Candidates for Laser Fusion Energy with Minimized Radioactivity

The new candidates for laser fusion energy with minimized radioactivity were presented. The possibility of side-on laser ignition of H–¹¹B with negligible radioactivity encouraged to study the fusion of solid state H–⁷Li fuel which again turns out to be only about ten times more difficult than the side-on ignition of solid deuterium–tritium using petawatt-picosecond laser pulses at anomalous interaction conditions if very high contrast ratio. Updated cross sections of the nuclear reaction are included. In other words, the specific approach discussed here involves inducing a fusion burn wave without radioactivity by laser-driven impact of a relatively large block of plasma on the outside of a solid density H–¹¹B and H–⁷Li targets.     

Investigation of Tokamak Plasma MHD Activity Using FFT Analysis of Mirnov Coils Oscillations

A tokamak plasma mode was analyzed using the Fast Fourier Transform (FFT). Fourier analysis is reliable technique for mode detection in tokamaks. For this purpose, we used a poloidal array of Mirnov coils. After Fourier analysis on Mirnov coils data, Power Spectral Density (PSD) diagram was plotted. PSD describes how the power of a signal is distributed with frequency. In this contribution we also determined edge safety factor and safety factor from Fourier based derived mode numbers q = m/n. We obtained the maximum MHD activity using power spectrum in the frequency of 33 kHz. Also the edge safety factor determined small than 3, and the values of obtained safety factor from the mode numbers are between 2 ≤ q ≤ 5.     

Two new methods for ranking of Z‐numbers based on sigmoid function and sign method

This paper introduces two processes of ranking methods on Z‐numbers that are effectively able to deal with uncertain decision‐making data. Decision making is based on recommended Z‐ numbers. For this purpose first, the Z‐number is transformed to a fuzzy number and then the ranking method by using the sigmoid function and the sign method is used to mention fuzzy numbers. For the next step, the method is extended to related Z‐numbers. Finally, we use it to prioritize the items and solve some examples.     

Turbulent Transport in the Tokamak Edge Plasma and SOL Region

In this work, the turbulent transport in the edge plasma and Scrape-Off Layer (SOL) region of IR-T1 tokamak at the presence of biased limiter has been investigated and analyzed. The time and radial evolution of floating potential, electric field and turbulent transport have been measured by using two arrays of the Langmuir probes in both the radial and poloidal directions. The analyses have been done by the Fast Fourier Transport method and spectral features of them are obtained with the help of the standard Auto-Correlation technique and modified covariance power spectral density estimate. The probability distribution function and actual transfer function magnitude of the radial and poloidal turbulent transport (Γ_r and Γ_p) have been investigated and compared in the edge plasma and SOL region. Also the histogram of turbulent transport has been analyzed and compared in the edge and SOL at presence of positive limiter biasing. The results show that in the edge plasma poloidal turbulent transport (Γ_p) is about of 60 % more than SOL region whereas radial turbulent transport (Γ_r) is about of 40 % less. During the application of positive biasing, it was found that Γ_r in the IR-T1 reduces by about 80 % in the edge plasma and 45–50 % in the SOL. Increase of Γ_p is about of 50 % after applied positive biasing in the edge while it increases 70 % nearly, in the SOL. Consequently, the improvement in confinement can be obtained for positive limiter biasing.     

Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes

Two types of Artificial Neural Networks (ANNs), a Multi-Layer Perceptron (MLP) and a Generalized Regression Neural Network (GRNN), have been used for the validation of a fluid bed granulation process. The training capacity and the accuracy of these two types of networks were compared. The variations of the ratio of binder solution to feed material, product bed temperature, atomizing air pressure, binder spray rate, air velocity and batch size were taken as input variables for training the MLP and GRNN. The properties of size, size distribution, flow rate, angle of repose and Hausner's ratio of granules produced, were measured and used as output variables. Qualitatively, the two networks gave comparable results, as both pointed out the importance of the binder spray rate and the atomizing air pressure to the granulation process. However, the averaged absolute error of the MLP was higher than the averaged absolute error of the GRNN. Furthermore, the correlation coefficients between the experimentally determined and the calculated output values, the corresponding prediction accuracy for the different granule properties as well as the overall prediction accuracy using GRNN were better than using MLP. In conclusion, the comparison of two different networks (MLP, a so-called feed-forward back-propagation network and GRNN, a so-called Bayesian Neural Network) showed the higher capacity of the latter for validation of such granulation processes.