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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Crystal structure and shape selection in the growth of 3D metallic crystallites on layered materials: Fe on MoS 2

Nucleation and growth of supported 3D metal clusters or crystallites during deposition on MoS 2 , or on other weakly-adhering layered materials, can potentially produce diverse growth shapes, and even crystal structures differing from the bulk metal. For Fe deposition on MoS 2 , SEM and AFM observations reveal three distinct crystallite shapes. By comparison with atomistic structure models incorporating realistic Fe-MoS 2 interface structures, here we conclude that these are: triangular fcc(111) pyramids with sloped {100} side facets; bcc(110) A-frame tents with sloped {100} side facets; and bcc(110) mesas with vertical {100} and {110} side facets. The following picture is proposed for the competitive formation of clusters and crystallites with different structures: (i) small nanoclusters formed at the onset of deposition exhibit facile fluxional dynamics allowing sampling of different crystal structures and shapes; (ii) sufficient fluxionality implies a Boltzmann distribution of sampled structures, and thus coexistence of different structures follows from the demonstrated similar energies for those structures; (iii) growing clusters reach a threshold size above which the characteristic time scale for restructuring exceeds that for cluster growth. Thereafter, clusters are locked-in to a specific crystal structure and shape as revealed by imaging of larger crystallites. Despite a penalty for fcc(111) over bcc(111) pyramids based on bulk energetics, favorable surface and interface energies makes them preferable for smaller sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Walker Branch Watershed: Daily Stream Metabolism and Organic Carbon Spiraling Metrics in the West Fork of Walker Branch, Tennessee, USA, 2004-2010

This dataset contains daily metabolism estimates of gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP), in addition to organic carbon spiraling length (SOC) and mineralization velocity (VfOC) estimates at the West Fork of Walker Branch, a small headwater stream, in the Walker Branch Watershed, Tennessee, USA. Observations were made from 2004-2010 (2004-01-01 to 2010-12-31). These data were generated to assess seasonal and interannual variability in metabolism and organic carbon spiraling and to explore potential driver variables, as analyses of intra- and interannual variability in metabolism and organic carbon spiraling are currently limited, leaving knowledge gaps in the driving mechanisms of and future changes to stream metabolism and carbon processing under climate change. Additionally, measurements of discharge (Q), stream width, stream- and canopy-level photosynthetically active radiation (PAR), water temperature, and precipitation from this time frame are included. This dataset contains one data file in comma separated (*.csv) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Performance Results for Sensor Assignment Problem as Solved on a Multi-Node Cluster

An earlier report described a procedure for optimal sensor set selection and its implementation on a computational cluster. This new and innovative capability was developed to facilitate a reduction in operations staffing levels to improve plant economics. By automating surveillance and maintenance tasks through early detection of degrading sensors and equipment, staff can be more efficiently deployed. The method uses automated reasoning and domain knowledge in the form of the conservation equations to infer from plant measurements the state of equipment health. Inclusion of domain knowledge addresses the problem that exists with pure data-driven methods that there are no rigorous guidelines for determining what constitutes an adequate sensor set. Formalizing the procedure for sensor set selection as we have done results in a more reliable and explainable diagnosis of plant equipment health. Importantly, from the standpoint of the plant owner, personnel are provided with an early and explicit diagnosis of an equipment problem. That in principle automates the process and eliminates having to send personnel into the plant to find the cause as typically occurs when a data-driven method detects an anomaly. In this report we describe first results obtained using a computational cluster to solve the sensor set selection problem as framed above. The case described addresses the problem of equipment health monitoring in the high-pressure (HP) feedwater system of a pressurized light water reactor as seen through the eyes of our collaborating utility partner. Maintenance of this system can amount to millions of dollars per year if equipment health issues go undiagnosed and lead to loss of function. On examining the potential that is inherent in the installed sensor set for diagnosing equipment health degradation, it was found that greater fault resolution capability can be achieved using a sensor set that is 20 percent fewer in number. The take-away is that compared to the installed sensor set there exists a more strategic assignment of sensors that will furnish better health monitoring capability and with fewer sensors. Where the problem defies solution by manual inspection, as is the case here, one can be found by an algorithm. The solution was obtained in four hours using 30 computational cores. The HP feedwater problem as posed above illustrates the added value of approaching the sensor selection problem as one amenable to algorithmic solution. This problem is of interest to advanced reactor designers and to utilities that are setting up remote monitoring and diagnostic centers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Validation of a Custom Ball-on-Ring Apparatus and Consideration of Common Issues for Use in Further Testing (SULI Deliverables)

