sfepy.homogenization.coefs_phononic module

class sfepy.homogenization.coefs_phononic.AcousticMassLiquidTensor(name, problem, kwargs)

get_coefs(freq)

Get frequency-dependent coefficients.

class sfepy.homogenization.coefs_phononic.AcousticMassTensor(name, problem, kwargs)

The acoustic mass tensor for a given frequency.

Returns

selfAcousticMassTensor instance

This class instance whose evaluate() method computes for a given frequency the required tensor.

Notes

eigenmomenta, eigs should contain only valid resonances.

evaluate(freq)

get_coefs(freq)

Get frequency-dependent coefficients.

to_file_txt = None

class sfepy.homogenization.coefs_phononic.AppliedLoadTensor(name, problem, kwargs)

The applied load tensor for a given frequency.

Returns

selfAppliedLoadTensor instance

This class instance whose evaluate() method computes for a given frequency the required tensor.

Notes

eigenmomenta, ueigenmomenta, eigs should contain only valid resonances.

evaluate(freq)

to_file_txt = None

class sfepy.homogenization.coefs_phononic.BandGaps(name, problem, kwargs)

Band gaps detection.

Parameters

eigensolverstr

The name of the eigensolver for mass matrix eigenvalues.

eig_range(int, int)

The eigenvalues range (squared frequency) to consider.

freq_margins(float, float)

Margins in percents of initial frequency range given by eig_range by which the range is increased.

fixed_freq_range(float, float)

The frequency range to consider. Has precedence over eig_range and freq_margins.

freq_stepfloat

The frequency step for tracing, in percent of the frequency range.

freq_epsfloat

The frequency difference smaller than freq_eps is considered zero.

zero_epsfloat

The tolerance for finding zeros of mass matrix eigenvalues.

detect_funcallable

The function for detecting the band gaps. Default is detect_band_gaps().

log_save_namestr

If not None, the band gaps log is to be saved under the given name.

raw_log_save_namestr

If not None, the raw band gaps log is to be saved under the given name.

fix_eig_range(n_eigs)

process_options()

Setup application-specific options.

Subclasses should implement this method as needed.

Returns

app_optionsStruct instance

The application options.

static save_log(filename, float_format, bg)

Save band gaps, valid flags and eigenfrequencies.

static to_file_txt(fd, float_format, bg)

class sfepy.homogenization.coefs_phononic.ChristoffelAcousticTensor(name, problem, kwargs)

process_options()

Setup application-specific options.

Subclasses should implement this method as needed.

Returns

app_optionsStruct instance

The application options.

class sfepy.homogenization.coefs_phononic.DensityVolumeInfo(name, problem, kwargs)

Determine densities of regions specified in region_to_material, and compute average density based on region volumes.

static to_file_txt(fd, float_format, dv_info)

class sfepy.homogenization.coefs_phononic.Eigenmomenta(name, problem, kwargs)

Eigenmomenta corresponding to eigenvectors.

Parameters

var_namestr

The name of the variable used in the integral.

thresholdfloat

The threshold under which an eigenmomentum is considered zero.

threshold_is_relativebool

If True, the threshold is relative w.r.t. max. norm of eigenmomenta.

transformcallable, optional

Optional function for transforming the eigenvectors before computing the eigenmomenta.

Returns

eigenmomentaStruct

The resulting eigenmomenta. An eigenmomentum above threshold is marked by the attribute ‘valid’ set to True.

process_options()

Setup application-specific options.

Subclasses should implement this method as needed.

Returns

app_optionsStruct instance

The application options.

class sfepy.homogenization.coefs_phononic.PhaseVelocity(name, problem, kwargs)

Compute phase velocity.

process_options()

Setup application-specific options.

Subclasses should implement this method as needed.

Returns

app_optionsStruct instance

The application options.

class sfepy.homogenization.coefs_phononic.PolarizationAngles(name, problem, kwargs)

Compute polarization angles, i.e., angles between incident wave direction and wave vectors. Vector length does not matter - eigenvectors are used directly.

process_options()

Setup application-specific options.

Subclasses should implement this method as needed.

