def __init__(self, y, u, R11, R12, R22, R33, omega, Retau):
self.y = np.copy(y)
ny = np.size(self.y)
self.q = np.zeros(6*ny, dtype=np.complex)
self.q[0:6*ny:6] = u[:]
self.q[1:6*ny:6] = R11[:]
self.q[2:6*ny:6] = R12[:]
self.q[3:6*ny:6] = R22[:]
self.q[4:6*ny:6] = R33[:]
self.q[5:6*ny:6] = omega[:]
self.Retau = Retau
self.writedir = "."
self.tol = 1e-11
self.ny = ny
self.n = self.ny*6
self.maxiter = 30
self.dt = 1e6
self.force_boundary = False
self.neq = 6
self.nu = 1e-4
self.rho = 1.0
self.dp = calc_dp(self.Retau, self.nu)
self.beta = np.ones(ny*4, dtype=y.dtype)
self.objective = TestObjective()
def __init__(self, y, u, k, omega, Retau, verbose=False, model=None):
self.y = np.copy(y)
ny = np.size(self.y)
self.verbose = verbose
self.q = np.zeros(3*ny, dtype=np.float)
self.Retau = Retau
self.nu = 1e-4
self.q[0:3*ny:3] = u[:]
self.q[1:3*ny:3] = k[:]
self.q[2:3*ny:3] = omega[:]
self.writedir = "."
self.tol = 1e-11
self.ny = ny
self.n = self.ny*3
self.maxiter = 10
self.dt = 1e6
self.force_boundary = False
self.neq = 1
self.rho = 1.0
self.dp = calc_dp(self.Retau, self.nu)
self.sigma_w = 0.5
self.beta_0 = 0.0708
self.gamma_w = 13.0/25.0
self.sigma_k = 0.6
self.beta_s = 0.09
self.model = model
if self.model == None or self.model == "linear":
self.beta = np.ones(ny, dtype=np.float)
elif self.model == 'nn':
self.nn = NeuralNetwork(sizes = [1, 3, 1])
self.beta = np.random.randn(self.nn.n)*1e-2
self.nn.set_from_vector(self.beta)
def __init__(self, y, u, k, omega, Retau, verbose=False):
self.y = np.copy(y)
ny = np.size(self.y)
self.verbose = verbose
self.q = np.zeros(3*ny, dtype=np.complex)
self.Retau = Retau
self.nu = 1e-4
self.q[0:3*ny:3] = u[:]
self.q[1:3*ny:3] = k[:]
self.q[2:3*ny:3] = omega[:]
self.writedir = "."
self.tol = 1e-11
self.ny = ny
self.n = self.ny*3
self.maxiter = 10
self.dt = 1e6
self.force_boundary = False
self.neq = 1
self.rho = 1.0
self.dp = calc_dp(self.Retau, self.nu)
self.sigma_w = 0.5
self.beta_0 = 0.0708
self.gamma_w = 13.0/25.0
self.sigma_k = 0.6
self.beta_s = 0.09
self.beta = np.ones(ny, dtype=np.complex)
def __init__(self, y, u, k, tau, Retau):
self.y = np.copy(y)
ny = np.size(self.y)
self.q = np.zeros(3*ny, dtype=np.complex)
self.q[0:3*ny:3] = u[:]
self.q[1:3*ny:3] = k[:]
self.q[2:3*ny:3] = tau[:]
self.Retau = Retau
self.writedir = "."
self.tol = 1e-11
self.ny = ny
self.n = self.ny*3
self.maxiter = 1000
self.dt = 1e6
self.force_boundary = True
self.neq = 1
self.nu = 1e-4
self.rho = 1.0
self.dp = calc_dp(self.Retau, self.nu)
self.sigma_k = 1.36
self.cmu = 0.09
self.sigma_tau2 = self.sigma_tau1 = self.sigma_k = 1.36
self.Ceps1 = 1.44
self.Ceps2 = 1.83
self.A2 = 4.9
def __init__(self, y, u, Retau):
self.y = np.copy(y)
self.q = np.copy(u.astype(np.complex))
self.Retau = Retau
#
self.n = np.size(y)
self.writedir = "."
self.maxiter = 20
self.tol = 1e-13
self.dt = 1e10
self.neq = 1
self.nu = 1e-4
self.rho = 1.0
self.dp = calc_dp(self.Retau, self.nu)
self.beta = np.ones_like(u)
self.objective = TestObjective()
def __init__(self, y, u, Retau):
self.y = np.copy(y)
self.q = np.copy(u.astype(np.float))
self.Retau = Retau
self.verbose = True
self.n = np.size(y)
self.writedir = "."
self.maxiter = 20
self.tol = 1e-13
self.dt = 1e10
self.neq = 1
self.nu = 1e-4
self.rho = 1.0
self.dp = calc_dp(self.Retau, self.nu)
self.beta = np.ones_like(u)
self.objective = TestObjective()
