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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Three-pole wigglers at NSLS-II

The National Synchrotron Light Source-II (NSLS-II) at Brookhaven National Lab (BNL) is a third-generation medium-energy storage ring with 3 GeV and sub-nm-rad horizontal emittance, equipped with a top-off capability of 500 mA. It is designed to deliver an extremely intense photon beam across a wide spectral range, from the far-infrared to the very hard X-ray region. This outstanding performance is achieved through a combination of bending magnets (BM), three-pole wigglers (3 PW), and advanced insertion device (ID) sources. Six three-pole wigglers have been constructed at the NSLS-II IDs Lab. These devices are used to produce broadband radiation with lower angular power density and to monitor the electron beam emittance and energy spread. Here, this paper describes the R&D activities focused on developing the required magnetic and mechanical designs, along with magnetic field optimization and the final magnetic measurement results. It also covers the spectral brightness, flux, power density, spatial and angular output properties of the 3 PWs, including their commissioning results and their effects on the performance of the NSLS-II storage ring.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-Line Superconducting Waveguides Based on YBCO-on-Kapton Technology

Coplanar (with the ground) and stripline 25 cm long waveguides are manufactured from the YBCO-Kapton material. The waveguides are intended as the key components of a scalable signal delivery for large quantum computing systems and detectors. The waveguide is coupled with an integrated power divider that replaces the standard attenuator. The divider is designed to return most of the power to the previous thermalization stage. The method is projected to reduce the active dissipation by ≈10. We investigate the effect of internal strain in YBCO layer on the device's mechanical stability. Here, we show that adhesives with high stiffness are essential for the reliability of YBCO-Kapton devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Enhancing Oxygen Evolution Reaction and Stability in Proton-Conducting Solid Oxide Electrolysis Cells (p-SOECs) via a Porous Gadolinium-Doped Ceria Interlayer

Proton-conducting solid oxide electrolysis cells (p-SOECs) offer a promising pathway for intermediate temperature (400-600 °C) hydrogen production. However, they still face critical challenges related to sluggish oxygen evolution reaction (OER) kinetics and low Faradaic efficiencies. Here, in this work, we demonstrate that introducing a thin (~0.8 µm) porous Gd 0.1 Ce 0.9 O 1.95 (GDC) interlayer between a BaCo 0.8 Zr 0.1 Zn 0.1 O 3-δ (BCZZ) oxygen electrode and electrolyte significantly enhances p-SOEC performance. The GDC interlayer reduces polarization resistance by 48% (0.54 to 0.28 O cm 2 ) and increases Faradaic efficiency from 63% to 81% at -0.8 A/cm 2 and 600 °C. GDC interlayer p-SOECs display elevated effective H 2 current densities compared to control p-SOECs and reach up to -1.22 A/cm 2 at 1.3 V. Mechanistic studies on the interactions between GDC and BCZZ reveal that GDC intrinsically promotes OER kinetics by significantly reducing the polarization activation energy (Ea p ), dropping from 1.45 to 1.22 eV for full p-SOECs and 0.98 to 0.76 eV for symmetric cells. This promotional effect is localized in the electrochemically active region near the electrolyte interface. Durability testing for over 1500 hours under 50% H 2 O conditions indicates that the GDC interlayer also improves long-term stability, with a degradation rate 53% lower than control p-SOECs. By pinpointing the interfacial region where GDC exerts its promotional effect, highlighting its role in enhancing OER kinetics, and establishing interlayer engineering as a powerful technique, this work provides a unified pathway to simultaneously improve p-SOEC activity, Faradaic efficiency, and durability.

08 - HYDROGEN↗
DOE OSTI2023

Water Power Technologies Office: 2021-2022 Accomplishments Report

The report details key performance goals for each of WPTO’s activity areas until 2025 and includes long-term, follow-on objectives through 2030. This report serves as a strategic vision and operational guide to help WPTO manage and coordinate its future activities and communicate its mission, goals, and plans. The Multi-Year Program Plan was an integral part of the 2022 Peer Review, providing reviewers insight and guidance on the office’s strategy to which WPTO-funded projects must align.

