OP2 Introduction

The Jupyter notebook for this demo can be found in: - docs/quick_start/demo/op2_demo.ipynb - https://github.com/SteveDoyle2/pyNastran/tree/master/docs/quick_start/demo/op2_demo.ipynb

Why use the OP2? Why not use the F06/PCH file?

Most people are comfortable with the F06. However, it’s: - Ironically, a lot harder to parse. The OP2 is very structured. - Much, much, much slower. We can read entire blocks of arrays with a single call. The data is already typed. - Much, much more memory inefficient because we aren’t appending strings onto lists and turning that into a numpy array.

F06 parsers get ridiculously hard when you start do complicated results, like: - single subcase buckling - superelements - SOL 200 optimization with sub-optimization - SPOINTs

The pyNastran OP2 Reader is fast, highly validated, and it supports most result types. The data in the OP2 is also more accurate because there is no rounding.

Validating an OP2

The test_op2 script is created when you run python setup.py develop or python setup.py install on pyNastran. Assuming it’s on your path (it’ll be in Python27:raw-latex:`Scripts `or something similar), you can run:

>>> test_op2 -f solid_bending.op2

The -f tells us to print out solid_bending.test_op2.f06, which can be compared to your F06 for a small file to build confidence in the reader. It’s also useful when you want an F06 of your model without rerunning Nastran just to see what’s in it.

If you have a large model, you can make test_op2 run much, much faster. The -c flag disables double-reading of the OP2. By default, test_op2 uses two different read methods (the old method and new method) to ensure that results are read in properly. When running the code, this is turned off, but is turned on for test_op2.

>>> test_op2 -fc solid_bending.op2

Import the packages

import os
import copy
import numpy as np
np.set_printoptions(precision=2, threshold=20, suppress=True)

import pyNastran
pkg_path = pyNastran.__path__[0]

from pyNastran.utils import print_bad_path
from pyNastran.op2.op2 import read_op2
from pyNastran.utils import object_methods, object_attributes
from pyNastran.utils.nastran_utils import run_nastran

import pandas as pd

Sets default precision of real numbers for pandas output

pd.set_option('precision', 3)
np.set_printoptions(precision=3, threshold=20)

As with the BDF, we can use the long form and the short form. However, the long form for the OP2 doesn’t really add anything. So, let’s just use the short form.

In addition to the default numpy support, there is also ``pandas`` dataframe support.

#op2_filename = r'D:\work\pynastran_0.8.0\models\iSat\ISat_Launch_Sm_Rgd.op2'
#op2_filename = r'D:\work\pynastran_0.8.0\models\iSat\ISat_Launch_Sm_4pt.op2'
bdf_filename = os.path.abspath(os.path.join(pkg_path, '..', 'models', 'iSat', 'ISat_Launch_Sm_4pt.dat'))
op2_filename = os.path.abspath(os.path.join(pkg_path, '..', 'models', 'iSat', 'ISat_Launch_Sm_4pt.op2'))

if 1:
    from pyNastran.bdf.bdf import read_bdf
    op2_filename = os.path.abspath(os.path.join(pkg_path, '..', 'models', 'iSat', 'ISat_Launch_Sm_4pt2.op2'))
    model = read_bdf(bdf_filename, debug=None)
    model.set_param('POSTEXT', 'YES')
    model.set_param('POST', -2)
    bdf_filename = os.path.abspath(os.path.join(pkg_path, '..', 'models', 'iSat', 'ISat_Launch_Sm_4pt2.bdf'))
    model.write_bdf(bdf_filename)

if not os.path.exists(op2_filename):
    run_nastran(bdf_filename)
assert os.path.exists(op2_filename), print_bad_path(op2_filename)

# define the input file with a file path
op2 = read_op2(op2_filename, build_dataframe=True, debug=False)
WARNING: shell.py:2544 PSHELL pid=1 midsurface: z1=0.4 z2=-0.4 t=0.036 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=2 midsurface: z1=0.4 z2=-0.4 t=0.054 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=3 midsurface: z1=0.4 z2=-0.4 t=0.018 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=7 midsurface: z1=0.418 z2=-0.418 t=0.036 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=34 midsurface: z1=0.194 z2=-0.194 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=38 midsurface: z1=0.284 z2=-0.284 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=46 midsurface: z1=0.199 z2=-0.199 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=37 midsurface: z1=0.309 z2=-0.309 t=0.0186 not in range of -1.5t < zi < 1.5t INFO: op2_scalar.py:1588 op2_filename = 'c:\\nasa\\m4\\formats\\git\\pynastran\\models\\iSat\\ISat_Launch_Sm_4pt2.op2'

OP2 Introspection

The get_op2_stats() function lets you quickly understand what in an op2.

print(op2.get_op2_stats())
params:
  AUTOSPC = 'YES'
  GRDPNT = 0
  K6ROT = 100.0
  OMODES = 11
  POST = -2
  POSTEXT = 'YES'
GridPointWeight['']:  reference_point=0
  mass=[    1.7746     1.7746     1.7746]
  cg  =[-6.02244e-18    -2.5306   -18.4677]
       [-0.0338514 -1.01609e-17   -18.4677]
       [-0.0338514    -2.5306 -6.56299e-20]

