2nd High Lift Prediction Workshop

Alpha = 7deg , Re = 15E+06 , DLR - F11 configuration

2ndHLPW

References

High Lift Workshop

Define case name

This is the solver case to be analysed

In [1]:
case_name = 'v2'

Define Data Location

For remote data the interaction will use ssh to securely interact with the data
This uses the reverse connection capability in paraview so that the paraview server can be submitted to a job scheduler
Note: The default paraview server connection will use port 11111

In [2]:
remote_data = True
data_dir='/gpfs/cfms/workarea/projects/hyper_flux/high_lift/7deg_v3'

data_host='[email protected]'

remote_server_auto = True

paraview_cmd='mpiexec /gpfs/cfms/apps/bin/pvserver'
if not remote_server_auto:
    paraview_cmd=None

if not remote_data:
    data_host='localhost'
    paraview_cmd=None

Initialise Environment

In [3]:
%pylab inline
from paraview.simple import *
paraview.simple._DisableFirstRenderCameraReset()
import pylab as pl
import math
import numpy as np
Populating the interactive namespace from numpy and matplotlib
paraview version 4.2.0-75-gff3f889

Data Connection

This starts paraview server on remote host and connects

In [4]:
from zutil.post import pvserver_connect
if remote_data:
    pvserver_connect(data_host=data_host,data_dir=data_dir,paraview_cmd=paraview_cmd)
[[email protected]] Executing task 'port_test'
Selected Port: 12000
[[email protected]] Executing task 'pvserver'
[[email protected]] run: /bin/bash -l -c "cd /gpfs/cfms/workarea/projects/hyper_flux/high_lift/7deg_v3 && sleep 2;mpiexec /gpfs/cfms/apps/bin/pvserver -rc --client-host=localhost -sp=12000"
[[email protected]] out: 
[[email protected]] out: 		   _____ ______ __  __  _____ 
[[email protected]] out: 		  / ____|  ____|  \/  |/ ____|
[[email protected]] out: 		 | |    | |__  | \  / | (___  
[[email protected]] out: 		 | |    |  __| | |\/| |\___ \ 
[[email protected]] out: 		 | |____| |    | |  | |____) |
[[email protected]] out: 		  \_____|_|    |_|  |_|_____/ 
[[email protected]] out: 
[[email protected]] out: ++++++++++++++++++++++++++++: System Data :++++++++++++++++++++++++++++
[[email protected]] out: + Hostname = vis03
[[email protected]] out: + Kernel = 2.6.32-358.el6.x86_64
[[email protected]] out: + RHEL Release = Red Hat Enterprise Linux Server release 6.4 (Santiago)
[[email protected]] out: + Uptime = 11:33:51 up 27 days, 1:18, 14 users,
[[email protected]] out: ++++++++++++++++++++++++++++: User Data :++++++++++++++++++++++++++++++
[[email protected]] out: + Username = acimpoeru
[[email protected]] out: +++++++++++++++++++++++: Contact Information :+++++++++++++++++++++++++
[[email protected]] out: + in case of any problems, contact: [email protected]
[[email protected]] out: + for feedback, contact: [email protected] 
[[email protected]] out: +++++++++++++++++++++: Maintenance Information :+++++++++++++++++++++++
[[email protected]] out: + There is no planned maintenance taking place this week
[[email protected]] out: +++++: Group Home Directory Quota Usage (updated every 10 mins)   +++++
[[email protected]] out: + 
[[email protected]] out: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
[[email protected]] out: Warning: Conflicting CPU frequencies detected, using: 3059.000000.
[[email protected]] out: [[email protected]] rtunnel: opened reverse tunnel: (u'127.0.0.1', 51370) -> ('172.20.1.25', 22) -> ('localhost', 11111)
Connecting to client (reverse connection requested)...
[[email protected]] out: Connection URL: csrc://localhost:12000
[[email protected]] out: Client connected.
[[email protected]] out: 

Get control dictionary

In [17]:
from zutil.post import get_case_parameters,print_html_parameters
parameters=get_case_parameters(case_name,data_host=data_host,data_dir=data_dir)

