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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assessing the impact of human mobility to predict regional excess death in Ecuador

Abstract COVID-19 outbreaks have had high mortality in low- and middle-income countries such as Ecuador. Human mobility is an important factor influencing the spread of diseases possibly leading to a high burden of disease at the country level. Drastic control measures, such as complete lockdown, are effective epidemic controls, yet in practice one hopes that a partial shutdown would suffice. It is an open problem to determine how much mobility can be allowed while controlling an outbreak. In this paper, we use statistical models to relate human mobility to the excess death in Ecuador while controlling for demographic factors. The mobility index provided by GRANDATA, based on mobile phone users, represents the change of number of out-of-home events with respect to a benchmark date (March 2nd, 2020). The study confirms the global trend that more men are dying than expected compared to women, and that people under 30 show less deaths than expected, particularly individuals younger than 20 with a death rate reduction between 22 and 27%. The weekly median mobility time series shows a sharp decrease in human mobility immediately after a national lockdown was declared on March 17, 2020 and a progressive increase towards the pre-lockdown level within two months. Relating median mobility to excess deaths shows a lag in its effect: first, a decrease in mobility in the previous two to three weeks decreases excess death and, more novel, we found an increase of mobility variability four weeks prior increases the number of excess deaths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Control simulations of many-body quantum systems by a synergism of discrete real-time learning and optimal control theory

We present a self-consistent algorithm for optimal control simulations of many-body quantum systems. The algorithm features a two-step synergism that combines discrete real-time machine learning (DRTL) with Quantum Optimal Control Theory (QOCT) using the time-dependent Schrödinger equation. Specifically, in step (1), DRTL is employed to identify a compact working space (i.e., the important portion of the Hilbert space) for the time evolution of the many-body quantum system in the presence of a control field (i.e., the initial or previously updated field), and in step (2), QOCT utilizes the DRTL-determined working space to find a newly updated control field for a chosen objective. Steps 1 and 2 are iterated until a self-consistent control objective value is reached such that the resulting optimal control field yields the same targeted objective value when the corresponding working space is systematically enlarged. Furthermore, to demonstrate this two-step self-consistent DRTL-QOCT synergistic algorithm, we perform optimal control simulations of strongly interacting 1D as well as 2D Heisenberg spin systems. In both scenarios, only a single spin (at the left end site for 1D and the upper left corner site for 2D) is driven by the time-dependent control fields to create an excitation at the opposite site as the target. It is found that, starting from all spin-down zero excitation states, the synergistic method is able to identify working spaces and convergence of the desired controlled dynamics with just a few iterations of the overall algorithm. In the cases studied, the dimensionality of the working space scales only quasi-linearly with the number of spins.

