rootSolve (version 1.8.2.4)

steady.1D: Steady-state solver for multicomponent 1-D ordinary differential equations

Description

Estimates the steady-state condition for a system of ordinary differential equations that result from 1-Dimensional partial differential equation models that have been converted to ODEs by numerical differencing.

It is assumed that exchange occurs only between adjacent layers.

Usage

steady.1D(y, time = NULL, func, parms = NULL, 
          nspec = NULL, dimens = NULL, 
          names = NULL, method = "stode", jactype = NULL, 
          cyclicBnd = NULL, bandwidth = 1, times = time, ...)

Value

A list containing

y

if names is not given: a vector with the state variable values from the last iteration during estimation of steady-state condition of the system of equations. if names is given, a matrix with one column for every steady-state *component*.

...

the number of "global" values returned.

The output will have the attribute steady, which returns TRUE, if steady-state has been reached and the attribute

precis with the precision attained during each iteration.

Arguments

y

the initial guess of (state) values for the ODE system, a vector.

time, times

time for which steady-state is wanted; the default is times=0 (for method = "stode" or method = "stodes", and times = c(0,Inf) for method = "runsteady" ). (note- since version 1.7, 'times' has been added as an alias to 'time').

func

either an R-function that computes the values of the derivatives in the ode system (the model defininition) at time time, or a character string giving the name of a compiled function in a dynamically loaded shared library.

If func is an R-function, it must be defined as: yprime = func(t, y, parms,...). t is the current time point in the integration, y is the current estimate of the variables in the ODE system. If the initial values y has a names attribute, the names will be available inside func. parms is a vector or list of parameters; ... (optional) are any other arguments passed to the function.

The return value of func should be a list, whose first element is a vector containing the derivatives of y with respect to time, and whose next elements are global values whose steady-state value is also required.

The derivatives should be specified in the same order as the state variables y.

parms

parameters passed to func.

nspec

the number of *species* (components) in the model. If NULL, then dimens should be specified.

dimens

the number of *boxes* in the model. If NULL, then nspec should be specified.

names

the names of the components; used to label the output, which will be written as a matrix.

method

the solution method, one of "stode", "stodes", or "runsteady". When method = 'runsteady', then solver lsode is used by default, unless argument jactype is set to "sparse", in which case lsodes is used and the structure of the jacobian is determined by the solver.

jactype

the jacobian type - default is a regular 1-D structure where layers only interact with adjacent layers. If the structure does not comply with this, the jacobian can be set equal to 'sparse' if method = stodes. If jactype = 'sparse' and method = runsteady then lsodes, the sparse integrator, will be used.

cyclicBnd

if a cyclic boundary exists, a value of 1 else NULL; see details.

bandwidth

the number of adjacent boxes over which transport occurs. Normally equal to 1 (box i only interacts with box i-1, and i+1). Values larger than 1 will not work with method = "stodes".

...

additional arguments passed to the solver function as defined by method. See example for the use of argument positive to enforce positive values (pos = TRUE).

Author

Karline Soetaert <[e-mail]>

Details

This is the method of choice for multi-species 1-dimensional models, that are only subjected to transport between adjacent layers

More specifically, this method is to be used if the state variables are arranged per species:

A[1],A[2],A[3],....B[1],B[2],B[3],.... (for species A, B))

Two methods are implemented.

• The default method rearranges the state variables as A[1],B[1],...A[2],B[2],...A[3],B[3],.... This reformulation leads to a banded Jacobian with (upper and lower) half bandwidth = number of species. Then function stode solves the banded problem.

• The second method uses function stodes. Based on the dimension of the problem, the method first calculates the sparsity pattern of the Jacobian, under the assumption that transport is only occurring between adjacent layers. Then stodes is called to solve the problem.

  As stodes is used to estimate steady-state, it may be necessary to specify the length of the real work array, lrw.

  Although a reasonable guess of lrw is made, it is possible that this will be too low. In this case, steady.1D will return with an error message telling the size of the work array actually needed. In the second try then, set lrw equal to this number.

For single-species 1-D models, use steady.band.

If state variables are arranged as (e.g. A[1],B[1],A[2],B[2],A[3],B[3],... then the model should be solved with steady.band

In some cases, a cyclic boundary condition exists. This is when the first box interacts with the last box and vice versa. In this case, there will be extra non-zero fringes in the Jacobian which need to be taken into account. The occurrence of cyclic boundaries can be toggled on by specifying argument cyclicBnd=1. If this is the case, then the steady-state will be estimated using stodes. The default is no cyclic boundaries.