Get status file

In [18]:
from zutil.post import get_status_dict
status=get_status_dict(case_name,data_host=data_host,data_dir=data_dir)
num_procs = str(status['num processor'])

Define test conditions - ETW experiment

In [25]:
from IPython.display import HTML
HTML(print_html_parameters(parameters))

ref_area = 419130 
alpha = 7.0

Read experimental data from prediction workshop

In [20]:
from zutil.post import get_csv_data

data_root = 'data/results/results_csv_format'

#meta_comp_results = 'data/results/metacomp_results'
#fluent_comp_results = 'data/results/fluent_results'


exp_data = {'ps01' : {'wing' : {'location': (0.0, 209.67 ,0.0),
                                }, 
                      'flap': {'location': (197.8982043488),
                               'normal' : (0.003629,-0.996368,-0.085077),
                               }, 
                      'slat': {'location': (-217.4733989342),
                               }, 
                      'eta' : 0.15,
                      'eta_index' : 15,
                      },
            'ps02' : {'wing' : {'location': (0.0, 403.87 ,0.0),
                                }, 
                      'flap': {'location': (392.1763409519),
                               'normal' : (0.003626,-0.996367,-0.085081),
                               },
                      'slat': {'location':(-411.7473427342),
                               }, 
                      'eta' : 0.285,
                      'eta_index' : 29,
                      },
            'ps04' : {'wing' : {'location': (0.0, 629.14 ,0.0)},
                      'flap': {'location': (-617.8606667446),
                               'normal' : (0.000246,0.989663,0.143413)
                               },
                      'slat': {'location':(-637.4499817342)
                               },
                      'eta' : 0.449,
                      'eta_index' : 45,
                      },
            'ps05' : {'wing' : {'location': (0.0, 760.30 ,0.0)},
                      'flap': {'location': (-749.2356867151),
                               'normal' : (0.000241,0.989664,0.143404)
                               },
                      'slat': {'location':(-768.8322037342)
                               },
                      'eta' : 0.543,
                       'eta_index' : 54,
                      },
            'ps06' : {'wing' : {'location': (0.0, 952.95 ,0.0)},
                      'flap': {'location': (-942.2476198083),
                               'normal' : (0.000247,0.989663,0.14341)
                               },
                      'slat': {'location':(-961.8352640342)
                               },
                      'eta' : 0.681,
                       'eta_index' : 68,
                      },
            'ps07' : {'wing' : {'location': (0.0, 1001.28 ,0.0)},
                      'flap': {'location': (-990.6836221217),
                               'normal' : (0.000229,0.989666,0.143389)
                               },
                      'slat': {'location':(-1010.3007059342)
                               },
                      'eta' : 0.715,
                       'eta_index' : 72,
                      },
            'ps08' : {'wing' : {'location': (0.0, 1051.35 ,0.0)},
                      'flap': {'location': (-1040.0478582652),
                               'normal' : (0.000244,0.990009,0.141001),
                               },
                      'slat': {'location':(-1060.4835625342),
                               },
                      'eta' : 0.751,
                       'eta_index' : 75,
                      },
            'ps09' : {'wing' : {'location': (0.0, 1145.79 ,0.0)
                                },
                      'flap': {'location': (-1134.5926478846),
                               'normal' : (0.000193,0.990019,0.140931)
                               },
                      'slat': {'location':(-1155.1131106342)
                               }, 
                      'eta' : 0.818,
                       'eta_index' : 82,
                      },
            'ps10' : {'wing' : {'location': (0.0, 1247.68 ,0.0)
                                },
                      'flap': {'location': (-1236.8582935410),
                               'normal' : (0.000301,0.989998,0.141080)
                               },
                      'slat': {'location':(-1257.1962384342)
                               }, 
                      'eta' : 0.891,
                       'eta_index' : 89,
                      },
            'ps11' : {'wing' : {'location': (0.0, 1349.83 ,0.0)
                                },
                      'flap': {'location': (-1339.1385759566),
                               'normal' : (0.000256,0.990007,0.141018)
                               },
                      'slat': {'location':(-1359.5541526342)
                               },
                      'eta' : 0.964,
                       'eta_index' : 96,
                      },
            }

exp_locations = { 'wing': {'normal':[0.0,1.0,0.0],
                           },
                  'slat' : {'normal': [0.003861,0.999926,0.011494],
                            },
                   'flap' : {'normal' : [],
                            },
                 }

