On the simulation of the UPMSAT-2 microsatellite power

Simulation of satellite subsystems behaviour is extramely important in the design at early stages. The subsystems are normally simulated in the both ways : isolated and as part of more complex simulation that takes into account imputs from other subsystems (concurrent design). In the present work, a simple concurrent simulation of the power subsystem of a microsatellite, UPMSat-2, is described. The aim of the work is to obtain the performance profile of the system (battery charging level, power consumption by the payloads, power supply from solar panels....). Different situations such as battery critical low or high level, effects of high current charging due to the low temperature of solar panels after eclipse,DoD margins..., were analysed, and different safety strategies studied using the developed tool (simulator) to fulfil the mission requirements. Also, failure cases were analysed in order to study the robustness of the system. The mentioned simulator has been programed taking into account the power consumption performances (average and maximum consumptions per orbit/day) of small part of the subsystem (SELEX GALILEO SPVS modular generators built with Azur Space solar cells, SAFT VES16 6P4S Li-ion battery, SSBV magnetometers, TECNOBIT and DATSI/UPM On Board Data Handling -OBDH-...). The developed tool is then intended to be a modular simulator, with the chance of use any other components implementing some standard data.

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.