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[{ReN(PMe2Ph)3}{ReO3N}]2 – Structural Evidence for the Nitridotrioxorhenate(VII) Anion, [ReO3N]2−
(2005)
The Ministry of Science and Research in North Rhine-Westphalia created eight platforms of excellence, one in the research area „Energy and Environment“ in 2002 at ACUAS. This platform concentrates the research and development of 13 professors in Jülich and Aachen and of two scientific institutes with different topics: – NOWUM-Energy with emphasis on efficient and economic energy conversion – The Solar Institute Jülich – SIJ – being the largest research institute in the field of renewables at a University of Applied Sciences in Germany With this platform each possible energy conversion – nuclear, fossil, renewable- can be dealt with to help solving the two most important problems of mankind, energy and potable water. At the CSE are presented the historical development, some research results and the combined master studies in „Energy Systems“ and „Nuclear Applications“
One of the most important parameters in a burning chamber - in power stations, in waste to energy plants - is the temperature. This temperature is in the range of 700-1500 °C - one of the most advanced measuring methods being the acoustic pyrometry with the possibility of producing temperature mapping in one level of the burning chamber - comparable to computer tomography. The results of these measurements discussed in the presentation can be used - to fulfil the legal requirements in the FRG or in the EU - to equalise the temperature in one level of the burning chamber to optimise the steam production (better efficiency of the plant) and to minimise the production of temperature controlled flue gas components (NO, CO a. o.) - to control the SNCR-process if used.
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).