A computational modeling study of streamer propagation in a cold, atmospheric-pressure, helium jet in ambient air is presented. A self-consistent, multi-species, multi-temperature plasma model with detailed finite-rate chemistry and photoionization effects is used to provide fundamental insights into the structure and dynamics of the streamers. A parametric study of the streamer properties as a function of important discharge geometric and operating conditions is performed. The fluid mechanical mixing layer between the helium jet core and the ambient air is instrumental in guiding the propagation direction of the streamer and gives the plasma jet a visibly collimated appearance. The key chemical reactions which drive the streamer propagation are electron-impact ionization of helium neutral and nitrogen molecules. Photoionization plays a role in enhancing the propagation speed of the streamer, but is not necessary to sustain the streamer. The streamer yields a large radical concentration through chemical reactions in the streamer head and the body. The streamer propagation speed increases with reduced helium jet radius and increased helium-air mixing layer width. Impurities in the helium jet result in a significant increase in the discharge propagation speed within the tube through photoionization, but not after the streamer propagates into the open ambient region. It is also observed that thinner electrodes produce stronger electric-field concentrations that increase discharge propagation speeds within the tube but have a smaller influence on the discharge after it emerges out of the tube as a streamer.

A luminous plasma jet is produced when helium gas issuing into atmospheric pressure ambient air is excited by high voltage nanosecond pulsing of a dielectric covered electrode. A detailed computational modeling study of such a discharge is presented. The dynamics of streamer propagation, its dependence on the diffusional mixing layer between helium and air species, and the role of photoionization are discussed.

The problem of optimal data collection to efficiently learn the model parameters of a graphite nitridation experiment is studied in the context of Bayesian analysis using both synthetic and real experimental data. The paper emphasizes that the optimal design can be obtained as a result of an information theoretic sensitivity analysis. Thus, the preferred design is where the statistical dependence between the model parameters and observables is the highest possible. In this paper, the statistical dependence between random variables is quantified by mutual information and estimated using a k nearest neighbor based approximation. It is shown, that by monitoring the inference process via measures such as entropy or Kullback-Leibler divergence, one can determine when to stop the data collection process. The methodology is applied to select the most informative designs on both a simulated data set and on an experimental data set, previously published in the literature. It is also shown that the sequential Bayesian analysis used in the experimental design can also be useful in detecting conflicting information between measurements and model predictions.

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