# Coupled Spring Mass ODE System¶

## Introduction¶

In this tutorial Modulus is used to solve a system of coupled ordinary differential equations. Since the APIs used for this problem have already been covered in a previous tutorial, only the problem description is discussed without going into the details of the code.

Note

This tutorial assumes that you have completed tutorial Lid Driven Cavity Background and have familiarized yourself with the basics of the Modulus APIs. Also, we recommend you to refer to tutorial 1D Wave Equation for information on defining new differential equations, and solving time dependent problems in Modulus.

## Problem Description¶

In this tutorial, a simple spring mass system as shown in Fig. 45 is solved. The systems shows three masses attached to each other by four springs. The springs slide along a friction-less horizontal surface. The masses are assumed to be point masses and the springs are mass-less. The problem is solved so that the masses ($$m's$$) and the spring constants ($$k's$$) are constants, but they can later be parameterized if you intend to solve the parameterized problem (Tutorial Parameterized 3D Heat Sink).

The model’s equations are given as below:

(132)$\begin{split}\begin{split} m_1 x_1''(t) &= -k_1 x_1(t) + k_2(x_2(t) - x_1(t)),\\ m_2 x_2''(t) &= -k_2 (x_2(t) - x_1(t))+ k_3(x_3(t) - x_2(t)),\\ m_3 x_3''(t) &= -k_3 (x_3(t) - x_2(t)) - k_4 x_3(t). \end{split}\end{split}$

Where, $$x_1(t), x_2(t), \text{and } x_3(t)$$ denote the mass positions along the horizontal surface measured from their equilibrium position, plus right and minus left. As shown in the figure, first and the last spring are fixed to the walls.

For this tutorial, assume the following conditions:

(133)$\begin{split}\begin{split} [m_1, m_2, m_3] &= [1, 1, 1],\\ [k_1, k_2, k_3, k_4] &= [2, 1, 1, 2],\\ [x_1(0), x_2(0), x_3(0)] &= [1, 0, 0],\\ [x_1'(0), x_2'(0), x_3'(0)] &= [0, 0, 0]. \end{split}\end{split}$ Fig. 45 Three masses connected by four springs on a friction-less surface¶

## Case Setup¶

The case setup for this problem is very similar to the setup for the tutorial 1D Wave Equation. Define the differential equations in spring_mass_ode.py and then define the domain and the solver in spring_mass_solver.py.

Note

The python scripts for this problem can be found at examples/ode_spring_mass/.

### Defining the Equations¶

The equations of the system (132) can be coded using the sympy notation similar to tutorial 1D Wave Equation.

from sympy import Symbol, Function, Number
from modulus.pdes import PDES

class SpringMass(PDES):
name = "SpringMass"

def __init__(self, k=(2, 1, 1, 2), m=(1, 1, 1)):

self.k = k
self.m = m

k1 = k
k2 = k
k3 = k
k4 = k
m1 = m
m2 = m
m3 = m

t = Symbol("t")
input_variables = {"t": t}

x1 = Function("x1")(*input_variables)
x2 = Function("x2")(*input_variables)
x3 = Function("x3")(*input_variables)

if type(k1) is str:
k1 = Function(k1)(*input_variables)
elif type(k1) in [float, int]:
k1 = Number(k1)
if type(k2) is str:
k2 = Function(k2)(*input_variables)
elif type(k2) in [float, int]:
k2 = Number(k2)
if type(k3) is str:
k3 = Function(k3)(*input_variables)
elif type(k3) in [float, int]:
k3 = Number(k3)
if type(k4) is str:
k4 = Function(k4)(*input_variables)
elif type(k4) in [float, int]:
k4 = Number(k4)

if type(m1) is str:
m1 = Function(m1)(*input_variables)
elif type(m1) in [float, int]:
m1 = Number(m1)
if type(m2) is str:
m2 = Function(m2)(*input_variables)
elif type(m2) in [float, int]:
m2 = Number(m2)
if type(m3) is str:
m3 = Function(m3)(*input_variables)
elif type(m3) in [float, int]:
m3 = Number(m3)

self.equations = {}
self.equations["ode_x1"] = m1 * (x1.diff(t)).diff(t) + k1 * x1 - k2 * (x2 - x1)
self.equations["ode_x2"] = (
m2 * (x2.diff(t)).diff(t) + k2 * (x2 - x1) - k3 * (x3 - x2)
)
self.equations["ode_x3"] = m3 * (x3.diff(t)).diff(t) + k3 * (x3 - x2) + k4 * x3


Here, each parameter $$(k's \text{ and } m's)$$ is written as a function and substitute it as a number if it’s constant. This allows you to parameterize any of this constants by passing them as a string.

