def quilt(Swimmers, influence_type, NT, NI, i):
"""Constructs a full transformation matrix that includes all Swimmers.
The target is always the Bodies' collocation points, but the influence
could be the Bodies, Edges, or Wakes.
Args:
Swimmers: List of Swimmer objects being simulated.
influence_type: Type of influencing panel (Body, Edge, or Wake).
NT: Total number of target panels (across all Swimmers).
NI: Total number of influence panels (across all Swimmers).
i: Time step number.
Returns:
xp1: Transformed coordinate matrix from panels' left endpoints.
xp2: Transformed coordinate matrix from panels' right endpoints.
zp: Transformed coordinate matrix from panels' z-levels.
"""
xp1 = np.empty((NT, NI))
xp2 = np.empty((NT, NI))
zp = np.empty((NT, NI))
for SwimT in Swimmers: # Target Swimmer (rows)
(r0, rn) = (SwimT.i_b, SwimT.i_b + SwimT.Body.N) # Insertion row range
for SwimI in Swimmers: # Influencing Swimmer (columns)
if influence_type == 'Body':
(c0, cn) = (SwimI.i_b, SwimI.i_b + SwimI.Body.N
) # Insertion column range
(xi, zi) = (SwimI.Body.AF.x, SwimI.Body.AF.z
) # Coordinates of influences
elif influence_type == 'Edge':
(c0, cn) = (SwimI.i_e, SwimI.i_e + SwimI.Edge.N)
(xi, zi) = (SwimI.Edge.x, SwimI.Edge.z)
elif influence_type == 'Wake':
(c0, cn) = (SwimI.i_w, SwimI.i_w + i)
(xi, zi) = (SwimI.Wake.x[:i + 1], SwimI.Wake.z[:i + 1])
else:
print 'ERROR! Invalid influence type.'
(xp1[r0:rn, c0:cn], xp2[r0:rn, c0:cn], zp[r0:rn, c0:cn]) = \
transformation(SwimT.Body.AF.x_col, SwimT.Body.AF.z_col, xi, zi)
return (xp1, xp2, zp)
def quilt(Swimmers, influence_type, NT, NI, i):
"""Constructs a full transformation matrix that includes all Swimmers.
The target is always the Bodies' collocation points, but the influence
could be the Bodies, Edges, or Wakes.
Args:
Swimmers: List of Swimmer objects being simulated.
influence_type: Type of influencing panel (Body, Edge, or Wake).
NT: Total number of target panels (across all Swimmers).
NI: Total number of influence panels (across all Swimmers).
i: Time step number.
Returns:
xp1: Transformed coordinate matrix from panels' left endpoints.
xp2: Transformed coordinate matrix from panels' right endpoints.
zp: Transformed coordinate matrix from panels' z-levels.
"""
xp1 = np.empty((NT,NI))
xp2 = np.empty((NT,NI))
zp = np.empty((NT,NI))
for SwimT in Swimmers: # Target Swimmer (rows)
(r0, rn) = (SwimT.i_b, SwimT.i_b+SwimT.Body.N) # Insertion row range
for SwimI in Swimmers: # Influencing Swimmer (columns)
if influence_type == 'Body':
(c0, cn) = (SwimI.i_b, SwimI.i_b+SwimI.Body.N) # Insertion column range
(xi, yi, zi) = (SwimI.Body.AF.x, SwimI.Body.AF.z) # Coordinates of influences
elif influence_type == 'Edge':
(c0, cn) = (SwimI.i_e, SwimI.i_e+SwimI.Edge.N)
(xi, yi, zi) = (SwimI.Edge.x, SwimI.Edge.z)
elif influence_type == 'Wake':
(c0, cn) = (SwimI.i_w, SwimI.i_w+i)
(xi, yi, zi) = (SwimI.Wake.x[:i+1], SwimI.Wake.z[:i+1])
else:
print 'ERROR! Invalid influence type.'
