By Xiangju Zhang, Chaglin Gui
The inner combustion is standard as an influence resource in engineering. because the calls for put upon engines have elevated, tribology has come to play an more and more vital position of their development.
This ebook is an inventive blend of clever layout know-how and the tribological layout of engines: engine tribology, info technology, synthetic intelligence, non numerical algorithms, glossy layout expertise and dynamics to suggest new technique and expertise for tribological engine layout. It not just provides an efficient method of l engine layout but in addition explores a brand new development for examine and l layout method.
· an important reference for the layout of more advantageous and effective engines
· Proposes new suggestions for tribological engine design
· Combines complex layout applied sciences with conventional tribological layout equipment
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Additional info for An Intelligent System for Engine Tribological Design
There are more and more actual and effective influencing factors being gradually considered into the lubrication analysis and calculation of an engine bearing system. The relevant research work may be categorized into several aspects, which are introduced as follows accompanied with the introduction to the basic tribological design principles adopted in this book. W. Hahn^^l, Holland's approach by J. F. Booker^^l The principles of Hahn's approach are described as follows. , rotational motion and squeeze motion.
Expert systems in engineering applications. Berlin: Springer-Ver lag, 1993 12. C. Shakeri, I. Deif, P. Katragadda, et al.. Intelligent design system for design automation. Intelligent systems in Design and manufacturing III B. Gopalakrishnan and A. ), Boston: SPLE, 2000, pp 2735 Engine Tribological Design Methodology and Intelligent System 35 13. F. Kimura. Architecture and implementation subgroup—summary of the workshop discussion. Intelligent CAD, III,, H. Yoshikawa, F. Arbab, and T. , 1991, pp 43-49 14.
Under the application of the reproduction operator, the population can evolve, while improving the average quality of the population. The crossover operator produces new offspring by swapping gene segments of two parent samples. It selects a certain proportion of individuals within a population according to the crossover rate, Pc, and swaps them with fitter samples at a crossover site to generate new offspring, as shown in Fig. 1-11. The mutation operator randomly selects a gene sample according to the mutation rate, Pm, and randomly alternates its individual gene bit from 1 to 0, or vice versa, as shown in Fig.