# -*- coding: utf-8 -*-
"""
Created on Mon Nov 2 15:23:15 2015
@author: dave
"""
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import range
from builtins import zip
from builtins import dict
from builtins import str
from builtins import int
from future import standard_library
standard_library.install_aliases()
from builtins import object
import os
import numpy as np
#import scipy.interpolate as interpolate
import pandas as pd
from matplotlib import pyplot as plt
from wetb.prepost import Simulations as sim
from wetb.prepost import dlcdefs
from wetb.prepost import hawcstab2 as hs2
from wetb.prepost import mplutils
class ConfigBase(object):
def __init__(self):
pass
def set_master_defaults(self):
"""Create a set of default master tags that are required for proper
compatibility with Simulations.py
"""
mt = {}
# =====================================================================
# required tags and their defaults
# =====================================================================
mt['[dt_sim]'] = 0.01
mt['[hawc2_exe]'] = 'hawc2-latest'
# convergence_limits 0.001 0.005 0.005 ;
# critical one, risidual on the forces: 0.0001 = 1e-4
mt['[epsresq]'] = '1.0' # default=10.0
# increment residual
mt['[epsresd]'] = '0.5' # default= 1.0
# constraint equation residual
mt['[epsresg]'] = '1e-8' # default= 1e-7
# folder names for the saved results, htc, data, zip files
# Following dirs are relative to the model_dir_server and they specify
# the location of where the results, logfiles, animation files that where
# run on the server should be copied to after the simulation has finished.
# on the node, it will try to copy the turbulence files from these dirs
mt['[animation_dir]'] = 'animation/'
mt['[control_dir]'] = 'control/'
mt['[data_dir]'] = 'data/'
mt['[eigen_analysis]'] = False
mt['[eigenfreq_dir]'] = False
mt['[htc_dir]'] = 'htc/'
mt['[log_dir]'] = 'logfiles/'
mt['[meander_dir]'] = False
mt['[opt_dir]'] = False
mt['[pbs_out_dir]'] = 'pbs_out/'
mt['[res_dir]'] = 'res/'
mt['[iter_dir]'] = 'iter/'
mt['[turb_dir]'] = 'turb/'
mt['[turb_db_dir]'] = '../turb/'
mt['[wake_dir]'] = False
mt['[hydro_dir]'] = False
mt['[mooring_dir]'] = False
mt['[externalforce]'] = False
mt['[Case folder]'] = 'NoCaseFolder'
# zip_root_files only is used when copy to run_dir and zip creation, define
# in the HtcMaster object
mt['[zip_root_files]'] = []
# only active on PBS level, so files have to be present in the run_dir
mt['[copyback_files]'] = [] # copyback_resultfile
mt['[copyback_frename]'] = [] # copyback_resultrename
mt['[copyto_files]'] = [] # copyto_inputfile
mt['[copyto_generic]'] = [] # copyto_input_required_defaultname
# =====================================================================
# required tags by HtcMaster and PBS in order to function properly
# =====================================================================
# the express queue ('#PBS -q xpresq') has a maximum walltime of 1h
mt['[pbs_queue_command]'] = '#PBS -q workq'
# walltime should have following format: hh:mm:ss
mt['[walltime]'] = '04:00:00'
mt['[auto_walltime]'] = False
return mt
def opt_tags_h2_eigenanalysis(self, basename):
"""Return opt_tags suitable for a standstill HAWC2 eigen analysis.
"""
opt_tags = [self.opt_h2.copy()]
opt_tags[0].update(self.eigenan.copy())
opt_tags[0]['[Case id.]'] = '%s_hawc2_eigenanalysis' % basename
opt_tags[0]['[blade_damp_x]'] = 0.0
opt_tags[0]['[blade_damp_y]'] = 0.0
opt_tags[0]['[blade_damp_z]'] = 0.0
opt_tags[0]['[blade_nbodies]'] = 1
opt_tags[0]['[Windspeed]'] = 0.0
opt_tags[0]['[initspeed_rotor_rads]'] = 0.0
opt_tags[0]['[operational_data]'] = 'case-turbine2-empty.opt'
return opt_tags
def opt_tags_hs_structure_body_eigen(self, basename):
"""Return opt_tags suitable for a standstill HAWCStab2 body eigen
analysis, at 0 RPM.
"""
opt_tags = [self.opt_hs2.copy()]
opt_tags[0]['[Case id.]'] = '%s_hawc2_eigenanalysis' % basename
opt_tags[0]['[blade_damp_x]'] = 0.0
opt_tags[0]['[blade_damp_y]'] = 0.0
opt_tags[0]['[blade_damp_z]'] = 0.0
opt_tags[0]['[blade_nbodies]'] = 1
opt_tags[0]['[Windspeed]'] = 0.0
opt_tags[0]['[initspeed_rotor_rads]'] = 0.0
opt_tags[0]['[fixspeed_rotor_rads]'] = 0.0
opt_tags[0]['[operational_data]'] = 'case-turbine2-empty.opt'
return opt_tags
def opt_tags_hs2(self, basename):
opt_tags = [self.opt_hs2.copy()]
opt_tags[0]['[Case id.]'] = '%s_hawcstab2' % basename
return opt_tags
def set_hs2opdata(self, master, basename):
"""Load the HS2 operational data file and create opt_tags for HAWC2
cases.
Returns
-------
opt_tags : list of dicts
"""
fpath = os.path.join(master.tags['[data_dir]'],
master.tags['[operational_data]'])
hs2_res = hs2.results()
hs2_res.load_operation(fpath)
omegas = hs2_res.operation.rotorspeed_rpm.values*np.pi/30.0
winds = hs2_res.operation.windspeed.values
pitchs = -1.0*hs2_res.operation.pitch_deg.values
return self.set_opdata(winds, pitchs, omegas, basename=basename)
def set_opdata(self, winds, pitchs, omegas, basename=None):
"""Return opt_tags for HAWC2 based on an HAWCStab2 operational data
file.
Parameters
----------
winds : ndarray(n)
wind speed for given operating point [m/s]
pitchs : ndarray(n)
pitch angle at given operating point [deg]
omegas : ndarray(n)
rotor speed at given operating point [rad/s]
basename : str, default=None
If not None, the [Case id.] tag is composed out of the basename,
wind speed, pitch angle and rotor speed. If set to None, the
[Case id.] tag is not set.
