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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Protoplast fusion in Bacillus species produces frequent, unbiased, genome-wide homologous recombination

Abstract In eukaryotes, fine-scale maps of meiotic recombination events have greatly advanced our understanding of the factors that affect genomic variation patterns and evolution of traits. However, in bacteria that lack natural systems for sexual reproduction, unbiased characterization of recombination landscapes has remained challenging due to variable rates of genetic exchange and influence of natural selection. Here, to overcome these limitations and to gain a genome-wide view on recombination, we crossed Bacillus strains with different genetic distances using protoplast fusion. The offspring displayed complex inheritance patterns with one of the parents consistently contributing the major part of the chromosome backbone and multiple unselected fragments originating from the second parent. Our results demonstrate that this bias was in part due to the action of restriction–modification systems, whereas genome features like GC content and local nucleotide identity did not affect distribution of recombination events around the chromosome. Furthermore, we found that recombination occurred uniformly across the genome without concentration into hotspots. Notably, our results show that species-level genetic distance did not affect genome-wide recombination. This study provides a new insight into the dynamics of recombination in bacteria and a platform for studying recombination patterns in diverse bacterial species.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Investigations into an unknown organism on the martian meteorite Allan Hills 84001

Examination of fracture surfaces near the fusion crust of the martian meteorite Allan Hills (ALH) 84001 have been conducted using scanning electron microscopy (SEM) and atomic force microscopy (AFM) and has revealed structures strongly resembling mycelium. These structures were compared with similar structures found in Antarctic cryptoendolithic communities. On morphology alone, we conclude that these features are not only terrestrial in origin but probably belong to a member of the Actinomycetales, which we consider was introduced during the Antarctic residency of this meteorite. If true, this is the first documented account of terrestrial microbial activity within a meteorite from the Antarctic blue ice fields. These structures, however, do not bear any resemblance to those postulated to be martian biota, although they are a probable source of the organic contaminants previously reported in this meteorite.

NASA Center JSC↗
DOE OSTI2024

Effect of Heat Treatment on Microstructure and Mechanical Property of 316L Stainless Steel Produced by Laser Powder Bed Fusion

The advanced non-light water reactor designs (Gen IV reactors), including molten salt/ very high temperature/ sodium-cooled and lead-cooled fast reactors, typically operate at higher temperatures and more extreme radiation conditions than light water reactors. An intrinsic part of the deployment and progress of Gen IV reactor designs is selecting the most suitable structural material for a specific application. Additive manufacturing (AM), a fairly new process of making physical, three-dimensional objects from a computer design file, is going to completely change the way of design, build and certify nuclear systems. It offers a range of opportunities to produce complex geometries from existing materials, offers new routes for processing of previously difficult to process materials, allows for design of new high-performance materials, and finally facilitates hybridization of dissimilar materials. This emerging technology has successfully produced cars, wind turbine blade molds and even live cells. It could also open up big opportunities for the nuclear industry to quickly deploy technologies at a fraction of the cost. So far, AM techniques have been preliminarily applied in the field of nuclear reactors, including the classical parts such as the pressure vessel of a small reactor with 508-III steel, the bottom nozzle of a fuel assembly with 304L steel, the fuel cladding with zirconium alloy and the integrated impeller of a pump and the multi-channel valve body with 316L steel [6,7]. The AM applications for operating nuclear reactors started in auxiliary plant components and have slowly migrated to metallic reactors and core components, but many of these are not safety critical components. Although many parts used for nuclear reactors have been fabricated by AM techniques, practical applications in engineering are still a long way off due to the uncertainty factors focused on the processing, material properties, analysis methods and application standards, which feeds the safety and life-cycle of the nuclear reactor. Due to rapid, repeated heating and cooling during production, a high dislocation density was present in the AM material. This microstructure feature is unstable at elevated temperature while high temperature is one of the typical operation environments for nuclear reactors. Thus, it is important to understand the thermal effect on the microstructure of AM material. The objectives of this study are to investigate the effect of heat treatment on the microstructure and mechanical properties of 316L stainless steel produced by laser powder bed fusion additive manufacturing, and to determine an appropriate heat treatment practice that will be applied to the lightweight AM lattice-structured material with the same chemistry. The heat treatment study consisted of annealing the samples at a temperature range of 800 to 1200 oC with a 50 oC increment for different times (1-24 hours), followed by vacuum or air cooling. Microstructural characterization was carried out by Scanning Electron Microscope (SEM). Grain size and crystallographic orientation were investigated by Electron Backscatter Diffraction (EBSD). Vickers hardness tests with a 0.5 kg load were employed to determine the hardness of samples after different heat treatments. After heat treatment, the random crystallographic orientation was preserved, and the volume fraction of high-angle grain boundaries (grain boundary misorientation =15 oC) remained the same. The dislocation density decreased with annealing temperature due to recovery. The fine subgrain structures in the as-printed specimen were quite stable up to 1200 oC. Minimal recrystallization was observed up to 1200 oC. Recrystallization initiated only after 8.5 hours at 1200 oC. The SEM images did not show obvious dependence of microstructure on cooling rate. The hardness of the specimens decreased with increasing annealing temperature as a result of the decrease in dislocation density. It is interesting to note that the AM material showed very similar hardness to the wrought material when annealing at similar temperature, although the microstructures are very different. Annealing at 1050 oC for 1 hour followed by air cooling was selected as the heat treatment procedure for the lattice designed lightweight AM 316L material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution Systems

