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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

The Martian Dust Devil Electron Avalanche: Laboratory Measurements of the E-Field Fortifying Effects of Dust-Electron Absorption

Analogous to terrestrial dust devils, charged dust in Mars dust devils should become vertically stratified in the convective features, creating large scale E-fields. This E-field in a Martian-like atmosphere has been shown to stimulate the development of a Townsend discharge (electron avalanche) that acts to dissipate charge in regions where charge build-up occurs. While the stratification of the charged dust is a source of the electrical energy, the uncharged particulates in the dust population may absorb a portion of these avalanching electrons, thereby inhibiting dissipation and leading to the development of anomalously large E-field values. We performed a laboratory study that does indeed show the presence of enhanced E-field strengths between an anode and cathode when dust-absorbing filaments (acting as particulates) are placed in the avalanching electron flow. Further, the E-field threshold condition to create an impulsive spark discharge increases to larger values as more filaments are placed between the anode and cathode. We conclude that the spatially separated charged dust creates the charge centers and E-fields in a dust devil, but the under-charged portion of the population acts to reduce Townsend electron dissipation currents, further fortifying the development of larger-than-expected E-fields.

Farrell, W. M.↗
NASA NTRS1974

Electron avalanche in liquid xenon

Measurements in liquid Xe are reported of the characteristic function that describes the avalanche, the first Townsend coefficient. An electron avalanche occurs when the drifting electrons attain sufficient kinetic energy from the electric field to cause additional ionization. Data on the avalanche were obtained by studying the pulses in a liquid-filled proportional chamber as a function of applied voltage and anode diameter. The results of the investigation show that the avalanche of free electrons in purified liquid Xe is adequately described by the same formalism used for the avalanche of electrons in gases.

Derenzo, S. E.↗
NASA NTRS2010

Martian Dust Devil Electron Avalanche Process and Associated Electrochemistry

Mars' dynamic atmosphere displays localized dust devils and larger, global dust storms. Based on terrestrial analog studies, electrostatic modeling, and laboratory work these features will contain large electrostatic fields formed via triboelectric processes. In the low-pressure Martian atmosphere, these fields may create an electron avalanche and collisional plasma due to an increase in electron density driven by the internal electrical forces. To test the hypothesis that an electron avalanche is sustained under these conditions, a self-consistent atmospheric process model is created including electron impact ionization sources and electron losses via dust absorption, electron dissociation attachment, and electron/ion recombination. This new model is called the Dust Devil Electron Avalanche Model (DDEAM). This model solves simultaneously nine continuity equations describing the evolution of the primary gaseous chemical species involved in the electrochemistry. DDEAM monitors the evolution of the electrons and primary gas constituents, including electron/water interactions. We especially focus on electron dynamics and follow the electrons as they evolve in the E field driven collisional gas. When sources and losses are self-consistently included in the electron continuity equation, the electron density grows exponentially with increasing electric field, reaching an equilibrium that forms a sustained time-stable collisional plasma. However, the character of this plasma differs depending upon the assumed growth rate saturation process (chemical saturation versus space charge). DDEAM also shows the possibility of the loss of atmospheric methane as a function of electric field due to electron dissociative attachment of the hydrocarbon. The methane destruction rates are presented and can be included in other larger atmospheric models.

Jackson, Telana L.↗
DOE OSTI2024

Wall heating by subcritical energetic electrons generated by the runaway electron avalanche source *