Returns
-------
opt_tags : list of dicts
"""
# the HAWC2 cases
opt_tags = []
for wind, pitch, omega in zip(winds, pitchs, omegas):
opt_dict = {}
opt_dict.update(self.opt_h2.copy())
opt_dict.update(self.fix_op.copy())
rpl = (basename, wind, pitch, omega)
if basename is not None:
tmp = '%s_%02.0fms_%04.01fdeg_%04.02frads_hawc2' % rpl
opt_dict['[Case id.]'] = tmp
opt_dict['[Windspeed]'] = wind
opt_dict['[blade_pitch_deg]'] = pitch
opt_dict['[fixspeed_rotor_rads]'] = omega
opt_dict['[initspeed_rotor_rads]'] = omega
# opt_dict['[t0]'] = int(2000.0/opt_dict['[Windspeed]']) # or 2000?
# opt_dict['[time stop]'] = opt_dict['[t0]']+100
# opt_dict['[time_stop]'] = opt_dict['[t0]']+100
opt_tags.append(opt_dict.copy())
return opt_tags
class Sims(object):
def __init__(self, sim_id, P_MASTERFILE, MASTERFILE, P_SOURCE, P_RUN,
PROJECT, POST_DIR, master_tags_default):
"""
Create HtcMaster() object
=========================
the HtcMaster contains all the settings to start creating htc files.
It holds the master file, server paths and more.
The master.tags dictionary holds those tags who do not vary for different
cases. Variable tags, i.e. tags who are a function of other variables
or other tags, are defined in the function variable_tag_func().
It is considered as good practice to define the default values for all
the variable tags in the master_tags
Parameters
----------
sim_id : str
P_MASTERFILE : str
MASTERFILE : str
P_SOURCE : str
P_RUN : str
PROJECT : str
POST_DIR : str
master_tags_default : dict
Dictionary with the default master tag values. Should be created
by the turbine specific class Configurations.set_master_defaults()
Members
-------
Returns
-------
"""
self.sim_id = sim_id
self.P_MASTERFILE = P_MASTERFILE
self.MASTERFILE = MASTERFILE
self.P_SOURCE = P_SOURCE
self.P_RUN = P_RUN
self.PROJECT = PROJECT
self.POST_DIR = POST_DIR
# TODO: write a lot of logical tests for the tags!!
# TODO: tests to check if the dirs are setup properly (ending slahses)
# FIXME: some tags are still variable! Only static tags here that do
# not depent on any other variable that can change
self.master = sim.HtcMaster()
self.master.tags.update(master_tags_default)
def _var_tag_func(self, master, case_id_short=False):
"""
Function which updates HtcMaster.tags and returns an HtcMaster object
Only use lower case characters for case_id since a hawc2 result and
logfile are always in lower case characters. Simulations.prepare_launch
will force the value of the tags as defined in master.output_dirs
to lower case.
BE CAREFULL: if you change a master tag that is used to dynamically
calculate an other tag, that change will be propageted over all cases,
for example:
master.tags['tag1'] *= master.tags[tag2]*master.tags[tag3']
it will accumlate over each new case. After 20 cases
master.tags['tag1'] = (master.tags[tag2]*master.tags[tag3'])^20
which is not wanted, you should do
master.tags['tag1'] = tag1_base*master.tags[tag2]*master.tags[tag3']
"""
mt = master.tags
dlc_case = mt['[Case folder]']
mt['[data_dir]'] = 'data/'
mt['[res_dir]'] = 'res/%s/' % dlc_case
mt['[log_dir]'] = 'logfiles/%s/' % dlc_case
mt['[htc_dir]'] = 'htc/%s/' % dlc_case
mt['[case_id]'] = mt['[Case id.]']
mt['[DLC]'] = dlc_case
mt['[pbs_out_dir]'] = 'pbs_out/%s/' % dlc_case
mt['[pbs_in_dir]'] = 'pbs_in/%s/' % dlc_case
mt['[iter_dir]'] = 'iter/%s/' % dlc_case
if mt['[eigen_analysis]']:
rpl = os.path.join(dlc_case, mt['[Case id.]'])
mt['[eigenfreq_dir]'] = 'res_eigen/%s/' % rpl
# for HAWCStab2 certain things have to be done differently
if mt['[hs2]']:
mt['[htc_dir]'] = ''
mt['[t0]'] = 0
mt['[time stop]'] = 1
mt['[hawc2]'] = False
mt['[output]'] = False
mt['[copyback_files]'] = ['./*.ind', './*.pwr', './*.log',
'./*.cmb', './*.bea']
mt['[copyback_frename]'] = [mt['[res_dir]'], mt['[res_dir]'],
mt['[log_dir]'], mt['[res_dir]'],
mt['[res_dir]']]
if mt['[hs2_bladedeform_switch]']:
mt['[hs2_bladedeform]'] = 'bladedeform'
else:
mt['[hs2_bladedeform]'] = 'nobladedeform'
if int(mt['[tip_loss]']) == 1:
mt['[hs2_tipcorrect]'] = 'tipcorrect'
else:
mt['[hs2_tipcorrect]'] = 'notipcorrect'
if int(mt['[Induction]']) == 1:
mt['[hs2_induction]'] = 'induction'
else:
mt['[hs2_induction]'] = 'noinduction'
if mt['[hs2_gradients_switch]']:
mt['[hs2_gradients]'] = 'gradients'
else:
mt['[hs2_gradients]'] = 'nogradients'
