الصفحة 1
الصفحة 1
Reliability-based Structural Design
Reliability-based Structural Design provides readers with an understanding of the fundamentals and applications of structural reliability, stochastic finite element method, reliability analysis via stochastic expansion, and optimization under uncertainty. Probability theory, statistic methods, and reliability analysis methods including Monte Carlo sampling, Latin hypercube sampling, first and second-order reliability methods, stochastic finite element method, and stochastic optimization are discussed. In addition, the use of stochastic expansions, including polynomial chaos expansion and Karhunen-Loeve expansion, for the reliability analysis of practical engineering problems is also examined. Detailed examples of practical engineering applications including an uninhabited joined-wing aircraft and a supercavitating torpedo are presented to illustrate the effectiveness of these methods.
Design and Analysis of Simulation Experiments
This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS.
