In this work, we obtained a charge state distribution inside an Ar plasma produced by an electron–cyclotron-resonance ion source. The processes that lead to the observed lines in x-ray spectra are identified and included in the simulated x-ray spectrum. The geometrical constraints of the flat double crystal spectrometer, used to measure the x-ray spectrum, are investigated as they are crucial for correctly obtaining the ion densities from the observed transition amplitudes. Multiple electron impact ionization is included, and a realistic electron velocity distribution is taken into account. The charge state distribution of the Ar ions is compared to measured extracted ionic currents.

This work refers to experimental and 3D nonlinear FEA on punching. Numerical results were compared with experimental ones in order to benchmark the FE model and afterwards a parametric study was conducted, changing the reinforcement ratio, slab thickness, concrete strength and column dimensions, running a total of 360 models, where their effect on punching capacity is shown. EC2 and MC2010 provisions agreed approximately with experimental and FEA results. Based in the FEA results it is proposed an equation to predict the punching capacity with the introduction of fracture mechanics parameter, which was compared with several experimental results, giving good approximation.

The sand pile model, in conjunction with Bean model, is often applied to describe single grain bulk superconductors. However, in several applications such as electric motors, multiseeded bulks are needed, due to the need to increase sample dimensions. In this paper, an extension of the sand pile model is presented in order to manage this type of materials. Multiseeded HTS bulk superconductors, produced, e.g., by the top-seeded melt growth process, are characterized by intra- and intergrain currents, and these are reflected in the model. However, identifying these currents from flux density measurements is not straightforward, when considering more than one grain. In fact, the number of currents increases with the number of grains, and these have to be identified from the measured field surface. A method to identify these currents based on genetic algorithms is validated with artificial data and then used in real measurements.

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