mt['[windspeed]'] = mt['[Windspeed]']
mt['[time_stop]'] = mt['[time stop]']
mt['[duration]'] = str(float(mt['[time_stop]']) - float(mt['[t0]']))
return master
def _set_path_auto_config(self, verbose=True):
"""
auto configure directories: assume you are running in the root of the
relevant HAWC2 model
and assume we are in a simulation case of a certain turbine/project
"""
(self.P_RUN, self.P_SOURCE, self.PROJECT,
self.sim_id, self.P_MASTERFILE,
self.MASTERFILE, self.POST_DIR) = dlcdefs.configure_dirs(verbose=verbose)
def _set_path_config(self, runmethod='here'):
"""
Set the path configuration into the tags
"""
self.runmethod = runmethod
if runmethod == 'here':
self._set_path_auto_config()
elif runmethod in ['local', 'local-script', 'none', 'local-ram']:
self.p_root = '/home/dave/SimResults/h2_vs_hs2/'
elif runmethod == 'windows-script':
self.p_root = '/mnt/D16731/dave/Documents/_SimResults'
elif runmethod == 'gorm':
self.p_root = '/mnt/hawc2sim/h2_vs_hs2'
elif runmethod == 'jess':
self.p_root = '/mnt/hawc2sim/h2_vs_hs2'
else:
msg='unsupported runmethod, options: none, local, gorm or opt'
raise ValueError(msg)
if not runmethod == 'here':
self.P_RUN = os.path.join(self.p_root, self.PROJECT, self.sim_id)
self.master.tags['[master_htc_file]'] = self.MASTERFILE
self.master.tags['[master_htc_dir]'] = self.P_MASTERFILE
# directory to data, htc, SOURCE DIR
if self.P_SOURCE[-1] == os.sep:
self.master.tags['[model_dir_local]'] = self.P_SOURCE
else:
self.master.tags['[model_dir_local]'] = self.P_SOURCE + os.sep
if self.P_RUN[-1] == os.sep:
self.master.tags['[run_dir]'] = self.P_RUN
else:
self.master.tags['[run_dir]'] = self.P_RUN + os.sep
self.master.tags['[post_dir]'] = self.POST_DIR
self.master.tags['[sim_id]'] = self.sim_id
# set the model_zip tag to include the sim_id
rpl = (self.PROJECT, self.master.tags['[sim_id]'])
self.master.tags['[model_zip]'] = '%s_%s.zip' % rpl
def get_dlc_casedefs(self):
"""
Create iter_dict and opt_tags based on spreadsheets
"""
iter_dict = dict()
iter_dict['[empty]'] = [False]
# see if a htc/DLCs dir exists
dlcs_dir = os.path.join(self.P_SOURCE, 'htc', 'DLCs')
if os.path.exists(dlcs_dir):
opt_tags = dlcdefs.excel_stabcon(dlcs_dir)
else:
opt_tags = dlcdefs.excel_stabcon(os.path.join(self.P_SOURCE, 'htc'))
if len(opt_tags) < 1:
raise ValueError('There are is not a single case defined. Make sure '
'the DLC spreadsheets are configured properly.')
# add all the root files, except anything with *.zip
f_ziproot = []
for (dirpath, dirnames, fnames) in os.walk(self.P_SOURCE):
# remove all zip files
for i, fname in enumerate(fnames):
if fname.endswith('.zip'):
fnames.pop(i)
f_ziproot.extend(fnames)
break
# and add those files
for opt in opt_tags:
opt['[zip_root_files]'] = f_ziproot
self.master.output_dirs.extend('[Case folder]')
self.master.output_dirs.extend('[Case id.]')
return iter_dict, opt_tags
def create_inputs(self, iter_dict, opt_tags):
sim.prepare_launch(iter_dict, opt_tags, self.master, self._var_tag_func,
write_htc=True, runmethod=self.runmethod, verbose=False,
copyback_turb=False, msg='', update_cases=False,
ignore_non_unique=False, run_only_new=False,
pbs_fname_appendix=False, short_job_names=False)
def get_control_tuning(self, fpath):
"""
Read a HAWCStab2 controller tuning file and return as tags
"""
tuning = hs2.hs2_control_tuning()
tuning.read_parameters(fpath)
tune_tags = {}
tune_tags['[pi_gen_reg1.K]'] = tuning.pi_gen_reg1.K
tune_tags['[pi_gen_reg2.I]'] = tuning.pi_gen_reg2.I
tune_tags['[pi_gen_reg2.Kp]'] = tuning.pi_gen_reg2.Kp
tune_tags['[pi_gen_reg2.Ki]'] = tuning.pi_gen_reg2.Ki
tune_tags['[pi_pitch_reg3.Kp]'] = tuning.pi_pitch_reg3.Kp
tune_tags['[pi_pitch_reg3.Ki]'] = tuning.pi_pitch_reg3.Ki
tune_tags['[pi_pitch_reg3.K1]'] = tuning.pi_pitch_reg3.K1
tune_tags['[pi_pitch_reg3.K2]'] = tuning.pi_pitch_reg3.K2
tune_tags['[aero_damp.Kp2]'] = tuning.aero_damp.Kp2
tune_tags['[aero_damp.Ko1]'] = tuning.aero_damp.Ko1
tune_tags['[aero_damp.Ko2]'] = tuning.aero_damp.Ko2
return tune_tags
def post_processing(self, statistics=True, resdir=None,
calc_mech_power=False):
"""
Parameters
----------
resdir : str, default=None
Defaults to reading the results from the [run_dir] tag.
Force to any other directory using this variable. You can also use
the presets as defined for runmethod in _set_path_config.