(xp1[r0:rn, c0:cn], xp2[r0:rn, c0:cn], zp[r0:rn, c0:cn]) = \
transformation(SwimT.Body.AF.x_col, SwimT.Body.AF.y_col, SwimT.Body.AF.z_col, xi, yi, zi)
return(xp1, xp2, zp)
def wake_rollup(Swimmers, DEL_T, i):
"""Performs wake rollup on the swimmers' wake panels.
Args:
Swimmers: List of Swimmer objects being simulated.
DEL_T: Time step length.
i: Time step number.
"""
# Wake panels initialize when i==1
if i == 0:
pass
else:
NT = i # Number of targets (wake panel points that are rolling up)
for SwimT in Swimmers:
SwimT.Wake.vx = np.zeros(NT)
SwimT.Wake.vz = np.zeros(NT)
DELTA_CORE = SwimT.DELTA_CORE
for SwimI in Swimmers:
# Coordinate transformation for body panels influencing wake
(xp1, xp2, zp) = transformation(SwimT.Wake.x[1:i+1], SwimT.Wake.z[1:i+1], SwimI.Body.AF.x, SwimI.Body.AF.z)
# Angle of normal vector with respect to global z-axis
(nx, nz) = panel_vectors(SwimI.Body.AF.x, SwimI.Body.AF.z)[2:4]
beta = np.arctan2(-nx, nz)
# Katz-Plotkin eqns 10.20 and 10.21 for body source influence
dummy1 = np.log((xp1**2+zp**2)/(xp2**2+zp**2))/(4*np.pi)
dummy2 = (np.arctan2(zp,xp2)-np.arctan2(zp,xp1))/(2*np.pi)
# Rotate back to global coordinates
dummy3 = dummy1*np.cos(beta) - dummy2*np.sin(beta)
dummy4 = dummy1*np.sin(beta) + dummy2*np.cos(beta)
# Finish eqns 10.20 and 10.21 for induced velocity by multiplying with sigma
SwimT.Wake.vx += np.dot(dummy3, SwimI.Body.sigma)
SwimT.Wake.vz += np.dot(dummy4, SwimI.Body.sigma)
# Formation of (x-x0) and (z-z0) matrices, similar to xp1/xp2/zp but coordinate transformation is not necessary
NI = SwimI.Body.N+1
xp = np.repeat(SwimT.Wake.x[1:i+1,np.newaxis], NI, 1) - np.repeat(SwimI.Body.AF.x[:,np.newaxis].T, NT, 0)
zp = np.repeat(SwimT.Wake.z[1:i+1,np.newaxis], NI, 1) - np.repeat(SwimI.Body.AF.z[:,np.newaxis].T, NT, 0)
# Find distance r_b between each influence/target
r_b = np.sqrt(xp**2+zp**2)
# Katz-Plotkin eqns 10.9 and 10.10 for body doublet (represented as point vortices) influence
dummy1 = zp/(2*np.pi*(r_b**2+DELTA_CORE**2))
dummy2 = -xp/(2*np.pi*(r_b**2+DELTA_CORE**2))
# Finish eqns 10.9 and 10.10 by multiplying with Body.gamma, add to induced velocity
SwimT.Wake.vx += np.dot(dummy1, SwimI.Body.gamma)
SwimT.Wake.vz += np.dot(dummy2, SwimI.Body.gamma)
# Formation of (x-x0) and (z-z0) matrices, similar to xp1/xp2/zp but coordinate transformation is not necessary
NI = SwimI.Edge.N+1
xp = np.repeat(SwimT.Wake.x[1:i+1,np.newaxis], NI, 1) - np.repeat(SwimI.Edge.x[:,np.newaxis].T, NT, 0)
zp = np.repeat(SwimT.Wake.z[1:i+1,np.newaxis], NI, 1) - np.repeat(SwimI.Edge.z[:,np.newaxis].T, NT, 0)
# Find distance r_e between each influence/target
r_e = np.sqrt(xp**2+zp**2)