Abstract Subcritical energetic electrons (SEEs) produced by the runaway electron (RE) avalanche source at energies below the runaway threshold are found to be the primary contributor to surface heating of plasma-facing components (PFCs) during final loss events. This finding is supported by theoretical analysis, computational modeling with the Kinetic Orbit Runaway electrons Code (KORC), and qualitative agreement with DIII-D experimental observations. The avalanche source generates significantly more secondary electrons below the runaway threshold, which thermalize rapidly when well-confined. However, during a final loss event, the RE beam impacts the first wall, and SEEs are deconfined before they can thermalize. Additionally, because the energy deposition length decreases faster than energy, the deposited energy density, and thus the maximum PFC surface temperature change, is larger for SEEs than REs. KORC simulations employ an analytic first wall to model particle deconfinement onto a non-axisymmetric wall composed of individual tiles. PFC surface heating is calculated using a 1D model extended to include an energy-dependent deposition length scale. Simulations of DIII-D qualitatively agree with infrared (IR) imaging only when SEEs from the avalanche source are included. These results demonstrate that SEEs are the dominant contributor to PFC surface heating and indicate that the avalanche source plays a critical role in the PFC damage caused during final loss events. The prominence of SEEs also has important implications for interpreting IR imaging, one of the primary diagnostics for RE-wall interaction diagnosis, despite REs dominating the energy and current density. This result improves predictions of wall damage due to post-disruption REs to estimate material lifetime and design RE mitigation systems for ITER and future reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A physics-constrained deep learning surrogate model of the runaway electron avalanche growth rate

A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic Fokker–Planck equation. The resulting PINN is able to evaluate the runaway probability function across a broad range of parameters in the absence of any synthetic or experimental data. This surrogate of the adjoint relativistic Fokker–Planck equation is then used to infer the avalanche growth rate as a function of the electric field, synchrotron radiation and effective charge. Predictions of the avalanche PINN are compared against first principle calculations of the avalanche growth rate with excellent agreement observed across a broad range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The impact of collisionality on the runaway electron avalanche during a tokamak disruption

The exponential growth (avalanching) of runaway electrons (REs) during a tokamak disruption continues to be a large uncertainty in RE modeling. The present work investigates the impact of tokamak geometry on the efficiency of the avalanche mechanism across a broad range of disruption scenarios. It is found that the parameter ν *,crit ⁠, describing the collisionality at the critical energy to run away, delineates how toroidal geometry impacts RE formation. In particular, utilizing a reduced but self-consistent description of plasma power balance, it is shown that for a high-density deuterium-dominated plasma, ν *,crit is robustly less than one, resulting in a substantial decrease in the efficiency of the RE avalanche compared to predictions from slab geometry. In contrast, for plasmas containing a substantial quantity of neon or argon, ν *,crit ≳1, no reduction of the avalanche is observed due to toroidal geometry. This sharp contrast in the impact of low- vs high-Z material results primarily from the relatively strong radiative cooling from high-Z impurities, enabling the plasma to be radiatively pinned at low temperatures and thus large electric fields, even for modest quantities of high-Z material.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1974

Random walk theory applied to electron avalanche formation

Use of microscopic detail in random walk theory describing the initial formations of a large number of avalanches shows that concomitant electron transport coefficients quickly relax to equilibrium values. This enables the use of random walks having step sizes and probabilities based only on local electric field strengths and densities. A self-consistent avalanche solution which accounts for collective long range Coulomb interactions as well as short range elastic and inelastic collisions between electrons and background atoms is demonstrated for helium. Avalanche growth retardation followed by an abrupt growth augmentation as time proceeds is shown to be associated with the formation of regions of charge density extrema near the avalanche axis and within the axial distance covered by the electron swarm.

Englert, G. W.↗
NASA NTRS1973

Electron avalanche structure determined by random walk theory

A self-consistent avalanche solution which accounts for collective long range Coulomb interactions as well as short range elastic and inelastic collisions between electrons and background atoms is made possible by a random walk technique. Results show that the electric field patterns in the early formation stages of avalanches in helium are close to those obtained from theory based on constant transport coefficients. Regions of maximum and minimum induced electrostatic potential phi are located on the axis of symmetry and within the volume covered by the electron swarm. As formation time continues, however, the region of minimum phi moves to slightly higher radii and the electric field between the extrema becomes somewhat erratic. In the intermediate formation periods the avalanche growth is slightly retarded by the high concentration of ions in the tail which oppose the external electric field. Eventually the formation of ions and electrons in the localized regions of high field strength more than offset this effect causing a very abrupt increase in avalanche growth.

Englert, G. W.↗
NASA NTRS2015

Is the Electron Avalanche Process in a Martian Dust Devil Self-Quenching?