Artificial neural networks↗
DOE OSTI2024

Final Technical Report

The capture of CO2 and its simultaneously conversion to useful chemical fuels driven by solar energy represents one of the best solutions to resolve our growing energy and environmental concerns. The most critical challenge to this endeavor is the rational design of a photocatalytic architecture that can effectively couple a given photosensitizer (PS) with an appropriate catalyst, thereby enabling efficient photosensitization of a multi-electron reduction catalysis. This research program aims to address this challenge using an interdisciplinary approach that combines innovative material design and synthesis, fundamental mechanistic studies, and photocatalytic performance evaluation. The strategies include 1) constructing and investigating a novel class of 2D COF hybrid photocatalysts with an effective photoactive organic building block as PS and a precisely incorporated CO2 reduction molecular catalyst (MC); and 2) mechanistic origins of CO2 photoreduction using a set of complementary time-resolved and in situ spectroscopic techniques. The novelty of the proposed hybrid system lies in the unprecedented combination of the unique advantage of porous crystalline COF PS with the precise catalytic function of MC for photocatalytic CO2 reduction. In the periods of the support (09/01/2019-12/31/2022), we have made research progress in four projects: 1) Exploring 2D COFs with incorporated Mn complex for light driven CO2 reduction; 2) The dependence of excited state and charge transfer dynamics on monomer structure of 2D COFs; and 3) Control over Charge Separation by Imine Structural Isomerization in Covalent Organic Frameworks with Implications on CO2 Photoreduction; and 4) The impact of monomer structure on the photoluminescence properties of COFs. We found that both monomer structure and linker chemistry can effectively impact the excited state dynamics, charge transfer properties, and photoluminescence quantum yields, the important properties that dictate their applications in photocatalysis. In addition, we found that the direction of imine linker determines charge transfer direction and thus controls the types of catalytic reactions (e.g. water oxidation or CO2 reduction reactions). The result from these fundamental studies provides important information for correlating the structure of the COF photocatalysts with their photophysical properties and catalytic functions, paving the way for their novel application in photocatalysis. We expect that our findings will contribute to addressing current shortcomings of semiconductor- and molecular-based photocatalytic systems that suffer from inefficient light harvesting and charge separation and poor adsorption and activation of reactants. In turn, this research will contribute towards the development of novel photocatalytic systems for CO2 reduction to generate renewable chemical fuels and simultaneously address the problem of mitigating climate change due to CO2 accumulation. In addition, the experimental approaches employed in this research can be easily transferred to other energy technologies and are expected to broadly impact fields involving photocatalysis, optoelectronic devices, and solar energy conversion. The proposed research has also been integrated with educational activities and serve as a basis to raise awareness around the critical issues of global energy production and consumption, and to develop the next generation of solar energy scientists.

14 SOLAR ENERGY↗
DOE OSTI2025

Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest

Abstract. Wind farm flow control is a strategy to increase the efficiency and therefore lower the levelized cost of energy of a wind farm. This is done using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the flow behind the turbine, affecting downstream turbines in the farm. Two inherently different wind farm flow control methods have been identified in the literature: wake steering and wake mixing. This paper focuses on comparing the turbine quantities of interest between these methods for a simple two-turbine wind farm setup, while a companion article (Brown et al., 2025) focuses on the wake quantities of interest for a single wind turbine setup. Both papers use the same set of wind farm simulations based on high-fidelity large-eddy simulations (LESs) coupled with OpenFAST turbine models. First, precursor simulations are executed in order to match wind conditions measured with lidars in an offshore wind farm off the east coast of the USA. These measurements show general wind conditions that exhibit substantially higher vertical wind shear and veer than any of the LES studies performed with wind farm flow control strategies currently available in the literature. The precursors are used to evaluate the effectiveness of the control methods. In the LES, the wind veer leads to highly skewed wakes, which have considerable influence on the power uplift of wind farm flow control strategies. In addition to a baseline controller, four different control strategies, each of which uses either pitch or yaw control, are performed on the upstream turbine of a simple two-turbine wind farm. Assuming that the wind direction is known and constant over time, the simulations show that wake steering is generally the superior wind farm flow control strategy, considering both wind farm power production and turbine damage equivalent loads when substantial wind veer is present. This result is consistent over different wind speeds and wind directions. On the other hand, for similar wind conditions with lower veer, wake mixing was found to yield the highest power production, although at the expense of generally higher loads. This leads us to conclude that the effect of wind veer, which has so far not usually been considered, can not be neglected when determining the optimal wind farm flow control strategy.

17 WIND ENERGY↗
DOE OSTI2021

Suppression of MHD modes with active phase-control of probe-injected currents

Abstract Active phase-control of probe-injected current is shown to both suppress and amplify long-wavelength rotating magnetohydrodynamic instabilities in the HBT-EP tokamak. Four probes are connected in quadrature and energized to drive non-axisymmetric currents through the edge of the tokamak, creating magnetic perturbations comparable to previously-studied saturated kink modes or resonant magnetic perturbations that are generated by an external control coil array. Measurements of the magnetic perturbations from the probe-injected currents determine a set of current-carrying helical filaments used to model active feedback control of resistive wall modes. These experiments suggest current-injection feedback may be an effective alternative to external control coils for control of RWMs and other long-wavelength kink-like modes at the edge of tokamaks.

