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We present a fully relativistic analytical model for calculating synthetic spectra from beam–target fusion reactions. When the target particle is assumed at rest, Monte Carlo sampling of reactant velocities can be avoided, and spectrum computations are considerably faster. A fully analytical treatment additionally gives more insight into the spectrum formation. The fully relativistic formulation now makes it possible to handle massless particles in the model, for example from one-step gamma-ray reactions, and the results are corroborated by simulations from established codes. [ABSTRACT FROM AUTHOR]

During bursty MHD events, transient ion cyclotron emission (ICE) is observed from deuterium plasmas in the large helical device (LHD) heliotron-stellarator. Unusually, the frequencies of the successive ICE spectral peaks are not close to integer multiples of the local cyclotron frequency of an energetic ion population in the likely emitting region. We show that this ICE is probably driven by a subset of the fusion-born protons near their birth energy E H = 3.02 MeV. This subset has a kinetic energy component parallel to the magnetic field, m H v ∥ 2 / 2 , significantly greater than its perpendicular energy m H v ⊥ 2 / 2 , for which v ⊥ ∼ V A , the AlfvĂ©n speed. First principles computations of the collective relaxation of this proton population, within a majority thermal deuterium plasma, are carried out using a particle-in-cell approach. This captures the full gyro-orbit kinetics of all ions which, together with an electron fluid, evolve self-consistently with the electric and magnetic fields under the Maxwellâ€"Lorentz equations. The simulated ICE spectra are derived from the Fourier transform of the fields which are excited. We find substantial frequency shifts in the peaks of the simulated ICE spectra, which correspond closely to the measured ICE spectra following the resonance condition ω = k ∥ v ∥ + n Ω H for n th proton harmonic. This suggests that the transient ICE in LHD is generated by the identified subset of the fusion-born protons, relaxing under the magnetoacoustic cyclotron instability. So far as is known, this is the first report of a collective radiation signal from fusion-born ions in anon-tokamak magnetically confined plasma. Disambiguation between two or more energetic ion species that could potentially generate complex observed ICE spectra is an increasing challenge, and the results and methodology developed here will assist this. Our approach is also expected to be relevant to ICE driven by ion beams with lower parallel velocities, for example in cylindrical plasma experiments. [ABSTRACT FROM AUTHOR]

This paper introduces a new model to find the velocity-space location of energetic ions generating ion cyclotron emission (ICE) in plasmas. ICE is thought to be generated due to inverted gradients in the v ⊥ direction of the velocity distribution function or due to anisotropies, i.e., strong gradients in the pitch direction. Here, we invert synthetic ICE spectra generated from first principles PIC-hybrid computations to find the locations of these ICE-generating ions in velocity space in terms of a probability distribution function. To this end, we compute 2D ICE weight functions based on the magnetoacoustic cyclotron instability, which reveals the velocity-space sensitivity of ICE measurements. As an example, we analyze the velocity-space sensitivity of synthetic ICE measurements near the first 15 harmonics for plasma parameters typical for the Large Helical Device. Furthermore, we investigate the applicability of a least-square subset search, Tikhonov regularization, and Lasso regularization to obtain the locations in velocity space of the ions generating the ICE. [ABSTRACT FROM AUTHOR]

The relationship between simulated ion cyclotron emission (ICE) signals s and the corresponding 1D velocity distribution function f v ⊥ of the fast ions triggering the ICE is modeled using a two-layer deep neural network. The network architecture (number of layers and number of computational nodes in each layer) and hyperparameters (learning rate and number of learning iterations) are fine-tuned using a bottom-up approach based on cross-validation. Thus, the optimal mapping g s ; θ of the neural network in terms of the number of nodes, the number of layers, and the values of the hyperparameters, where θ is the learned model parameters, is determined by comparing many different configurations of the network on the same training and test set and choosing the best one based on its average test error. The training and test sets are generated by computing random ICE velocity distribution functions f and their corresponding ICE signals s by modeling the relationship as the linear matrix equation Wf = s. The simulated ICE signals are modeled as edge ICE signals at LHD. The network predictions for f based on ICE signals s are on many simulated ICE signal examples closer to the true velocity distribution function than that obtained by 0th-order Tikhonov regularization, although there might be qualitative differences in which features one technique is better at predicting than the other. Additionally, the network computations are much faster. Adapted versions of the network can be applied to future experimental ICE data to infer fast-ion velocity distribution functions. [ABSTRACT FROM AUTHOR]

Three-dimensional simulations of partially magnetized plasma are real challenges that actually limit the understanding of the discharge operations such as the role of kinetic instabilities using explicit Particle-In-Cell (PIC) schemes. The transition to high performance computing cannot overcome all the limits inherent to very high plasma densities and thin mesh sizes employed to avoid numerical heating. We have applied a recent method proposed in the literature [L. F. Ricketson and A. J. Cerfon, Plasma Phys. Controlled Fusion 59, 024002 (2017)] to model low temperature plasmas. This new approach, namely, the sparse grid combination technique, offers a gain in computational time by solving the problem on a reduced number of grid cells, hence allowing also the reduction of the total number of macroparticles in the system. We have modeled the example of the two-dimensional electron drift instability, which was extensively studied in the literature to explain the anomalous electron transport in a Hall thruster. Comparisons between standard and sparse grid PIC methods show an encouraging gain in the computational time with an acceptable level of error. This method offers a unique opportunity for future three-dimensional simulations of instabilities in partially magnetized low temperature plasmas. [ABSTRACT FROM AUTHOR]

Negative hydrogen or deuterium ions are the precursor particles used to generate a high power beam of neutrals in order to heat the tokamak plasma core of magnetic fusion devices, inject current, and to some extent control instabilities. In the case of ITER, for instance, the negative ions are produced inside a high power large volume low-pressure tandem type magnetized ion source and extracted toward an electrostatic accelerator which accelerates them to 1 MeV before entering a neutralizer converting the ions into a neutral beam. This so-called neutral beam injector relies on the production of negative ions on the surface facing the plasma of the ion source extraction electrode. The latter is covered by a cesium layer in order to increase the negative ion yield. The use of cesium is currently an issue as it may diffuse outside of the source and induce secondary particle production or voltage breakdowns inside the accelerator vessel requiring a regular maintenance in a nuclear environment. In this work, we analyze numerically with a 2.5D particle-in-cell model the production rate and transport of negative ions in a linear device used as an ion source. The negative ions are generated via a dissociative attachment process with a hydrogen molecule in the volume of a magnetized cesium-free plasma. The linear device in the model has a large aspect ratio with a radius of 5 and a length of 100 cm and the magnetic field strength ranges from 100 to 400 G. We show that the shape and depth of the plasma potential profile may be controlled by biasing the end-plates which in turn strongly influence the residence time of the electrons and hence the negative ion yield. We observe the formation of large-scale rotating structures when the positive ions become magnetized with a rotation velocity in the kHz range. [ABSTRACT FROM AUTHOR]