locate them with arc-minute precision. When a GRB is detected, the rapidly slewing spacecraft will point the 5 photometric channel Photo-z Infra-Red Telescope (PIRT) to identify high redshift (z < 6) long GRBs within 100s and send an alert within 1000s of the GRB trigger. An L2 orbit provides < 95% observing efficiency with pointing optimized for follow up by the James Webb Space Telescope (JWST) and ground observatories. The predicted Gamow Explorer high-z rate is <10 times that of the Neil Gehrels Swift Observatory. The instrument and mission capabilities also enable rapid identification of short GRBs and their afterglows associated with GW events. The Gamow Explorer will be proposed to the 2021 NASA MIDEX call and if approved, launched in 2028.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Directional dependency of electronic stopping in nickel, projectile’s excited charge state and momentum transfer

Abstract We studied the directional dependency of electronic stopping power of swift light ions in nickel using real-time time-dependent density functional theory. We report a variation of electronic stopping for moving ions as the projectile probes different electronic densities of the host material. These results show that while the predicted magnitude stays in reasonable agreement with experiment, for $$v > 2$$ v > 2 . a.u. simulating only low index crystallographic directions is not enough to sample the experimental average values. The ab initio simulations give us access to microscopic quantities, such as non-adiabatic forces, momentum transfer and transient excited state charges of the projectile and host ions, which are not available through other methods. We report these quantities for the first time.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Elephants Sharing the Highway: Studying TCP Fairness in Large Transfers over High Throughput Links

Escalating bandwidth demand strains high-performance data networks, posing potential performance risks. TCP congestion control algorithms enhance reliability and optimize bandwidth usage. Network performance is influenced by factors such as AQM algorithms and router buffer size. In the context of constrained network resources, understanding how TCP flows share networks and the resulting performance impact is essential. This paper introduces insights into TCP fairness and performance involving a comparison of TCP CUBIC, Reno, Hamilton, and BBR versions 1 and 2 across real-world networks supporting high bandwidths of up to 25 Gbps. The research explores TCP behaviors with AQM algorithms like FIFO, FQ_CODEL, and RED, alongside diverse buffer sizes. Notably, findings reveal that manipulating buffers and queuing methods yields contrasting outcomes based on bandwidth. BBRv2 emerges as a superior fair algorithm, pivotal for swift transfers, particularly in scientific data scenarios. These results provide crucial guidance for future network design, ensuring equitable performance optimization.

Kiran, Mariam↗
DOE OSTI2023

Smart Preprocessing & Robust Integration Emulator

To achieve the desired particle size of biomass feedstocks during preprocessing for trouble-free handling and conversion to produce biofuels and bioproducts, the raw materials must undergo a crucial milling process. The particle size of biomass plays a critical role in subsequent biofuel manufacturing, where a larger area-to-volume ratio facilitates efficient synthesis while balancing the impact of moisture on biomass storage. To optimize biofuel production efficiency and overcome these challenges, it is imperative to accurately predict the particle size distribution (PSD) of the biomass in the design of efficient preprocessing systems. The population balance model (PBM), upon empirical calibration and validation, can provide rapid prediction of post-milling PSD of granular biomass. However, PSD has limitations related to mass conservation and the absence of moisture considerations. To overcome these drawbacks, a deep learning model called the enhanced deep neural operator (DNO+) is implemented in the code. This model not only retains the capabilities of the PBM in handling complex mapping functions but also incorporates additional factors influencing the system. By considering various experimental conditions such as sieve size and moisture content, the trained DNO+ model can effectively predict the PSD after milling for any given feed PSD. To further reduce the reliance on experimental data, the PBM is integrated into the DNO+ model, resulting in a physics-informed DNO+ (PIDNO+). The PIDNO+ model addresses the non-conservation of quality exhibited by the PBM while inheriting the advantages of the DNO+ model in considering multiple influencing factors. Moreover, the PIDNO+ model significantly reduces the amount of data required for model training. Both deep learning models, i.e., DNO+ and PIDNO+, are excellent in predictive performance, offering swift and accurate machine learning-based predictions. The use of this code that contains these models will assist in guiding the proper milling equipment selection and operational conditions to achieve the desired biomass particle sizes, ensuring the efficiency of subsequent biofuel and bioproduct production processes.

Xia, Yidong [Idaho National Laboratory (INL), Idah↗
DOE OSTI2024

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Geochemistry & Geophysics↗
DOE OSTI2021

PNNL - WRF-LES - Convective - TTU

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

17 WIND ENERGY↗