Physics↗
DOE OSTI2023

Application of NEAMS Multiphysics Framework for Species Tracking in Molten Salt Reactors

This report from Idaho National Laboratory (INL) summarizes the key modeling and simulation activities conducted under the Department of Energy (DOE) Molten Salt Reactor (MSR) Campaign during the Fiscal Year 2023 (FY23). The focus of the work was to leverage state-of-the-art modeling capabilities from the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes to enable novel multiphysics and multiscale modeling and simulation of MSRs. Through collaboration with NEAMS code developers, advanced multiphysics analysis capabilities for MSR systems were demonstrated by coupling depletion, thermal-hydraulics, and thermochemistry into an innovative framework for chemical species transport in MSRs. As a result, the framework can track nuclides throughout their lifetimes in the core, from production (depletion) to advection throughout the salt volume (thermal-hydraulics) and off-gassing or precipitation outside of the salt (thermochemistry). This work supports the near-term deployment of MSRs by integrating the synergistic efforts between the DOE’s MSR Campaign and NEAMS program. The resulting framework will help better connect system design modelers with experimentalists to better understand and predict complex physical behaviors in MSRs. Researchers and MSR developers alike can now leverage these new modeling and simulation capabilities to perform novel analyses with applications including: • MSR dynamics during normal operational transients and accident scenarios • Off-gas system design and performance for fuel cycle and depletion analysis • Corrosion and active chemistry control for reactor component health and lifetime determination • Source term, decay heat and activity determination in accident scenarios • Special nuclear material accountancy and chemical forensic analysis for safeguards • Digital twin development of experiments and experimental reactor demonstrations • Measurement requirements for instrumentation and control design • Uncertainty and sensitivity analysis of missing data to inform future experimental data collection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Distributed control of electric power grids

A method for determining power output levels of a plurality of nodes in an electric power system includes receiving, at a first node of the plurality of nodes, voltage information and multipliers of all neighboring nodes of the first node within the electric power system, determining, by the first node, a local power generation and a local voltage using the voltage information and the multipliers of the neighboring nodes and distributing the local power generation and the local voltage to the neighboring nodes, determining, by the first node, an estimated voltage of each of the neighboring nodes and distributing the estimated voltage to each of the neighboring nodes, and updating, by the first node, a local multiplier using the voltage information received from the neighboring nodes and the estimated voltage of each of the neighboring nodes determined by the node.

Ghosh, Soumyadip↗
DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗
DOE OSTI2021

Kinetically Controlled Linker Binding in Rare Earth-2,5-Dihydroxyterepthalic Acid Metal–Organic Frameworks and Its Predicted Effects on Acid Gas Adsorption

In the pursuit of highly stable and selective metal–organic frameworks (MOFs) for the adsorption of caustic acid gas species, an entire series of rare earth MOFs have been explored. Each of the MOFs in this series (RE-DOBDC; RE = La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu; DOBDC = 2,5-dihydroxyterepthalic acid) was synthesized in the tetragonal space group I4/m. Crystallized MOF samples, specifically Eu-DOBDC, were seen to have a combination of monodentate and bidentate binding when synthesized under typical reaction conditions, resulting in a contortion of the structure. However, extended crystallization times determined that this binding is kinetically controlled and that the monodentate binding option was crystallographically eliminated by extended reaction times at higher temperatures. Furthermore, this series allows for the direct study of the effect of the metal center on the structure of the of the MOF; herein, the lanthanide metal ionic radii contraction across the periodic table results in a reduction of the MOF pore size and lattice parameters. Scanning electron microscopy–energy-dispersive spectroscopy was used to investigate the stages of crystal growth for these RE-DOBDC MOFs. All MOFs, except Er-DOBDC had a minimum of two stages of growth. These analogues were demonstrated by analysis of neutron diffraction (PND) to exhibit a cooperative rotational distortion of the secondary building unit, resulting in two crystallographically distinct linker sublattices. Computational modeling efforts were used to show distinct differences on acid gas (NO 2 and SO 2 ) binding energies for RE-DOBDC MOFs when comparing the monodentate/bidentate combined linker with the bidentate-only linker crystal structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Randomized Sketching Algorithms for Low-Memory Dynamic Optimization