# Read data

for key,value in exp_data.iteritems():
    value['wing']['data'] = get_csv_data(data_root+'/cp_'+key+'.csv',
                                header=True,remote=False,delim=',')
    #value['wing']['fluent-data'] = get_csv_data(fluent_comp_results+'/wing_'+str(value['eta_index'])+'_coarse.csv',
    #                                header=True,remote=False,delim=',')
    #value['wing']['metacomp-data'] = get_csv_data(meta_comp_results+'/wing_'+str(value['eta_index'])+'_coarse.csv',
    #                                header=True,remote=False,delim=',')
    value['flap']['data'] = get_csv_data(data_root+'/cp_'+key+'_flap.csv',
                                header=True,remote=False,delim=',')    
    #value['flap']['fluent-data'] = get_csv_data(fluent_comp_results+'/flap_'+str(value['eta_index'])+'_coarse.csv',
    #                                header=True,remote=False,delim=',')
    #value['flap']['metacomp-data'] = get_csv_data(meta_comp_results+'/flap_'+str(value['eta_index'])+'_coarse.csv',
    #                                header=True,remote=False,delim=',')
    value['slat']['data'] = get_csv_data(data_root+'/cp_'+key+'_slat.csv',
                                header=True,remote=False,delim=',')
    #value['slat']['fluent-data'] = get_csv_data(fluent_comp_results+'/slat_'+str(value['eta_index'])+'_coarse.csv',
    #                                header=True,remote=False,delim=',')
    #value['slat']['metacomp-data'] = get_csv_data(meta_comp_results+'/slat_'+str(value['eta_index'])+'_coarse.csv',
    #                                header=True,remote=False,delim=',')

Cp profiles at different positions

In [21]:
from zutil.post import cp_profile_wall_from_file

def get_cp_profile(file_root,section_loc,section_normal,section_data):
    force_data = cp_profile_wall_from_file(file_root,
                              section_normal,
                              section_loc,
                              func=cp_array,
                              section_data=section_data,
                              alpha=0.0)
    
def cp_array(data_array,pts_array,**kwargs):
    cp_array = data_array.GetPointData()['cp']
    section_data = kwargs['section_data']
    
    section_data.append((pts_array.GetPoints(),cp_array))
    
In [22]:
from zutil.post import get_case_root

section_data = {}

for station,val in exp_data.items():
    
    print 'Extracting station: '+station
    
    section_data[station] = {}
    
    for key,data in val.items():
        section_data[station][key] = []
        if key == 'wing':
            #print 'Location: ' + key
            get_cp_profile(get_case_root(case_name,num_procs),
                           data['location'],
                           exp_locations[key]['normal'],
                           section_data[station][key]
                           )
                        
        if key == 'flap':
            get_cp_profile(get_case_root(case_name,num_procs),
                           [0.0,-data['location']/data['normal'][1],0.0],
                           data['normal'],
                           section_data[station][key]
                           )
        if key == 'slat':
            get_cp_profile(get_case_root(case_name,num_procs),
                           [0.0,-data['location']/exp_locations[key]['normal'][1],0.0],
                           exp_locations[key]['normal'],
                           section_data[station][key]
                           )
            
Extracting station: ps09
Extracting station: ps08
Extracting station: ps07
Extracting station: ps06
Extracting station: ps05
Extracting station: ps04
Extracting station: ps10
Extracting station: ps02
Extracting station: ps01
Extracting station: ps11
//Applications/paraview.app/Contents/Python/paraview/vtk/numpy_interface/dataset_adapter.py:126: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  if array == None:
In [23]:
for station,val in exp_data.items():
    
    for key,data in val.items():

        section = section_data[station][key]
        
        if len(section) == 3:      
            # First get min loc for each loop
            min_loc = []
            for loop in section:
                pts = loop[0]
                lmin = np.amin(pts[:,0])
                min_loc.append(lmin)

