def _calcTimeSeries(self, ):
"""
Compute the u,v,w, timeseries based on the spectral, coherence
and Reynold's stress models.
This method performs the work of taking a specified spectrum
and coherence function and transforming it into a spatial
timeseries. It performs the steps outlined in Veers84's [1]_
equations 7 and 8.
Returns
-------
turb : the turbulent velocity timeseries array (3 x nz x ny x
nt) for this PyTurbSim run.
Notes
-----
1) Veers84's equation 7 [1]_ is actually a 'Cholesky
Factorization'. Therefore, rather than writing this
functionality explicitly we call 'cholesky' routines to do
this work.
2) This function uses one of two methods for computing the
Cholesky factorization. If the Fortran library tslib is
available it is used (it is much more efficient), otherwise
the numpy implementation of Cholesky is used.
.. [1] Veers, Paul (1984) 'Modeling Stochastic Wind Loads on
Vertical Axis Wind Turbines', Sandia Report 1909, 17
pages.
"""
grid = self.grid
tmp = np.zeros((grid.n_comp, grid.n_z, grid.n_y, grid.n_f + 1),
dtype=ts_complex)
if dbg:
self.timer.start()
# First calculate the 'base' set of random phases:
phases = self.phase(self)
# Now correlate the phases at each point to set the Reynold's stress:
phases = self.stress.calc_phases(phases)
# Now correlate the phases between points to set the spatial coherence:
phases = self.cohere.calc_phases(phases)
# Now multiply the phases by the spectrum...
tmp[..., 1:] = np.sqrt(self.spec.array) * grid.reshape(phases)
# and compute the inverse fft to produce the timeseries:
ts = irfft(tmp)
if dbg:
self.timer.stop()
# Select only the time period requested:
# Grab a random number of where to cut the timeseries.
i0_out = self.randgen.randint(grid.n_t - grid.n_t_out + 1)
ts = ts[..., i0_out:i0_out + grid.n_t_out] / (grid.dt / grid.n_f)**0.5
ts -= ts.mean(-1)[..., None] # Make sure the turbulence has zero mean.
return ts
def ctke(self, ):
return 0.5 * np.sqrt((self.stress**2).mean(-1).sum(0))
def _sumdict(self):
out = dict()
# Start by pulling values from the config file
# if there was one.
if 'config' in self.info:
out.update(self.info['config'])
uhub = out['uhub'] = statObj(self.uhub)
out['vhub'] = statObj(self.vhub, uhub.mean)
out['whub'] = statObj(self.whub, uhub.mean)
out['hhub'] = statObj(np.sqrt(self.uhub**2 + self.vhub**2))
out['grid'] = self.grid
out['upvp'] = statObj(self.uhub * self.vhub)
out['upwp'] = statObj(self.vhub * self.whub)
out['vpwp'] = statObj(self.vhub * self.whub)
out['upvp'].scale = 1
out['upwp'].scale = 1
out['vpwp'].scale = 1
out['tke'] = statObj((self.uturb**2).sum(0))
out['tke'] = statObj((self.uturb**2).sum(0))
out['ctke'] = statObj(0.5 *
np.sqrt((self.uturb[0] * self.uturb[1])**2 +
(self.uturb[0] * self.uturb[2])**2 +
(self.uturb[1] * self.uturb[2])**2))
out['u_sigma'] = self.uturb[0].flatten().std()
out['v_sigma'] = self.uturb[1].flatten().std()
out['w_sigma'] = self.uturb[2].flatten().std()
out['TurbModel_desc'] = self.info['specModel']['description']
out['RandSeed1'] = self.info['RandSeed']
out['profModel_sumstring'] = self.info['profModel']['sumstring']
out['specModel_sumstring'] = self.info['specModel']['sumstring']
out['stressModel_sumstring'] = self.info['stressModel']['sumstring']
out['cohereModel_sumstring'] = self.info['cohereModel']['sumstring']
out['ver'] = ver
out['NowDate'] = time.strftime('%a %b %d, %Y', self.info['StartTime'])
out['NowTime'] = time.strftime('%H:%M:%S', self.info['StartTime'])
out['RunTime'] = self.info['RunTime']
out['FreqNyquist'] = self.f[-1]
out['GridBase'] = self.grid.z[0]
out['HeightOffset'] = 0.0 # Is this correct?
out['ydata'] = self.grid.y
out['z_ustd'] = np.concatenate(
(self.grid.z[:, None], self.uturb[0].std(-1)), axis=1)
out['z_vstd'] = np.concatenate(
(self.grid.z[:, None], self.uturb[1].std(-1)), axis=1)
out['z_wstd'] = np.concatenate(
(self.grid.z[:, None], self.uturb[2].std(-1)), axis=1)
u, v, w = self.uprof.mean(-1)[:, :, None]
out['WINDSPEEDPROFILE'] = np.concatenate((
self.grid.z[:, None],
np.sqrt(u**2 + v**2),
np.angle(u + 1j * v) * 180 / np.pi,
u,
v,
w,
),
axis=1)
out['HFlowAng'] = np.angle(self.uprof[0][self.ihub] +
1j * self.uprof[1][self.ihub])
out['VFlowAng'] = np.angle(self.uprof[0][self.ihub] +
1j * self.uprof[2][self.ihub])
out['TurbModel'] = self.info['specModel']['name']
out['gridheader'] = '--------- ' * self.grid.n_y
for nm in [
'Zref',
'RefHt',
'ZRef',
]:
if nm in self.info['profModel']['params']:
out['RefHt'] = self.info['profModel']['params'][nm]
for nm in [
'URef',
'Uref',
]:
if nm in self.info['profModel']['params']:
out['URef'] = self.info['profModel']['params'][nm]
out['PLExp'] = self.info['profModel']['params'].get('PLexp', None)
return out