Extracting harmonic equations

Harmonic Balance method

Once a DifferentialEquation is defined and its harmonics specified, one can extract the harmonic equations using get_harmonic_equations, which itself is composed of the subroutines harmonic_ansatz, slow_flow, fourier_transform! and drop_powers.

The harmonic equations use an additional time variable specified as slow_time in get_harmonic_equations. This is essentially a label distinguishing the time dependence of the harmonic variables (expected to be slow) from that of the oscillating terms (expected to be fast). When the equations are Fourier-transformed to remove oscillating terms, slow_time is treated as a constant. Such an approach is exact when looking for steady states.

HarmonicBalance.get_harmonic_equations Function

get_harmonic_equations(
    diff_eom::DifferentialEquation;
    fast_time,
    slow_time,
    degree,
    jacobian
) -> HarmonicEquation

Apply the harmonic ansatz, followed by the slow-flow, Fourier transform and dropping higher-order derivatives to obtain a set of ODEs (the harmonic equations) governing the harmonics of diff_eom.

The harmonics evolve in slow_time, the oscillating terms themselves in fast_time. If no input is used, a variable T is defined for slow_time and fast_time is taken as the independent variable of diff_eom.

By default, all products of order > 1 of slow_time-derivatives are dropped, which means the equations are linear in the time-derivatives.

Example

julia> @variables t, x(t), ω0, ω, F;

# enter the simple harmonic oscillator
julia> diff_eom = DifferentialEquation( d(x,t,2) + ω0^2 * x ~ F *cos(ω*t), x);

# expand x in the harmonic ω
julia> add_harmonic!(diff_eom, x, ω);

# get equations for the harmonics evolving in the slow time T
julia> harmonic_eom = get_harmonic_equations(diff_eom)

A set of 2 harmonic equations
Variables: u1(T), v1(T)
Parameters: ω0, ω, F

Harmonic ansatz:
x(t) = u1*cos(ωt) + v1*sin(ωt)

Harmonic equations:

(ω0^2)*u1(T) + (2//1)*ω*Differential(T)(v1(T)) - (ω^2)*u1(T) ~ F

(ω0^2)*v1(T) - (ω^2)*v1(T) - (2//1)*ω*Differential(T)(u1(T)) ~ 0

source

HarmonicVariable and HarmonicEquation types

The equations governing the harmonics are stored using the two following structs. When going from the original to the harmonic equations, the harmonic ansatz $x_i(t)=\sum _{j=1}^M{u}_{i,j}(T)\cos ({\omega }_{i,j}t)+v_{i,j}(T)\sin ({\omega }_{i,j}t)$ is used. Internally, each pair $(u_{i,j},v_{i,j})$ is stored as a HarmonicVariable. This includes the identification of ${\omega }_{i,j}$ and $x_i(t)$, which is needed to later reconstruct $x_i(t)$.

HarmonicBalance.HarmonicVariable Type

mutable struct HarmonicVariable

Holds a variable stored under symbol describing the harmonic ω of natural_variable.

Fields

• symbol::Num: Symbol of the variable in the HarmonicBalance namespace.

• name::String: Human-readable labels of the variable, used for plotting.

• type::String: Type of the variable (u or v for quadratures, a for a constant, Hopf for Hopf etc.)

• ω::Num: The harmonic being described.

• natural_variable::Num: The natural variable whose harmonic is being described.

source

When the full set of equations of motion is expanded using the harmonic ansatz, the result is stored as a HarmonicEquation. For an initial equation of motion consisting of $M$ variables, each expanded in $N$ harmonics, the resulting HarmonicEquation holds $2NM$ equations of $2NM$ variables. Each symbol not corresponding to a variable is identified as a parameter.

A HarmonicEquation can be either parsed into a steady-state HarmonicBalance.Problem or solved using a dynamical ODE solver.

HarmonicBalance.HarmonicEquation Type

mutable struct HarmonicEquation

Holds a set of algebraic equations governing the harmonics of a DifferentialEquation.

Fields

• equations::Vector{Equation}: A set of equations governing the harmonics.

• variables::Vector{HarmonicVariable}: A set of variables describing the harmonics.

• parameters::Vector{Num}: The parameters of the equation set.

• natural_equation::DifferentialEquation: The natural equation (before the harmonic ansatz was used).

• jacobian::Matrix{Num}: The Jacobian of the natural equation.

source

Krylov-Bogoliubov Averaging Method

The Krylov-Bogoliubov averaging method is an alternative high-frequency expansion technique used to analyze dynamical systems. Unlike the Harmonic Balance method, which is detailed in the background section, the Krylov-Bogoliubov method excels in computing higher orders in $1/\omega$, enabling the capture of faster dynamics within a system.

HarmonicBalance.KrylovBogoliubov.get_krylov_equations Function

get_krylov_equations(
    diff_eom::DifferentialEquation;
    order,
    fast_time,
    slow_time
)

Apply the Krylov-Bogoliubov averaging method to a specific order to obtain a set of ODEs (the slow-flow equations) governing the harmonics of diff_eom.

The harmonics evolve in slow_time, the oscillating terms themselves in fast_time. If no input is used, a variable T is defined for slow_time and fast_time is taken as the independent variable of diff_eom.

Krylov-Bogoliubov averaging method can be applied up to order = 2.

Example

julia> @variables t, x(t), ω0, ω, F;

# enter the simple harmonic oscillator
julia> diff_eom = DifferentialEquation( d(x,t,2) + ω0^2 * x ~ F *cos(ω*t), x);

# expand x in the harmonic ω
julia> add_harmonic!(diff_eom, x, ω);

# get equations for the harmonics evolving in the slow time T to first order
julia> harmonic_eom = get_krylov_equations(diff_eom, order = 1)

A set of 2 harmonic equations
Variables: u1(T), v1(T)
Parameters: ω, F, ω0

Harmonic ansatz:
xˍt(t) =
x(t) = u1(T)*cos(ωt) + v1(T)*sin(ωt)

Harmonic equations:

((1//2)*(ω^2)*v1(T) - (1//2)*(ω0^2)*v1(T)) / ω ~ Differential(T)(u1(T))

((1//2)*(ω0^2)*u1(T) - (1//2)*F - (1//2)*(ω^2)*u1(T)) / ω ~ Differential(T)(v1(T))

source

Purpose and Advantages

The primary advantage of the Krylov-Bogoliubov method lies in its ability to delve deeper into high-frequency components, allowing a more comprehensive understanding of fast dynamical behaviors. By leveraging this technique, one can obtain higher-order approximations that shed light on intricate system dynamics.

However, it's essential to note a limitation: this method cannot handle multiple harmonics within a single variable, unlike some other high-frequency expansion methods.

For further information and a detailed understanding of this method, refer to Krylov-Bogoliubov averaging method on Wikipedia.