def FindSpectralPeak(data: np.ndarray, threshold: float = 90):
# peaks, _ = find_peaks(data.__abs__(), height=threshold)
# max_peak = np.where(data == max(data[peaks]))
max_peak_index = np.where(data.__abs__() == max(data.__abs__()))
if max_peak_index is not None:
return max_peak_index, data[max_peak_index]
else:
return -1, 0
def _get_matrix_coefficients(self, x_arr: np.ndarray,
T_cool_fluid_arr: np.ndarray):
dx = max(x_arr.__abs__()) / (self.node_num - 1)
A_arr, B_arr, C_arr, D_arr = [], [], [], []
for x, T_cool_fluid in zip(x_arr, T_cool_fluid_arr):
A = 1 / dx**2
C = 1 / dx**2
k_out = 1 / (1 / self.alpha_out(x) +
self.cover_thickness / self.lam_cover)
B = -2 / dx**2 - (k_out + self.alpha_cool(x, T_cool_fluid)) / (
self.lam_blade(self.T_wall_av) * self.wall_thickness)
D = -(k_out * self.T_out_stag(x) +
self.alpha_cool(x, T_cool_fluid) * T_cool_fluid) / (
self.lam_blade(self.T_wall_av) * self.wall_thickness)
A_arr.append(A)
B_arr.append(B)
C_arr.append(C)
D_arr.append(D)
M = np.zeros([self.node_num, self.node_num])
N = np.zeros([self.node_num])
for i1 in range(self.node_num):
for i2 in range(self.node_num):
if i1 == 0:
N[i1] = 0
M[i1, 0] = 1
M[i1, 1] = -1
elif i1 == self.node_num - 1:
N[i1] = 0
M[i1, i1] = 1
M[i1, i1 - 1] = -1
else:
N[i1] = D_arr[i1]
M[i1, i1 - 1] = A_arr[i1]
M[i1, i1] = B_arr[i1]
M[i1, i1 + 1] = C_arr[i1]
return M, N