"""
post_dir = self.POST_DIR
# =========================================================================
# check logfiles, results files, pbs output files
# logfile analysis is written to a csv file in logfiles directory
# =========================================================================
# load the file saved in post_dir
cc = sim.Cases(post_dir, self.sim_id, rem_failed=False)
if resdir is None:
# we keep the run_dir as defined during launch
run_root = None
elif resdir in ['local', 'local-script', 'none', 'local-ram']:
run_root = '/home/dave/SimResults'
elif resdir == 'windows-script':
run_root = '/mnt/D16731/dave/Documents/_SimResults'
elif resdir == 'gorm':
run_root = '/mnt/hawc2sim/h2_vs_hs2'
elif resdir == 'jess':
run_root = '/mnt/hawc2sim/h2_vs_hs2'
else:
run_root = None
cc.change_results_dir(resdir)
if isinstance(run_root, str):
forcedir = os.path.join(run_root, self.PROJECT, self.sim_id)
cc.change_results_dir(forcedir)
cc.post_launch()
cc.remove_failed()
if statistics:
tags=['[windspeed]']
stats_df = cc.statistics(calc_mech_power=calc_mech_power,
ch_fatigue=[], tags=tags, update=False)
ftarget = os.path.join(self.POST_DIR, '%s_statistics.xlsx')
stats_df.to_excel(ftarget % self.sim_id)
class MappingsH2HS2(object):
def __init__(self, config):
"""
Parameters
----------
config : Config class based on ConfigBase
"""
self.hs2_res = hs2.results()
self.h2_maps = config.h2_maps
self.units = {'curved_s': '[m]',
'Cl': '[-]',
'Cd': '[-]',
'Ct': '[-]',
'Cp': '[-]',
'ax_ind': '[-]',
'tan_ind': '[-]',
'vrel': '[m/s]',
'inflow_angle': '[deg]',
'AoA': '[deg]',
'pos_x': '[m]',
'pos_y': '[m]',
'pos_z': '[m]',
'def_x': '[m]',
'def_y': '[m]',
'def_z': '[m]',
'torsion': '[deg]',
'twist': '[deg]',
'ax_ind_vel': '[m/s]',
'tan_ind_vel': '[m/s]',
'F_x': '[N/m]',
'F_y': '[N/m]',
'M': '[Nm/m]',
'chord': '[m]'}
def powercurve(self, h2_df_stats, fname_hs):
self._powercurve_h2(h2_df_stats)
self._powercurve_hs2(fname_hs)
def _powercurve_h2(self, df_stats):
df_stats.sort_values('[windspeed]', inplace=True)
df_mean = pd.DataFrame()
df_std = pd.DataFrame()
for key, value in self.h2_maps.items():
tmp = df_stats[df_stats['channel']==key]
if len(tmp) == 0:
rpl = (key, value)
msg = 'HAWC2 channel %s is needed for %s but is missing' % rpl
raise ValueError(msg)
df_mean[value] = tmp['mean'].values.copy()
df_std[value] = tmp['std'].values.copy()
# also add the wind speed
df_mean['windspeed'] = tmp['[windspeed]'].values.copy()
df_std['windspeed'] = tmp['[windspeed]'].values.copy()
self.pwr_h2_mean = df_mean
self.pwr_h2_std = df_std
self.h2_df_stats = df_stats
def _powercurve_hs2(self, fname):
mappings = {'P [kW]' :'P_aero',
'T [kN]' :'T_aero',
'V [m/s]' :'windspeed'}
df_pwr, units = self.hs2_res.load_pwr_df(fname)
self.pwr_hs = pd.DataFrame()
for key, value in mappings.items():
self.pwr_hs[value] = df_pwr[key].values.copy()
def blade_distribution(self, fname_h2, fname_hs2, h2_df_stats=None,
fname_h2_tors=None):
self.hs2_res.load_ind(fname_hs2)
self.h2_res = sim.windIO.ReadOutputAtTime(fname_h2)
self._distribution_hs2()
self._distribution_h2()
if h2_df_stats is not None:
self.h2_df_stats = h2_df_stats
if fname_h2_tors is not None:
self.distribution_stats_h2(fname_h2_tors, 'Tors_e', 'torsion')
def _distribution_hs2(self):
"""Read a HAWCStab2 *.ind file (blade distribution loading)
rot_angle and rot_vec_123 in HS2 should be in rotor polar coordinates
"""
mapping_hs2 = {'s [m]' :'curved_s',
'CL0 [-]' :'Cl',
'CD0 [-]' :'Cd',
'CT [-]' :'Ct',
'CP [-]' :'Cp',
'A [-]' :'ax_ind',
'AP [-]' :'tan_ind',
'U0 [m/s]' :'vrel',
'PHI0 [rad]' :'inflow_angle',
'ALPHA0 [rad]':'AoA',
'X_AC0 [m]' :'pos_x',
'Y_AC0 [m]' :'pos_y',
'Z_AC0 [m]' :'pos_z',
'UX0 [m]' :'def_x',
'UY0 [m]' :'def_y',
'UZ0 [m]' :'def_z',
'Tors. [rad]' :'torsion',
'Twist[rad]' :'twist',
'V_a [m/s]' :'ax_ind_vel',
'V_t [m/s]' :'tan_ind_vel',
'FX0 [N/m]' :'F_x',
'FY0 [N/m]' :'F_y',
'M0 [Nm/m]' :'M',
'chord [m]' :'chord',
'angle [rad]' :'rot_angle',
'v_1 [-]' :'rot_vec_1',
'v_2 [-]' :'rot_vec_2',
'v_3 [-]' :'rot_vec_3'}
try:
hs2_cols = list(mapping_hs2)
# select only the HS channels that will be used for the mapping
std_cols = list(mapping_hs2.values())
self.hs_aero = self.hs2_res.ind.df_data[hs2_cols].copy()
except KeyError:
# some results have been created with older HAWCStab2 that did not
# include CT and CP columns
mapping_hs2.pop('CT [-]')
mapping_hs2.pop('CP [-]')
hs2_cols = list(mapping_hs2)
std_cols = list(mapping_hs2.values())
# select only the HS channels that will be used for the mapping
self.hs_aero = self.hs2_res.ind.df_data[hs2_cols].copy()
# change column names to the standard form that is shared with H2
self.hs_aero.columns = std_cols
chord12 = self.hs_aero['chord'] / 2.0
self.hs_aero['pos_x'] -= (np.cos(self.hs_aero['twist'])*chord12)
self.hs_aero['pos_y'] += (np.sin(self.hs_aero['twist'])*chord12)
self.hs_aero['AoA'] *= (180.0/np.pi)
self.hs_aero['inflow_angle'] *= (180.0/np.pi)
self.hs_aero['torsion'] *= (180.0/np.pi)
self.hs_aero['twist'] *= (180.0/np.pi)
def _distribution_h2(self):
mapping_h2 = { 'Radius_s' :'curved_s',
'Cl' :'Cl',
'Cd' :'Cd',
'Ct_local' :'Ct',
'Cq_local' :'Cq',
'Induc_RPy' :'ax_ind_vel',
'Induc_RPx' :'tan_ind_vel',
'Vrel' :'vrel',
'Inflow_ang':'inflow_angle',
'alfa' :'AoA',
'pos_RP_x' :'pos_x',
'pos_RP_y' :'pos_y',
'pos_RP_z' :'pos_z',
'Chord' :'chord',
'Secfrc_RPx':'F_x',
'Secfrc_RPy':'F_y',
'Secmom_RPz':'M'}
h2_cols = list(mapping_h2)
std_cols = list(mapping_h2.values())
# select only the h2 channels that will be used for the mapping
h2_aero = self.h2_res[h2_cols].copy()
# change column names to the standard form that is shared with HS
h2_aero.columns = std_cols
h2_aero['def_x'] = self.h2_res['Pos_B_x'] - self.h2_res['Inipos_x_x']
h2_aero['def_y'] = self.h2_res['Pos_B_y'] - self.h2_res['Inipos_y_y']
h2_aero['def_z'] = self.h2_res['Pos_B_z'] - self.h2_res['Inipos_z_z']
h2_aero['ax_ind_vel'] *= (-1.0)
# h2_aero['pos_x'] += (self.h2_res['Chord'] / 2.0)
h2_aero['F_x'] *= (1e3)
h2_aero['F_y'] *= (1e3)
h2_aero['M'] *= (1e3)
h2_aero['M'] -= (h2_aero['F_y']*h2_aero['chord']/2.0)
h2_aero['twist'] = np.nan
# # HAWC2 includes root and tip nodes, while HAWC2 doesn't. Remove them
# h2_aero = h2_aero[1:-1]
self.h2_aero = h2_aero
def distribution_stats_h2(self, fname_h2, sensortype, newname):
"""Determine blade distribution sensor from the HAWC2 statistics.