Ceramic materials are well-known for their high hardness and strength but are limited in their application due to low toughness and sudden failure. As a potential solution, inspiration can be taken from dental enamel nanostructure, where undulating rods cause cracks to branch or deflect, increasing the energy needed to cause total fracture of a ceramic part. Following the dental enamel structure, a novel ceramic which uses 3D printed Yttria stabilized Zirconia rods in an alumina matrix was developed. To test this bio-inspired ceramic material, a proper testing apparatus needed to be created and tested to verify its accuracy. For this project, a bespoke ball on ring testing apparatus was created and tested using both conventionally sintered alumina disks and purchased alumina disks to validate its accuracy. By comparing the Weibull distribution of rupture strengths measured by the tests to literature values, it was shown that the testing frame had a wide distribution of strength which did not align with literature values on the lower end. Through fractography, it was found that some samples fractured from the contact stress induced by the ball indenter, which could not be used to calculate rupture strength. This fracture was often linked to low stress to failure, which was initiated by a flaw on the surface near the indenter which acted as a stress concentrator. Removal of these samples from the data set increased the accuracy of the reported rupture strength values for the ceramic. Considering the equations for the magnitude of contact and flexural strength, along with observations of initiating flaws, several measures can be taken for testing the bio-inspired composite. These measures include proper polishing of both sides of the sample, reducing sample thickness, and potentially using a softer indenter material.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Non-relativistic geometry and the equivalence principle

Abstract We describe a geometric and symmetry-based formulation of the equivalence principle in non-relativistic physics. It applies both on the classical and quantum levels and states that the Newtonian potential can be eliminated in favor of a curved and time-dependent spatial metric. It is this requirement that forces the gravitational mass to be equal to the inertial mass. We identify the symmetry responsible for the equivalence principle as the remnant of time-reparameterization symmetry of the relativistic theory. We also clarify the transformation properties of the Schrödinger wave-function under arbitrary frame changes.

Astronomy & Astrophysics↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Observational window effects on multi-object reverberation mapping

ABSTRACT Contemporary reverberation mapping campaigns are employing wide-area photometric data and high-multiplex spectroscopy to efficiently monitor hundreds of active galactic nuclei (AGNs). However, the interaction of the window function(s) imposed by the observation cadence with the reverberation lag and AGN variability time-scales (intrinsic to each source over a range of luminosities) impact our ability to recover these fundamental physical properties. Time dilation effects due to the sample source redshift distribution introduce added complexity. We present comprehensive analysis of the implications of observational cadence, seasonal gaps, and campaign baseline duration (i.e. the survey window function) for reverberation lag recovery. We find that the presence of a significant seasonal gap dominates the efficacy of any given campaign strategy for lag recovery across the parameter space, particularly for those sources with observed-frame lags above 100 d. Using the Australian Dark Energy Survey as a baseline, we consider the implications of this analysis for the 4MOST/Time-Domain Extragalactic Survey campaign providing concurrent follow-up of the Legacy Survey of Space and Time deep-drilling fields, as well as upcoming programmes. We conclude that the success of such surveys will be critically limited by the seasonal visibility of some potential field choices, but show significant improvement from extending the baseline. Optimizing the sample selection to fit the window function will improve survey efficacy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Fragmentation dynamics of CS 2 dications and trications following S 2p ionization