# Katz-Plotkin eqns 10.9 and 10.10 for edge (as point vortices) influence
dummy1 = zp/(2*np.pi*(r_e**2+DELTA_CORE**2))
dummy2 = -xp/(2*np.pi*(r_e**2+DELTA_CORE**2))
# Finish eqns 10.9 and 10.10 by multiplying with Edge.gamma, add to induced velocity
SwimT.Wake.vx += np.dot(dummy1, SwimI.Edge.gamma)
SwimT.Wake.vz += np.dot(dummy2, SwimI.Edge.gamma)
# Formation of (x-x0) and (z-z0) matrices, similar to xp1/xp2/zp but coordinate transformation is not necessary
NI = i+1
xp = np.repeat(SwimT.Wake.x[1:i+1,np.newaxis], NI, 1) - np.repeat(SwimI.Wake.x[:i+1,np.newaxis].T, NT, 0)
zp = np.repeat(SwimT.Wake.z[1:i+1,np.newaxis], NI, 1) - np.repeat(SwimI.Wake.z[:i+1,np.newaxis].T, NT, 0)
# Find distance r_w between each influence/target
r_w = np.sqrt(xp**2+zp**2)
# Katz-Plotkin eqns 10.9 and 10.10 for wake (as point vortices) influence
dummy1 = zp/(2*np.pi*(r_w**2+DELTA_CORE**2))
dummy2 = -xp/(2*np.pi*(r_w**2+DELTA_CORE**2))
# Finish eqns 10.9 and 10.10 by multiplying with Wake.gamma, add to induced velocity
SwimT.Wake.vx += np.dot(dummy1, SwimI.Wake.gamma[:i+1])
SwimT.Wake.vz += np.dot(dummy2, SwimI.Wake.gamma[:i+1])
for Swim in Swimmers:
# Modify wake with the total induced velocity
Swim.Wake.x[1:i+1] += Swim.Wake.vx*DEL_T
Swim.Wake.z[1:i+1] += Swim.Wake.vz*DEL_T
def wake_rollup(Swimmers, DEL_T, i, P):
"""Performs wake rollup on the swimmers' wake panels.
Args:
Swimmers: List of Swimmer objects being simulated.
DEL_T: Time step length.
i: Time step number.
"""
if (P['SW_ROLLUP']):
# Wake panels initialize when i==1
if i == 0:
pass
else:
NT = i # Number of targets (wake panel points that are rolling up)
for SwimT in Swimmers:
SwimT.Wake.vx = np.zeros(NT)
SwimT.Wake.vz = np.zeros(NT)
DELTA_CORE = SwimT.DELTA_CORE
for SwimI in Swimmers:
# Coordinate transformation for body panels influencing wake
(xp1, xp2, zp) = transformation(SwimT.Wake.x[1:i + 1],
SwimT.Wake.z[1:i + 1],
SwimI.Body.AF.x,
SwimI.Body.AF.z)
# Angle of normal vector with respect to global z-axis
(nx, nz) = panel_vectors(SwimI.Body.AF.x,
SwimI.Body.AF.z)[2:4]
beta = np.arctan2(-nx, nz)
# Katz-Plotkin eqns 10.20 and 10.21 for body source influence
dummy1 = np.log(
(xp1**2 + zp**2) / (xp2**2 + zp**2)) / (4 * np.pi)
dummy2 = (np.arctan2(zp, xp2) -
np.arctan2(zp, xp1)) / (2 * np.pi)
# Rotate back to global coordinates
dummy3 = dummy1 * np.cos(beta) - dummy2 * np.sin(beta)
dummy4 = dummy1 * np.sin(beta) + dummy2 * np.cos(beta)
# Finish eqns 10.20 and 10.21 for induced velocity by multiplying with sigma
SwimT.Wake.vx += np.dot(dummy3, SwimI.Body.sigma)
SwimT.Wake.vz += np.dot(dummy4, SwimI.Body.sigma)
