Simulation of the Euler-Bernoulli Beam¶

In this example, the hyperbolic equation of an euler bernoulli beam, clamped at one side is considered. The domain of the vertical beam excitation $x(z,t)$ is regarded to be $[0, 1] \times \mathbb{R}^+$ .

The governing equation reads:

$\begin{align*} \partial^2_t x(z,t) &= - \frac{EI}{\mu} \partial^4_z x(z,t)\\ x(0,t) &= 0 \\ \partial_z x(0,t) &= 0 \\ \partial^2_z x(0,t) &= 0 \\ \partial^3_z x(0,t) &= u(t) \end{align*}$

With the E-module $E$ , the second moment of area $I$ and the specific density $\mu$ . In this example, the input $u(t)$ mimics the force impulse occurring if the beam is hit by a hammer.

Spatial disretization¶

For further analysis let $D_z(x) = - \frac{EI}{\mu} \partial^4_z x$ denote the spatial operator and

$R(x) = \begin{pmatrix} x(0,t) \\ \partial_z x(0,t) \\ \partial^2_z x(1,t) \\ \partial^3_z x(1,t) \end{pmatrix} = \boldsymbol{0}$

denote the boundary operator.

Repeated partial integration of the expression

$\begin{align*} \left< D_z x | \varphi \right> &= \frac{EI}{\mu} \left< \partial^4_z x | y \right>\\ &= \frac{EI}{\mu} \left( \left[\partial^3_z x \varphi \right]_0^1 -\left[\partial^2_z x \partial_z\varphi \right]_0^1 \left[\partial^1_z x \partial^2_z\varphi \right]_0^1 -\left[ x \partial^3_z\varphi \right]_0^1 \right)\\ &\quad + \frac{EI}{\mu} \left< x | \partial^4_z y \right> \end{align*}$

and application of the boundary conditions shows that $\left< D_z x | y \right> = \left< x | D_z y \right>$ if $Rx = R\varphi$ . Therefore, the spatial operator is self-adjoint.

Alternative Variant¶

Using the weak formulation, which is gained by projecting the original equation on a set of test functions and fully shifting the spatial operator onto the test functions and substituting the boundary conditions

$\begin{align*} \left< D_z x | \varphi \right> &= \frac{EI}{\mu} \left< \partial^4_z x | y \right>\\ &= \frac{EI}{\mu} \big( u(t) \varphi(1) - \partial^3_z x(0) \varphi(0) + \partial^2_z x(0) \partial_z\varphi(0) \\ &\qquad + \partial_z x(1) \partial^2_z\varphi(1) - x(1) \partial^3_z\varphi(1) \\ &\qquad + \left< x | \partial^4_z y \right> \big) \end{align*}$

and inserting the modal approximation from above, the system can be simulated for every arbitrary input $u(t)$ . Note that this approximation converges over the whole spatial domain, but not punctually, since using the eigenvectors $\partial^3_z \varphi(1) = 0$ but $\partial^3_z x(1) = u(t)$ .

source code:

"""
This example simulates an euler-bernoulli beam, please refer to the
documentation for an exhaustive explanation.
"""

import numpy as np
import sympy as sp
import pyinduct as pi
from matplotlib import pyplot as plt


class ImpulseExcitation(pi.SimulationInput):
    """
    Simulate that the free end of the beam is hit by a hammer
    """

    def _calc_output(self, **kwargs):
        t = kwargs["time"]
        a = 1/20
        value = 100 / (a * np.sqrt(np.pi)) * np.exp(-((t-1)/a)**2)
        return dict(output=value)


def calc_eigen(order, l_value, EI, mu, der_order=4, debug=False):
    r"""
    Solve the eigenvalue problem and return the eigenvectors

    Args:
        order: Approximation order.
        l_value: Length of the spatial domain.
        EI: Product of e-module and second moment of inertia.
        mu: Specific density.
        der_order: Required derivative order of the generated functions.

    Returns:
        pi.Base: Modal base.
    """
    C, D, E, F = sp.symbols("C D E F")
    gamma, l = sp.symbols("gamma l")
    z = sp.symbols("z")

    eig_func = (C*sp.cos(gamma*z)
                + D*sp.sin(gamma*z)
                + E*sp.cosh(gamma*z)
                + F*sp.sinh(gamma*z))

    bcs = [eig_func.subs(z, 0),
           eig_func.diff(z, 1).subs(z, 0),
           eig_func.diff(z, 2).subs(z, l),
           eig_func.diff(z, 3).subs(z, l),
           ]
    e_sol = sp.solve(bcs[0], E)[0]
    f_sol = sp.solve(bcs[1], F)[0]
    new_bcs = [bc.subs([(E, e_sol), (F, f_sol)]) for bc in bcs[2:]]
    d_sol = sp.solve(new_bcs[0], D)[0]
    char_eq = new_bcs[1].subs([(D, d_sol), (l, l_value), (C, 1)])
    char_func = sp.lambdify(gamma, char_eq, modules="numpy")

    def char_wrapper(z):
        try:
            return char_func(z)
        except FloatingPointError:
            return 1

    grid = np.linspace(-1, 30, num=1000)
    roots = pi.find_roots(char_wrapper, grid, n_roots=order)
    if debug:
        pi.visualize_roots(roots, grid, char_func)