  IS  =[    705.69   -1.56673   0.141188]
       [  -1.56673    621.837    135.836]
       [  0.141188    135.836    415.862]

  IQ  =[   689.184                      ]
       [              348.385           ]
       [                         705.821]

  Q  = [ 0.0884613 0.00159687   0.996078]
       [ -0.892013  -0.444887  0.0799325]
       [   0.44327  -0.895585 -0.0379308]
op2_results.eqexin: EQEXIN(nid, ndof, doftype); nnodes=5379
op2_results.bgpdt: BGPDT(cd, xyz); nnodes=5379
op2_results.force.cbar_force[1]
  type=RealCBarForceArray ntimes=167 nelements=827; table_name='OEF1X'
  data: [ntimes, nnodes, 8] where 8=[bending_moment_a1, bending_moment_a2, bending_moment_b1, bending_moment_b2, shear1, shear2, axial, torque]
  data.shape = (167, 827, 8)
  element.shape = (827,)
  element name: CBAR-34
  sort1
  modes = [  1   2   3 ... 165 166 167]
  eigns = [   2757.98     3568.25     9686.269 ... 6162759.    6169884.5
 6229575.5  ]
  cycles = [  8.358   9.507  15.664 ... 395.101 395.329 397.237]

eigenvectors[1]
  isubcase = 1
  type=RealEigenvectorArray ntimes=167 nnodes=5379, table_name=OUGV1
  data: [t1, t2, t3, r1, r2, r3] shape=[167, 5379, 6] dtype=float32
  node_gridtype.shape = (5379, 2)
  sort1
  modes = [  1   2   3 ... 165 166 167]
  eigns = [   2757.98     3568.25     9686.269 ... 6162759.    6169884.5
 6229575.5  ]
  mode_cycles = [  8.358   9.507  15.664 ... 395.101 395.329 397.237]

ctria3_stress[1]
  type=RealPlateStressArray ntimes=167 nelements=32 nnodes_per_element=1 nlayers=2 ntotal=64
  data: [ntimes, ntotal, 8] where 8=[fiber_distance, oxx, oyy, txy, angle, omax, omin, von_mises]
  element_node.shape = (64, 2)
  data.shape=(167, 64, 8)
  element type: CTRIA3-74
  s_code: 1
  sort1
  modes = [  1   2   3 ... 165 166 167]
  eigns = [   2757.98     3568.25     9686.269 ... 6162759.    6169884.5
 6229575.5  ]
  mode2s = [0 0 0 ... 0 0 0]
  cycles = [  8.358   9.507  15.664 ... 395.101 395.329 397.237]

cquad4_stress[1]
  type=RealPlateStressArray ntimes=167 nelements=4580 nnodes_per_element=1 nlayers=2 ntotal=9160
  data: [ntimes, ntotal, 8] where 8=[fiber_distance, oxx, oyy, txy, angle, omax, omin, von_mises]
  element_node.shape = (9160, 2)
  data.shape=(167, 9160, 8)
  element type: CQUAD4-33
  s_code: 1
  sort1
  modes = [  1   2   3 ... 165 166 167]
  eigns = [   2757.98     3568.25     9686.269 ... 6162759.    6169884.5
 6229575.5  ]
  mode2s = [0 0 0 ... 0 0 0]
  cycles = [  8.358   9.507  15.664 ... 395.101 395.329 397.237]

eigenvalues[ISAT_SM_LAUNCH_4PT MODES TO 400 HZ]
  type=RealEigenvalues neigenvalues=167
  title, extraction_order, eigenvalues, radians, cycles, generalized_mass, generalized_stiffness

If that’s too long…

print(op2.get_op2_stats(short=True))
params:
  AUTOSPC = 'YES'
  GRDPNT = 0
  K6ROT = 100.0
  OMODES = 11
  POST = -2
  POSTEXT = 'YES'
GridPointWeight['']: ref_point=0 mass=1.7746; [reference_point, M0, S, mass, cg, IS, IQ, Q]
op2_results.eqexin: EQEXIN(nid, ndof, doftype); nnodes=5379
op2_results.bgpdt: BGPDT(cd, xyz); nnodes=5379
op2_results.force.cbar_force[1]
eigenvectors[1]
ctria3_stress[1]
cquad4_stress[1]
eigenvalues['ISAT_SM_LAUNCH_4PT MODES TO 400 HZ']

Accessing the Eigenvectors object

Eigenvectors are the simplest object. They use the same class as for displacements, velocity, acceleration, SPC Forces, MPC Forces, Applied Loads, etc. These are all node-based tables with TX, TY, TZ, RX, RY, RZ. Results are in the analysis coordinate frame (CD), which is defined by the GRID card.

Numpy-based Approach

We’ll first show off the standard numpy based results on a transient case. Static results are the same, except that you’ll always use the 0th index for the “time” index.