Returns

app_optionsStruct instance

The application options.

class sfepy.homogenization.coefs_phononic.SchurEVP(name, problem, kwargs)

Schur complement eigenvalue problem.

post_process(eigs, mtx_s_phi, mtx_dib, problem)

prepare_matrices(problem)

A = K + B^T D^{-1} B

class sfepy.homogenization.coefs_phononic.SimpleEVP(name, problem, kwargs)

Simple eigenvalue problem.

post_process(eigs, mtx_s_phi, data, problem)

prepare_matrices(problem)

process_options()

Setup application-specific options.

Subclasses should implement this method as needed.

Returns

app_optionsStruct instance

The application options.

save(eigs, mtx_phi, problem)

sfepy.homogenization.coefs_phononic.compute_cat_dim_dim(coef, iw_dir)

Christoffel acoustic tensor part of dielectric tensor dimension.

sfepy.homogenization.coefs_phononic.compute_cat_dim_sym(coef, iw_dir)

Christoffel acoustic tensor part of piezo-coupling tensor dimension.

sfepy.homogenization.coefs_phononic.compute_cat_sym_sym(coef, iw_dir)

Christoffel acoustic tensor (part) of elasticity tensor dimension.

sfepy.homogenization.coefs_phononic.compute_eigenmomenta(em_equation, var_name, problem, eig_vectors, transform=None)

Compute the eigenmomenta corresponding to given eigenvectors.

sfepy.homogenization.coefs_phononic.cut_freq_range(freq_range, eigs, valid, freq_margins, eig_range, fixed_freq_range, freq_eps)

Cut off masked resonance frequencies. Margins are preserved, like no resonances were cut.

Returns

freq_rangearray

The new range of frequencies.

freq_range_marginsarray

The range of frequencies with prepended/appended margins equal to fixed_freq_range if it is not None.

sfepy.homogenization.coefs_phononic.describe_gaps(gaps)

sfepy.homogenization.coefs_phononic.detect_band_gaps(mass, freq_info, opts, gap_kind='normal', mtx_b=None)

Detect band gaps given solution to eigenproblem (eigs, eig_vectors). Only valid resonance frequencies (e.i. those for which corresponding eigenmomenta are above a given threshold) are taken into account.

Notes

• make freq_eps relative to ]f0, f1[ size?

sfepy.homogenization.coefs_phononic.find_zero(f0, f1, callback, freq_eps, zero_eps, mode)

For f in ]f0, f1[ find frequency f for which either the smallest (mode = 0) or the largest (mode = 1) eigenvalue of problem P given by callback is zero.

Returns

flag0, 1, or 2

The flag, see Notes below.

frequencyfloat

The found frequency.

eigenvaluefloat

The eigenvalue corresponding to the found frequency.

Notes

Meaning of the return value combinations:

mode	flag	meaning
0, 1	0	eigenvalue -> 0 for f in ]f0, f1[
0	1	f -> f1, smallest eigenvalue < 0
0	2	f -> f0, smallest eigenvalue > 0 and -> -infty
1	1	f -> f1, largest eigenvalue < 0 and -> +infty
1	2	f -> f0, largest eigenvalue > 0

sfepy.homogenization.coefs_phononic.get_callback(mass, method, mtx_b=None, mode='trace')

Return callback to solve band gaps or dispersion eigenproblem P.

Notes

Find zero callbacks return:

eigenvalues

Trace callbacks return:

(eigenvalues,)

or

(eigenvalues, eigenvectors) (in full (dispoersion) mode)

If mtx_b is None, the problem P is

M w = lambda w,

otherwise it is

omega^2 M w = eta B w

sfepy.homogenization.coefs_phononic.get_gap_ranges(freq_range, gaps, kinds)

For each (potential) band gap in gaps, return the frequency ranges of its parts according to kinds.

sfepy.homogenization.coefs_phononic.get_log_freqs(f0, f1, df, freq_eps, n_point_min, n_point_max)

Get logging frequencies.

The frequencies get denser towards the interval boundaries.

sfepy.homogenization.coefs_phononic.get_ranges(freq_range, eigs)

Get an eigenvalue range slice and a corresponding initial frequency range within a given frequency range.

sfepy.homogenization.coefs_phononic.split_chunks(indx)

Split index vector to chunks of consecutive numbers.