Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Assessment of energy and emissions saving solutions in urban rail-based transport systems

Global warming and climate change are indisputable theories. Since the Industrial Revolution, the mean temperature of the planet has increased by 1°C. Now, temperatures are approaching a higher stage of +1.5°C and the attention is on both CO2 emissions and energy consumption. Transportation is a major component of the environmental impact, accounting for approximately 30% of air pollution and energy consumption. Due to the rapid urbanization in the EU, with an estimated 74.3% of the population living in cities, forecasted to rise to 80% by 2050, urban mobility is dramatically increasing its relevance. Therefore, a reduction in energy consumption and pollutant emissions is a crucial factor to consider in developing urban transportation and particularly rail-based systems, able to provide energy saving transport services by improving urban environment. Several methods and techniques are under development to improve the energy performance of Light Rail Transport (LRT), which spread from different typologies of power supply to improving energy efficiency. The purpose of this paper is to start from the last developments and innovative energy sources for LRT systems. The focus is on two parts: a) trams running on Hydrogen in parallel with on board batteries with energy saving control techniques, b) potential renewable energy sources to meet power demand. The comparison is with traditional power sources and equipment (e.g. Catenary-based). The methods, based on selected indicators, are under development and test by calculations and simulations with reference to the case study of the new tramlines in the city of Brescia (Italy)