This requires that for each aerodynamic calculation point there should
be an output sensor defined manually in the output section.
Parameters
----------
fname_h2
sensortype
newname
"""
if not hasattr(self, 'h2_aero'):
raise UserWarning('first run blade_distribution')
# load the HAWC2 .sel file for the channels
fpath = os.path.dirname(fname_h2)
fname = os.path.basename(fname_h2)
res = sim.windIO.LoadResults(fpath, fname, readdata=False)
sel = res.ch_df[res.ch_df.sensortype == sensortype].copy()
if len(sel) == 0:
msg = 'HAWC2 sensor type "%s" is missing, are they defined?'
raise ValueError(msg % sensortype)
sel.sort_values(['radius'], inplace=True)
tors_e_channels = sel.ch_name.tolist()
# find the current case in the statistics DataFrame
case = fname.replace('.htc', '')
df_case = self.h2_df_stats[self.h2_df_stats['[case_id]']==case].copy()
# and select all the torsion channels
df_tors_e = df_case[df_case.channel.isin(tors_e_channels)].copy()
# join the stats with the channel descriptions DataFrames, have the
# same name on the joining column
df_tors_e.set_index('channel', inplace=True)
sel.set_index('ch_name', inplace=True)
# joining happens on the index, and for which the same channel has been
# used: the unique HAWC2 channel naming scheme
df_tors_e = pd.concat([df_tors_e, sel], axis=1)
df_tors_e.radius = df_tors_e.radius.astype(np.float64)
# sorting on radius, combine with ch_df
df_tors_e.sort_values(['radius'], inplace=True)
# FIXME: what if number of torsion outputs is less than aero
# calculation points?
self.h2_aero['%s' % newname] = df_tors_e['mean'].values.copy()
self.h2_aero['%s_std' % newname] = df_tors_e['std'].values.copy()
self.h2_aero['%s_radius_s' % newname] = df_tors_e['radius'].values.copy()
def body_structure_modes(self, fname_h2, fname_hs):
self._body_structure_modes_h2(fname_h2)
self._body_structure_modes_hs(fname_hs)
def _body_structure_modes_h2(self, fname):
self.body_freq_h2 = sim.windIO.ReadEigenBody(fname)
blade_h2 = self.body_freq_h2[self.body_freq_h2['body']=='blade1'].copy()
# because HAWCStab2 is sorted by frequency
blade_h2.sort_values('Fd_hz', inplace=True)
# HAWC2 usually has a lot of duplicate entries
blade_h2.drop_duplicates('Fd_hz', keep='first', inplace=True)
# also drop the ones with very high damping, and 0 frequency
query = '(log_decr_pct < 500 and log_decr_pct > -500) and Fd_hz > 0.0'
self.blade_body_freq_h2 = blade_h2.query(query)
def _body_structure_modes_hs(self, fname):
self.body_freq_hs = hs2.results().load_cmb_df(fname)
class Plots(object):
"""
Comparison plots between HACW2 and HAWCStab2. This is done based on
the HAWC2 output output_at_time, and HAWCStab2 output *.ind
"""
def __init__(self, config):
"""
Parameters
----------
config : Config class based on ConfigBase
"""
self.h2c = 'b'
self.h2ms = '+'
self.h2ls = '-'
self.hsc = 'r'
self.hsms = 'x'
self.hsls = '--'
self.errls = '-'
self.errc = 'k'
self.errms = 'x'
# self.errlab = 'diff [\\%]'
self.errlab = 'diff'
self.interactive = False
self.config = config
self.dist_size = (16, 11)
self.dist_nrows = 3
self.dist_ncols = 4
self.dist_channels = ['pos_x', 'pos_y', 'AoA', 'inflow_angle',
'Cl', 'Cd', 'vrel', 'ax_ind_vel',
'F_x', 'F_y', 'M', 'torsion']
def load_h2(self, fname_h2, h2_df_stats=None, fname_h2_tors=None):
res = MappingsH2HS2(self.config)
res.h2_res = sim.windIO.ReadOutputAtTime(fname_h2)
self.units = res.units
res._distribution_h2()
if h2_df_stats is not None:
res.h2_df_stats = h2_df_stats
if fname_h2_tors is not None:
res.distribution_stats_h2(fname_h2_tors, 'Tors_e', 'torsion')
return res
def load_hs(self, fname_hs):
res = MappingsH2HS2(self.config)
res.hs2_res.load_ind(fname_hs)
self.units = res.units
res._distribution_hs2()
return res
def new_fig(self, title=None, nrows=2, ncols=1, dpi=150, size=(12.0, 5.0)):
if self.interactive:
subplots = plt.subplots
else:
subplots = mplutils.subplots
fig, axes = subplots(nrows=nrows, ncols=ncols, dpi=dpi, figsize=size)
if isinstance(axes, np.ndarray):
axes = axes.ravel()
else:
axes = [axes]
if title is not None:
fig.suptitle(title)
return fig, axes
def set_axes_label_grid(self, axes, setlegend=False):
if isinstance(axes, np.ndarray):
axes = axes.ravel()
for ax in axes:
if setlegend:
leg = ax.legend(loc='best')
if leg is not None:
leg.get_frame().set_alpha(0.5)
ax.grid(True)
return axes
def save_fig(self, fig, axes, fname):
fig.tight_layout()
fig.subplots_adjust(top=0.89)
fig.savefig(fname, dpi=150)
fig.clear()
print('saved:', fname)
def distribution(self, results, labels, title, channels, x_ax='pos_z',
xlabel='Z-coordinate [m]', nrows=2, ncols=4, size=(16, 5),