13 HYDRO ENERGY↗
DOE OSTI2023

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Energetic Control of Redox-Active Polymers toward Safe Organic Bioelectronic Materials

Avoiding faradaic side reactions during the operation of electrochemical devices is important to enhance the device stability, to achieve low power consumption, and to prevent the formation of reactive side-products. This is particularly important for bioelectronic devices, which are designed to operate in biological systems. While redox-active materials based on conducting and semiconducting polymers represent an exciting class of materials for bioelectronic devices, they are susceptible to electrochemical side-reactions with molecular oxygen during device operation. In this work, electrochemical side reactions with molecular oxygen are shown to occur during organic electrochemical transistor (OECT) operation using high-performance, state-of-the-art OECT materials. Depending on the choice of the active material, such reactions yield hydrogen peroxide (H 2 O 2 ), a reactive side-product, which may be harmful to the local biological environment and may also accelerate device degradation. A design strategy is reported for the development of redox-active organic semiconductors based on donor–acceptor copolymers that prevents the formation of H 2 O 2 during device operation. This study elucidates the previously overlooked side-reactions between redox-active conjugated polymers and molecular oxygen in electrochemical devices for bioelectronics, which is critical for the operation of electrolyte-gated devices in application-relevant environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microscopic activated dynamics theory of the shear rheology and stress overshoot in ultradense glass-forming fluids and colloidal suspensions

We formulate a particle and force level, activated dynamics-based statistical mechanical theory for the continuous startup nonlinear shear rheology of ultradense glass-forming hard sphere fluids and colloidal suspensions in the context of the elastically collective nonlinear Langevin equation approach and a generalized Maxwell model constitutive equation. Activated structural relaxation is described as a coupled local-nonlocal event involving caging and longer range collective elasticity which controls the characteristic stress relaxation time. Theoretical predictions for the deformation-induced enhancement of mobility, the onset of relaxation acceleration at remarkably low values of stress, strain, or shear rate, apparent power law thinning of the steady-state structural relaxation time and viscosity, a nonvanishing activation barrier in the shear thinning regime, an apparent Herschel–Buckley form of the shear rate dependence of the steady-state shear stress, exponential growth of different measures of a yield or flow stress with packing fraction, and reduced fragility and dynamic heterogeneity under deformation were previously shown to be in good agreement with experiments. The central new question we address here is the defining feature of the transient response—the stress overshoot. In contrast to the steady-state flow regime, understanding the transient response requires an explicit treatment of the coupled nonequilibrium evolution of structure, elastic modulus, and stress relaxation time. We formulate a new quantitative model for this aspect in a physically motivated and computationally tractable manner. Theoretical predictions for the stress overshoot are shown to be in good agreement with experimental observations in the metastable ultradense regime of hard sphere colloidal suspensions as a function of shear rate and packing fraction, and accounting for deformation-assisted activated motion appears to be crucial for both the transient and steady-state responses.

Mechanics↗
DOE OSTI2024

Grid connection barriers to renewable energy deployment in the United States

Bulk-power grid connection is an emerging bottleneck to the entry of wind, solar, and storage but has been understudied due to a lack of data. We create and analyze two novel interconnection datasets with more than 38,000 project-level observations that provide new information documenting interconnection challenges in the United States. Active grid connection requests are more than double the total installed capacity of the US power plant fleet (2,600 vs. 1,280 GW). The time required to secure a connection has increased by 70% over the last decade, and withdrawal rates remain high at 80%, suggesting a constrained transmission system that jeopardizes energy transition targets. Wide distributions of interconnection costs indicate the inherent uncertainty of the interconnection process. Interconnection requests that identify large transmission upgrades tend to withdraw from the process. These findings suggest the need for interconnection reforms, tighter links between long-term transmission planning and project-level interconnection processes, and more interconnection outcome transparency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

The first high-redshift cavity power measurements of cool-core galaxy clusters with the International LOFAR Telescope

Radio-mode feedback associated with the active galactic nuclei (AGNs) at the cores of galaxy clusters injects a large amount of energy into the intracluster medium (ICM), offsetting radiative losses through X-ray emission. This mechanism prevents the ICM from rapidly cooling down and fueling extreme starburst activity as it accretes onto the central galaxies, and it is therefore a key ingredient in the evolution of galaxy clusters. However, the influence and mode of feedback at high redshifts (z ∼ 1) remains largely unknown. Low-frequency sub-arcsecond-resolution radio observations taken with the International LOFAR Telescope have demonstrated their ability to assist X-ray observations with constraining the energy output from the AGNs (or “cavity power”) in galaxy clusters, thereby enabling research at higher redshifts than before. In this pilot project, we tested this hybrid method on a high-redshift (0.6 < z < 1.3) sample of 13 galaxy clusters for the first time with the aim of verifying the performance of this method at these redshifts and providing the first estimates of the cavity power associated with the central AGN for a sample of distant clusters. We were able to detect clear radio lobes in three out of 13 galaxy clusters at redshifts of 0.7 < z < 0.9, and we used these detections in combination with ICM pressures surrounding the radio lobes obtained from standard profiles to calculate the corresponding cavity powers of the AGNs. Combining our results with the literature, the current data appear to suggest that the average cavity power peaked at a redshift ofz ∼ 0.4 and slowly decreases toward higher redshifts. However, we require more and tighter constraints on the cavity volume and a better understanding of our observational systematics to confirm any deviation of the cavity power trend from a constant level.

