A Thesis Defense Session for MSc/Mohammad Ali Ebrahimi

//A Thesis Defense Session for MSc/Mohammad Ali Ebrahimi

Title: Optimal System Design of GEO Communication Satellite Under Uncertainty

By Mohammad Ali Ebrahimi
Supervisor: Dr. Mehran Mirshams
Chairmen: Dr. Jafar Roshanian (K. N. Toosi University of Technology), Dr. Mahdi Fakoor (University of Tehran)
December 18, 2019

Geo Communication Satellite are most practical and profitable satellites.They have defined with plenty of applications. The application interested in this dissertation are providing Broadcasting services for other Ground station.Design and  Manufacturing this kind of satellite is too expensive. In this thesis tried to achieve robust optimization design of this kind of satellite and compare it with case study. For this purpose a convenient method to design is submitted. Modeling this satellite is considered of two segment Bus, Payload. Payload have a responsibility to do a mission of sending and receiving of digital data that is known as Communication Payload. Bus includes of five subsystems (Propulsion, Power, ADCS, Thermal Control and Structure) that have responsibility to support Payload. Also by considering the fuel of launcher the costs of manufacturing, Maintenance and launch will be counted. Furthermore, in order to be more beneficial for multiobjective optimization method used. Therefore customer could select desired option between pareto-front solutions. The objectives is intended to use in this dissertation are increasing revenue and decreasing cost. At last, by considering Probability distribution (Uncertainty) of variables design Optimal and Robust design against their effects will be presents. The method used for satellite optimization design is Non-dominated sorting genetic algorithm and the ones used for sampling uncertainties are Monte Carlo Sampling. In this dissertation Results of Modeling evaluated with case study and less difference reported. Also by Optimization Design the optimized solution than the case study has been achieved. At last impression of uncertainty decreased by robust optimization design.