This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

MODELING AND PARAMETRIC STUDY OF END-GAS AUTOIGNITION TO ALLOW THE REALIZATION OF ULTRA-LOW EMISSIONS, HIGH-EFFICIENCY HEAVY-DUTY SPARK-IGNITED NATURAL GAS ENGINES

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the low emission limits and the wide range of chemical reactivity in pipeline-quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its Methane Number (MN), which is analogous to the Octane Number for liquid SI fuels, can span from 65 to 95. In order to realize diesel-like efficiencies, SI NG engines must be designed to operate at high Brake Mean Effective Pressures (BMEP), near or beyond knock limits, over a wide range of fuel reactivity. This requires a deep understanding of the combustion-engine interactions pertaining to flame propagation and End-Gas Autoignition (EGAI), i.e., the autoignition of the unburned gas (end gas) ahead of the flame front. However, EGAI, if controlled, provides an opportunity to increase SI NG engine efficiency by increasing the combustion rate and the total fraction of burned fuel, mitigating the effects of the slow flame speeds characteristic of natural gas fuels, which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, to realize diesel-like efficiencies and ultra-low emissions on SI NG engines, this work proposes the study of the main parameters influencing the modeling and prediction of NG EGAI to allow for its control. In this work, a novel EGAI detection and onset determination method was developed to reliably quantify EGAI for data analysis and engine control. The new method allowed the prediction of EGAI on SI NG engines without the need to use engine- and operating-condition-dependent thresholds and reduced the error in quantifying the fraction of the total energy released by the EGAI event by up to 40%pts. One- and three-dimensional engine models were then developed to study the engine/fuel interactions that lead to NG EGAI and its performance benefits. These models, although having decent agreement with experimental data, showed the need to account for NOx chemistry when predicting NG EGAI due to a consistently later prediction of the EGAI onset (~1.65 crank-angle degrees) and thus, a new reduced chemical mechanism for real NG fuels was developed containing NOx chemistry. The new reduced mechanism improved the EGAI onset prediction agreement to within ±0.5 crank-angle degrees and decreased simulation time during combustion by nearly 50% when using the further reduced AREIS50NOx chemical mechanism. These models were then used to study the role of NG composition on EGAI, evaluate the engine/fuel interactions leading to NG EGAI, and perform engine optimization while leveraging EGAI to increase thermal efficiency. Piston design optimization combined with a Controlled EGAI (C-EGAI) combustion mode allowed a Heavy-Duty (HD) SI NG engine to operate at diesel-like efficiencies, i.e., Brake Thermal Efficiency (BTE) ≥44%. Experimental and modeling data analysis revealed that earlier and faster heat release increases combustion efficiency by an average of 1%pts, increases work transferred to the piston resulting in a decrease in exhaust losses by 50% depending on the engine operating condition while slightly increasing heat losses. Finally, the simulation results revealed an opportunity to further enhance the BTE (up to 50%) by enabling C-EGAI combustion at leaner conditions, λ=1.4-1.6.

Bestel, Diego Bernardi↗
DOE OSTI2022

Aggregate attack surface management for network discovery of operational technology

Interconnectivity has become a substratum of technology as the benefits of data-driven functionality are being realized in nearly all industries. Increased connectivity of Operational Technology (OT) exacerbates cyber risks because Industrial Control Systems (ICS) are becoming exposed to the Internet. These exposures are often done inadvertently through misconfigurations as additional network devices come online. Attack surface management (ASM) platforms can be used to identify vulnerabilities by performing external network discovery over the Internet using web spiders. These web spiders enable big data analytics of Internet of Things (IoT) devices as identifiable information of Internet-exposed equipment are archived in searchable databases that are made publicly available. There are a multitude of ASM service providers on the market. Here, this study was conducted to evaluate several commonly known tools to determine the aggregate attack surface of control systems. Queries were crafted by targeting commonly known manufacturers and communication protocols found in OT networks. Identified devices were that categorized based on technology types. Each query was replicated between several tools to target identical ICS equipment. Findings in this paper suggested a significant variance in the exposures discovered by each tool, but unique contributions were identified for each tool when a merged attack surface was derived. Therefore, all tools should be used in aggregate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted–Evans–Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. Here, we explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