    # build eigenvectors
    eig_vec = eig_func.subs([(E, e_sol),
                             (F, f_sol),
                             (D, d_sol),
                             (l, l_value),
                             (C, 1)])

    # print(sp.latex(eig_vec))

    # build derivatives
    eig_vec_derivatives = [eig_vec]
    for i in range(der_order):
        eig_vec_derivatives.append(eig_vec_derivatives[-1].diff(z, 1))

    # construct functions
    eig_fractions = []
    for root in roots:
        # localize and lambdify
        callbacks = [sp.lambdify(z, vec.subs(gamma, root), modules="numpy")
                     for vec in eig_vec_derivatives]

        frac = pi.Function(domain=(0, l_value),
                           eval_handle=callbacks[0],
                           derivative_handles=callbacks[1:])
        frac.eigenvalue = - root**4 * EI / mu
        eig_fractions.append(frac)

    eig_base = pi.Base(eig_fractions)
    normed_eig_base = pi.normalize_base(eig_base)

    if debug:
        pi.visualize_functions(eig_base.fractions)
        pi.visualize_functions(normed_eig_base.fractions)

    return normed_eig_base


def run(show_plots):
    sys_name = 'euler bernoulli beam'

    # domains
    spat_bounds = (0, 1)
    spat_domain = pi.Domain(bounds=spat_bounds, num=101)
    temp_domain = pi.Domain(bounds=(0, 10), num=1000)

    if 0:
        # physical properties
        height = .1  # [m]
        width = .1  # [m]
        e_module = 210e9  # [Pa]
        EI = 210e9 * (width * height**3)/12
        mu = 1e6  # [kg/m]
    else:
        # normed properties
        EI = 1e0
        mu = 1e0

    # define approximation bases
    if 0:
        # somehow, fem is still problematic
        approx_base = pi.LagrangeNthOrder.cure_interval(spat_domain,
                                                        order=4)
        approx_lbl = "complete_base"
    else:
        approx_base = calc_eigen(7, 1, EI, mu)
        approx_lbl = "eig_base"

    pi.register_base(approx_lbl, approx_base)

    # system definition

    u = ImpulseExcitation("Hammer")
    x = pi.FieldVariable(approx_lbl)
    phi = pi.TestFunction(approx_lbl)

    weak_form = pi.WeakFormulation([
        pi.ScalarTerm(pi.Product(pi.Input(u), phi(1)), scale=EI),
        pi.ScalarTerm(pi.Product(x.derive(spat_order=3)(0), phi(0)),
                      scale=-EI),
        pi.ScalarTerm(pi.Product(x.derive(spat_order=2)(0), phi.derive(1)(0)),
                      scale=EI),
        pi.ScalarTerm(pi.Product(x.derive(spat_order=1)(1), phi.derive(2)(1)),
                      scale=EI),
        pi.ScalarTerm(pi.Product(x(1), phi.derive(3)(1)),
                      scale=-EI),
        pi.IntegralTerm(pi.Product(x, phi.derive(4)),
                        spat_bounds,
                        scale=EI),
        pi.IntegralTerm(pi.Product(x.derive(temp_order=2), phi),
                        spat_bounds,
                        scale=mu),
    ], name=sys_name)

    # initial conditions
    init_form = pi.ConstantFunction(0, domain=spat_bounds)
    init_form_dt = pi.ConstantFunction(0, domain=spat_bounds)
    initial_conditions = [init_form, init_form_dt]

    # simulation
    with np.errstate(under="ignore"):
        eval_data = pi.simulate_system(weak_form,
                                       initial_conditions,
                                       temp_domain,
                                       spat_domain,
                                       settings=dict(name="vode",
                                                     method="bdf",
                                                     order=5,
                                                     nsteps=1e8,
                                                     max_step=temp_domain.step))
    pi.tear_down([approx_lbl])

    # recover the input trajectory
    u_data = u.get_results(eval_data[0].input_data[0], as_eval_data=True)

    # visualization
    if show_plots:
        plt.plot(u_data.input_data[0], u_data.output_data)
        win1 = pi.PgAnimatedPlot(eval_data,
                                 labels=dict(left='x(z,t)', bottom='z'))
        pi.show()


if __name__ == "__main__":
    run(True)

Simulation of the Euler-Bernoulli Beam¶

Spatial disretization¶

Alternative Variant¶

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Related Topics

Simulation of the Euler-Bernoulli Beam¶

Spatial disretization¶

Modal Analysis¶

Alternative Variant¶