By R. Hampel, W. Kästner, A. Fenske (auth.), Dr. Da Ruan (eds.)
Fuzzy structures and gentle computing are new computing ideas which are tolerant to imprecision, uncertainty and partial truths. purposes of those suggestions in nuclear engineering current a massive problem as a result of its strict nuclear protection law. The fields of nuclear engineering, fuzzy structures and tender computing have however matured significantly over the last decade. This ebook offers new software potentials for Fuzzy structures and tender Computing in Nuclear Engineering. the foundation of this ebook might be traced again to the sequence of the 1st, moment and 3rd overseas workshops on Fuzzy common sense and clever applied sciences in Nuclear technology (FUNS), which have been effectively held in Mol, September 14-16, 1994 (FLINS'94), in Mol, September 25-27, 1996 (FLINS'96), and in Antwerp, September 14-16, 1998 (FLINS'98). The meetings have been organised by means of the Belgian Nuclear examine Centre (SCKeCEN) and geared toward bringing jointly scientists, researchers, and engineers from academia and undefined, at introducing the foundations of fuzzy good judgment, neural networks, genetic algorithms and different tender computing methodologies, to the sector of nuclear engineering, and at using those strategies to advanced challenge fixing inside of nuclear and comparable learn fields. This booklet, as its identify indicates, involves nuclear engineering purposes of fuzzy structures (Chapters 1-10) and delicate computing (Chapters 11-21). 9 pertinent chapters are according to the prolonged model of papers at FLINS'98 and the opposite 12 chapters are unique contributions with up to date assurance of fuzzy and gentle computing purposes by means of top researchers written completely for this book.
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Extra info for Fuzzy Systems and Soft Computing in Nuclear Engineering
1 Preliminary Remarks Stochastic disturbances on the measuring signals have a negative influence on the estimation quality of the Observer. The estimation error which is amplified beyond the observer gain matrix K consists of a deterministic part (characterizing the estimation quality) and a stochastic part of the signal (characterizing the quality of filtering). The different signal parts require different values of gain matrix elements. The constant gain elements of the classical observer have to be replaced by variable elements which are determined in dependence on the stochastic features of the estimation error.
J! 6 \1 \ I ! ~. 3 02/f \\ I \ 01.. 6. 5 Pressure I MPa Membership function of the linguistic variable pressure Rule base The correlations between the linguistic variables are defined with the help of single rules which are summarized in form of the rule base. 7. The time characteristics of the heat flux determined by the dynamic fuzzy controller correspond with the process behaviour calculated by the ATHLET-simulation very well. 34 t Heatflux/W -> Time I s Fig. 7. 3 Results of State Estimation Using the Fuzzy-Based Description of Heat Flux The high quality of heat flux reproduction by the fuzzy controller with feed back leads also to an improvement of the state estimation of the collapsed levels by the hybrid non-linear operating point observer.
5. Values of the linguistic variables pressure pet) and matrix elements b l and b2 bl P I U<:l. (,,~~. , ,11\ -,I , I b2 VS (very small) I ~. 8. 0 Po (MPa) Fig. 7. 9 he" (m) Fig. 8. 6). 6. 4). 20 t Matrix element ~ / mlMPa 60 60 50 50 "-'" 40 40 30 30 20 20 10 10 Fig. 9. 4, the sufficient quality of the calculated values for b 1f leads to a good correspondence between state observation and experimental results. So it could be shown, that the matrix elements can be reproduced by the fuzzybased interpolation between the output variables of the fuzzy controllers designed for the reference points in a sufficient quality.