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. In conclusion, the framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Parametric dependencies of resonant layer responses across linear, two-fluid, drift-MHD regimes

Non-axisymmetric magnetic fields arising in a tokamak either by external or internal perturbations can induce complex non-ideal MHD responses in their resonant surfaces while remaining ideally evolved elsewhere. This layer response can be characterized in a linear regime by a single parameter called the inner-layer Δ, which enables outer-layer matching and the prediction of torque balance to non-linear island regimes. In this work, we follow strictly one of the most comprehensive analytic treatments including two-fluid and drift MHD effects and keep the fidelity of the formulation by incorporating the numerical method based on the Riccati transformation when quantifying the inner-layer Δ. The proposed scheme reproduces not only the predicted responses in essentially all asymptotic regimes but also with continuous transitions as well as improved accuracies. In particular, the Δ variations across the inertial regimes with viscous or semi-collisional effects have been further resolved, in comparison with additional analytic solutions. The results imply greater shielding of the electromagnetic torque at the layer than what would be expected by earlier work when the viscous or semi-collisional effects can compete against the inertial effects, and also due to the intermediate regulation by kinetic Alfvén wave resonances as rotation slows down. These are important features that can alter the non-axisymmetric plasma responses including the field penetration by external fields or island seeding process in rotating tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Surface smoothing for laser powder-bed Ti-6Al-4V by a transient liquid phase

Surface roughness is the primary driver of fatigue for additively manufactured metals. To address surface roughness, this work introduces a new method to smooth features beyond line-of-sight without material removal. The method applies a coating that triggers local surface remelting by activating a eutectic reaction during heat treatment. The associated liquid phase then wets and isothermally solidifies into a smoother surface. For Ti-6Al-4V fabricated with laser powder bed fusion, samples with and without TLP smoothing (using a Cu coating) were characterized with a suite of techniques, including mechanical testing, electron backscatter diffraction, synchrotron X-ray tomography, and fractography. TLP smoothing reduced surface roughness by 80% and amplified compressive residual stress at the surface by about 50%. With statistically equivalent virtual microstructures, crystal plasticity scrutinized the roles of phases, porosity, and surface roughness. Although the tensile strain-to-failure was reduced to 1% strain, the TLP smoothing process increased high-cycle fatigue strength by about 20% compared to control samples, pointing to future opportunities to optimize the new process through various coating compositions and heat treatment schedules. Overall, this work establishes a new paradigm for treating surfaces of materials for smoothness and compressive residual stress.

Additive manufacturing↗
DOE OSTI2021

Mechanistic Duality of Bacterial Efflux Substrates and Inhibitors: Example of Simple Substituted Cinnamoyl and Naphthyl Amides

Antibiotic resistance poses an immediate and growing threat to human health. Multidrug efflux pumps are promising targets for overcoming antibiotic resistance with small-molecule therapeutics. Previously, we identified a diaminoquinoline acrylamide, NSC-33353, as a potent inhibitor of the AcrAB–TolC efflux pump in Escherichia coli. This inhibitor potentiates the antibacterial activities of novobiocin and erythromycin upon binding to the membrane fusion protein AcrA. It is also a substrate for efflux and lacks appreciable intrinsic antibacterial activity of its own in wild-type cells. Here, we have modified the substituents of the cinnamoyl group of NSC-33353, giving rise to analogs that retain the ability to inhibit efflux, lost the features of the efflux substrates, and gained antibacterial activity in wild-type cells. The replacement of the cinnamoyl group with naphthyl isosteres generated compounds that lack antibacterial activity but are both excellent efflux pump inhibitors and substrates. Surprisingly, these inhibitors potentiate the antibacterial activity of novobiocin but not erythromycin. Surface plasmon resonance experiments and molecular docking suggest that the replacement of the cinnamoyl group with naphthyl shifts the affinity of the compounds away from AcrA to the AcrB transporter, making them better efflux substrates and changing their mechanism of inhibition. These results provide new insights into the duality of efflux substrate/inhibitor features in chemical scaffolds that will facilitate the development of new efflux pump inhibitors.

