def run():
# constants
input_vel = 0.001
nu = input_vel * (10.0)
Ndim = shape
boundary = make_lid_boundary(shape=Ndim)
# domain
domain = dom.Domain("D2Q9", nu, Ndim, boundary)
# make lattice state, boundary and input velocity
initialize_step = lid_init_step(domain, value=0.08)
setup_step = lid_setup_step(domain, value=input_vel)
# init things
init = tf.global_variables_initializer()
# start sess
sess = tf.Session()
# init variables
sess.run(init)
# run steps
domain.Solve(sess, 1000, initialize_step, setup_step, lid_save, 60)
def run():
# constants
input_vel = 0.1
nu = input_vel * (0.0015)
Ndim = shape
boundary = make_car_boundary(shape=Ndim,
car_shape=(int(Ndim[1] / 4.3),
int(Ndim[0] / 2.3)))
# domain
domain = dom.Domain("D2Q9", nu, Ndim, boundary)
# make lattice state, boundary and input velocity
initialize_step = car_init_step(domain, value=0.08)
setup_step = car_setup_step(domain, value=input_vel)
# init things
init = tf.global_variables_initializer()
# start sess
sess = tf.Session()
# init variables
sess.run(init)
# run steps
domain.Solve(sess, 400, initialize_step, setup_step, car_save, 60)
def run():
# simulation constants
input_vel = 0.1
nu = 0.025
Ndim = shape
boundary = make_flow_boundary(shape=Ndim)
# les train details
batch_size = 4
les_ratio = 2
# placeholders
flow_in = tf.placeholder(tf.float32, [batch_size] + shape + [9],
name="flow_in")
# domains
domain = dom.Domain("D2Q9", nu, Ndim, boundary, les=False)
domain_les = dom.Domain("D2Q9",
nu,
Ndim,
boundary,
les=True,
train_les=True)
# unroll solvers
domain
# make lattice state, boundary and input velocity
initialize_step = flow_init_step(domain, value=input_vel)
setup_step = flow_setup_step(domain, value=input_vel)
# train op
train_op = tf.train.AdamOptimizer(lr).minimize(total_loss)
# init things
init = tf.global_variables_initializer()
# start sess
sess = tf.Session()
# init variables
sess.run(init)
# run steps
domain.Solve(sess, 4000, initialize_step, setup_step, flow_save, 60)