The tutorial is intetionally just accessing the objects in a very clear, though inefficient way. The OP2 objects can take full advantage of the numpy operations.

# what modes did we analyze:  1 to 167
eigenvector_keys = list(op2.eigenvectors.keys())
print("loadcases = %s" % eigenvector_keys)

# get subcase 1
eig1 = op2.eigenvectors[1]

modes = eig1.modes
times = eig1._times #  the generic version of modes
print("modes = %s\n" % modes)
print("times = %s\n" % times)

imode2 = 1 # corresponds to mode 2
mode2 = eig1.data[imode2, :, :]

print('first 10 nodes and grid types\nNid Gridtype\n%s' % eig1.node_gridtype[:10, :])
node_ids = eig1.node_gridtype[:, 0]

index_node10 = np.where(node_ids == 10)[0]  # we add the [0] because it's 1d
mode2_node10 = mode2[index_node10]
print("translation mode2_node10 = %s" % eig1.data[imode2, index_node10, :3].ravel())
print("rotations mode2_node10 = %s" % eig1.data[imode2, index_node10, 3:].ravel())
loadcases = [1]
modes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167]

times = [  1.   2.   3. ... 165. 166. 167.]

first 10 nodes and grid types
Nid Gridtype
[[ 1  1]
 [ 2  1]
 [ 3  1]
 [ 4  1]
 [ 5  1]
 [ 6  1]
 [ 7  1]
 [ 8  1]
 [ 9  1]
 [10  1]]
translation mode2_node10 = [0.    0.008 0.002]
rotations mode2_node10 = [-0.  0. -0.]

Pandas-based Approach

If you like pandas, you can access all the OP2 objects, which is very useful within the Jupyter Notebook. Different objects will look differently, but you can change the layout.

If you’re trying to learn pandas, there are many tutorials online, such as: http://pandas.pydata.org/pandas-docs/stable/10min.html

or a very long, but good video:

from IPython.display import YouTubeVideo
YouTubeVideo('5JnMutdy6Fw')
#https://www.youtube.com/watch?v=5JnMutdy6Fw
# get subcase 1
eig1 = op2.eigenvectors[1]

eig1.data_frame
Mode 1 2 3 4 5 6 7 8 9 10 ... 158 159 160 161 162 163 164 165 166 167
Freq 8.358 9.507 15.664 20.229 20.306 20.548 21.500 21.701 21.716 28.444 ... 382.715 385.300 387.258 390.518 390.989 391.049 393.165 395.101 395.329 397.237
Eigenvalue 2.758e+03 3.568e+03 9.686e+03 1.615e+04 1.628e+04 1.667e+04 1.825e+04 1.859e+04 1.862e+04 3.194e+04 ... 5.782e+06 5.861e+06 5.921e+06 6.021e+06 6.035e+06 6.037e+06 6.103e+06 6.163e+06 6.170e+06 6.230e+06
Radians 52.516 59.735 98.419 127.102 127.585 129.107 135.087 136.351 136.445 178.722 ... 2404.668 2420.914 2433.217 2453.694 2456.658 2457.035 2470.327 2482.490 2483.925 2495.912
NodeID Item
1 t1 5.548e-03 4.669e-06 1.816e-04 -5.670e-02 1.721e-04 4.175e-02 -8.661e-05 1.341e-03 1.582e-03 -2.439e-01 ... -5.721e-02 5.368e-02 3.839e-02 -0.133 1.974e-02 -0.028 -0.033 0.104 0.069 1.901e-02
t2 -2.133e-04 5.699e-03 -2.393e-02 5.802e-04 -1.812e-04 -1.971e-04 6.490e-05 3.562e-02 -3.164e-02 -1.291e-02 ... 3.090e-01 3.746e-01 5.836e-02 -0.024 5.890e-02 -0.015 -0.177 0.010 -0.053 -1.187e-01
t3 8.469e-04 1.512e-03 -7.038e-03 -8.160e-03 -1.385e-03 6.209e-03 -1.005e-04 9.286e-03 -7.856e-03 -3.757e-02 ... 4.531e-02 -1.270e-01 -2.550e-01 -0.179 1.140e-03 -0.042 0.037 -0.263 -0.213 1.474e-01
r1 8.399e-06 -2.241e-04 1.035e-03 -4.509e-05 6.317e-05 9.634e-06 -2.505e-06 -1.322e-03 1.172e-03 5.433e-04 ... 3.061e-02 9.862e-04 -2.992e-02 -0.035 -1.128e-04 -0.007 0.053 0.004 -0.024 3.404e-02
r2 2.507e-04 1.228e-06 8.731e-06 -2.571e-03 6.177e-06 1.767e-03 -3.825e-06 5.682e-05 5.614e-05 -1.009e-02 ... 1.174e-02 -1.239e-03 -1.026e-02 -0.031 4.137e-03 -0.011 -0.026 -0.010 -0.007 9.107e-04
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
5633 t2 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00 0.000 0.000e+00 0.000 0.000 0.000 0.000 0.000e+00
t3 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00 0.000 0.000e+00 0.000 0.000 0.000 0.000 0.000e+00
r1 -3.006e-07 5.476e-05 -6.343e-04 6.336e-06 2.494e-06 -2.716e-06 -5.488e-07 2.376e-04 -2.019e-04 -6.017e-05 ... 4.288e-04 3.523e-03 -9.686e-04 -0.007 -9.864e-04 -0.001 0.014 0.008 -0.028 2.645e-02
r2 -1.723e-06 1.278e-06 1.805e-06 1.940e-04 3.376e-07 8.449e-06 -3.548e-08 4.728e-05 4.650e-05 -2.129e-04 ... -2.084e-02 -1.173e-03 6.237e-03 -0.013 1.097e-03 -0.005 -0.006 -0.005 -0.005 -6.870e-03
r3 7.271e-06 3.394e-06 2.716e-06 -1.478e-04 4.099e-07 5.572e-05 -2.954e-07 1.383e-05 -1.663e-05 -6.515e-04 ... -2.914e-02 -1.307e-03 6.513e-03 -0.014 1.144e-03 -0.005 -0.008 -0.008 -0.007 -8.940e-03