i0=1, iplot_legend=0, legloc='best'):
"""
Compare blade distribution results
"""
res1 = results[0]
res2 = results[1]
lab1 = labels[0]
lab2 = labels[1]
radius1 = res1[x_ax].values
radius2 = res2[x_ax].values
fig, axes = self.new_fig(title=title, nrows=nrows, ncols=ncols, size=size)
if isinstance(axes, np.ndarray):
axesflat = axes.ravel()
else:
axesflat = axes
for i, chan in enumerate(channels):
ax = axesflat[i]
ax.plot(radius1, res1[chan].values, color=self.h2c,
label=lab1, alpha=0.9, marker=self.h2ms, ls=self.h2ls)
ax.plot(radius2, res2[chan].values, color=self.hsc,
label=lab2, alpha=0.7, marker=self.hsms, ls=self.hsls)
ax.set_ylabel('%s %s' % (chan.replace('_', '\\_'), self.units[chan]))
xlim = max(radius1.max(), radius2.max())
ax.set_xlim([0, xlim])
# if len(radius1) > len(radius2):
# radius = res1.hs_aero['pos_z'].values[n0:]
# x = res2.hs_aero['pos_z'].values[n0:]
# y = res2.hs_aero[chan].values[n0:]
# qq1 = res1.hs_aero[chan].values[n0:]
# qq2 = interpolate.griddata(x, y, radius)
# elif len(radius1) < len(radius2):
# radius = res2.hs_aero['pos_z'].values[n0:]
# x = res1.hs_aero['pos_z'].values[n0:]
# y = res1.hs_aero[chan].values[n0:]
# qq1 = interpolate.griddata(x, y, radius)
# qq2 = res2.hs_aero[chan].values[n0:]
# else:
# if np.allclose(radius1, radius2):
# radius = res1.hs_aero['pos_z'].values[n0:]
# qq1 = res1.hs_aero[chan].values[n0:]
# qq2 = res2.hs_aero[chan].values[n0:]
# else:
# radius = res1.hs_aero['pos_z'].values[n0:]
# x = res2.hs_aero['pos_z'].values[n0:]
# y = res2.hs_aero[chan].values[n0:]
# qq1 = res1.hs_aero[chan].values[n0:]
# qq2 = interpolate.griddata(x, y, radius)
# relative errors on the right axes
# err = np.abs(1.0 - (res1[chan].values / res2[chan].values))*100.0
# absolute errors on the right axes
err = res1[chan].values[i0:] - res2[chan].values[i0:]
axr = ax.twinx()
axr.plot(radius1[i0:], err, color=self.errc, ls=self.errls,
alpha=0.6, label=self.errlab, marker=self.errms)
# if err.max() > 50:
# axr.set_ylim([0, 35])
# use axr for the legend, but only for defined plot
if i == iplot_legend:
lines = ax.lines + axr.lines
labels = [l.get_label() for l in lines]
leg = axr.legend(lines, labels, loc=legloc)
leg.get_frame().set_alpha(0.5)
# x-label only on the last row
for k in range(ncols):
axesflat[-k-1].set_xlabel(xlabel)
axes = self.set_axes_label_grid(axes)
return fig, axes
def all_h2_channels(self, results, labels, fpath, channels=None,
size=(10,5)):
"""Results is a list of res (=HAWC2 results object)"""
for chan, details in results[0].ch_dict.items():
if channels is None or chan not in channels:
continue
resp = []
for res in results:
resp.append([res.sig[:,0], res.sig[:,details['chi']]])
fig, axes = self.new_fig(title=chan.replace('_', '\\_'),
size=size)
try:
mplutils.time_psd(resp, labels, axes, alphas=[1.0, 0.7], NFFT=None,
colors=['k-', 'r-'], res_param=250, f0=0, f1=5,
nr_peaks=10, min_h=15, mark_peaks=False)
except Exception as e:
print('****** FAILED')
print(e)
continue
axes[0].set_xlim([0,5])
axes[1].set_xlim(res.sig[[0,-1],0])
fname = os.path.join(fpath, chan + '.png')
self.save_fig(fig, axes, fname)
def h2_blade_distribution(self, fname_1, fname_2, title, labels, n0=0,
df_stats1=None, df_stats2=None,
iplot_legend=0, legloc='best'):
"""
Compare blade distribution aerodynamics of two HAWC2 cases.
"""
tors1 = fname_1.split('_aero_at_tstop')[0]
res1 = self.load_h2(fname_1, h2_df_stats=df_stats1, fname_h2_tors=tors1)
tors2 = fname_2.split('_aero_at_tstop')[0]
res2 = self.load_h2(fname_2, h2_df_stats=df_stats2, fname_h2_tors=tors2)
results = [res1.h2_aero[n0+1:], res2.h2_aero[n0+1:]]
fig, axes = self.distribution(results, labels, title, self.dist_channels,
x_ax='pos_z', xlabel='Z-coordinate [m]',
nrows=self.dist_nrows,
ncols=self.dist_ncols,
size=self.dist_size,
iplot_legend=iplot_legend, legloc=legloc)
return fig, axes
def hs_blade_distribution(self, fname_1, fname_2, title, labels, n0=0,
iplot_legend=0, legloc='best'):
res1 = self.load_hs(fname_1)
res2 = self.load_hs(fname_2)
results = [res1.hs_aero[n0:], res2.hs_aero[n0:]]
# channels = ['pos_x', 'pos_y', 'AoA', 'inflow_angle', 'Cl', 'Cd',
# 'vrel', 'ax_ind_vel']
fig, axes = self.distribution(results, labels, title, self.dist_channels,
x_ax='pos_z', xlabel='Z-coordinate [m]',
nrows=self.dist_nrows,
ncols=self.dist_ncols,
size=self.dist_size,
iplot_legend=iplot_legend, legloc=legloc)
return fig, axes
def blade_distribution(self, fname_h2, fname_hs2, title, n0=0,
h2_df_stats=None, fname_h2_tors=None,
iplot_legend=0, legloc='best'):
"""Compare aerodynamics, blade deflections between HAWC2 and HAWCStab2.