# Formation of (x-x0) and (z-z0) matrices, similar to xp1/xp2/zp but coordinate transformation is not necessary
NI = SwimI.Body.N + 1
xp = np.repeat(SwimT.Wake.x[1:i + 1, np.newaxis], NI,
1) - np.repeat(
SwimI.Body.AF.x[:, np.newaxis].T, NT, 0)
zp = np.repeat(SwimT.Wake.z[1:i + 1, np.newaxis], NI,
1) - np.repeat(
SwimI.Body.AF.z[:, np.newaxis].T, NT, 0)
# Find distance r_b between each influence/target
r_b = np.sqrt(xp**2 + zp**2)
# Katz-Plotkin eqns 10.9 and 10.10 for body doublet (represented as point vortices) influence
dummy1 = zp / (2 * np.pi * (r_b**2 + DELTA_CORE**2))
dummy2 = -xp / (2 * np.pi * (r_b**2 + DELTA_CORE**2))
# Finish eqns 10.9 and 10.10 by multiplying with Body.gamma, add to induced velocity
SwimT.Wake.vx += np.dot(dummy1, SwimI.Body.gamma)
SwimT.Wake.vz += np.dot(dummy2, SwimI.Body.gamma)
# Formation of (x-x0) and (z-z0) matrices, similar to xp1/xp2/zp but coordinate transformation is not necessary
NI = SwimI.Edge.N + 1
xp = np.repeat(SwimT.Wake.x[1:i + 1, np.newaxis], NI,
1) - np.repeat(
SwimI.Edge.x[:, np.newaxis].T, NT, 0)
zp = np.repeat(SwimT.Wake.z[1:i + 1, np.newaxis], NI,
1) - np.repeat(
SwimI.Edge.z[:, np.newaxis].T, NT, 0)
# Find distance r_e between each influence/target
r_e = np.sqrt(xp**2 + zp**2)
# Katz-Plotkin eqns 10.9 and 10.10 for edge (as point vortices) influence
dummy1 = zp / (2 * np.pi * (r_e**2 + DELTA_CORE**2))
dummy2 = -xp / (2 * np.pi * (r_e**2 + DELTA_CORE**2))
# Finish eqns 10.9 and 10.10 by multiplying with Edge.gamma, add to induced velocity
SwimT.Wake.vx += np.dot(dummy1, SwimI.Edge.gamma)
SwimT.Wake.vz += np.dot(dummy2, SwimI.Edge.gamma)
# Formation of (x-x0) and (z-z0) matrices, similar to xp1/xp2/zp but coordinate transformation is not necessary
NI = i + 1
xp = np.repeat(
SwimT.Wake.x[1:i + 1, np.newaxis], NI, 1) - np.repeat(
SwimI.Wake.x[:i + 1, np.newaxis].T, NT, 0)
zp = np.repeat(
SwimT.Wake.z[1:i + 1, np.newaxis], NI, 1) - np.repeat(
SwimI.Wake.z[:i + 1, np.newaxis].T, NT, 0)
# Find distance r_w between each influence/target
r_w = np.sqrt(xp**2 + zp**2)
# Katz-Plotkin eqns 10.9 and 10.10 for wake (as point vortices) influence
dummy1 = zp / (2 * np.pi * (r_w**2 + DELTA_CORE**2))
dummy2 = -xp / (2 * np.pi * (r_w**2 + DELTA_CORE**2))
# Finish eqns 10.9 and 10.10 by multiplying with Wake.gamma, add to induced velocity
SwimT.Wake.vx += np.dot(dummy1, SwimI.Wake.gamma[:i + 1])
SwimT.Wake.vz += np.dot(dummy2, SwimI.Wake.gamma[:i + 1])
for Swim in Swimmers:
# Modify wake with the total induced velocity
Swim.Wake.x[1:i + 1] += Swim.Wake.vx * DEL_T
Swim.Wake.z[1:i + 1] += Swim.Wake.vz * DEL_T
def quilt(Swimmers, RHO, DEL_T, i):
"""Constructs the influence coefficient matrices and solves using Kutta condition.