32274 rows × 167 columns

Accessing the plate stress/strain

Results are stored on a per element type basis.

The OP2 is the same as an F06, so CQUAD4 elements have centroidal-based results or centroidal-based as well as the results at the 4 corner nodes.

Be careful about what you’re accessing.

# element forces/stresses/strains are by element type consistent with the F06, so...
plate_stress = op2.cquad4_stress[1]
print("plate_stress_obj = %s" % type(plate_stress))

# the set of variables in the RealPlateStressArray
print("plate_stress = %s\n" % plate_stress.__dict__.keys())

# list of parameters that define the object (e.g. what is the nonlinear variable name
print("data_code_keys = %s\n" % plate_stress.data_code.keys())

# nonlinear variable name
name = plate_stress.data_code['name']
print("name = %r" % plate_stress.data_code['name'])

print("list-type variables = %s" % plate_stress.data_code['data_names'])

# the special loop parameter
# for modal analysis, it's "modes"
# for transient, it's "times"
# or be lazy and use "_times"
print("modes = %s" % plate_stress.modes) # name + 's'


# extra list-type parameter for modal analysis; see data_names
#print("mode_cycles =", plate_stress.mode_cycles)
plate_stress_obj = <class 'pyNastran.op2.tables.oes_stressStrain.real.oes_plates.RealPlateStressArray'>
plate_stress = dict_keys(['element_type', 'element_name', 'nonlinear_factor', '_times', 'result_name', 'approach_code', 'analysis_code', 'data', 'isubcase', 'ogs', 'pval_step', 'name', 'superelement_adaptivity_index', '_count', 'is_built', 'format_code', 'sort_code', 'table_code', 'title', 'subtitle', 'label', 'num_wide', 'device_code', 'table_name', 'data_frame', 'size', 'dt', 'ntimes', 'ntotal', '_ntotals', 'load_as_h5', 'h5_file', 'data_code', 'ielement', 'nelements', 'nnodes', '_encoding', '_times_dtype', 'cycle', 'data_names', 'eign', 'is_msc', 'is_nasa95', 'is_strain_flag', 'is_stress_flag', 'load_set', 'mode', 'mode2', 's_code', 'sort_bits', 'sort_method', 'stress_bits', 'subtitle_original', 'tCode', 'thermal', 'thermal_bits', 'modes', 'eigns', 'mode2s', 'cycles', 'itotal', 'itime', 'element_node', 'words'])

data_code_keys = dict_keys(['_encoding', 'load_as_h5', 'size', 'is_msc', 'is_nasa95', 'table_name', 'approach_code', 'isubcase', 'table_code', 'tCode', 'sort_code', 'sort_method', 'device_code', 'analysis_code', 'sort_bits', 'element_type', 'load_set', 'format_code', 'num_wide', 's_code', 'thermal', 'nonlinear_factor', 'name', 'mode', 'eign', 'mode2', 'cycle', 'data_names', '_times_dtype', 'thermal_bits', 'element_name', 'subtitle', 'subtitle_original', 'pval_step', 'superelement_adaptivity_index', 'label', 'title', 'stress_bits', 'is_stress_flag', 'is_strain_flag', 'result_name', '_count'])

name = 'mode'
list-type variables = ['mode', 'eign', 'mode2', 'cycle']
modes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167]