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

Systems and methods for customizing kernel machines with deep neural networks

A method including receiving an input data set. The input data set can include one of a feature domain set or a kernel matrix. The method also can include constructing dense embeddings using: (i) Nyström approximations on the input data set when the input data set comprises the kernel matrix, and (ii) clustered Nyström approximations on the input data set when the input data set comprises the feature domain set. The method additionally can include performing representation learning on each of the dense embeddings using a multi-layer fully-connected network for each of the dense embeddings to generate latent representations corresponding to each of the dense embeddings. The method further can include applying a fusion layer to the latent representations corresponding to the dense embeddings to generate a combined representation. The method additionally can include performing classification on the combined representation. Other embodiments of related systems and methods are also disclosed.

Song, Huan↗
DOE OSTI2025

Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation

In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers found using TORIC in certain HHFW heating scenarios. The outliers are observed to be spurious ion Bernstein wave (IBW)-like modes caused by a wavelength control algorithm designed to address challenging scenarios with high perpendicular wavenumbers. The effect arises from the modulation in the perpendicular susceptibility, which can induce sign reversal and IBW-like propagation for scenarios featuring normalized ion Larmor radius λ i ≫ 1. We use TORIC with this algorithm disabled to generate a novel HHFW-NSTX database that is free of outliers. Surrogate models trained on this database, including Random Forest Regressor (RFR), Multi-Layer Perceptrons, and Gaussian Process Regressors (GPR), demonstrate the ability to accurately predict HHFW heating profiles, with regression scores of R 2 ∈[0.93−0.99]. Additionally we demonstrate that it is possible to generalize predictions beyond training data by the use of both RFR and GPR models, enabling the prediction of scenarios previously limited to the original model. GPR models also provide uncertainty quantification, offering insights into model confidence. This work introduces a comprehensive Verification, Validation, and Uncertainty Quantification methodology for surrogate modeling, applicable not only to ICRF heating but also to other RF heating challenges and fusion physics problems. Beyond accelerated inference, these models show effective extrapolation capabilities, providing an alternative for addressing numerical challenges.

Artificial neural networks↗
DOE OSTI2025

Resolving discrepancies in bang-time predictions for indirect-drive ICF experiments on the NIF: Insights from the Build-A-Hohlraum campaign

This study investigated discrepancies between measured and simulated x-ray drive in Indirect-Drive Inertial Confinement Fusion (ID-ICF) hohlraums at the National Ignition Facility. Despite advances in radiation-hydrodynamic simulations, a consistent “drive deficit” remains. Experimentally measured ID-ICF capsule bang-times are systematically 400–700 ps later than simulations predict. The Build-A-Hohlraum (BAH) campaign explored potential causes for this discrepancy by systematically varying hohlraum features, including laser entrance hole (LEH) windows, capsules, and gas fills. Overall, the agreement between simulated and experimental x-ray drive was found to be largely unaffected by these changes. The data allow us to exclude some hypotheses put forward to potentially explain the discrepancy. Errors in the local thermodynamic equilibrium (LTE) atomic modeling, errors in the modeling of LEH closure, and errors due to a lack of plasma species mix physics in simulations are shown to be inconsistent with our measurements. Instead, the data support the hypothesis that errors in NLTE emission modeling are a significant contributor to the discrepancy. X-ray emission in the 2–4 keV range is found to be approximately 30% lower than in simulations. This is accompanied by higher than predicted electron temperatures in the gold bubble region, pointing to errors in non-LTE modeling. Introducing an opacity multiplier of 0.87 on energy groups above 1.8 keV improves agreement with experimental data, reducing the bang-time discrepancy from 300 to 100 ps. These results underscore the need for refined NLTE opacity models to enhance the predictive power of hohlraum simulations.

Band emission↗
DOE OSTI2026

Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training. Here, we demonstrate that our neural network, trained solely on megahertz-scale turbulence measurements from one machine (DIII-D), forecasts Type-I edge localized mode (ELM) onsets in a different tokamak (KSTAR) through zero-shot weight transfer following physics-consistent preprocessing without device-specific retraining. Through an explainable AI framework combining gradient-weighted class activation mapping with physics validation, we reveal that our network can internalize physics relationships governing the ELM instabilities rather than memorizing device-specific patterns. The network perceives spatiotemporal features that correlate consistently with independently calculated instability growth rates, magnetohydrodynamic stability limits, and pedestal structure dynamics. Statistical analyses of dimensionally-reduced saliency features reveal the identical triangular features between the saliency representations, instability growth rates, and prediction probability across tokamaks, providing evidence that our forecasting system can show tokamak-agnostic generalization. This work contributes to a foundation for explainable scientific AI systems, where cross-system developments are essential for transcending traditional domain-specific constraints.

