the position of a styrofoam ball in a vertical tube is controlled by a fan on the lower end of the tube
simulation.py
import numpy as np
import system_model
from scipy.integrate import solve_ivp
from ackrep_core import ResultContainer
from ackrep_core.system_model_management import save_plot_in_dir
import matplotlib.pyplot as plt
import os
# link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html
def simulate():
"""
simulate the system model with scipy.integrate.solve_ivp
:return: result of solve_ivp, might contains input function
"""
model = system_model.Model()
rhs_xx_pp_symb = model.get_rhs_symbolic()
print("Computational Equations:\n")
for i, eq in enumerate(rhs_xx_pp_symb):
print(f"dot_x{i+1} =", eq)
rhs = model.get_rhs_func()
# ---------start of edit section--------------------------------------
# initial state values
xx0 = [456, 0, 0]
t_end = 10
tt = np.linspace(0, t_end, 1000)
simulation_data = solve_ivp(rhs, (0, t_end), xx0, t_eval=tt)
# ---------end of edit section----------------------------------------
save_plot(simulation_data)
return simulation_data
def save_plot(simulation_data):
"""
plot your data and save the plot
access to data via: simulation_data.t array of time values
simulation_data.y array of data components
simulation_data.uu array of input values
:param simulation_data: simulation_data of system_model
:return: None
"""
# ---------start of edit section--------------------------------------
# create figure + 2x2 axes array
fig1, axs = plt.subplots(nrows=3, ncols=1, figsize=(12.8, 9.6))
axs[0].plot(simulation_data.t, simulation_data.y[1])
axs[0].set_ylabel("Height of the ball [m]") # y-label
axs[0].grid()
axs[1].plot(simulation_data.t, simulation_data.y[2])
axs[1].set_ylabel("Velocity of the ball [m/s]") # y-label
axs[1].grid()
axs[2].plot(simulation_data.t, simulation_data.y[0])
axs[2].set_ylabel("Rotation speed [U/min]") # y-label
axs[2].set_xlabel("Time [s]") # x-Label
axs[2].grid()
# ---------end of edit section----------------------------------------
plt.tight_layout()
save_plot_in_dir()
def evaluate_simulation(simulation_data):
"""
assert that the simulation results are as expected
:param simulation_data: simulation_data of system_model
:return:
"""
# ---------start of edit section--------------------------------------
# fill in final states of simulation to check your model
# simulation_data.y[i][-1]
expected_final_state = [1965.06846972, 2.1096, 0.2131639]
# ---------end of edit section----------------------------------------
rc = ResultContainer(score=1.0)
simulated_final_state = simulation_data.y[:, -1]
rc.final_state_errors = [
simulated_final_state[i] - expected_final_state[i] for i in np.arange(0, len(simulated_final_state))
]
rc.success = np.allclose(expected_final_state, simulated_final_state, rtol=0, atol=1e-2)
return rc
system_model.py
import sympy as sp
import symbtools as st
import importlib
import sys, os
# from ipydex import IPS, activate_ips_on_exception
from random import randrange
from ackrep_core.system_model_management import GenericModel, import_parameters
# Import parameter_file
params = import_parameters()
# link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html
class Model(GenericModel):
def initialize(self):
"""
this function is called by the constructor of GenericModel
:return: None
"""
# ---------start of edit section--------------------------------------
# Define number of inputs -- MODEL DEPENDENT
self.u_dim = 1
# Set "sys_dim" to constant value, if system dimension is constant
self.sys_dim = 3
# ---------end of edit section----------------------------------------
# check existence of params file
self.has_params = True
self.params = params
