In f0nzie/zFactor: Calculate the Compressibility Factor 'z' for Hydrocarbon Gases

knitr::opts_chunk$set(echo=T, comment=NA, error=T, warning=F, message = F, fig.align = 'center')

# source("moody.R")
library(rNodal)

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

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

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

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

sapply(blasius, rNodal:::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))

Reading from a dataframe

# source("moody.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 = rNodal:::blasius.0, 
                bla1 = rNodal:::blasius.1, 
                bla2 = rNodal:::blasius.2)

#blasius <- c(blasius.0, blasius.1, blasius.2, blasius.0, blasius.1)
blasius <- list(rNodal:::blasius.0, 
                rNodal:::blasius.1, 
                rNodal:::blasius.2, 
                rNodal:::blasius.0, 
                rNodal:::blasius.1)

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

ed <- c(0.05, 0.03, 0.01, 0.005, 0.002)
re <- c(5000, 10000, 50000, 100000, 500000)
blasius <- list(rNodal:::blasius.0, 
                rNodal:::blasius.1, 
                rNodal:::blasius.2, 
                rNodal:::blasius.0, 
                rNodal:::blasius.1)

mapply(rNodal:::friction.factor, re, ed, blasius)
# this will not work as intended because is not nested
# only the first values of each vector will be taken

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

re <- c(5000, 10000, 50000, 100000, 500000)
sapply(re, function(x) rNodal:::friction.factor(x, ED = 0.005))

################################### this gives ERROR
# the condition has length > 1 and only the first element will be used
re <- c(5000, 10000, 50000, 100000, 500000)
sapply(re, function(x, y) rNodal:::friction.factor(x, y), y = c(0.005, 0.002))

# 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(0.1, 0.01, 0.005, 0.002)

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

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

friction.factor returns a list

# The friction_factor function returns 3 values:
#   fgi: initial guess 
#   FF: friction factor
#   iter: number of iterations run
rNodal:::friction.factor(REY = 5000, f1 = rNodal:::blasius.0, ED = 0.005)

Friction factors for different Blasius equations and Reynolds

If we are looking only for the friction factor:

# Get only the friction factor
rNodal:::friction.factor(REY = 5000, f1 = rNodal:::blasius.0, ED = 0.005)$FF

# Friction factors for different Blasius equations and Reynolds

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

# Reynolds as columns
# Relative Roughness as rows
# get only the friction factor
res <- sapply(blasius, function(x) 
  sapply(re, function(y) rNodal:::friction.factor(y, ED = 0.005, x)$FF)) # get only FF

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

Nested `sapply` to find best initial guess

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


blasius <- list(bla0 = rNodal:::blasius.0, 
                bla1 = rNodal:::blasius.1, 
                bla2 = rNodal:::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) rNodal:::friction.factor(y, z, x)$iter)))

The third Blasius equation takes less iterations.

# source("moody.R")
rNodal:::moody.ff(5000, 0.005)

Matrix table with nested sapply

Get the relative roughness at various Reynolds right first.

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

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

Make the nested sapply:

# source("moody.R")
# table of Reynolds vs. Relative Roughness

re <- c(2000, 7000, 10000, 50000, 100000, 500000)                  # Reynolds values
ed <- c(0.05, 0.04, 0.03, 0.02, 0.015, 0.01, 0.005, 0.002, 0.001)  # Relative roughness

res <- sapply(re, function(x) 
  sapply(ed, function(y) rNodal:::moody.ff(x, y)))   # res is a matrix

rownames(res) <- as.character(ed)    # row names
colnames(res) <- as.character(re)    # column names
res