AI↗
DOE OSTI2023

Investigation of interfacial strength in nacre-mimicking tungsten heavy alloys for nuclear fusion applications

Abstract Tungsten heavy alloys have been proposed as plasma facing material components in nuclear fusion reactors and require experimental investigation in their confirmation. For this purpose, a 90W–7Ni–3Fe alloy has been selected and microstructurally manipulated to present a multiphase brick-and-mortar structure of W-phase ‘bricks’ surrounded by a ductile ‘mortar’. This work draws inspiration from nature to artificially imitate the extraordinary combination of strength and stiffness exhibited by mollusks and produce a nacre-mimicking metal matrix composite capable of withstanding the extremely hostile environment of the reactor interior and maintaining structural integrity. The underlying mechanisms behind this integrity have been probed through high-resolution structural and chemical characterization techniques and have revealed chemically diffuse phase boundaries exhibiting unexpected lattice coherency. These features have been attributed to an increase in the energy required for interfacial decohesion in these systems and the simultaneous expression of high strength and toughness in tungsten heavy alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Autoregressive long-horizon prediction of plasma edge dynamics *

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

autoregressive deep learning↗
DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗
DOE OSTI2025

Detailed simulations of the first deuterium-tritium-filled double shell implosions on the National Ignition Facility

The first indirectly driven, liquid DT-filled double shell inertial confinement fusion (ICF) implosions have recently been successfully performed on the National Ignition Facility (NIF). Double shells are a class of alternative designs that use a low-Z outer shell to compress a foam cushion that accelerates a high-Z inner shell to efficiently compress a liquid DT core. Double shells are challenging to fabricate, field, and model. Important engineering features enabling double shell fabrication include a fill-tube penetrating all shells and a carefully designed and very narrow (few μm) step-joint in the ablator. Due to the higher density materials involved, high Atwood number instabilities are also important at many material interfaces. In this paper, numerical simulations of double shell implosions using the Los Alamos National Laboratory multi-physics radiation-hydrodynamics code xRAGE will be discussed. An extensive effort has been under way for several years to develop the code capabilities for ICF simulations in a common modeling framework to allow ease of simulation setup and standardization of the computational methodology. This paper will present a wide range of simulation results capturing, quantifying, and comparing the impact of all these degradation mechanisms on implosion performance. Brief comparisons with recent experimental results and suggestions for future improvements will also be discussed. Our results suggest that capsule surface roughness and the step-joint gap have the largest impact on implosion performance. Initial experimental data may suggest that the sensitivity to the step-joint gap could provide the dominant explanation for DT-filled double shell experiments that have been fielded on NIF thus far.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗
DOE OSTI2023

Broadly neutralizing anti-S2 antibodies protect against all three human betacoronaviruses that cause deadly disease

Pan-betacoronavirus neutralizing antibodies may hold the key to developing broadly protective vaccines against novel pandemic coronaviruses and to more effectively respond to SARS-CoV-2 variants. The emergence of Omicron and subvariants of SARS-CoV-2 illustrates the limitations of solely targeting the receptor-binding domain (RBD) of the spike (S) protein. Here, we isolated a large panel of broadly neutralizing antibodies (bnAbs) from SARS-CoV-2 recovered-vaccinated donors, which targets a conserved S2 region in the betacoronavirus spike fusion machinery. Select bnAbs showed broad in vivo protection against all three deadly betacoronaviruses, SARS-CoV-1, SARS-CoV-2, and MERS-CoV, which have spilled over into humans in the past two decades. Structural studies of these bnAbs delineated the molecular basis for their broad reactivity and revealed common antibody features targetable by broad vaccination strategies. These bnAbs provide new insights and opportunities for antibody-based interventions and for developing pan-betacoronavirus vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Electron Modulational Instability in the Strong Turbulent Regime for an Electron Beam Propagating in a Background Plasma

We study collective processes for an electron beam propagating through a background plasma using simulations and analytical theory. A new regime where the instability of a Langmuir wave packet can grow locally much faster than ion frequency is clearly identified. The key feature of this new regime is an electron modulational instability that rapidly creates a local Langmuir wave packet, which in its turn produces local charge separation and strong ion density perturbations because of the action of the ponderomotive force, such that the beam-plasma wave interaction stops being resonant. Three evolution stages of the process and observed periodic burst features are discussed. In conclusion, different physical regimes in the plasma and beam parameter space are demonstrated for the first time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