View source: R/model_turbulence.R
| model_turbulence | R Documentation | 
The function calculates the seismic spectrum as predicted by the model of Gimbert et al. (2014) for hydraulic turbulence. The code was written to R by Sophie Lagarde and integrated to the R package 'eseis' by Michael Dietze.
model_turbulence(
  d_s,
  s_s,
  r_s = 2650,
  h_w,
  w_w,
  a_w,
  f = c(1, 100),
  r_0,
  f_0,
  q_0,
  v_0,
  p_0,
  n_0,
  res = 1000,
  eseis = FALSE,
  ...
)
| d_s | 
 | 
| s_s | 
 | 
| r_s | 
 | 
| h_w | 
 | 
| w_w | 
 | 
| a_w | 
 | 
| f | 
 | 
| r_0 | 
 | 
| f_0 | 
 | 
| q_0 | 
 | 
| v_0 | 
 | 
| p_0 | 
 | 
| n_0 | 
 | 
| res | 
 | 
| eseis | 
 | 
| ... | Further arguments passed to the function. | 
The model uses a set of predefined constants. These can also be changed
by the user, using the ... argument:
c = 0.5, instantaneous fluid-grain friction coefficient 
(dimensionless)
g = 9.81, gravitational acceleration (m/s^2)
k = 0.5, Kolmogrov constant (dimensionless)
k_s = 3 * d_s, roughness length (m)
h = k_s / 2, reference height of the measurement (m)
e_0 = 0, exponent of Q increase with frequency 
(dimensionless)
r_w = 1000, specific density of the fluid (kg/m^3)
c_w = 0.5, instantaneous fluid-grain friction coefficient
(dimensionless)
eseis object containing the modelled spectrum.
Sophie Lagarde, Michael Dietze
## model the turbulence-related power spectrum
P <- model_turbulence(d_s = 0.03, # 3 cm mean grain-size
                      s_s = 1.35, # 1.35 log standard deviation
                      r_s = 2650, # 2.65 g/cm^3 sediment density
                      h_w = 0.8, # 80 cm water level
                      w_w = 40, # 40 m river width
                      a_w = 0.0075, # 0.0075 rad river inclination
                      f = c(1, 200), # 1-200 Hz frequency range
                      r_0 = 10, # 10 m distance to the river
                      f_0 = 1, # 1 Hz Null frequency 
                      q_0 = 10, # 10 quality factor at f = 1 Hz
                      v_0 = 2175, # 2175 m/s phase velocity
                      p_0 = 0.48, # 0.48 power law variation coefficient
                      n_0 = c(0.6, 0.8), # Greens function estimates
                      res = 1000) # 1000 values build the output resolution
## plot the power spectrum
plot_spectrum(data = P)
              
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