Here, we present the results from a detailed study of the fragmentation dynamics of CS$_2^{2+}$ and CS$_2^{3+}$, formed in intense femtosecond soft x-ray pulses above the sulfur 2p edge, primarily through single core photoionization from the S 2p site, and subsequent Auger–Meitner decay(s). By combining three-dimensional velocity map imaging with covariance analysis, we determine the relative momenta of the ions produced in each two- and three-body fragmentation channel, at significantly higher ion count rates than conventional coincidence measurements. We shed new light on the wide range of fragmentation channels observed from the CS 2 dication and trication, including channels that involve ionization-induced bond formation and fragmentations producing undetected neutral cofragments. In the latter case, a “native frames” approach is used to isolate contributions from concerted and sequential fragmentations and extract dynamical information about each step of a concerted fragmentation process. While dications often fragment sequentially, the trication is dominated by concerted fragmentation. The main trication fragmentation channel into S + + C + + S + can be well-approximated by classical Coulombic simulations of the ground-state geometry distribution, reflecting both the nature of the trication potential energy surface and the rapid multiple ionization prior to substantial structural dynamics. This study demonstrates ways in which fundamental insights into the fragmentation dynamics of polycations following x-ray ionization may be extracted, which will be beneficial to future studies that employ time-resolved x-ray Coulomb explosion imaging to study ultrafast photochemistry.

Allum, Felix [Deutsches Elektronen-Synchrotron (DE↗
DOE OSTI2022

Development of a Markerless Deletion Mutagenesis System in Nitrate-Reducing Bacterium Rhodanobacter denitrificans

Rhodanobacter has been found as the dominant genus in aquifers contaminated with high concentrations of nitrate and uranium in Oak Ridge, TN, USA. The in situ stimulation of denitrification has been proposed as a potential method to remediate nitrate and uranium contamination. Among the Rhodanobacter species, Rhodanobacter denitrificans strains have been reported to be capable of denitrification and contain abundant metal resistance genes. However, due to the lack of a mutagenesis system in these strains, our understanding of the mechanisms underlying low-pH resistance and the ability to dominate in the contaminated environment remains limited. Here, we developed an in-frame markerless deletion system in two R. denitrificans strains. First, we optimized the growth conditions, tested antibiotic resistance, and determined appropriate transformation parameters in 10 Rhodanobacter strains. We then deleted the upp gene, which encodes uracil phosphoribosyltransferase, in R. denitrificans strains FW104-R3 and FW104-R5. The resulting strains were designated R3_Δupp and R5_Δupp and used as host strains for mutagenesis with 5-fluorouracil (5-FU) resistance as the counterselection marker to generate markerless deletion mutants. Further, to test the developed protocol, the narG gene encoding nitrate reductase was knocked out in the R3_Δupp and R5_Δupp host strains. As expected, the narG mutants could not grow in anoxic medium with nitrate as the electron acceptor. Overall, these results show that the in-frame markerless deletion system is effective in two R. denitrificans strains, which will allow for future functional genomic studies in these strains furthering our understanding of the metabolic and resistance mechanisms present in Rhodanobacter species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sovereign Credit Rating Processes Adapted to Critical Infrastructure Cyber Risk Assessment

United States critical infrastructure entities are increasingly targeted by motivated and capable threat actors and must be prepared to assess and treat a diverse range of cyber risks. Consequently, this necessitates some form of analytical process to evaluate risks and inform cyber security investment decisions. A potential solution for structuring cyber risk evaluation exists within the field of sovereign credit ratings – where agencies employ mature approaches that integrate quantitative and qualitative data to produce a singular value of assessment. Adapting such approaches, we present a novel criterion and methodology for measuring and communicating the likelihood element of cyber risk. The methodology is composed of three sequential phases: a quantitative baseline organized by distinct capability frames, a bounded qualitative adjustment per frame, and a greater-bounded qualitative adjustment spanning the entire process. The process culminates in publication of a cyber capability rating that communicates a critical infrastructure entity’s ability and willingness to mitigate discontinuous function due to cyberattack.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Detecting CAN Masquerade Attacks with Signal Clustering Similarity

