The efficiency of the optimized PAT is improved by 1.71% and the maximum static stress on the blade is reduced by 7.98%, which shows that this method is feasible. Based on this optimization method, the PAT blade is optimized and improved. Latin hypercube sampling operates in the following manner to generate a sample of size nS from xx 1,x 2,x nX in consistency with the distributions D 1,D 2,D nX indicated in Eq. Finally, the multi-disciplinary optimization design problem of PAT blade is solved by the optimization technology combining GA-BP neural network and NSGA-II algorithm. Thus, Latin hypercube sampling displays properties between random sampling, which involves no stratification, and stratified sampling, which stratifies on S su. The Journal of Computational Finance (81111) Volume 13/Number 3, Spring 2010 Latin hypercube sampling with dependence and applications in finance Natalie. In order to save calculation time of the whole optimization design, the multi-disciplinary performance analysis of each sample in the optimization process is completed by single-coupling method. Latin-hypercube designs can be created using the following simple syntax: > lhs (n, samples, criterion, iterations) where. The hydraulic performance of each sample point (including the hydraulic pressure load on the blade surface) and the strength performance analysis of blades are completed by CFD and FEA technology respectively. The LHS experimental design method obtains the sample points of training GA-BP neural network in the design space of variables. In Monte Carlo simulation, Latin hypercube sampling (LHS) (McKay et al (1979)) is a well-known variance reduction technique for vectors of independent random variables.
Specifically, a parameterized PAT blade with cubic non-uniform B-spline curve is adopted, and the control point of blade geometry is taken as the design variable. X lhsnorm(mu,sigma,n) returns an n-by-p matrix, X, containing a Latin hypercube sample of size n from a p-dimensional multivariate normal distribution with mean vector, mu, and covariance matrix, sigma. This method includes blade parametric design, Latin Hypercube Sampling (LHS) experimental design, CFD technology, FEA technology, GA-BP neural network and NSGA-II algorithm. In order to make the pump as turbine (PAT) run efficiently and safely, a multidisciplinary optimization design method for PAT blade, which gives consideration to both the hydraulic and intensity performances, is proposed based on multidisciplinary feasibility (MDF) optimization strategy.