Stochastic Methods in Engineering

Stochastic Methods in Engineering
Author :
Publisher : WIT Press
Total Pages : 379
Release :
ISBN-10 : 9781845646264
ISBN-13 : 1845646266
Rating : 4/5 (266 Downloads)

Book Synopsis Stochastic Methods in Engineering by : I. St Doltsinis

Download or read book Stochastic Methods in Engineering written by I. St Doltsinis and published by WIT Press. This book was released on 2012 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing industrial demand for reliable quantification and management of uncertainty in product performance forces engineers to employ probabilistic models in analysis and design, a fact that has occasioned considerable research and development activities in the field. Notes on Stochastics eventually address the topic of computational stochastic mechanics. The single volume uniquely presents tutorials on essential probabilistics and statistics, recent finite element methods for stochastic analysis by Taylor series expansion as well as Monte Carlo simulation techniques. Design improvement and robust optimisation represent key issues as does reliability assessment. The subject is developed for solids and structures of elastic and elasto-plastic material, large displacements and material deformation processes; principles are transferable to various disciplines. A chapter is devoted to the statistical comparison of systems exhibiting random scatter. Where appropriate examples illustrate the theory, problems to solve appear instructive; applications are presented with relevance to engineering practice. The book, emanating from a university course, includes research and development in the field of computational stochastic analysis and optimization. It is intended for advanced students in engineering and for professionals who wish to extend their knowledge and skills in computational mechanics to the domain of stochastics. Contents: Introduction, Randomness, Structural analysis by Taylor series expansion, Design optimization, Robustness, Monte Carlo techniques for system response and design improvement, Reliability, Time variant phenomena, Material deformation processes, Analysis and comparison of data sets, Probability distribution of test functions.


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