import numpy as np
import openseespy.opensees as ops
[docs]
def gen_grav_load(
ts_tag: int,
pattern_tag: int,
exclude_nodes: list = None,
direction: str = "Z",
factor: float = -9.81,
):
"""Applying the gravity loads.
Notes
-----
The mass values are from ``nodeMass(nodeTag)`` command, i.e., ones set in ``mass()`` command.
Parameters
-----------
ts_tag: int
The timeSeries tag you must assign.
pattern_tag: int
The pattern tag you must assign.
exclude_nodes: list, default=None
Excluded node tags, whose masses will not be used to generate gravity loads.
direction: str, default="Z"
The gravity load direction.
factor: float, default=-9.81
The factor applied to the mass values, it should be the multiplication of gravitational acceleration
and directional indicators, e.g., -9.81, where 9.81 is the gravitational acceleration
and -1 indicates along the negative Z axis.
Of course, it can be multiplied by an additional factor to account for additional constant loads,
e.g., 1.05 * (-9.81).
Returns
--------
None
"""
direction = direction.upper()
ops.timeSeries("Linear", int(ts_tag))
ops.pattern("Plain", int(pattern_tag), int(ts_tag))
node_tags = ops.getNodeTags()
if exclude_nodes is not None:
node_tags = [tag for tag in node_tags if tag not in exclude_nodes]
load_fact_6d = dict(
Z=np.array([0.0, 0.0, factor, 0.0, 0.0, 0.0]),
Y=np.array([0.0, factor, 0.0, 0.0, 0.0, 0.0]),
X=np.array([factor, 0.0, 0.0, 0.0, 0.0, 0.0]),
)
load_fact_3d = dict(
Z=np.array([0.0, 0.0, factor]),
Y=np.array([0.0, factor, 0]),
X=np.array([factor, 0.0, 0.0]),
)
load_fact_2d = dict(
Y=np.array([0.0, factor]),
X=np.array([factor, 0.0]),
)
load_fact_1d = dict(X=np.array([factor, 0.0]))
load_fact = {6: load_fact_6d, 3: load_fact_3d, 2: load_fact_2d, 1: load_fact_1d}
for nodetag in node_tags:
mass = np.array(ops.nodeMass(nodetag))
loadValues = mass * load_fact[len(mass)][direction]
ops.load(nodetag, *loadValues)