Similar to the BDF, we can use object_attributes/methods

#print "attributes =", object_attributes(plate_stress)
print("methods = %s\n" % object_methods(plate_stress))
print('methods2= %s\n' % plate_stress.object_methods())
print("headers = %s\n" % plate_stress.get_headers())
methods = ['add_new_eid_sort1', 'add_new_eid_sort2', 'add_sort1', 'add_sort2', 'apply_data_code', 'approach_code_str', 'build', 'build_dataframe', 'cast_grid_type', 'code_information', 'eid_to_element_node_index', 'export_to_hdf5', 'finalize', 'get_data_code', 'get_disp_temp', 'get_element_index', 'get_element_type', 'get_force_flux', 'get_headers', 'get_nnodes_bilinear', 'get_stats', 'get_table_code_name', 'get_unsteady_value', 'is_bilinear', 'is_magnitude_phase', 'is_sort1_new', 'is_thermal', 'object_attributes', 'object_methods', 'print_data_members', 'print_table_code', 'recast_gridtype_as_string', 'set_as_sort1', 'set_table_type', 'update_data_code', 'update_dt', 'update_t_code', 'write_f06', 'write_op2']

methods2= ['add_new_eid_sort1', 'add_new_eid_sort2', 'add_sort1', 'add_sort2', 'apply_data_code', 'approach_code_str', 'build', 'build_dataframe', 'cast_grid_type', 'code_information', 'eid_to_element_node_index', 'export_to_hdf5', 'finalize', 'get_data_code', 'get_disp_temp', 'get_element_index', 'get_element_type', 'get_force_flux', 'get_headers', 'get_nnodes_bilinear', 'get_stats', 'get_table_code_name', 'get_unsteady_value', 'is_bilinear', 'is_magnitude_phase', 'is_sort1_new', 'is_thermal', 'print_data_members', 'print_table_code', 'recast_gridtype_as_string', 'set_as_sort1', 'set_table_type', 'update_data_code', 'update_dt', 'update_t_code', 'write_f06', 'write_op2']

headers = ['fiber_distance', 'oxx', 'oyy', 'txy', 'angle', 'omax', 'omin', 'von_mises']

Number of Nodes on a CQUAD4

  • For CENT, there is 1 centroidal stress at two locations

  • For BILIN, there are 5 stresses at two locations (4 nodes + centroidal)

  • node_id=0 indicates a centroidal quantity

  • CTRIA3s are always centroidal

What sets this?

STRESS(real, sort1, BILIN) = ALL   # centroid + 4 corner nodes
STRESS(real, sort1, CENT) = ALL    # centroid

STRAIN(real, sort1, BILIN) = ALL   # centroid + 4 corner nodes
STRAIN(real, sort1, CENT) = ALL    # centroid

How do we know if we’re bilinear?

print("is_bilinear = %s\n" % plate_stress.is_bilinear())

What locations are chosen?

That depends on fiber distance/fiber curvature… - fiber_curvature - mean stress (\(\sigma_{alt}\)) & slope (\(\sigma_{mean}\))

$$ \sigma_{top} = \sigma_{alt} + \frac{t}{2} \sigma_{mean}$$

$$ \sigma_{btm} = \sigma_{alt} + \frac{t}{2} \sigma_{mean}$$
  • fiber_distance - upper and lower surface stress (o_top; o_btm)

  • If you have stress, fiber_distance is always returned regardless of your option.

What sets this?

STRAIN(real, sort1, FIBER) = ALL   # fiber distance/default
STRAIN(real, sort1, STRCUR) = ALL  # strain curvature

How do we know if we’re using fiber_distance?

print("is_fiber_distance = %s" % plate_stress.is_fiber_distance)

Accessing results

# element forces/stresses/strains are by element type consistent
# with the F06, so...

def abs_max_min(vals):
    absvals = list(abs(vals))
    maxval = max(absvals)
    i = absvals.index(maxval)
    return vals[i]

#-----------------------------
# again, we have linear quads, so two locations per element
print("element_node[:10, :] =\n%s..." % plate_stress.element_node[:10, :])

# lets get the stress for the first 3 CQUAD4 elements
eids = plate_stress.element_node[:, 0]
ueids = np.unique(eids)
print('ueids = %s' % ueids[:3])

# get the first index of the first 5 elements
ieids = np.searchsorted(eids, ueids[:3])
print('ieids = %s' % ieids)

# the easy way to slice data for linear plates
ieids5 = np.vstack([ieids, ieids + 1]).ravel()
ieids5.sort()

print('verify5:\n%s' % ieids5)

#-----------------------------
itime = 0 # static analysis / mode 1
if plate_stress.is_von_mises:  # True
    ovm = plate_stress.data[itime, :, 7]
    print('we have von mises data; ovm=%s\n' % ovm)
else:
    omax_shear = plate_stress.data[itime, :, 7]
    print('we have max shear data; omax_shear=%s\n' % omax_shear)


print("[layer1, layer2, ...] = %s" % ovm[ieids5])

ieid1000 = np.where(eids == 1000)[0]
print('ieid1000 = %s' % ieid1000)
ovm_mode6_eid1000 = ovm[ieid1000]
print("ovm_mode6_eid1000 = %s -> %s" % (ovm_mode6_eid1000, abs_max_min(ovm_mode6_eid1000)))
element_node[:10, :] =
[[1 0]
 [1 0]
 [2 0]
 [2 0]
 [3 0]
 [3 0]
 [4 0]
 [4 0]
 [5 0]
 [5 0]]...
ueids = [1 2 3]
ieids = [0 2 4]
verify5:
[0 1 2 3 4 5]
we have von mises data; ovm=[54.223  5.041 13.143 ...  2.34   6.146  7.368]

