# 1 A simple differential equation

The examples in this section show how a single ordinary differential equation can be solved with wasora. Indeed this is one of its main features, namely the ability to solve systems of differential-algebraic equations written as natural algebraic expressions. In particular, the equation the examples solve is

\frac{dx}{dt} = -x

with the initial condition x_0 = 1, which has the trivial analytical solution x(t) = e^{-t}.

## 1.1 exp.was

As clearly defined in wasoraâ€™s design basis, simple problems ought to be solved by means of simple inputs. Here is a solid example of this behavior.

end_time = 1        # transient problem
PHASE_SPACE x       # DAE problem with one variable
x_0 = 1             # initial condition
x_dot .= -x         # differential equation
PRINT t x HEADER    # output
# exercise: plot dt vs t and see what happens
$wasora exp.was | qdp -o exp --pi 1$ 

By default, wasora adjusts the time step so an estimation of the relative numerical error is bounded within a range given by the variable rel_error, which has an educated guess by default. It can be seen in the figure that dt starts with small values and grows as the conditions allow it.

## 1.2 exp-dt.was

The time steps wasora take can be limited by means of the special variables min_dt and max_dt. If they both are zero (as they are by default), wasora is free to choose dt as it considers appropriate. If max_dt is non-zero, dt will be bounded even if the conditions are such that bigger time steps would not introduce large errors. On the other hand, if min_dt is non-zero, the time step is guaranteed not to be smaller that the specified value. However, it should be noted that wasora may need to take several internal times step to keep the error bounded. In the limiting case where min_dt = max_dt, the time step can be set exactly although, again, wasora may take internal steps.

This situation is illustrated with the following input, which is run for five combinations of min_dt and max_dt.

end_time = 2
min_dt = $1 max_dt =$2
rel_error = 1e-3
PHASE_SPACE x
x_0 = 1
x_dot .= -x
PRINT t x (x-exp(-t))/x
$wasora exp-dt.was 0 0 > exp-dt1.dat$ wasora exp-dt.was 0.1 0   > exp-dt2.dat
$wasora exp-dt.was 0 0.1 > exp-dt3.dat$ wasora exp-dt.was 0.1 0.1 > exp-dt4.dat
$wasora exp-dt.was 1 1 > exp-dt5.dat$ pyxplot exp-dt.ppl; pdf2svg exp-dt.pdf exp-dt.svg; rm -f exp-dt.pdf
$pyxplot exp-error.ppl; pdf2svg exp-error.pdf exp-error.svg; rm -f exp-error.pdf$ 

It can be seen that all the solutions coincide with the analytical expression. Even if the time step is set to a big fixed value, the error commited by the numerical solver with respect to the exact solution is the same as wasora iterates internally as needed. In general, the fastest condition is where dt is not bounded as wasora minimizes iterations by automatically adjusting its value. However, it is clear that controlling the time step can be useful some times. A further control can be obtained by means of the TIME_PATH keyword.