            sort_index = np.argsort(np.array(min_loc))   

            if key == 'wing':
                section_data[station][key] = [section[sort_index[1]]]
            
            if key == 'slat':
                section_data[station][key] = [section[sort_index[0]]]
            
            if key == 'flap':
                section_data[station][key] = [section[sort_index[2]]]

            for loop in section:
                pts = loop[0]
                cp  = loop[1]

                lmin = np.amin(pts[:,0])
                lmax = np.amax(pts[:,0])

                pts[:,0] = (pts[:,0]-lmin)/(lmax-lmin)
                
                zmin = np.amin(pts[:,2])
                zmax = np.amax(pts[:,2])
                
                pts[:,2] = (pts[:,2]-zmin)/(lmax-lmin)
        
        
    #print val['flap']['fluent-data']
    #lmin = np.amin(val['slat']['fluent-data']['X'])
    #lmax = np.amax(val['slat']['fluent-data']['X'])   
    #val['slat']['fluent-data']['X'] = (val['slat']['fluent-data']['X']-lmin)/(lmax-lmin)
    #lmin = np.amin(val['wing']['fluent-data']['X'])
    #lmax = np.amax(val['wing']['fluent-data']['X'])   
    #val['wing']['fluent-data']['X'] = (val['wing']['fluent-data']['X']-lmin)/(lmax-lmin)
    #lmin = np.amin(val['flap']['fluent-data']['X'])
    #lmax = np.amax(val['flap']['fluent-data']['X'])   
    #val['flap']['fluent-data']['X'] = (val['flap']['fluent-data']['X']-lmin)/(lmax-lmin)
    
    #lmin = np.amin(val['slat']['metacomp-data']['X'])
    #lmax = np.amax(val['slat']['metacomp-data']['X'])   
    #val['slat']['metacomp-data']['X'] = (val['slat']['metacomp-data']['X']-lmin)/(lmax-lmin)
    #lmin = np.amin(val['wing']['metacomp-data']['X'])
    #lmax = np.amax(val['wing']['metacomp-data']['X'])   
    #val['wing']['metacomp-data']['X'] = (val['wing']['metacomp-data']['X']-lmin)/(lmax-lmin)
    #lmin = np.amin(val['flap']['metacomp-data']['X'])
    #lmax = np.amax(val['flap']['metacomp-data']['X'])   
    #val['flap']['metacomp-data']['X'] = (val['flap']['metacomp-data']['X']-lmin)/(lmax-lmin)
In [31]:
from matplotlib.backends.backend_pdf import PdfPages
from zutil.post import calc_force_wall
from IPython.display import FileLink, display 
from collections import OrderedDict
import matplotlib.font_manager as fm

prop=fm.FontProperties(size=20)

pressure_force, friction_force = calc_force_wall(get_case_root(case_name,num_procs),
                                                 [],half_model=False,
                                                 alpha=alpha)

C_L = (pressure_force[2] + friction_force[2])/ref_area
C_D = (pressure_force[0] + friction_force[0])/ref_area

print "C_L and C_D are  = ",C_L, C_D

for station in sorted(exp_data):

    val = exp_data[station]
    
    comp_fig1 = pl.figure(figsize=(30, 10),dpi=150, facecolor='w', edgecolor='k')
    comp_fig1.suptitle('Eta: '+str(val['eta']), fontsize=30, fontweight='bold')

    comp_ax   = comp_fig1.add_subplot(1,3,1)
    comp_ax_w = comp_fig1.add_subplot(1,3,2)
    comp_ax_f = comp_fig1.add_subplot(1,3,3)

    comp_ax.grid(True)
    comp_ax.set_xlabel('x/c', fontsize=20, fontweight='bold')
    comp_ax.set_ylabel('Cp', fontsize=20, fontweight='bold')
    comp_ax.set_title('Slat', fontsize=20, fontweight='bold')
    comp_ax.axis([0,1,1.5,-4.])
    comp_ax2 = comp_ax.twinx()
    comp_ax2.axis([0,1,0,1])

