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Register a new userMonte Carlo Raytracing Method for Calculating Secondary Electron Emission from Micro-Architected Surfaces
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Secondary electron emission (SEE) from inner linings of plasma chambers in electric thrusters for space propulsion can have a disruptive effect on device performance and efficiency. SEE is typically calculated using elastic and inelastic electron scattering theory by way of Monte Carlo simulations of independent electron trajectories. However, in practice the method can only be applied for ideally smooth surfaces and thin films, not representative of real material surfaces. Recently, micro-architected surfaces with nanometric features have been proposed to mitigate SEE and ion-induced erosion in plasma-exposed thruster linings. In this paper, we propose an approach for calculating secondary electron yields from surfaces with arbitrarily-complex geometries using an extension of the emph{ray tracing} Monte Carlo (RTMC) technique. We study nanofoam structures with varying porosities as representative micro-architected surfaces, and use RTMC to generate primary electron trajectories and track secondary electrons until their escape from the outer surface. Actual surfaces are represented as a discrete finite element meshes obtained from X-ray tomography images of tungsten nanofoams. At the local level, primary rays impinging into surface elements produce daughter rays of secondary electrons whose number, energies and angular characteristics are set by pre-calculated tables of SEE yields and energies from ideally-flat surfaces. We find that these micro-architected geometries can reduce SEE by up to 50% with respect to flat surfaces depending on porosity and primary electron energy.
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Surface erosion and secondary electron emission (SEE) have been identified as the most critical life-limiting factors in channel walls of Hall-effect thrusters for space propulsion. Recent wall concepts based on micro-architected surfaces have been proposed to mitigate surface erosion and SEE. The idea behind these designs is to take advantage of very-high surface-to-volume ratios to reduce SEE and ion erosion by internal trapping and redeposition. This has resulted in renewed interest to study electron-electron processes in relevant thruster wall materials. In this work, we present calculations of SEE yields in micro-porous hexagonal BN surfaces using stochastic simulations of electron-material interactions in discretized surface geometries. Our model consists of two complementary parts. First we study SEE as a function of primary electron energy and incidence angle in flat surfaces using Monte Carlo simulations of electron multi-scattering processes. The results are then used to represent the response function of discrete surface elements to individual electron rays generated using a ray-tracing Monte Carlo model. We find that micro-porous surfaces result in SEE yield reductions of over 50% in the energy range experienced in Hall thrusters. This points to the suitability of these micro-architected surface concepts to mitigate SEE-related issues in compact electric propulsion devices.
MAST-SEY is an open-source Monte Carlo code capable of calculating secondary electron emission using input data generated entirely from first principle (density functional theory) calculations. It utilizes the complex dielectric function and Penns theory for inelastic scattering processes, and relativistic Schrodinger theory by means of a partial-wave expansion method to govern elastic scattering. It allows the user to include explicitly calculated momentum dependence of the dielectric function, as well as to utilize first-principle density of states in secondary electron generation, which provides a more complete description of the underlying physics. In this paper we thoroughly describe the theoretical aspects of the modeling, as used in the code, and present sample results obtained for copper and aluminum.
We present density-functional theory (DFT) and quantum Monte Carlo (QMC) calculations designed to resolve experimental and theoretical controversies over the optical properties of H-terminated C nanoparticles (diamondoids). The QMC results follow the trends of well-converged plane-wave DFT calculations for the size dependence of the optical gap, but they predict gaps that are 1-2 eV higher. They confirm that quantum confinement effects disappear in diamondoids larger than 1 nm, which have gaps below that of bulk diamond. Our QMC calculations predict a small exciton binding energy and a negative electron affinity (NEA) for diamondoids up to 1 nm, resulting from the delocalized nature of the lowest unoccupied molecular orbital. The NEA suggests a range of possible applications of diamondoids as low-voltage electron emitters.
We present calculations of secondary electron emission (SEE) yields in tungsten as a function of primary electron energies between 50 eV and 1 keV and incidence angles between 0 and 90{deg}. We conduct a review of the established Monte Carlo methods to simulate multiple electron scattering in solids and select the best suited to study SEE in high-Z metals. We generate secondary electron yield and emission energy functions of the incident energy and angle and fit them to bivariate fitting functions using symbolic regression. We compare the numerical results with experimental data, with good agreement found. Our calculations are the first step towards studying SEE in nanoarchitected surfaces for electric propulsion chamber walls.
We present a new efficient method for Monte Carlo simulations of diffusion-reaction processes. First introduced by us in [Phys. Rev. Lett., 97:230602, 2006], the new algorithm skips the traditional small diffusion hops and propagates the diffusing particles over long distances through a sequence of super-hops, one particle at a time. By partitioning the simulation space into non-overlapping protecting domains each containing only one or two particles, the algorithm factorizes the N-body problem of collisions among multiple Brownian particles into a set of much simpler single-body and two-body problems. Efficient propagation of particles inside their protective domains is enabled through the use of time-dependent Greens functions (propagators) obtained as solutions for the first-passage statistics of random walks. The resulting Monte Carlo algorithm is event-driven and asynchronous; each Brownian particle propagates inside its own protective domain and on its own time clock. The algorithm reproduces the statistics of the underlying Monte-Carlo model exactly. Extensive numerical examples demonstrate that for an important class of diffusion-reaction models the new algorithm is efficient at low particle densities, where other existing algorithms slow down severely.
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