In f0nzie/rNodal: Oil and Gas Well Nodal Analysis for Production Petroleum Engineering

source("../R/friction.R")

# calculate `f` for various Reynolds numbers
re <- c(5000, 10000, 50000, 100000, 500000)
sapply(re, friction.factor, ED = 0.005, f1 = blasius.0)

# THis chunk giving error. REVISE!
# calculate `f` for various Reynolds numbers
re <- c(5000, 10000, 50000, 100000, 500000)
sapply(re, function(x) friction.factor(re, ED = 0.002))

# calculate `f` for various relative roughness values
ed <- c(0.05, 0.03, 0.01, 0.005, 0.002)
sapply(ed, friction.factor, REY = 1E5, f1 = blasius.0)

# calculate `f` for various Blasius equations
blasius <- list(bla0 = blasius.0, bla1 = blasius.1, bla2 = blasius.2)
sapply(blasius, friction.factor, REY = 1E5, ED = 0.005)

# using apply

# http://stackoverflow.com/questions/17685866/apply-function-using-multiple-changing-arguments-in-r

myfunc <- function(var1, var2, var3){
  result <- var1*var2*var3
  return(result)
}

data <-data.frame(a=c(1,2,3), b=c(4,5,6),c= c(7,8,9))
apply(data, 1, function(x, y, z) myfunc(data$a, data$b, data$c))

# using a list and sapply
myfunc <- function(var1, var2, var3){
  result <- var1*var2*var3
  return(result)
}

data <- list(a=c(1,2,3), b=c(4,5,6),c= c(7,8,9))
sapply(data, function(x, y, z) myfunc(data$a, data$b, data$c))

a<- 1:5
b<-sapply(a, function(x) sapply(a, function(y) x+y))
b

sapply(a, identity)

sapply(a, function(x) a)
sapply(a, function(x) c(a, a*a))
sapply(a, function(x) a*a)

foo <- function(bar) { return( 2 + bar ) }
foo2 <- function(bar) { return( 2 * bar ) }
df <- data.frame(c('foo', 'foo2'), stringsAsFactors = FALSE)

do.call(df[1, 1], list(4))
do.call(df[2, 1], list(4))

####################### giving error
source("../R/friction.R")

ed <- c(0.05, 0.03, 0.01, 0.005, 0.002)
re <- c(5000, 10000, 50000, 100000, 500000)

# calculate `f` for various Blasius equations
blasius <- list(bla0 = blasius.0, bla1 = blasius.1, bla2 = blasius.2)
#blasius <- c(blasius.0, blasius.1, blasius.2, blasius.0, blasius.1)
blasius <- list(blasius.0, blasius.1, blasius.2, blasius.0, blasius.1)

# data <- list(re = re, ed = ed, fun = blasius)
data <- data.frame(re = re, ed = ed)
sapply(data, function(x) friction.factor(data$re, ED=0.005, f1=blasius.0))

blasius <- list(blasius.0, blasius.1, blasius.2, blasius.0, blasius.1)

mapply(friction.factor, re, ed, blasius)

# nested sapply
cols <- 1:4
rows <- 1:10
sapply(cols, function(x) sapply(rows, function(y) y))

sapply(re, function(x) friction.factor(x, ED = 0.005))

################################### this gives ERROR
# the condition has length > 1 and only the first element will be used
sapply(re, function(x, y) friction.factor(x, y), y = c(0.005, 0.002))

####################################### giving error
# nested `sapply`. find FRiction Factors for different combinations
# of Reynolds and Relative Roughness
source("moody.R")

re <- c(5000, 10000, 50000, 100000, 500000)
ed <- c(0.01, 0.005, 0.002)

# Reynolds as columns
# Relative Roughness as rows
res <- sapply(re, function(x) 
  sapply(ed, function(y) friction.factor(x,y)))

rownames(res) <- as.character(ed)
colnames(res) <- as.character(re)
res

############################# giving error
# Friction factors for different Blasius equations and Reynolds
source("moody.R")

blasius <- list(bla0 = blasius.0, bla1 = blasius.1, bla2 = blasius.2)
re <- c(5000, 10000, 50000, 100000, 500000)

# Reynolds as columns
# Relative Roughness as rows
res <- sapply(blasius, function(x) 
  sapply(re, function(y) friction.factor(y, ED = 0.005, x)))

rownames(res) <- as.character(re)
res

# Sets of Friction Factors for different values of:
  # Blasius equation
  # Reynolds number
  # Relative Roughness

source("moody.R")

blasius <- list(bla0 = blasius.0, bla1 = blasius.1, bla2 = blasius.2)
re <- c(5000, 10000, 50000, 100000, 500000)
ed <- c(0.01, 0.005, 0.002)

# Reynolds as columns
# Relative Roughness as rows
lapply(blasius, function(x) 
  sapply(re, function(y) 
    sapply(ed, function(z) friction.factor(y, z, x))))

The third Blasius equation takes less iterations.

source("moody.R")
moody.ff(5000, 0.005)

source("moody.R")
re <- c(5000, 10000, 50000, 100000, 500000)

sapply(re, function(x) moody.ff(x, ed = 0.005))
# [1] 0.04261593 0.03761856 0.03214568 0.03127366 0.03052475

source("moody.R")

re <- c(2001, 7000, 10000, 50000, 100000, 500000)
ed <- c(0.05, 0.04, 0.03, 0.02, 0.015, 0.01, 0.005, 0.002, 0.001)

res <- sapply(re, function(x) 
  sapply(ed, function(y) moody.ff(x, y)))

rownames(res) <- as.character(ed)
colnames(res) <- as.character(re)
res

f0nzie/rNodal documentation built on May 6, 2019, 10:14 a.m.