Vehicular Controller Area Networks (CANs) are susceptible to cyber attacks of different levels of sophistication. Fabrication attacks are the easiest to administer—an adversary simply sends (extra) frames on a CAN—but also the easiest to detect because they disrupt frame frequency. To overcome time-based detection methods, adversaries must administer masquerade attacks by sending frames in lieu of (and therefore at the expected time of) benign frames but with malicious payloads. Research efforts have proven that CAN attacks, and masquerade attacks in particular, can affect vehicle functionality. Examples include causing unintended acceleration, deactivation of vehicle’s brakes, as well as steering the vehicle. We hypothesize that masquerade attacks modify the nuanced correlations of CAN signal time series and how they cluster together. Therefore, changes in cluster assignments should indicate anomalous behavior. We confirm this hypothesis by leveraging our previously developed capability for reverse engineering CAN signals (i.e., CAN-D [Controller Area Network Decoder]) and focus on advancing the state of the art for detecting masquerade attacks by analyzing time series extracted from raw CAN frames. Specifically, we demonstrate that masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle’s CAN signals (time series) and comparing the clustering similarity across CAN captures with and without attacks. We test our approach in a previously collected CAN dataset with masquerade attacks (i.e., the ROAD dataset) and develop a forensic tool as a proof of concept to demonstrate the potential of the proposed approach for detecting CAN masquerade attacks.

Moriano Salazar, Pablo↗
DOE OSTI2024

Inflaton Decay in No-Scale Supergravity and Starobinsky-like Models

We consider the decay of the inflaton in Starobinsky-like models arising from either an R+R 2 theory of gravity or N = 1 no-scale supergravity models. If Standard Model matter is simply introduced to the R+R 2 theory, the inflaton (which appears when the theory is conformally transformed into the Einstein frame) couples to matter predominantly in Standard Model Higgs kinetic terms. This will typically lead to a reheating temperature of ~3 × 10 9 GeV. However, if the Standard Model Higgs is conformally coupled to curvature, the decay rate may be suppressed and vanishes for conformal coupling ζ = 1/6. Nevertheless, the inflaton decays through the conformal anomaly, leading to a reheating temperature of the order of 10 8 GeV. The Starobinsky potential may also arise in no-scale supergravity. In this case, the inflaton decays if there is a direct coupling of the inflaton to matter in the superpotential or to gauge fields through the gauge kinetic function. We also discuss the relation between the theories and demonstrate the correspondence between the no-scale models and the conformally coupled R+R 2 theory (with ζ = 1/6).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗
DOE OSTI2022

Grid Energy Storage: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the Federal Government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the Federal Government to build more secure and diverse U.S. supply chains, including energy supply chains. To combat the climate crisis and avoid the most severe impacts of climate change, the U.S. is committed to achieving a 50 to 52 percent reduction from 2005 levels in economy-wide net greenhouse gas pollution by 2030, creating a carbon pollution-free power sector by 2035, and achieving net zero emissions economy-wide by no later than 2050. The U.S. Department of Energy (DOE) recognizes that a secure, resilient supply chain will be critical in harnessing emissions outcomes and capturing the economic opportunity inherent in the energy sector transition. Potential vulnerabilities and risks to the energy sector industrial base must be addressed throughout every stage of this transition. The DOE energy supply chain strategy report summarizes the key elements of the energy supply chain as well as the strategies the U.S. Government is starting to employ to address them. Additionally, it describes recommendations for Congressional action. DOE has identified technologies and crosscutting topics for analysis in the one-year time frame set by the Executive Order. Along with the capstone policy report, DOE is releasing 11 deep dive assessment documents, including this one, covering the following technology sectors: carbon capture materials; electric grid including transformers and high voltage direct current (HVDC); energy storage; fuel cells and electrolyzers; hydropower including pumped storage hydropower (PSH); neodymium magnets; nuclear energy; platinum group metals and other catalysts; semiconductors; solar photovoltaics (PV); and wind. DOE is also releasing two deep dive assessments on the following crosscutting topics: Commercialization and competitiveness; and cybersecurity and digital components. More information can be found at www.energy.gov/policy/supplychains.

25 ENERGY STORAGE↗
DOE OSTI2022

Direct Higgs-top CP-phase measurement with $t\overline{t}h$ at the 14 TeV LHC and 100 TeV FCC

The study of the Higgs boson’s properties is a cornerstone of the LHC and future collider programs. In this paper, we examine the potential to directly probe the Higgs-top interaction strength and CP-structure in the $t\overline{t}h$ channel with the Higgs boson decaying to bottom-quark pairs and top-quarks in the di-leptonic mode. We adopt the BDRS algorithm to tag the boosted Higgs and exploit the M 2 -assisted reconstruction to compute observables sensitive to the CP-phase at the $t\bar{t}$ rest frame, where the new physics sensitivity can be enhanced. Performing a side-band analysis at the LHC to control the continuum $t\bar{t}b\bar{b}$ background, we find that the Higgs-top strength and CP-phase can be probed up to δ κt ≲ 20% and |a| ≲ 36° at 95% CL, respectively. We also derive that a similar analysis at a 100 TeV future collider could further improve the precision to δ κt ≲ 1% and |a| ≲ 1.5° where the CP-odd observables play a crucial role, boosting the sensitivity on the CP-phase