Args:
Swimmers: List of Swimmer objects being simulated.
RHO: Fluid density.
DEL_T: Time step length.
i: Time step number.
"""
n_b = 0 # n_b is really a constant, only needs to be calculated once
n_e = 0 # also a constant
n_w = 0
for Swim in Swimmers:
Swim.i_b = n_b
Swim.i_e = n_e
Swim.i_w = n_w
n_b += Swim.Body.N
n_e += 1
n_w += i
xp1 = np.empty((n_b,n_b))
xp2 = np.empty((n_b,n_b))
zp = np.empty((n_b,n_b))
sigma_all = np.empty(n_b)
for SwimI in Swimmers: # Influencing Swimmer (rows)
(r0, rn) = (SwimI.i_b, SwimI.i_b+SwimI.Body.N) # Insertion row range
sigma_all[r0:rn] = SwimI.Body.sigma[:]
for SwimT in Swimmers: # Target Swimmer (columns)
(c0, cn) = (SwimT.i_b, SwimT.i_b+SwimT.Body.N) # Insertion column range
(xp1[r0:rn, c0:cn], xp2[r0:rn, c0:cn], zp[r0:rn, c0:cn]) = \
transformation(SwimT.Body.AF.x_col, SwimT.Body.AF.z_col, SwimI.Body.AF.x, SwimI.Body.AF.z)
# Body source singularities influencing the bodies
# Transpose so that row elements represent the effect on the (row number)th panel
b_s = np.transpose((xp1 * np.log(xp1**2 + zp**2) - xp2 * np.log(xp2**2 + zp**2) \
+ 2*zp*(np.arctan2(zp,xp2) - np.arctan2(zp,xp1)))/(4*np.pi))
# Body doublet singularities influencing bodies themselves
# Transpose similar to phi_s
a = np.transpose(-(np.arctan2(zp,xp2)\
- np.arctan2(zp,xp1))/(2*np.pi))
xp1 = np.empty((n_e,n_b))
xp2 = np.empty((n_e,n_b))
zp = np.empty((n_e,n_b))
for SwimI in Swimmers:
r0 = SwimI.i_e
for SwimT in Swimmers:
(c0, cn) = (SwimT.i_b, SwimT.i_b+SwimT.Body.N)
(xp1[r0, c0:cn], xp2[r0, c0:cn], zp[r0, c0:cn]) = \
transformation(SwimT.Body.AF.x_col, SwimT.Body.AF.z_col, SwimI.Edge.x, SwimI.Edge.z)
# Edge doublet singularities influencing the bodies
b_de = np.transpose(-(np.arctan2(zp,xp2) - np.arctan2(zp,xp1))/(2*np.pi))
if i > 0: # There are no wake panels until i==1
xp1 = np.empty((n_w,n_b))
xp2 = np.empty((n_w,n_b))
zp = np.empty((n_w,n_b))
mu_w_all = np.empty(n_w)
for SwimI in Swimmers:
(r0, rn) = (SwimI.i_w, SwimI.i_w+i)
mu_w_all[r0:rn] = SwimI.Wake.mu[:i]
for SwimT in Swimmers:
(c0, cn) = (SwimT.i_b, SwimT.i_b+SwimT.Body.N)
(xp1[r0:rn, c0:cn], xp2[r0:rn, c0:cn], zp[r0:rn, c0:cn]) = \
transformation(SwimT.Body.AF.x_col, SwimT.Body.AF.z_col, SwimI.Wake.x[:i+1], SwimI.Wake.z[:i+1])
# Wake doublet singularities influencing the bodies
b_dw = np.transpose(-(np.arctan2(zp,xp2) - np.arctan2(zp,xp1))/(2*np.pi))
# SOLVING TIME
n_iter = 0
while True:
n_iter += 1
if n_iter == 1:
# Begin with explicit Kutta condition as first guess
# Construct the augmented body matrix by combining body and trailing edge panel arrays
c = np.zeros((n_b,n_b))