See Also

plot.steady1D for plotting the output of steady.1D

steady, for a general interface to most of the steady-state solvers

steady.band, to find the steady-state of ODE models with a banded Jacobian

steady.2D, steady.3D, steady-state solvers for 2-D and 3-D partial differential equations.

stode, iterative steady-state solver for ODEs with full or banded Jacobian.

stodes, iterative steady-state solver for ODEs with arbitrary sparse Jacobian.

runsteady, steady-state solver by dynamically running to steady-state

Examples

## =======================================================================
##  EXAMPLE 1: BOD + O2                                
## =======================================================================
## Biochemical Oxygen Demand (BOD) and oxygen (O2) dynamics
## in a river

#==================#
# Model equations  #
#==================#
O2BOD <- function(t, state, pars) {

  BOD <- state[1:N]
  O2  <- state[(N+1):(2*N)]

# BOD dynamics
  FluxBOD <-  v * c(BOD_0, BOD)  # fluxes due to water transport
  FluxO2  <-  v * c(O2_0, O2)
  
  BODrate <- r*BOD*O2/(O2+10)  # 1-st order consumption, Monod in oxygen

#rate of change = flux gradient - consumption  + reaeration (O2)
  dBOD         <- -diff(FluxBOD)/dx  - BODrate
  dO2          <- -diff(FluxO2)/dx   - BODrate + p*(O2sat-O2)

  return(list(c(dBOD = dBOD, dO2 = dO2), BODrate = BODrate))

}    # END O2BOD
 
 
#==================#
# Model application#
#==================#
# parameters
dx      <- 100       # grid size, meters
v       <- 1e2       # velocity, m/day
x       <- seq(dx/2, 10000, by = dx)  # m, distance from river
N       <- length(x)
r       <- 0.1       # /day, first-order decay of BOD
p       <- 0.1       # /day, air-sea exchange rate
O2sat   <- 300       # mmol/m3 saturated oxygen conc
O2_0    <- 50        # mmol/m3 riverine oxygen conc
BOD_0   <- 1500      # mmol/m3 riverine BOD concentration

# initial guess:
state <- c(rep(200, N), rep(200, N))

# solving the model - note: pos=TRUE only allows positive concentrations
print(system.time(
 out   <- steady.1D (y = state, func = O2BOD, parms = NULL,
                     nspec = 2, pos = TRUE, names = c("BOD", "O2"))))

#==================#
# Plotting output  #
#==================#
mf <- par(mfrow = c(2, 2))
plot(x, out$y[ ,"O2"], xlab =  "Distance from river",
     ylab = "mmol/m3", main = "Oxygen", type = "l")

plot(x, out$y[ ,"BOD"], xlab = "Distance from river",
     ylab = "mmol/m3", main = "BOD", type = "l")

plot(x, out$BODrate, xlab = "Distance from river",
     ylab = "mmol/m3/d", main = "BOD decay rate", type = "l")
par(mfrow=mf)

# same plot in one command
plot(out, which = c("O2","BOD","BODrate"),xlab = "Distance from river",
     ylab = c("mmol/m3","mmol/m3","mmol/m3/d"), 
     main = c("Oxygen","BOD","BOD decay rate"), type = "l")

# same, but now running dynamically to steady-state - no need to set pos = TRUE
print(system.time(
out2 <- steady.1D (y = state, func = O2BOD, parms = NULL, nspec = 2,
                   time = c(0, 1000), method = "runsteady", 
                   names = c("BOD", "O2"))))
                    
# plot all state variables at once, against "x":
plot(out2, grid=x, xlab = "Distance from river",
     ylab = "mmol/m3", type = "l", lwd = 2)
                         
plot(out, out2, grid=x, xlab = "Distance from river", which = "BODrate",
     ylab = "mmol/m3", type = "l", lwd = 2)

## =======================================================================
##   EXAMPLE 2: Silicate diagenesis                      
## =======================================================================
## Example from the book:
## Soetaert and Herman (2009).
## a practical guide to ecological modelling -
## using R as a simulation platform.
## Springer

#====================#
# Model equations    #
#====================#

SiDIAmodel <- function (time = 0,    # time, not used here
                        Conc,        # concentrations: BSi, DSi
                        parms = NULL) # parameter values; not used
{
 BSi<- Conc[1:N]
 DSi<- Conc[(N+1):(2*N)]

# transport           
# diffusive fluxes at upper interface of each layer

# upper concentration imposed (bwDSi), lower: zero gradient
 DSiFlux <- -SedDisp *   IntPor *diff(c(bwDSi ,DSi,DSi[N]))/thick    
 BSiFlux <- -Db      *(1-IntPor)*diff(c(BSi[1],BSi,BSi[N]))/thick 