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Improving Rare-Earth Mineral Separation with Insights from Molecular Recognition: Functionalized Hydroxamic Acid Adsorption onto Bastnäsite and Calcite

Enhancing the separation of rare-earth elements (REEs) from gangue materials in mined ores requires an understanding of the fundamental interactions driving the adsorption of collector ligands onto mineral interfaces. In this work, we examine five functionalized hydroxamic acid ligands as potential collectors for the REE-containing bastnäsite mineral in froth flotation using density functional theory calculations and a suite of surface-sensitive analytical spectroscopies. These include vibrational sum frequency generation, attenuated total reflectance Fourier transform infrared, Raman, and X-ray photoelectron spectroscopies. Differences in the chemical makeup of these ligands on well-defined bastnäsite and calcite surfaces allow for a systematic relationship connecting the structure to adsorption activity to be framed in the context of interfacial molecular recognition. Here we show how the intramolecular hydrogen bonding of adsorbed ligands requires the inclusion of explicit water solvent molecules to correctly map energetic and structural trends measured by experiments. We anticipate that the results and insights from this work will motivate and inform the design of improved flotation collectors for REE ores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Legacy Well Leakage Risk Analysis at the Farnsworth Unit Site

This paper summarizes the results of the risk analysis and characterization of the CO 2 and brine leakage potential of Farnsworth Unit (FWU) site wells. The study is part of the U.S. DOE’s National Risk Assessment Partnership (NRAP) program, which aims to quantitatively evaluate long-term environmental risks under conditions of significant geologic uncertainty and variability. To achieve this, NRAP utilizes risk assessment and computational tools specifically designed to quantify uncertainties and calculate the risk associated with geologic carbon dioxide (CO 2 ) sequestration. For this study, we have developed a workflow that utilizes physics-based reservoir simulation results as input to perform leakage calculations using NRAP Tools, specifically NRAP-IAM-CS and RROM-Gen. These tools enable us to conduct leakage risk analysis based on ECLIPSE reservoir simulation results and to characterize wellbore leakage at the Farnsworth Unit Site. We analyze the risk of leakage from both individual wells and the entire field under various wellbore integrity distribution scenarios. The results of the risk analysis for the leakage potential of FWU wells indicate that, when compared to the total amount of CO 2 injected, the highest cemented well integrity distribution scenario (FutureGen high flow rate) exhibits approximately 0.01% cumulative CO 2 leakage for a 25-year CO 2 injection duration at the end of a 50-year post-injection monitoring period. In contrast, the highest possible leakage scenario (open well) shows approximately 0.1% cumulative CO 2 leakage over the same time frame.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Coriolis forces modify magnetostatic ponderomotive potentials

It is possible to produce a ponderomotive effect in a plasma system without time-varying fields, if the plasma flows over spatial oscillations in the field. This can be achieved by superimposing a spatially oscillatory perturbation on a guide field, then setting up an electric field perpendicular to the guide field to drive flow over the perturbation. However, subtle distinctions in the structure of the resulting electric field can entirely change the behavior of the resulting ponderomotive force. Previous work has shown that, in slab models, these distinctions can be explained in terms of the polarization of the effective wave that appears in the co-moving frame. Here, we consider what happens to this picture in a cylindrical system, where the transformation to the co-moving (rotating) frame is not inertial. It turns out that the non-inertial nature of this frame transformation can lead to counterintuitive behavior, partly due to the appearance of parallel (magnetic-field-aligned) electric fields in the rotating frame even in cases where none existed in the laboratory frame. Apart from the academic interest of this study, the practical impact lies in being better able to anticipate the antenna configuration on the plasma periphery of a cylindrical plasma that will lead to optimal ponderomotive barrier formation in the interior plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