This is based on HAWCSTab2 *.ind files, and an HAWC2 output_at_time
output file.
Parameters
----------
fname_h2
fname_hs2
title
n0 : int, default=0
Number of nodes to ignore at the blade root section
"""
results = MappingsH2HS2(self.config)
results.blade_distribution(fname_h2, fname_hs2, h2_df_stats=h2_df_stats,
fname_h2_tors=fname_h2_tors)
self.units = results.units
res = [results.h2_aero[n0+1:-1], results.hs_aero[n0:]]
# channels = ['pos_x', 'pos_y', 'AoA', 'inflow_angle', 'Cl', 'Cd',
# 'vrel', 'ax_ind_vel']
labels = ['HAWC2', 'HAWCStab2']
fig, axes = self.distribution(res, labels, title, self.dist_channels,
x_ax='pos_z', xlabel='Z-coordinate [m]',
nrows=self.dist_nrows,
ncols=self.dist_ncols,
size=self.dist_size,
iplot_legend=iplot_legend, legloc=legloc)
return fig, axes
def blade_distribution2(self, fname_h2, fname_hs2, title, n0=0,
iplot_legend=0, legloc='best'):
"""Compare aerodynamics, blade deflections between HAWC2 and HAWCStab2.
This is based on HAWCSTab2 *.ind files, and an HAWC2 output_at_time
output file.
"""
results = MappingsH2HS2(self.config)
results.blade_distribution(fname_h2, fname_hs2)
res = [results.h2_aero[n0+1:-1], results.hs_aero[n0:]]
channels = ['pos_x', 'pos_y', 'torsion', 'inflow_angle',
'Cl', 'Cd', 'vrel', 'AoA',
'F_x', 'F_y', 'M', 'ax_ind_vel', 'torsion']
labels = ['HAWC2', 'HAWCStab2']
fig, axes = self.distribution(res, labels, title, channels,
x_ax='pos_z', xlabel='Z-coordinate [m]',
nrows=3, ncols=4, size=(16, 12),
iplot_legend=iplot_legend, legloc=legloc)
return fig, axes
def powercurve(self, h2_df_stats, fname_hs, title, size=(8.6, 4)):
results = MappingsH2HS2(self.config)
results.powercurve(h2_df_stats, fname_hs)
fig, axes = self.new_fig(title=title, nrows=1, ncols=2, size=size)
wind_h2 = results.pwr_h2_mean['windspeed'].values
wind_hs = results.pwr_hs['windspeed'].values
# POWER ---------------------------------------------------------------
ax = axes[0]
ax.set_ylabel('Power [kW]')
ax.set_xlabel('Wind speed [m/s]')
# HAWC2
keys = ['P_aero', 'P_mech']
lss = [self.h2ls, '--', ':']
# HAWC2
for key, ls in zip(keys, lss):
# it is possible the mechanical power has not been calculated
if key not in results.pwr_h2_mean:
continue
label = 'HAWC2 %s' % (key.replace('_', '$_{') + '}$')
yerr = results.pwr_h2_std[key].values
c = self.h2c
ax.errorbar(wind_h2, results.pwr_h2_mean[key].values, color=c, ls=ls,
label=label, alpha=0.9, yerr=yerr, marker=self.h2ms)
# HAWCSTAB2
key = 'P_aero'
ax.plot(wind_hs, results.pwr_hs[key].values, label='HAWCStab2',
alpha=0.7, color=self.hsc, ls=self.hsls, marker=self.hsms)
# errors on the right axes
axr = ax.twinx()
assert np.allclose(wind_h2, wind_hs)
qq1 = results.pwr_h2_mean[key].values
qq2 = results.pwr_hs[key].values
err = qq1 - qq2
axr.plot(wind_hs, err, color=self.errc, ls=self.errls, alpha=0.6,
label=self.errlab + ' P$_{aero}$')
ax.set_xlim([wind_h2.min(), wind_h2.max()])
# legends
lines, labels = ax.get_legend_handles_labels()
linesr, labelsr = axr.get_legend_handles_labels()
leg = axr.legend(lines + linesr, labels + labelsr, loc='lower right')
leg.get_frame().set_alpha(0.5)
# THRUST --------------------------------------------------------------
ax = axes[1]
ax.set_ylabel('Thrust [kN]')
ax.set_xlabel('Wind speed [m/s]')
keys = ['T_aero', 'T_shafttip']
lss = [self.h2ls, '--', ':']
# HAWC2
for key, ls in zip(keys, lss):
label = 'HAWC2 %s' % (key.replace('_', '$_{') + '}$')
yerr = results.pwr_h2_std[key].values
c = self.h2c
ax.errorbar(wind_h2, results.pwr_h2_mean[key].values, color=c, ls=ls,
label=label, alpha=0.9, yerr=yerr, marker=self.h2ms)
# HAWCStab2
ax.plot(wind_hs, results.pwr_hs['T_aero'].values, color=self.hsc, alpha=0.7,
label='HAWCStab2 T$_{aero}$', marker=self.hsms, ls=self.hsls)
# errors on the right axes
axr = ax.twinx()
qq1 = results.pwr_h2_mean['T_aero'].values
qq2 = results.pwr_hs['T_aero'].values
err = qq1 - qq2
axr.plot(wind_hs, err, color=self.errc, ls=self.errls, alpha=0.6,
label=self.errlab + ' T$_{aero}$')
ax.set_xlim([wind_h2.min(), wind_h2.max()])
ax.set_xlabel('Wind speed [m/s]')
# legends
lines, labels = ax.get_legend_handles_labels()
linesr, labelsr = axr.get_legend_handles_labels()
leg = axr.legend(lines + linesr, labels + labelsr, loc='lower right')
