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)