Astronomy & Astrophysics↗
DOE OSTI2024

Active learning approach to simulations of strongly correlated matter with the ghost Gutzwiller approximation

Quantum embedding (QE) methods such as the ghost Gutzwiller approximation (gGA) offer a powerful approach to simulating strongly correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly correlated materials. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction

Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure–property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Concept Design of Active Shielding for Dynamic Wireless Charging of Light-duty EV

Dynamic wireless charging of electric vehicles is a flexible and state of the art charging technology with the potential capability of enabling fully automated in-motion charging. With charging power increasing to more than 100 kW for light duty vehicles, electromagnetic field (EMF) emission becomes a critical challenge. Due to the high costs of ferrite materials, this paper proposes an active shielding solution with multiple canceling coils installed on the ground side to supplement ferrite passive shielding to ensure electromagnetic safety. Two canceling coils are designed on two sides of the ground side coil. The canceling coils are small in size and 180 degree opposite in phase to the ground coil. Simulation and modeling shows that the canceling coils can reduce EMF emission from 37.2 µT to 18.2 µT at 0.8 m during 100 kW operation with only 2.5% of ground side current flowing in the canceling coils. These results have been preliminarily verified by inductance measurements and magnetic field measurement at 1.1 m distance without canceling coils. By further increasing the canceling coils’ currents to 4%, EMF emission can also be mitigated at 200 kW, although the canceling coils’ shape, position, and phase angle can be further optimized to improve the three-dimensional field distribution.

25 ENERGY STORAGE↗
DOE OSTI2020

The role of collective elasticity on activated structural relaxation, yielding, and steady state flow in hard sphere fluids and colloidal suspensions under strong deformation

In this work, we theoretically study the effect of external deformation on activated structural relaxation and aspects of the nonlinear mechanical response of glassy hard sphere fluids in the context of elastically collective nonlinear Langevin equation theory. This microscopic force-based approach describes activated relaxation as a coupled local–nonlocal event involving caging and longer range collective elasticity, with the latter becoming more important and ultimately dominant with increasing packing fraction under equilibrium conditions. The central new question we address is how this physical picture of activated relaxation, and the relative importance of local caging vs collective elasticity physics, depends on external deformation. Theoretical predictions are presented for deformation-induced enhancement of mobility, the onset of relaxation speed up at remarkably low values of stress, strain, or shear rate, apparent power law thinning of the steady state structural relaxation time and viscosity, a non-vanishing activation barrier in the shear thinning regime, an apparent Herschel–Bulkley form of the rate dependence of the steady state shear stress, exponential growth of different measures of a dynamic yield or flow stress with the packing fraction, and reduced fragility and dynamic heterogeneity under deformation. The results are contrasted with experiments and simulations, and qualitative or better agreement is found. An overarching conclusion is that deformation strongly reduces the importance of longer range collective elastic effects relative to the local caging aspect for most, but not all, physical questions, with deformation-dependent fragility and dynamic heterogeneity phenomena being qualitatively sensitive to collective elasticity. Overall, nonlinear rheology is predicted to be a more local problem than quiescent structural relaxation, albeit with deformation-modified activated processes still important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