leg.get_frame().set_alpha(0.5)
axes = self.set_axes_label_grid(axes, setlegend=False)
return fig, axes
def h2_powercurve(self, h2_df_stats1, h2_df_stats2, title, labels,
size=(8.6,4)):
res1 = MappingsH2HS2(self.config)
res1._powercurve_h2(h2_df_stats1)
wind1 = res1.pwr_h2_mean['windspeed'].values
res2 = MappingsH2HS2(self.config)
res2._powercurve_h2(h2_df_stats2)
wind2 = res2.pwr_h2_mean['windspeed'].values
fig, axes = self.new_fig(title=title, nrows=1, ncols=2, size=size)
# POWER
ax = axes[0]
key = 'P_aero'
# HAWC2
yerr1 = res1.pwr_h2_std[key].values
ax.errorbar(wind1, res1.pwr_h2_mean[key].values, color=self.h2c, yerr=yerr1,
marker=self.h2ms, ls=self.h2ls, label=labels[0], alpha=0.9)
yerr2 = res2.pwr_h2_std[key]
ax.errorbar(wind2, res2.pwr_h2_mean[key].values, color=self.hsc, yerr=yerr2,
marker=self.hsms, ls=self.hsls, label=labels[1], alpha=0.7)
ax.set_ylabel('Power [kW]')
ax.set_xlabel('Wind speed [m/s]')
# relative errors on the right axes
axr = ax.twinx()
assert np.allclose(wind1, wind2)
qq1 = res1.pwr_h2_mean[key].values
qq2 = res2.pwr_h2_mean[key].values
err = np.abs(1.0 - qq1 / qq2)*100.0
axr.plot(wind1, err, color=self.errc, ls=self.errls, alpha=0.6,
label=self.errlab)
# THRUST
ax = axes[1]
keys = ['T_aero', 'T_shafttip']
lss = [self.h2ls, '--', ':']
for key, ls in zip(keys, lss):
label = '%s %s' % (labels[0], key.replace('_', '$_{') + '}$')
yerr = res1.pwr_h2_std[key].values
c = self.h2c
ax.errorbar(wind1, res1.pwr_h2_mean[key].values, color=c, ls=ls,
label=label, alpha=0.9, yerr=yerr, marker=self.h2ms)
for key, ls in zip(keys, lss):
label = '%s %s' % (labels[1], key.replace('_', '$_{') + '}$')
yerr = res2.pwr_h2_std[key].values
c = self.hsc
ax.errorbar(wind2, res2.pwr_h2_mean[key].values, color=c, ls=ls,
label=label, alpha=0.9, yerr=yerr, marker=self.hsms)
# relative errors on the right axes
axr = ax.twinx()
qq1 = res1.pwr_h2_mean['T_aero'].values
qq2 = res2.pwr_h2_mean['T_aero'].values
err = np.abs(1.0 - (qq1 / qq2))*100.0
axr.plot(wind1, err, color=self.errc, ls=self.errls, alpha=0.6,
label=self.errlab)
ax.set_ylabel('Thrust [kN]')
ax.set_xlabel('Wind speed [m/s]')
axes = self.set_axes_label_grid(axes, setlegend=True)
# # use axr for the legend
# lines = ax.lines + axr.lines
# labels = [l.get_label() for l in lines]
# leg = axr.legend(lines, labels, loc='best')
# leg.get_frame().set_alpha(0.5)
return fig, axes
def hs_powercurve(self, fname1, fname2, title, labels, size=(8.6, 4)):
res1 = MappingsH2HS2(self.config)
res1._powercurve_hs2(fname1)
wind1 = res1.pwr_hs['windspeed'].values
res2 = MappingsH2HS2(self.config)
res2._powercurve_hs2(fname2)
wind2 = res2.pwr_hs['windspeed'].values
fig, axes = self.new_fig(title=title, nrows=1, ncols=2, size=size)
# POWER
ax = axes[0]
key = 'P_aero'
ax.plot(wind1, res1.pwr_hs['P_aero'].values, label=labels[0],
alpha=0.9, color=self.h2c, ls=self.h2ls, marker=self.h2ms)
ax.plot(wind2, res2.pwr_hs['P_aero'].values, label=labels[1],
alpha=0.7, color=self.hsc, ls=self.hsls, marker=self.hsms)
ax.set_ylabel('Power [kW]')
ax.set_xlabel('Wind speed [m/s]')
# relative errors on the right axes
axr = ax.twinx()
assert np.allclose(wind1, wind2)
qq1 = res1.pwr_hs[key].values
qq2 = res2.pwr_hs[key].values
err = np.abs(1.0 - qq1 / qq2)*100.0
axr.plot(wind1, err, color=self.errc, ls=self.errls, alpha=0.6,
label=self.errlab)
# axr.set_ylim([])
# THRUST
ax = axes[1]
ax.plot(wind1, res1.pwr_hs['T_aero'].values, color=self.h2c, alpha=0.9,
label=labels[0], marker=self.h2ms, ls=self.h2ls)
ax.plot(wind2, res2.pwr_hs['T_aero'].values, color=self.hsc, alpha=0.7,
label=labels[1], marker=self.hsms, ls=self.hsls)
# relative errors on the right axes
axr = ax.twinx()
qq1 = res1.pwr_hs['T_aero'].values
qq2 = res2.pwr_hs['T_aero'].values
err = np.abs(1.0 - (qq1 / qq2))*100.0
axr.plot(wind1, err, color=self.errc, ls=self.errls, alpha=0.6,
label=self.errlab)
ax.set_ylabel('Thrust [kN]')
ax.set_xlabel('Wind speed [m/s]')
axes = self.set_axes_label_grid(axes, setlegend=True)
# # use axr for the legend
# lines = ax.lines + axr.lines
# labels = [l.get_label() for l in lines]
# leg = axr.legend(lines, labels, loc='best')
# leg.get_frame().set_alpha(0.5)
return fig, axes
if __name__ == '__main__':
dummy = None