Viking era laboratory experiments show that mixing tribocharged grains in a low pressure CO2 gas can form a discharge that glows, indicating the presence of an excited electron population that persists over many seconds. Based on these early experiments, it has been predicted that martian dust devils and storms may also contain a plasma and new plasma chemical species as a result of dust grain tribo-charging. However, recent results from modeling suggest a contrasting result: that a sustained electron discharge may not be easily established since the increase in gas conductivity would act to short-out the local E-fields and quickly dissipate the charged grains driving the process. In essence, the system was thought to be self-quenching (i.e., turn itself off). In this work, we attempt to reconcile the difference between observation and model via new laboratory measurements. We conclude that in a Mars-like low pressure CO2 atmosphere and expected E-fields, the electron current remains (for the most part) below the expected driving tribo-electric dust currents (approx. 10 microA/m(exp. 2)), thereby making quenching unlikely.

Electron↗
NASA NTRS1979

First principles numerical model of avalanche-induced arc discharges in electron-irradiated dielectrics

The model consists of four phases: single electron dynamics, single electron avalanche, negative streamer development, and tree formation. Numerical algorithms and computer code implementations are presented for the first three phases. An approach to developing a code description of fourth phase is discussed. Numerical results are presented for a crude material model of Teflon.

Beers, B. L.↗
NASA NTRS1976

Calculations of the Electron Energy Distribution Function in a Uranium Plasma by Analytic and Monte Carlo Techniques

Electron energy distribution functions were calculated in a U235 plasma at 1 atmosphere for various plasma temperatures and neutron fluxes. The distributions are assumed to be a summation of a high energy tail and a Maxwellian distribution. The sources of energetic electrons considered are the fission-fragment induced ionization of uranium and the electron induced ionization of uranium. The calculation of the high energy tail is reduced to an electron slowing down calculation, from the most energetic source to the energy where the electron is assumed to be incorporated into the Maxwellian distribution. The pertinent collisional processes are electron-electron scattering and electron induced ionization and excitation of uranium. Two distinct methods were employed in the calculation of the distributions. One method is based upon the assumption of continuous slowing and yields a distribution inversely proportional to the stopping power. An iteration scheme is utilized to include the secondary electron avalanche. In the other method, a governing equation is derived without assuming continuous electron slowing. This equation is solved by a Monte Carlo technique.

Bathke, C. G.↗
DOE OSTI2026

Machine Learning for Multipactor Susceptibility Prediction in Planar RF Gaps

Multipactor discharge is a nonlinear electron avalanche that limits the performance of high-power radio-frequency (RF) and vacuum electronic devices. Predicting multipactor susceptibility traditionally relies on Monte Carlo or particle-in-cell (PIC) simulations, which become computationally expensive for large parametric studies. In this work, we present a supervised machine-learning (ML) framework for prediction of multipactor susceptibility in a two-surface planar geometry. The models are trained using high-fidelity PIC simulation generated susceptibility data and learn the relationship between operational parameters, geometry, and material-dependent secondary electron emission properties. The proposed approach enables rapid reconstruction of susceptibility charts while preserving the physical structure of multipactor growth regions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
NASA NTRS2012

Earth's Most Powerful Natural Particle Accelerator

Thunderstorms launch antimatter, gamma rays, and highly energetic electrons and neutrons to the edge of space. This witches' brew of radiation is generated at the edge of the stratopause, by the strong electric fields associated with lightning discharges. In less than a quarter millisecond, an explosive feedback process takes an initial seed population of electrons, perhaps produced by cosmic rays from dying stars, and amplifies them a billion billion-fold in the rarefied air over high altitude thunderheads. The electrons generate gamma radiation as they travel through the stratosphere and lower mesosphere, momentarily brighter and of harder spectrum than cosmic gamma ray bursts. These electrons ultimately are absorbed by the atmosphere, but the gamma rays continue on, into the upper reaches of the atmosphere, where they in turn generate a new population of electrons, positrons, and energetic neutrons. These secondary electrons and positrons move along the magnetic field, and can reach near-earth space, streaming through the inner radiation belts, and possibly contributing to the trapped populations there. First postulated by Wilson in 1925, and serendipitously discovered by the Compton Gamma Ray Observatory in 1994 [Fishman et al.], these events, known as "Terrestrial Gamma ray Flashes" (TGFs), represent the most intense episodes of particle acceleration on or near the Earth, resulting in electrons with energies up to 100 MeV. Recent observations by the RHESSI [Smith et al., 2004], Fermi [Briggs et al., 2010], and AGILE [Tavani et al., 2011] satellites, and theoretical and computational modeling, have suggested that the relativistic runaway electron avalanche (RREA) mechanism [Gurevich, 1992], and important modifications, such as the relativistic feedback discharge (RFD) model [Dwyer 2012] can best explain the observations at present. In these models, strong thunderstorm electric fields drive seed electrons, generated from cosmic ray interactions, into a runaway discharge, in which the seed electrons continually gain energy from the electric field, creating a host of secondaries as they interact with the background atmospheric gas. The feedback mechanisms include backwards-propagating positrons and gamma rays, which then can generate new "seed" electrons at the base of the acceleration region, and themselves generate further avalanche chain reactions, greatly amplifying the initial seed population. All these processes happen in the stratosphere, in the altitude range near 15-20 km, where the electric fields and mean free paths are appropriate to allow the discharge to develop.