42 ENGINEERING↗
DOE OSTI2024

Concept of Operations for Advanced Reactor Spent Nuclear Fuel Management

This presentation presents a preliminary description of a concept of operations to incorporate advanced-reactor spent nuclear fuel (SNF) into an integrated waste-management system (IWMS). The evaluation includes SNF from four advanced-reactor concepts with the following fuel types: (1) small modular reactors using oxide fuels, (2) tristructural-isotropic (TRISO) fuels, (3) metallic fuels, and (4) fuel salts. To provide context for the proposed concept of operations for advanced reactors, a comparison is made with traditional light-water reactors (LWRs) to identify potential gaps in the IWMS. The technical differences between advanced reactors and LWRs are assessed to determine the feasibility of managing advanced-reactor waste streams using existing operations and technology. This presentation emphasizes fuel types from Advanced Reactor Demonstration Program reactors: Xe 100, which uses TRISO fuel and Natrium, with its metallic fuels while also analyzing management options for molten-salt reactors and advanced light-water reactors (ALWRs). Understanding the storage, transportation, and disposal requirements of SNF is dependent on both the quantity and characteristics of the SNF generated by nuclear reactors. This presentation provides a high level overview, comparing the anticipated concept of operations for different SNFs from advanced reactors. The IWMS includes at-reactor storage, transportation, potential off-site storage, potential treatment, and disposition. To assess the potential effect of advanced-reactor concept of operations on the IWMS, estimates were made for fuel characteristics that contribute to storage, transportation, disposal, and possible treatment of advanced-reactor SNF. These include canister heat load, dose rates, and criticality-control limits, which are important for determining the condition and configuration of the advanced reactor SNF. At-reactor storage of LWR SNF traditionally involves a spent-fuel pool (SFP) before transfer to an independent spent-fuel storage installation. However, some advanced-reactor concepts, particularly those using TRISO and salt fuels, do not anticipate the use of an SFP. This difference in at-reactor storage could impact the IWMS. Additionally, transportation of advanced-reactor SNF may include additional processes tied to potential off gassing, and transportation of microreactor SNF may occur within the reactor vessel. Some advanced-reactor SNF could also undergo treatment to meet requirements of an acceptable waste form for disposition, and the treatment location will be a major contributor to efficiently performing IWMS responsibilities. Moreover, the quantity of SNF generated is an important consideration for IWMS because it could affect the size of the transportation fleet and potential off-site storage requirements. Additionally, volume and heat load are the primary drivers for SNF disposition. This presentation compares potential packaging options for advanced reactor SNF with traditional LWR packaging to provide a high-level comparison for the needs of the IWMS for advanced-reactor SNF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Integrating collapsible plasmonic gaps on near-field probes for polarization-resolved mapping of plasmon-enhanced emission in 2D material

Scanning near-field optical microscopy (SNOM) is an important technique used to study the optical properties of material systems at the nanoscale. In previous work, we reported on the use of nanoimprinting to improve the reproducibility and throughput of near-field probes including complicated optical antenna structures such as the ‘campanile’ probe. However, precise control over the plasmonic gap size, which determines the near-field enhancement and spatial resolution, remains a challenge. Here, we present a novel approach to fabricating a sub-20 nm plasmonic gap in a near-field plasmonic probe through the controlled collapse of imprinted nanostructures using atomic layer deposition (ALD) coatings to define the gap width. The resulting ultranarrow gap at the apex of the probe provides a strong polarization-sensitive near-field optical response, which results in an enhancement of the optical transmission in a broad wavelength range from 620 to 820 nm, enabling tip-enhanced photoluminescence (TEPL) mapping of 2-dimensional (2D) materials. We demonstrate the potential of this near-field probe by mapping a 2D exciton coupled to a linearly polarized plasmonic resonance with below 30 nm spatial resolution. This work proposes a novel approach for integrating a plasmonic antenna at the apex of the near-field probe, paving the way for the fundamental study of light-matter interactions at the nanoscale.