[layer1, layer2, ...] = [54.223  5.041 13.143 21.223 78.546 17.91 ]
ieid1000 = [1998 1999]
ovm_mode6_eid1000 = [90.618 94.093] -> 94.09257
# see the difference between "transient"/"modal"/"frequency"-style results
# and "nodal"/"elemental"-style results
# just change imode

imode = 5  # mode 6; could just as easily be dt
iele = 10  # element 10
ilayer = 1

ieid10 = np.where(eids == iele)[0][ilayer]
print('ieid10 = %s' % ieid10)
print(plate_stress.element_node[ieid10, :])


# headers = [u'fiber_distance', u'oxx', u'oyy', u'txy', u'angle', u'omax', u'omin', u'von_mises']
print("ps.modes = %s" % plate_stress.modes[imode])
print("ps.cycles = %s" % plate_stress.cycles[imode])
print("oxx = %s" % plate_stress.data[imode, ieid10, 1])
print("oyy = %s" % plate_stress.data[imode, ieid10, 2])
print("txy = %s" % plate_stress.data[imode, ieid10, 3])
print("omax = %s" % plate_stress.data[imode, ieid10, 5])
print("omin = %s" % plate_stress.data[imode, ieid10, 6])
print("ovm/max_shear = %s" % plate_stress.data[imode, ieid10, 7])

if plate_stress.is_fiber_distance:
    print("fiber_distance = %s" % plate_stress.data[imode, ieid10, 0])
else:
    print("curvature = %s" % plate_stress.data[imode, ieid10, 0])
ieid10 = 19
[10  0]
ps.modes = 6
ps.cycles = 20.548039949617547
oxx = 18.868874
oyy = 20.159315
txy = 8.309595
omax = 27.848701
omin = 11.179487
ovm/max_shear = 24.273378
fiber_distance = -0.4
from pyNastran.bdf.bdf import read_bdf
from pyNastran.bdf.mesh_utils.mass_properties import mass_properties
bdf_filename = os.path.abspath(os.path.join(pkg_path, '..', 'models', 'iSat', 'ISat_Launch_Sm_4pt.dat'))
model = read_bdf(bdf_filename, debug=False)
mass, cg, I = mass_properties(model)
WARNING: shell.py:2544 PSHELL pid=1 midsurface: z1=0.4 z2=-0.4 t=0.036 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=2 midsurface: z1=0.4 z2=-0.4 t=0.054 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=3 midsurface: z1=0.4 z2=-0.4 t=0.018 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=7 midsurface: z1=0.418 z2=-0.418 t=0.036 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=34 midsurface: z1=0.194 z2=-0.194 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=38 midsurface: z1=0.284 z2=-0.284 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=46 midsurface: z1=0.199 z2=-0.199 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=37 midsurface: z1=0.309 z2=-0.309 t=0.0186 not in range of -1.5t < zi < 1.5t

Let’s print out the actual mass properties from the OP2 and get the same result as the F06

We need PARAM,POSTEXT,YES in out BDF to get the Grid Point Weight Table

gpw = op2.grid_point_weight
#print(gpw)
if gpw:
    gpwi = gpw['']
    #print(gpw.object_attributes())
    #print(gpwi)
    gpwi.object_methods()
    #print(gpwi.object_attributes())
    #gpw.write_f06?
    print(gpwi.get_stats())
    print('M0 = ', gpwi.MO)
    print('S  = ', gpwi.S)
    print('mass = ', gpwi.mass)
    print('cg = ', gpwi.cg)
    print('IS = ', gpwi.IS)
    print('IQ = ', gpwi.IQ)
    print('Q  = ', gpwi.Q)
GridPointWeight['']: ref_point=0 mass=1.7746; [reference_point, M0, S, mass, cg, IS, IQ, Q]

M0 =  [[   1.775   -0.      -0.      -0.     -32.773    4.491]
 [  -0.       1.775    0.      32.773   -0.      -0.06 ]
 [  -0.       0.       1.775   -4.491    0.06    -0.   ]
 [  -0.      32.773   -4.491 1322.291    1.415   -1.251]
 [ -32.773   -0.       0.06     1.415 1227.076 -218.771]
 [   4.491   -0.06    -0.      -1.251 -218.771  427.228]]
S  =  [[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
mass =  [1.775 1.775 1.775]
cg =  [[ -0.     -2.531 -18.468]
 [ -0.034  -0.    -18.468]
 [ -0.034  -2.531  -0.   ]]
IS =  [[705.69   -1.567   0.141]
 [ -1.567 621.837 135.836]
 [  0.141 135.836 415.862]]
IQ =  [689.184 348.385 705.821]
Q  =  [[ 0.088  0.002  0.996]
 [-0.892 -0.445  0.08 ]
 [ 0.443 -0.896 -0.038]]

We can also write the full F06

import getpass
name = getpass.getuser()
os.chdir(os.path.join(r'C:\Users', name, 'Desktop'))

# write the F06 with Real/Imaginary or Magnitude/Phase
# only matters for complex results
op2.write_f06('isat.f06', is_mag_phase=False)