    comp_ax_w.grid(True)
    comp_ax_w.set_xlabel('x/c', fontsize=20, fontweight='bold')
    comp_ax_w.set_ylabel('Cp', fontsize=20, fontweight='bold')
    comp_ax_w.set_title('Main Wing', fontsize=20, fontweight='bold')
    comp_ax_w.axis([0.0,1.0,1.5,-4.])
    comp_ax2_w = comp_ax_w.twinx()
    comp_ax2_w.axis([0,1,-0.5,0.5])

    comp_ax_f.grid(True)
    comp_ax_f.set_xlabel('x/c', fontsize=20, fontweight='bold')
    comp_ax_f.set_ylabel('Cp', fontsize=20, fontweight='bold')
    comp_ax_f.set_title('Flap', fontsize=20, fontweight='bold')
    comp_ax_f.axis([0.0,1.0,1.5,-4.])
    comp_ax2_f = comp_ax_f.twinx()
    comp_ax2_f.axis([0,1,0,1])

    section = section_data[station]['slat'][0]    
    x_loc = section[0][:,0]
    z_loc = section[0][:,2]
    cp    = section[1]
        
    comp_ax.plot(val['slat']['data']['x/c_local'],val['slat']['data']['cp_ETW_RUN238'],'b.', color='b', markersize=25,label = 'Experiment')
    comp_ax.plot(x_loc,cp,'b.', color='r',markersize=20,label = 'zCFD')
    comp_ax2.plot(x_loc,z_loc, color='gray',markersize=20,label = 'Profile')
    #comp_ax.plot(val['slat']['metacomp-data']['X'],val['slat']['metacomp-data']['CP'],'b.',color='k',markersize=20,label='CFD++')
    #comp_ax.plot(val['slat']['fluent-data']['X'],val['slat']['fluent-data']['CP'],'b.',color='g',markersize=20,label='FLUENT')

    section = section_data[station]['wing'][0]    
    x_loc = section[0][:,0]
    z_loc = section[0][:,2]
    cp    = section[1]

    comp_ax_w.plot(val['wing']['data']['x/c_local'],val['wing']['data']['cp_ETW_RUN238'],'b.', color='b', markersize=25, label = 'Experiment')
    comp_ax_w.plot(x_loc,cp ,'b.',color='r',markersize=20,label = 'zCFD')
    comp_ax2_w.plot(x_loc,z_loc, color='gray',markersize=20,label = 'Profile')
    #comp_ax_w.plot(val['wing']['metacomp-data']['X'],val['wing']['metacomp-data']['CP'] ,'b.',color='k',markersize=20,label = 'CFD++')
    #comp_ax_w.plot(val['wing']['fluent-data']['X'],val['wing']['fluent-data']['CP'] ,'b.',color='g',markersize=20,label = 'FLUENT')

    section = section_data[station]['flap'][0]
    x_loc = section[0][:,0]
    z_loc = section[0][:,2]
    cp    = section[1]

    comp_ax_f.plot(val['flap']['data']['x/c_local'],val['flap']['data']['cp_ETW_RUN238'],'b.', color='b', markersize=25,label = 'Experiment')
    comp_ax_f.plot(x_loc,cp, 'b.', color ='r', markersize=20,label = 'zCFD')
    comp_ax2_f.plot(x_loc,z_loc, color='gray',markersize=20,label = 'Profile')
    #comp_ax_f.plot(val['flap']['metacomp-data']['X'],val['flap']['metacomp-data']['CP'], 'b.', color ='k', markersize=20,label = 'CFD++')
    #comp_ax_f.plot(val['flap']['fluent-data']['X'],val['flap']['fluent-data']['CP'], 'b.', color ='g', markersize=20,label = 'FLUENT')

    comp_ax.legend(loc = 'upper right',numpoints=1,prop = prop)
    comp_ax_w.legend(loc = 'upper right',numpoints=1,prop = prop)
    comp_ax_f.legend(loc = 'upper right',numpoints=1,prop = prop)

    comp_fig1.savefig('images/case2b_'+station+'.svg')
    show()
C_L and C_D are  =  1.84089358656 0.172667485486