Rowland, Doug↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
NASA NTRS2013

Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector

The purpose of this program was to develop single-photon-sensitive short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) avalanche photodiode (APD) receivers based on linear-mode HgCdTe APDs, for application by NASA in light detection and ranging (lidar) sensors. Linear-mode photon-counting APDs are desired for lidar because they have a shorter pixel dead time than Geiger APDs, and can detect sequential pulse returns from multiple objects that are closely spaced in range. Linear-mode APDs can also measure photon number, which Geiger APDs cannot, adding an extra dimension to lidar scene data for multi-photon returns. High-gain APDs with low multiplication noise are required for efficient linear-mode detection of single photons because of APD gain statistics -- a low-excess-noise APD will generate detectible current pulses from single photon input at a much higher rate of occurrence than will a noisy APD operated at the same average gain. MWIR and LWIR electron-avalanche HgCdTe APDs have been shown to operate in linear mode at high average avalanche gain (M > 1000) without excess multiplication noise (F = 1), and are therefore very good candidates for linear-mode photon counting. However, detectors fashioned from these narrow-bandgap alloys require aggressive cooling to control thermal dark current. Wider-bandgap SWIR HgCdTe APDs were investigated in this program as a strategy to reduce detector cooling requirements.

Huntington, Andrew↗
NASA NTRS2014

A Highly Sensitive Multi-Element HgCdTe E-APD Detector for IPDA Lidar Applications

An HgCdTe electron avalanche photodiode (e-APD) detector has been developed for lidar receivers, one application of which is integrated path differential absorption lidar measurements of such atmospheric trace gases as CO2 and CH4. The HgCdTe APD has a wide, visible to mid-wave-infrared, spectral response, high dynamic range, substantially improved sensitivity, and an expected improvement in operational lifetime. A demonstration sensor-chip assembly consisting of a 4.3 lm cutoff HgCdTe 4 9 4 APD detector array with 80 micrometer pitch pixels and a custom complementary metal-oxide-semiconductor readout integrated circuit was developed. For one typical array the APD gain was 654 at 12 V with corresponding gain normalized dark currents ranging from 1.2 fA to 3.2 fA. The 4 9 4 detector system was characterized at 77 K with a 1.55 micrometer wavelength, 1 microsecond wide, laser pulse. The measured unit gain detector photon conversion efficiency was 91.1%. At 11 V bias the mean measured APD gain at 77 K was 307.8 with sigma/mean uniformity of 1.23%. The average, noise-bandwidth normalized, system noise-equivalent power (NEP) was 1.04 fW/Hz(exp 1/2) with a sigma/mean of 3.8%. The measured, electronics-limited, bandwidth of 6.8 MHz was more than adequate for 1 microsecond pulse detection. The system had an NEP (3 MHz) of 0.4 fW/Hz(exp 1/2) at 12 V APD bias and a linear dynamic range close to 1000. A gain-independent quantum-limited SNR of 80% of full theoretical was indicative of a gain-independent excess noise factor very close to 1.0 and the expected APD mode quantum efficiency.

NEP↗