!head -n 40 isat.f06
                           O U T P U T   F R O M   G R I D   P O I N T   W E I G H T   G E N E R A T O R
0                                                     REFERENCE POINT =        0
                                                                M O
                      *  1.774604E+00 -2.303930E-19 -5.452775E-20 -1.068744E-17 -3.277277E+01  4.490806E+00 *
                      * -2.303930E-19  1.774604E+00  1.829591E-19  3.277277E+01 -1.803164E-17 -6.007288E-02 *
                      * -5.452775E-20  1.829591E-19  1.774604E+00 -4.490806E+00  6.007288E-02 -1.164670E-19 *
                      * -1.068744E-17  3.277277E+01 -4.490806E+00  1.322291E+03  1.414705E+00 -1.250593E+00 *
                      * -3.277277E+01 -1.803164E-17  6.007288E-02  1.414705E+00  1.227076E+03 -2.187709E+02 *
                      *  4.490806E+00 -6.007288E-02 -1.164670E-19 -1.250593E+00 -2.187709E+02  4.272284E+02 *
                                                                 S
                                           *  1.000000E+00  0.000000E+00  0.000000E+00 *
                                           *  0.000000E+00  1.000000E+00  0.000000E+00 *
                                           *  0.000000E+00  0.000000E+00  1.000000E+00 *
                               DIRECTION
                          MASS AXIS SYSTEM (S)     MASS              X-C.G.        Y-C.G.        Z-C.G.
                                  X            1.774604E+00     -6.022436E-18 -2.530596E+00 -1.846765E+01
                                  Y            1.774604E+00     -3.385143E-02 -1.016094E-17 -1.846765E+01
                                  Z            1.774604E+00     -3.385143E-02 -2.530596E+00 -6.562987E-20
                                                                I(S)
                                           *  7.056902E+02 -1.566725E+00  1.411882E-01 *
                                           * -1.566725E+00  6.218375E+02  1.358363E+02 *
                                           *  1.411882E-01  1.358363E+02  4.158619E+02 *
                                                                I(Q)
                                           *  6.891835E+02                             *
                                           *                3.483848E+02               *
                                           *                              7.058213E+02 *
                                                                 Q
                                           *  8.846130E-02  1.596867E-03  9.960783E-01 *
                                           * -8.920127E-01 -4.448868E-01  7.993249E-02 *
                                           *  4.432697E-01 -8.955854E-01 -3.793084E-02 *

1    ISAT_SM_LAUNCH_4PT MODES TO 400 HZ                                    JULY      16, 2020  pyNastran v1.4.0+dev.cc0dbf554  PAGE     1

1    ISAT_SM_LAUNCH_4PT MODES TO 400 HZ                                    JULY      16, 2020  pyNastran v1.4.0+dev.cc0dbf554  PAGE     2
     DEFAULT

                                              R E A L   E I G E N V A L U E S
                                             ISAT_SM_LAUNCH_4PT MODES TO 400 HZ
   MODE    EXTRACTION      EIGENVALUE            RADIANS             CYCLES            GENERALIZED         GENERALIZED
    NO.       ORDER                                                                       MASS              STIFFNESS
#from IPython.display import display, Math, Latex

The mass results are different as pyNastran’s mass assumes point masses

\[m_{plates} = A (\rho t + nsm)\]
\[m_{solid} = V \rho\]
\[m_{bars} = L (\rho A + nsm)\]
\[I = m r^2\]

The larger your model is and the further from the origin, the more accurate the result. For some applications (e.g. a weight breakdown), this is probably be fine.

print('cg =\n%s' % gpw[''].cg)
print('cg = %s' % cg)
cg =
[[ -0.     -2.531 -18.468]
 [ -0.034  -0.    -18.468]
 [ -0.034  -2.531  -0.   ]]
cg = [ -0.034  -2.531 -18.468]

It’s not like Nastran is perfect either.

Limitations

  1. You cannot do weight statements in Nastran by component/property/material.

  2. Everything is always summmed up (e.g. you can have different geometry in Subcase 2 and MPCs connecting physical geometry, with other parts flying off into space).

These are things that pyNastran can do.

from pyNastran.bdf.bdf import read_bdf
bdf_filename = os.path.abspath(os.path.join(pkg_path, '..', 'models', 'iSat', 'ISat_Launch_Sm_4pt.dat'))
model = read_bdf(bdf_filename, debug=False)
WARNING: shell.py:2544 PSHELL pid=1 midsurface: z1=0.4 z2=-0.4 t=0.036 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=2 midsurface: z1=0.4 z2=-0.4 t=0.054 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=3 midsurface: z1=0.4 z2=-0.4 t=0.018 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=7 midsurface: z1=0.418 z2=-0.418 t=0.036 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=34 midsurface: z1=0.194 z2=-0.194 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=38 midsurface: z1=0.284 z2=-0.284 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=46 midsurface: z1=0.199 z2=-0.199 t=0.0186 not in range of -1.5t < zi < 1.5t WARNING: shell.py:2544 PSHELL pid=37 midsurface: z1=0.309 z2=-0.309 t=0.0186 not in range of -1.5t < zi < 1.5t