Zhou, Junze (ORCID:000000020682767X)↗
DOE OSTI2024

Calibration and Measurement techniques in the LLRF systems of the Fermilab PIP-II Linac

he PIP-II Accelerator is an 800 MeV superconducting Linac in the injection chain of the Fermilab accelerator complex. The LLRF systems are a based on two different hardware platforms controlling a variety of cavity types and resonance control systems including temperature, pneumatic and piezzo tuners. The various calibrations required prior to beam operation include, signal power, gradient, amplifier characterization, cavity Q measurement and detune constants. Measurements such as piezzo capacitance, cavity piezo transfer function help in determining tuner health and in devising microphonics control strategies. These measurement and calibration methods of the PIP-II LLRF system are discussed here.

Varghese, P.↗
DOE OSTI2024

Transport from electron-scale turbulence in toroidal magnetic confinement devices

Plasma transport driven by turbulence ultimately determines the energy confinement performance of controlled fusion devices regardless of their confinement schemes and configurations. A large variety of plasma instabilities have been proposed for driving turbulence responsible for anomalous plasma transport beyond classical/neoclassical transport due to collisions. Although ion-scale turbulence usually dominates due to its large eddy size and saturation level, electron-scale turbulence has been recognized to be important in regions where ion-scale turbulence is suppressed (e.g., in internal transport barrier and in spherical tokamak H-mode plasmas) or is close to marginality. Electron-scale turbulence has been shown to nonlinearly interact with ion-scale turbulence, which modifies the dynamics of both and affects the resulting plasma transport, particularly when ion-scale instability is weakly driven. In this review paper, we focus on electron-scale turbulence that is believed to operate in magnetically confinement fusion devices and aim to provide a review of theoretical, numerical, and experimental developments in understanding electron-scale turbulence and its role in driving anomalous plasma turbulence. In particular, we focus on the electrostatic electron temperature gradient (ETG) mode which is the most widely recognized plasma instability underlying electron-scale turbulence observed in magnetically confined plasmas. Finally, we note that there are other less studied instabilities that might be responsible for observed electron-scale turbulence, most notably ubiquitous mode, and short-wavelength ion temperature gradient (SWITG) mode, which will be briefly touched on in this review.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Methane flux from transplanted soil monoliths depends on moisture, but not origin

Soils both produce and consume methane (CH 4 ), a potent greenhouse gas that contributes to climate change. In coastal forests, upland soils are shifting from being CH 4 sinks to sources as sea levels rise, increasingly flooding soils with little prior inundation history. Ecosystem CH 4 budgets are highly uncertain due in part to the difficulty in separating fluxes measured at the soil surface into individual production and consumption processes which are likely to have different responses to future environmental conditions. Here, we measured growing season CH 4 fluxes from soil monoliths transplanted four years prior along an inundation and salinity gradient to determine how changes in abiotic conditions control CH 4 flux rates. To parse net fluxes measured at the soil surface into their component gross rates, we paired field measurements with a stable isotope pool dilution incubation of surface soils. Throughout the growing season, net soil surface CH 4 flux was positively correlated with soil moisture (p < 0.01), with lowland-located soils tending towards CH 4 sources (mean 0.349 ± 1.11 mg CH 4 -C m -2 hr -1 , error is standard deviation) and upland-located soils tending towards CH 4 sinks (-0.003 ± 0.003 mg CH 4 -C m -2 hr -1 ). Transplanted soils’ fluxes were statistically identical to their native neighbors once microtopography-driven differences in soil moisture were controlled for. The pool dilution experiment revealed that production and consumption rates were similar in upland and lowland surface soils (2.82 ± 3.29 µmol CH 4 g -1 dry soil d -1 production, 3.47 ± 2.03 µmol CH 4 g -1 dry soil d -1 consumption), indicating the majority of production likely occurs at depth in lowland soils. Both gross and net fluxes from transplanted soils showed no effect of soil origin after four years, suggesting low resistance of CH 4 cycling to global change drivers. Our results indicate the strength of the coastal forest CH 4 sink is likely to decrease in proportion to sea-level rise.

59 BASIC BIOLOGICAL SCIENCES↗