# Harmonic Balance computations¶

## Treatments¶

Several specific treatments for Harmonic Balance computations are available such as:

## Specific function¶

antares.prepare4tsm(nharm, list_omega, list_nbblade)

Initialize the HbComputations for a TSM computation with two rows.

Parameters
• nharm – the number of harmonics of the computation

• list_omega – rotation speed of both rows expressed in radians per second

Returns

the two HbComputations

## HbComputation object¶

Defines an Almost-Periodic Computation.

The HbComputation object can ease setting up the Harmonic Balance computations.

### Input Parameters¶

The following keys can be set:

• frequencies – (type = numpy array ) – List of frequencies considered, for TSM put also the harmonics, not only the fundamental frequency

• timelevels – (type = numpy array ) – List of timelevels, default is evenly spaced timelevels on the smallest frequency

• phaselag – (type = numpy array ) – List of phaselags associated to each frequency

### Main functions¶

class antares.HbComputation

Defines an Almost-Periodic Computation. The IDFT and DFT Almost-Periodic Matrix can be computed. All the definitions are based on the following article

ap_dft_matrix(frequencies=None, timelevels=None)

Compute the Almost-Periodic DFT matrix

ap_idft_matrix(frequencies=None, timelevels=None)

Compute the Almost-Periodic IDFT matrix

ap_source_term(frequencies=None, timelevels=None)

Compute the Almost-Periodic source term which is to $$D_t[\cdot] = i A^{-1} P A$$, where $$A$$ denotes the DFT matrix, $$A^{-1}$$ the IDFT matrix and $$P = diag(-\omega_N,\cdots,\omega_0,\cdots,\omega_N )$$

conditionning()

Returns the condition number of the almost periodic IDFT matrix

get_evenly_spaced(base_frequency=None)

Set the timelevels vector as evenly spaced over the base frequency period.

optimize_timelevels(target=0.0)

p_source_term(frequencies=None, timelevels=None)