 BSiFlux[1] <- BSidepo                # upper boundary flux is imposed

# BSi dissolution     
 Dissolution <- rDissSi * BSi*(1.- DSi/EquilSi )^pow 
 Dissolution <- pmax(0,Dissolution)

# Rate of change= Flux gradient, corrected for porosity + dissolution
 dDSi     <- -diff(DSiFlux)/thick/Porosity      +    # transport
              Dissolution * (1-Porosity)/Porosity    # biogeochemistry

 dBSi     <- -diff(BSiFlux)/thick/(1-Porosity)  - Dissolution				

 return(list(c(dBSi, dDSi),           # Rates of changes
        Dissolution = Dissolution,    # Profile of dissolution rates
        DSiSurfFlux = DSiFlux[1],     # DSi sediment-water exchange rate 
        DSIDeepFlux = DSiFlux[N+1],   # DSi deep-water (burial) flux
        BSiDeepFlux = BSiFlux[N+1]))  # BSi deep-water (burial) flux
}

#====================#
# Model run          #
#====================#
# sediment parameters
thick    <- 0.1                       # thickness of sediment layers (cm)
Intdepth <- seq(0, 10, by = thick)    # depth at upper interface of layers
Nint     <- length(Intdepth)          # number of interfaces
Depth    <- 0.5*(Intdepth[-Nint] +Intdepth[-1]) # depth at middle of layers
N        <- length(Depth)                       # number of layers

por0    <- 0.9                         # surface porosity (-)
pordeep <- 0.7                         # deep porosity    (-)
porcoef <- 2                           # porosity decay coefficient  (/cm)
# porosity profile, middle of layers
Porosity <- pordeep + (por0-pordeep)*exp(-Depth*porcoef)    
# porosity profile, upper interface 
IntPor   <- pordeep + (por0-pordeep)*exp(-Intdepth*porcoef)  

dB0      <- 1/365           # cm2/day      - bioturbation coefficient
dBcoeff  <- 2
mixdepth <- 5               # cm
Db       <- pmin(dB0, dB0*exp(-(Intdepth-mixdepth)*dBcoeff))

# biogeochemical parameters
SedDisp  <- 0.4             # diffusion coefficient, cm2/d
rDissSi  <- 0.005           # dissolution rate, /day
EquilSi  <- 800             # equilibrium concentration
pow      <- 1
BSidepo  <- 0.2*100         # nmol/cm2/day
bwDSi    <- 150             # mmol/m3

# initial guess of state variables-just random numbers between 0,1
Conc     <- runif(2*N)

# three runs with different deposition rates                                      
BSidepo  <- 0.2*100          # nmol/cm2/day
sol  <- steady.1D (Conc, func = SiDIAmodel, parms = NULL, nspec = 2, 
                   names = c("DSi", "BSi"))

BSidepo  <- 2*100          # nmol/cm2/day
sol2 <- steady.1D (Conc, func = SiDIAmodel, parms = NULL, nspec = 2, 
                   names = c("DSi", "BSi"))

BSidepo  <- 3*100          # nmol/cm2/day
sol3 <- steady.1D (Conc, func = SiDIAmodel, parms = NULL, nspec = 2, 
                   names = c("DSi", "BSi"))

#====================#
# plotting output    #
#====================#
par(mfrow=c(2,2))

# Plot 3 runs 
plot(sol, sol2, sol3, xyswap = TRUE, mfrow = c(2, 2),
     xlab = c("mmolSi/m3 liquid", "mmolSi/m3 solid"), 
     ylab = "Depth", lwd = 2, lty = 1)
legend("bottom", c("0.2", "2", "3"), title = "mmol/m2/d",
       lwd = 2, col = 1:3)
plot(Porosity, Depth, ylim = c(10, 0), xlab = "-" ,
     main = "Porosity",    type = "l", lwd = 2)
plot(Db, Intdepth, ylim = c(10, 0), xlab = "cm2/d",
     main = "Bioturbation", type = "l", lwd = 2)
mtext(outer = TRUE, side = 3, line = -2, cex = 1.5, "SiDIAmodel")

# similar, but shorter
plot(sol, sol2, sol3, vertical =TRUE,
     lwd = 2, lty = 1,
     main = c("DSi [mmol/m3 liq]","BSi [mmol/m3 sol]"),
     ylab= "depth [cm]")
legend("bottom", c("0.2", "2", "3"), title = "mmol/m2/d",
       lwd = 2, col = 1:3)