# ----------- SET DEFAULT INPUT FUNCTION ---------- #
# --------------- Only for non-autonomous Systems
def uu_default_func(self):
"""
define input function
:return:(function with 2 args - t, xx_nv) default input function
"""
# ---------start of edit section--------------------------------------
def uu_rhs(t, xx_nv):
"""
sequence of numerical input values
:param t:(scalar or vector) time
:param xx_nv:(vector or array of vectors) numeric state vector
:return:(list) numeric inputs
"""
u = 120
return [u]
# ---------end of edit section----------------------------------------
return uu_rhs
# ----------- SYMBOLIC RHS FUNCTION ---------- #
def get_rhs_symbolic(self):
"""
define symbolic rhs function
:return: matrix of symbolic rhs-functions
"""
if self.dxx_dt_symb is not None:
return self.dxx_dt_symb
# ---------start of edit section--------------------------------------
x1, x2, x3 = self.xx_symb # state components
A_B, A_SP, m, g, T_M, k_M, k_V, k_L, n_0 = self.pp_symb # parameters
u1 = self.uu_symb[0] # inputs
# define symbolic rhs functions
dx1_dt = -60 / T_M * x1 + k_M / T_M * u1 * 60**2
dx2_dt = x3
dx3_dt = k_L / m * ((k_V * (x1 + n_0) / 60 - A_B * x3) / A_SP) ** 2 - g
# rhs functions matrix
self.dxx_dt_symb = sp.Matrix([dx1_dt, dx2_dt, dx3_dt])
# ---------end of edit section----------------------------------------
return self.dxx_dt_symb
parameters.py
import sys
import os
import numpy as np
import sympy as sp
import tabulate as tab
# link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html
# set model name
model_name = "Ball in tube"
# ---------- create symbolic parameters
pp_symb = [A_B, A_SP, m, g, T_M, k_M, k_V, k_L, n_0] = sp.symbols("A_B, A_SP, m, g, T_M, k_M, k_V, k_L, n_0", real=True)
# ---------- create symbolic parameter functions
# parameter values can be constant/fixed values OR set in relation to other parameters (for example: a = 2*b)
A_B_sf = 2.8274e-3
A_SP_sf = 0.4299e-3
m_sf = 2.8e-3
g_sf = 9.81
T_M_sf = 369e-3
k_M_sf = 0.273
k_V_sf = 12e-5 # 0.0001
k_L_sf = 2.823e-4
n_0_sf = 456
# list of symbolic parameter functions
# trailing "_sf" stands for "symbolic parameter function"
pp_sf = [A_B_sf, A_SP_sf, m_sf, g_sf, T_M_sf, k_M_sf, k_V_sf, k_L_sf, n_0_sf]
# ---------- list for substitution
# -- entries are tuples like: (independent symbolic parameter, numerical value)
pp_subs_list = []
# OPTONAL: Dictionary which defines how certain variables shall be written
# in the table - key: Symbolic Variable, Value: LaTeX Representation/Code
# useful for example for complex variables: {Z: r"\underline{Z}"}
latex_names = {}
# ---------- Define LaTeX table
# Define table header
# DON'T CHANGE FOLLOWING ENTRIES: "Symbol", "Value"
tabular_header = ["Parameter Name", "Symbol", "Value", "Unit"]
# Define column text alignments
col_alignment = ["left", "center", "left", "center"]
# Define Entries of all columns before the Symbol-Column
# --- Entries need to be latex code
col_1 = [
"ball cross-sectional area",
"air gap cross-sectional area",
"mass of the ball",
"acceleration due to gravitation",
"time constant",
"amplification",
"proportional factor",
"parameter",
"basic rotation speed",
]
# contains all lists of the columns before the "Symbol" Column
# --- Empty list, if there are no columns before the "Symbol" Column
start_columns_list = [col_1]
# Define Entries of the columns after the Value-Column
# --- Entries need to be latex code
col_4 = [
r"$m^2$",
r"$m^2$",
"kg",
r"$\frac{m}{s^2}$",
"s",
r"$s^{-1}$",
r"$m^3$",
r"$\frac{kg}{m}$",
r"$\frac{U}{min}$",
]
# contains all lists of columns after the FIX ENTRIES
# --- Empty list, if there are no columns after the "Value" column
end_columns_list = [col_4]