On hybrid scenarios in KSTAR

We report the status of hybrid scenario experiments in Korea Superconducting Tokamak Advanced Research (KSTAR). The hybrid scenario is defined as stationary discharges with ${\beta _{\rm{N}}}$ $ \geqslant $ 2.4 and ${H_{89}}$ $ \geqslant $ 2.0 at ${q_{95}}$ < 6.5 without or with very mild sawtooth activities in KSTAR. It is being developed towards reactor-relevant conditions. High performance of ${\beta _{\rm{N}}} \lesssim 3.0$, ${H_{89}} \lesssim 2.4$ and G-factor ($ \equiv {\beta _{\rm{N}}}{H_{89}}/q_{95}^2$) $ \lesssim 0.4$6 has been achieved and sustained for $ \gtrsim 40{{\tau }_{\text{E}}}$ at ne/nGW ~ 0.7 with heating power of $ \lesssim $5 MW. Some KSTAR hybrid discharges exhibit a unique feature of a slow transition from conventional H-mode to hybrid mode after the third neutral beam injection. The reason for the confinement enhancement is extensively studied in this transition period of a representative discharge exhibiting a common feature of KSTAR hybrid scenarios. 0D performance analysis with magnetohydrodynamic activities, 1D kinetic profile dynamics, power balance analysis, linear gyro-kinetic analysis and edge pedestal stability analysis were conducted. The enhancement is thought to be from both the core and the pedestal. The improvement in the core region of the ion energy channel is observed from the linear gyro-kinetic analysis considering the electromagnetic, the fast ion, the Shafranov shift, ${\it{\omega }_{E \times B}}$, and the magnetic shear effect. The electromagnetic finite $\beta $ stabilisation plays a role in the inner core region at ${\rho _{\rm{tor}}}\sim0.35$ together with the fast ion effect. The alpha stabilisation effect is also found at ${\rho _{\rm{tor}}}\sim0.5$. ${\omega _{E \times B}}$, which could reduce the linear growth of the ion temperature gradient mode in the outer core region at ${\rho _{\rm{tor}}}\sim0.5 - 0.7$ with the highest contribution from the toroidal rotation. Furthermore, regarding the improvement in the pedestal, Shafranov shift broadens the stability boundary of the pedestal in support of the diamagnetic effect. The pedestal height and width could be reproduced by the EPED model, while a realistic current profile is used to calculate the internal inductance for Shafranov shift. Based on these findings, a comprehensive confinement enhancement mechanism has been proposed by considering the core-edge interplay.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

TASTI-GRID: a holistic view of Florida's grid resilience opportunities

In December of 2025, the American Society of Civil Engineers updated Florida’s infrastructure grade to a ”C+” – reflecting overall investments in recent years. With the recommendation to further strengthen the electric grid and establish consistent building standards, extreme weather, aging infrastructure, and population in-migration are still compounding Florida’s grid vulnerabilities. Key challenges related to hurricane and other tropical & marine weather events are not unique to Florida. However, the state’s topography, location, demographics, and variation among rural and urban centers for tourism and industry, demonstrate the need for unique approaches to advancing critical infrastructure resilience, particularly for large concentrations of elderly residents or in areas of historic underinvestment. Florida’s grid asset advancements are credited with a reduction in average power outage durations. Yet, improvements since 2021 have set the stage for future resilience activities – as modernization efforts have greatly improved data collection – enabling better understanding of vulnerabilities and trends across counties and localities (particularly in Central Florida).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Methane Emissions Produced by Pneumatic Devices and Produced Water Tanks on Natural Gas Wellsites

In recent years, more scrutiny has been placed on the release of greenhouse gasses (GHGs) by the oil and gas industry in North America as climate concerns increase. In the coming years, stricter regulations proposed by the Environmental Protection Agency (EPA) concerning the release of GHGs will further direct attention to GHG emissions quantification, modeling, and overall reduction. Active natural gas wellsites employ production equipment that routinely emits methane during normal operation. Production equipment often includes, but is not limited to, gas production units (GPUs), pneumatic controllers (PCs) and actuators powered by produced natural gas, and produced water storage tanks. This work details direct methane emission measurements taken at active natural gas wellsites originating from intermittent pneumatic controllers and produced water storage tanks. Efforts to model methane flows originating from these sources are also detailed, and those results are compared to direct measurement. The direct methane emissions measurement campaign collected flow data from two active dry wellsites in the Marcellus shale region. These measurement campaigns captured, in total, several weeks of continuous data from these sites. Data included multiple pneumatic device vent flowrate channels, a single channel monitoring total emissions vented from the produced water storage tanks, and corresponding site weather data. Periodic composition measurements were taken from flows vented from produced water tanks. Computational modeling efforts attempted to mimic a given site’s methane emissions behavior given limited input parameters. Direct measurement data and simulation data yielded, through reduction and analysis, apparent methane emissions factors, statistics on pneumatic device actuation events, important relationships between GPU parameters and pneumatic device actuation events, produced water storage tank vent flows and flow compositions, and parameters most contributing to the magnitude and frequency of these fugitive methane emissions. Measurements, considering data from a 72hr period, produced PC emissions factors from 0.008 to 0.03 SCFH per device with an average device emitting 0.026 SCFH at the first site. The second site produced PC emissions factors spanning from 0.05 to 25.47 SCFH with an average device emitting 1.93 SCFH not including the 25.47 SCFH device, which was assumed to be malfunctioning. Two unique water tank emission measurements were made, 618 g CH4 per hour and 32.6 g CH4 per hour. On a water basis, the water tank emissions factors were 0.66 and 0.042 kg CH4 per bbl of water, respectively.

03 NATURAL GAS↗