Weight Statement

Let’s get the breakdown by property ID

#help(model.mass_properties)

pid_to_eids_map = model.get_element_ids_dict_with_pids()
#print(pid_to_eids_map.keys())
print('pid, mass, cg, [ixx, iyy, izz, ixy, ixz, iyz]')
for pid, eids in sorted(pid_to_eids_map.items()):
    mass, cg, inertia = mass_properties(model, element_ids=eids, mass_ids=[], reference_point=[0., 0., 0.])
    print('%-6s %-.6f %-38s %s' % (pid, mass, cg, inertia))

mass_ids = list(model.masses.keys())
mass, cg, inertia = mass_properties(model, element_ids=[], mass_ids=mass_ids, reference_point=[0., 0., 0.])
print('%-6s %-.6f %-38s %s' % ('mass', mass, cg, inertia))
pid, mass, cg, [ixx, iyy, izz, ixy, ixz, iyz]
1      0.027278 [  0.   0. -20.]                       [3.699 6.553 4.384 0.    0.    0.   ]
2      0.047993 [ -0.   0. -20.]                       [18.033 18.033 12.454 -0.    -0.     0.   ]
3      0.020998 [  0.  -0. -20.]                       [5.881 3.907 5.27  0.    0.    0.   ]
4      0.012216 [  0.043   0.438 -19.702]              [2.346 3.23  2.019 0.01  0.005 0.052]
5      0.330158 [  0.    2.2 -20. ]                    [63.317 28.366 41.752  0.     0.     0.   ]
7      0.027813 [  0.  -0. -20.]                       [ 8.141  8.141  9.438 -0.    -0.    -0.   ]
8      0.081584 [  0.   0. -20.]                       [15.087 15.087 30.174 -0.     0.     0.   ]
9      0.077642 [  0.   0. -20.]                       [17.017 17.017 18.911 -0.    -0.     0.   ]
10     0.000236 [  0.  -0. -20.]                       [ 0.035  0.035  0.057 -0.    -0.    -0.   ]
11     0.041700 [ -1.025  23.773 -12.016]              [ 0.666  0.988  0.348 -0.037  0.263 -0.056]
12     0.000457 [ 0.    -5.92  20.506]                 [0.013 0.013 0.    0.    0.    0.001]
13     0.003885 [ 0.    -6.949  9.892]                 [ 0.002  0.     0.002  0.     0.    -0.   ]
14     0.000353 [-0.     0.    14.391]                 [ 0.003  0.012  0.009  0.    -0.     0.   ]
15     0.003626 [0.    0.    7.867]                    [ 0.     0.092  0.091  0.    -0.     0.   ]
16     0.000000 [0. 0. 0.]                             [0. 0. 0. 0. 0. 0.]
19     0.017749 [ -0.23    6.021 -35.642]              [ 1.77   4.395  5.817 -0.053  0.005 -0.145]
20     0.163082 [  0.      0.    -18.545]              [ 9.01 34.77 25.76  0.    0.    0.  ]
21     0.003625 [ -0.  -0. -20.]                       [ 0.728  0.728  1.41  -0.    -0.    -0.   ]
22     0.000000 [0. 0. 0.]                             [0. 0. 0. 0. 0. 0.]
23     0.000000 [0. 0. 0.]                             [0. 0. 0. 0. 0. 0.]
33     0.001346 [-0.    -2.175  0.369]                 [ 0.077  0.085  0.162  0.    -0.    -0.001]
34     0.003561 [-0.    -0.    14.833]                 [ 0.271  0.271  0.067 -0.    -0.    -0.   ]
35     0.000000 [0. 0. 0.]                             [0. 0. 0. 0. 0. 0.]
36     0.007197 [  0.      0.    -14.783]              [ 0.835  3.605  3.02   0.     0.    -0.   ]
37     0.094566 [ -0.      0.    -19.499]              [ 8.975 52.72  46.49   0.    -0.     0.   ]
38     0.007602 [ 0.    -9.329 27.311]                 [0.681 1.214 0.562 0.    0.    0.07 ]
39     0.002433 [ 0.    -8.954  4.04 ]                 [ 0.022  0.045  0.024  0.    -0.    -0.003]
41     0.000735 [-0.    -0.     2.193]                 [ 0.009  0.057  0.062  0.     0.    -0.   ]
42     0.008854 [ -1.554  20.121 -19.007]              [ 1.166  1.459  0.293 -0.001  0.056  0.   ]
43     0.012241 [  0.      0.    -19.499]              [2.228 7.588 6.32  0.    0.    0.   ]
46     0.003671 [ 0.    0.   15.28]                    [ 0.178  0.348  0.335  0.    -0.     0.   ]
60     0.000000 [0. 0. 0.]                             [0. 0. 0. 0. 0. 0.]
61     0.000000 [0. 0. 0.]                             [0. 0. 0. 0. 0. 0.]
mass   0.772000 [  0.     -8.256 -18.238]              [392.813 338.699 118.704  -0.     -0.    138.698]