for SwimI in Swimmers:
c[:,SwimI.i_b] = -b_de[:, SwimI.i_e]
c[:,SwimI.i_b+SwimI.Body.N-1] = b_de[:, SwimI.i_e]
a_inv = np.linalg.inv(a + c)
# Get right-hand side
if i == 0:
b = -np.dot(b_s, sigma_all)
else:
b = -np.dot(b_s, sigma_all) - np.dot(b_dw, mu_w_all)
# Solve for bodies' doublet strengths using explicit Kutta
mu_b_all = np.dot(a_inv, b)
# First mu_guess (from explicit Kutta)
for Swim in Swimmers:
Swim.mu_guess = np.empty(2) # [0] is current guess, [1] is previous
Swim.delta_cp = np.empty(2) # [0] is current delta_cp, [1] is previous
Swim.Body.mu[:] = mu_b_all[Swim.i_b:Swim.i_b+Swim.Body.N]
Swim.mu_guess[0] = Swim.Body.mu[-1]-Swim.Body.mu[0]
else:
if n_iter == 2: # Make a second initial guess
# Update phi_dinv so it no longer includes explicit Kutta condition
a_inv = np.linalg.inv(a)
Swimmers[0].mu_guess[1] = Swimmers[0].mu_guess[0]
Swimmers[0].delta_cp[1] = Swimmers[0].delta_cp[0]
Swimmers[0].mu_guess[0] = 0.8*Swimmers[0].mu_guess[1] # Multiply first (explicit) guess by arbitrary constant to get second guess
else: # Newton method to get delta_cp == 0
# Get slope, which is change in delta_cp divided by change in mu_guess
slope = (Swimmers[0].delta_cp[0]-Swimmers[0].delta_cp[1])/(Swimmers[0].mu_guess[0]-Swimmers[0].mu_guess[1])
Swimmers[0].mu_guess[1] = Swimmers[0].mu_guess[0]
Swimmers[0].delta_cp[1] = Swimmers[0].delta_cp[0]
Swimmers[0].mu_guess[0] = Swimmers[0].mu_guess[1] - Swimmers[0].delta_cp[0]/slope
# Form right-hand side including mu_guess as an influence
if i == 0:
rhs = -np.dot(b_s, sigma_all) - np.squeeze(np.dot(b_de, Swimmers[0].mu_guess[0]))
else:
rhs = -np.dot(b_s, sigma_all) - np.dot(np.insert(b_dw, 0, b_de[:,0], axis=1), np.insert(Swimmers[0].Wake.mu[:i], 0, Swimmers[0].mu_guess[0]))
Swimmers[0].Body.mu = np.dot(a_inv, rhs)
for Swim in Swimmers:
Swim.Body.pressure(RHO, DEL_T, i)
if len(Swimmers) > 1 or Swimmers[0].SW_KUTTA == 0:
break
Swimmers[0].delta_cp[0] = np.absolute(Swimmers[0].Body.cp[-1]-Swimmers[0].Body.cp[0])
if Swimmers[0].delta_cp[0] < 0.0001 or n_iter >= 1000:
# if Swimmers[0].delta_cp[0] < 0.0001:
break
for Swim in Swimmers:
# mu_past used in differencing for pressure
Swim.Body.mu_past[1,:] = Swim.Body.mu_past[0,:]
Swim.Body.mu_past[0,:] = Swim.Body.mu
Swim.Edge.mu = Swim.mu_guess[0]
Swim.Edge.gamma[0] = -Swim.Edge.mu
Swim.Edge.gamma[1] = Swim.Edge.mu
# Get gamma of body panels for use in wake rollup
Swim.Body.gamma[0] = -Swim.Body.mu[0]
Swim.Body.gamma[1:-1] = Swim.Body.mu[:-1]-Swim.Body.mu[1:]
Swim.Body.gamma[-1] = Swim.Body.mu[-1]
