| ode.1D {deSolve} | R Documentation |
Solves a system of ordinary differential equations resulting from 1-Dimensional multi-component transport-reaction models that include transport only between adjacent layers.
ode.1D(y, times, func, parms, nspec=NULL, dimens=NULL,
method="lsode", ...)
y |
the initial (state) values for the ODE system, a vector. If y has a name attribute, the names will be used to label the output matrix. |
times |
time sequence for which output is wanted; the first value of times must be the initial time |
func |
either an R-function that computes the values of the derivatives in the ODE system (the model definition) at time t,
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 that are required at
each point in times.
If func is a character string then integrator lsodes will be used. See details |
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 |
method |
the integrator to use, one of "vode", "lsode", "lsoda", "lsodar", "lsodes" |
... |
additional arguments passed to the integrator |
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.
lsodes. 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 lsodes is called to solve the problem.
lsodes is used to integrate, it may be necessary to specify the length of the real work array, lrw.
lrw is made, it is possible that this will be too low.
In this case, ode.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.
If the model is specified in compiled code (in a DLL), then option 2, based on lsodes is the only solution method.
For single-species 1-D models, use ode.band.
See the selected integrator for the additional options
A matrix with up to as many rows as elements in times and as many columns as elements in y plus the number of "global" values returned
in the second element of the return from func, plus an additional column (the first) for the time value.
There will be one row for each element in times unless the integrator returns with an unrecoverable error.
If y has a names attribute, it will be used to label the columns of the output value.
The output will have the attributes istate, and rstate, two vectors with several useful elements.
The first element of istate returns the conditions under which the last call to the integrator returned. Normal is istate = 2.
If verbose = TRUE, the settings of istate and rstate will be written to the screen. See the help for the selected integrator for details.
It is advisable though not mandatory to specify BOTH nspec and dimens. In this case, the solver can check whether the input makes sense
(i.e. if nspec*dimens = length(y))
Karline Soetaert <k.soetaert@nioo.knaw.nl>
ode,
ode.band for solving models with a banded Jacobian
ode.2D for integrating 2-D models
lsoda, lsode, lsodes, lsodar, vode, daspk.
# example 1
#=======================================================
# a predator and its prey diffusing on a flat surface
# in concentric circles
# 1-D model with using cylindrical coordinates
# Lotka-Volterra type biology
#=======================================================
#==================#
# Model equations #
#==================#
lvmod <- function (time, state, parms,N,rr,ri,dr,dri)
{
with (as.list(parms),{
PREY <- state[1:N]
PRED <- state[(N+1):(2*N)]
# Fluxes due to diffusion
# at internal and external boundaries: zero gradient
FluxPrey <- -Da * diff(c(PREY[1],PREY,PREY[N]))/dri
FluxPred <- -Da * diff(c(PRED[1],PRED,PRED[N]))/dri
# Biology: Lotka-Volterra model
Ingestion <- rIng * PREY*PRED
GrowthPrey <- rGrow* PREY*(1-PREY/cap)
MortPredator <- rMort* PRED
# Rate of change = Flux gradient + Biology
dPREY <- -diff(ri * FluxPrey)/rr/dr +
GrowthPrey - Ingestion
dPRED <- -diff(ri * FluxPred)/rr/dr +
Ingestion*assEff -MortPredator
return (list(c(dPREY,dPRED)))
})
}
#==================#
# Model application#
#==================#
# model parameters:
R <- 20 # total radius of surface, m
N <- 100 # 100 concentric circles
dr <- R/N # thickness of each layer
r <- seq(dr/2,by=dr,len=N) # distance of center to mid-layer
ri <- seq(0,by=dr,len=N+1) # distance to layer interface
dri<- dr # dispersion distances
parms <- c( Da =0.05, # m2/d, dispersion coefficient
rIng =0.2, # /day, rate of ingestion
rGrow =1.0, # /day, growth rate of prey
rMort =0.2 , # /day, mortality rate of pred
assEff =0.5, # -, assimilation efficiency
cap =10 ) # density, carrying capacity
# Initial conditions: both present in central circle (box 1) only
state <- rep(0,2*N)
state[1] <- state[N+1] <- 10
# RUNNING the model: #
times <-seq(0,200,by=1) # output wanted at these time intervals
# the model is solved by the two implemented methods:
# 1. Default: banded reformulation
print(system.time(
out <- ode.1D(y=state,times=times,func=lvmod,parms=parms,nspec=2,
N=N,rr=r,ri=ri,dr=dr,dri=dri)
))
# 2. Using sparse method
print(system.time(
out2 <- ode.1D(y=state,times=times,func=lvmod,parms=parms,nspec=2,
N=N,rr=r,ri=ri,dr=dr,dri=dri,method="lsodes")
))
#==================#
# Plotting output #
#==================#
# the data in 'out' consist of: 1st col times, 2-N+1: the prey
# N+2:2*N+1: predators
PREY <- out[,2:(N +1)]
filled.contour(x=times,y=r,PREY,color= topo.colors,
xlab="time, days", ylab= "Distance, m",
main="Prey density")
# Example 2.
#=======================================================
# 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 # 1-st order consumption
#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)))
} # END O2BOD
#==================#
# Model application#
#==================#
# parameters
dx <- 25 # grid size of 25 meters
v <- 1e3 # velocity, m/day
x <- seq(dx/2,5000,by=dx) # m, distance from river
N <- length(x)
r <- 0.05 # /day, first-order decay of BOD
p <- 0.5 # /day, air-sea exchange rate
O2sat <- 300 # mmol/m3 saturated oxygen conc
O2_0 <- 200 # mmol/m3 riverine oxygen conc
BOD_0 <- 1000 # mmol/m3 riverine BOD concentration
# initial conditions:
state <- c(rep(200,N),rep(200,N))
times <- seq(0,20,by=1)
# running the model
# step 1 : model spinup
out <- ode.1D (y=state,times,O2BOD,parms=NULL,nspec=2)
#==================#
# Plotting output #
#==================#
# select oxygen (first column of out:time, then BOD, then O2
O2 <- out[,(N+2):(2*N+1)]
color= topo.colors
filled.contour(x=times,y=x,O2,color= color,nlevels=50,
xlab="time, days", ylab= "Distance from river, m",
main="Oxygen")