### Solving the ODEs: Creating Geometry, defining ICs and making the Neural Network Solver¶

Once you have the ODEs defined, you can easily form the constraints needed for optimization as seen in earlier tutorials. This example, uses Point1D geometry to create the point mass. You also have to define the time range of the solution and create symbol for time ($$t$$) to define the initial condition, etc. in the train domain. This code shows the geometry definition for this problem. Note that this tutorial does not use the x-coordinate ($$x$$) information of the point, it is only used to sample a point in space. The point is assigned different values for variable ($$t$$) only (initial conditions and ODEs over the time-range). The code to generate the nodes and relevant constraints is below:

import numpy as np
from sympy import Symbol, Eq

import modulus
from modulus.hydra import to_yaml, instantiate_arch
from modulus.hydra.config import ModulusConfig
from modulus.continuous.solvers.solver import Solver
from modulus.continuous.domain.domain import Domain
from modulus.geometry.csg.csg_1d import Point1D
from modulus.continuous.constraints.constraint import (
PointwiseBoundaryConstraint,
PointwiseBoundaryConstraint,
)
from modulus.continuous.validator.validator import PointwiseValidator
from modulus.key import Key
from modulus.node import Node

from spring_mass_ode import SpringMass

@modulus.main(config_path="conf", config_name="config")
def run(cfg: ModulusConfig) -> None:
print(to_yaml(cfg))
# make list of nodes to unroll graph on
sm = SpringMass(k=(2, 1, 1, 2), m=(1, 1, 1))
sm_net = instantiate_arch(
input_keys=[Key("t")],
output_keys=[Key("x1"), Key("x2"), Key("x3")],
cfg=cfg.arch.fully_connected,
)
nodes = sm.make_nodes() + [sm_net.make_node(name="spring_mass_network", jit=cfg.jit)]

# make geometry
geo = Point1D(0)
t_max = 10.0
t_symbol = Symbol("t")
x = Symbol("x")
time_range = {t_symbol: (0, t_max)}

# make domain
domain = Domain()

# initial conditions
IC = PointwiseBoundaryConstraint(
nodes=nodes,
geometry=geo,
outvar={"x1": 1.0, "x2": 0, "x3": 0, "x1__t": 0, "x2__t": 0, "x3__t": 0},
batch_size=cfg.batch_size.IC,
lambda_weighting={
"x1": 1.0,
"x2": 1.0,
"x3": 1.0,
"x1__t": 1.0,
"x2__t": 1.0,
"x3__t": 1.0,
},
param_ranges={t_symbol: 0},
)

# solve over given time period
interior = PointwiseBoundaryConstraint(
nodes=nodes,
geometry=geo,
outvar={"ode_x1": 0.0, "ode_x2": 0.0, "ode_x3": 0.0},
batch_size=cfg.batch_size.interior,
param_ranges=time_range,
)


Next define the validation data for this problem. The solution of this problem can be obtained analytically and the expression can be coded into dictionaries of numpy arrays for $$x_1, x_2, \text{and } x_3$$. This part of the code is similar to the tutorial 1D Wave Equation.


deltaT = 0.001
t = np.arange(0, t_max, deltaT)
t = np.expand_dims(t, axis=-1)
invar_numpy = {"t": t}
outvar_numpy = {
"x1": (1 / 6) * np.cos(t)
+ (1 / 2) * np.cos(np.sqrt(3) * t)
+ (1 / 3) * np.cos(2 * t),
"x2": (2 / 6) * np.cos(t)
+ (0 / 2) * np.cos(np.sqrt(3) * t)
- (1 / 3) * np.cos(2 * t),
"x3": (1 / 6) * np.cos(t)
- (1 / 2) * np.cos(np.sqrt(3) * t)
+ (1 / 3) * np.cos(2 * t),
}
validator = PointwiseValidator(invar_numpy, outvar_numpy, nodes, batch_size=1024)



Now that you have the definitions for the various constraints and domains complete, you can form the solver and run the problem. The code to do the same can be found below:

    slv = Solver(cfg, domain)

# start solver
slv.solve()

if __name__ == "__main__":
run()


Once the python file is setup, you can solve the problem by executing the solver script spring_mass_solver.py as seen in other tutorials.

## Results and Post-processing¶

The results for the Modulus simulation are compared against the analytical validation data. You can see that the solution converges to the analytical result very quickly. The plots can be created using the .npz files that are created in the validator/ directory in the network checkpoint.