simLife (version 0.3)

woehler: Woehler experiment

Description

Simulate a Woehler experiment

Usage

woehler(S, CL, param, opt, stress, fun = lapply, cl = NULL)

Arguments

S

non-overlapping object system

CL

predefined clustered regions of objects, see simFracture

param

parameter list for random generation of individual failure times

opt

control parameters, see simTimes

stress

list of stress levels

fun

optional, if fun=mclapply use mclapply for to parallelize simulations

cl

optional, parallel cluster object, see 'snow'

Value

matrix of failure times, first colunm corresponds to the times and the second to the stress level

Details

As done in a real life scenario the Woehler diagram measures the applied stress amplitude versus number of cycles to failure. For each given stress level the function is intended to be an all-in-one wrapper function for fatigue lifetime model simulations for different stress levels which returns a matrix of failure times where each row corresponds to one stress level. The Woehler experiments can be simulated in parallel based on using the package snow possibly together with Rmpi for an MPI cluster object.

Examples

# NOT RUN {
## For use with parallel package (recommended)
# library("parallel")
# options(mc.cores=detectCores())
# options(parallel.option="mclapply")

data(AL2MC_20p_k10_F2p_S)

## generate individual failure times
opt <- list("vickers"=107,"distTol"=1,"Tmax"=10^11,
		"inAreafactor"=1.56, "outAreafactor"=1.43,
		"pointsConvHull"=10, "scale"=1e+06,"pl"=0)

par <- list("P"=c(0.01,6,0.5,75,-15,3),
		"F"=c(0,0,0,105,-12,1),
		"const"=list("Em"=68.9,"Ef"=400,"nc"=28.2,"nu"=0.33,
				"pf"=0.138,"nE"=NULL,"sigref"=276,"Vref"=5891))

# number of simulations at each stress level
nsim <- 5
# stress levels
stress <- as.list(seq(from=90,to=140,by=10))

## using no cluster
cl <- NULL
## or for use of SOCKS or MPI cluster
# library("snow")
# cl <- makeCluster(6)
## or
# cl <- makeSOCKcluster(6)
# clusterEvalQ(cl,library("simLife"))

# the following code may take some time
W <- woehler(SF, CL=NULL, par, opt, stress=rep(stress,each=nsim))

woehlerDiagram(W, NULL, yrange=c(80,145), cl=cl)

## do not forget to stop cluster if used
# stopCluster(cl)

# }