van_der_pol Namespace Reference

Functions
void	dydt (const double t, const double mu, const double *__restrict__ y, double *__restrict__ dy)
	An implementation of the RHS of the van der Pol equation. More...
void	set_same_initial_conditions (int NUM, double **y_host, double **var_host)
	Set same ICs for all problems. More...
void	apply_mask (double *y_host)
	Not needed for van der Pol. More...
void	apply_reverse_mask (double *y_host)
	Not needed for van der Pol. More...
void	eval_jacob (const double t, const double mu, const double *__restrict__ y, double *__restrict__ jac)
	An implementation of the van der Pol jacobian. More...
void	sparse_multiplier (const double *A, const double *Vm, double *w)
	Implements Jacobian \ vector multiplication in sparse (or unrolled) form. More...

Function Documentation

apply_mask()

void van_der_pol::apply_mask

(

double *

y_host

)

Not needed for van der Pol.

In pyJac, these are used to transform the input/output vectors to deal with moving the last species mass fraction

Definition at line 41 of file ics.c.

apply_reverse_mask()

void van_der_pol::apply_reverse_mask

(

double *

y_host

)

Not needed for van der Pol.

In pyJac, these are used to transform the input/output vectors to deal with moving the last species mass fraction

Definition at line 48 of file ics.c.

dydt()

void van_der_pol::dydt	(	const double	t,
		const double	mu,
		const double *__restrict__	y,
		double *__restrict__	dy
	)

An implementation of the RHS of the van der Pol equation.

The y and dy vectors supplied here are local versions of the global state vectors. They have been transformed from the global Column major (Fortan) ordering to a local 1-D vector. Hence the vector accesses can be done in a simple manner below, i.e. y[0] -> \(y_1\), y[1] -> \(y_2\), etc.

See also

solver_generic.c

Parameters

[in]	t	The current system time
[in]	mu	The van der Pol parameter
[in]	y	The state vector
[out]	dy	The output RHS (dydt) vector

Definition at line 22 of file dydt.c.

eval_jacob()

void van_der_pol::eval_jacob	(	const double	t,
		const double	mu,
		const double *__restrict__	y,
		double *__restrict__	jac
	)

An implementation of the van der Pol jacobian.

The Jacobian is in a local Column-major (Fortran) order. As with dydt(), this function operates on local copies of the global state vector and jacobian. Hence simple linear indexing can be used here.

See also

solver_generic.c

Parameters

[in]	t	The current system time
[in]	mu	The van der Pol parameter
[in]	y	The state vector at time t
[out]	jac	The jacobian to populate

Note

To get the value at index [i, j] of the Jacobian, we multiply i by NSP, the size of the first dimension of Jacobian and add j, i.e.: jac[i, j] -> jac[i * NSP + j].

Remember that the Jacobian is flatten in column-major order, hence:

jac = [jac[0, 0], jac[1, 0], jac[0, 1], jac[1, 1]]

jac[0, 0] = \(\frac{\partial \dot{y_1}}{\partial y_1}\)

jac[1, 0] = \(\frac{\partial \dot{y_2}}{\partial y_1}\)

jac[0, 1] = \(\frac{\partial \dot{y_1}}{\partial y_2}\)

jac[1, 1] = \(\frac{\partial \dot{y_2}}{\partial y_2}\)

Definition at line 23 of file jacob.c.

set_same_initial_conditions()

void van_der_pol::set_same_initial_conditions	(	int	NUM,
		double **	y_host,
		double **	var_host
	)

Set same ICs for all problems.

Parameters

NUM	The number of initial value problems
y_host	The state vectors to initialize
var_host	The vector of \(mu\) parameters for the van der Pol equation

Definition at line 20 of file ics.c.

sparse_multiplier()

void van_der_pol::sparse_multiplier	(	const double *	A,
		const double *	Vm,
		double *	w
	)

Implements Jacobian \ vector multiplication in sparse (or unrolled) form.

Parameters

[in]	A	The (NSP x NSP) Jacobian matrix, see eval_jacob() for details on layout
[in]	Vm	The (NSP x 1) vector to multiply by
[out]	w	The (NSP x 1) vector to store the result in, \(w := A * Vm\)

Definition at line 21 of file sparse_multiplier.c.