• December 03, 2016

    Automatically 'discovering' the heat equation with reverse finite differencing


    To date, most higher level physical knowledge has been derived from simpler first principles. However, these techniques have still not given a fully predictive understanding of turbulent flow. We have the exact deterministic governing equations (Navier–Stokes), but can’t solve them for most useful cases. As such, it could be of interest to find a different quantity, other than momentum, for which we can find some conservation or transport equation, that will be feasible to solve, i.e., does not have the scale resolution requirements of a direct numerical simulation. Or, perhaps, we may want to follow one of the current turbulence modeling paradigms but uncover an equation to relate the unresolved to the resolved information, e.g., Reynolds stresses to mean velocity.

    As a first step towards this goal, we want to see if the process of deriving physical laws or theories can be automated. For example, can we posit a generic homogeneous PDE, such as solve for the coefficients $[A, B, C,…]$, eliminate the terms for which coefficients are small, then be left with an analytically or computationally feasible equation?

    For a first example, here we look at the 1-D heat or diffusion equation, for which an analytical solution can be obtained. We will use two terms we know apply (the time derivative $u_t$ and second spatial derivative $u_xx$), and three that don’t (first order linear transport $u_x$, nonlinear advection $uu_x$, and a constant offset $E$):

    with the initial condition

    We will use the analytical solution as our dataset on which we want to uncover the governing PDE:

    from which our method should be able to determine that $A=1$, $C=\nu = 0.1$, and $B=D=E=0$.

    First, we will use the analytical solution and analytical partial derivatives.

    import numpy as np
    from numpy.testing import assert_almost_equal
    # Specify diffusion coefficient
    nu = 0.1
    def analytical_soln(xmax=1.0, tmax=0.2, nx=1000, nt=1000):
        """Compute analytical solution."""
        x = np.linspace(0, xmax, num=nx)
        t = np.linspace(0, tmax, num=nt)
        u = np.zeros((len(t), len(x))) # rows are timesteps
        for n, t_ind in enumerate(t):
            u[n, :] = np.sin(4*np.pi*x)*np.exp(-16*np.pi**2*nu*t_ind)
        return u, x, t
    u, x, t = analytical_soln()
    # Create vectors for analytical partial derivatives
    u_t = np.zeros(u.shape)
    u_x = np.zeros(u.shape)
    u_xx = np.zeros(u.shape)
    for n in range(len(t)):
        u_t[n, :] = -16*np.pi**2*nu*np.sin(4*np.pi*x)*np.exp(-16*np.pi**2*nu*t[n])
        u_x[n, :] = 4*np.pi*np.cos(4*np.pi*x)*np.exp(-16*np.pi**2*nu*t[n])
        u_xx[n, :] = -16*np.pi**2*np.sin(4*np.pi*x)*np.exp(-16*np.pi**2*nu*t[n])
    # Compute the nonlinear convective term (that we know should have no effect)
    uu_x = u*u_x
    # Check to make sure some random point satisfies the PDE
    i, j = 15, 21
    assert_almost_equal(u_t[i, j] - nu*u_xx[i, j], 0.0)

    Okay, so now that we have our data to work on, we need to form a system of equations $KM=0$ to solve for the coefficients $M$:

    for which each of the subscript indices $[0…4]$ corresponds to random points in space and time.

    # Create K matrix from the input data using random indices
    nterms = 5 # total number of terms in the equation
    ni, nj = u.shape
    K = np.zeros((5, 5))
    # Pick data from different times and locations for each row
    for n in range(nterms):
        i = int(np.random.rand()*(ni - 1)) # time index
        j = int(np.random.rand()*(nj - 1)) # space index
        K[n, 0] = u_t[i, j]
        K[n, 1] = -u_x[i, j]
        K[n, 2] = -u_xx[i, j]
        K[n, 3] = -uu_x[i, j]
        K[n, 4] = -1.0
    # We can't solve this matrix because it's singular, but we can try singular value decomposition
    # I found this solution somewhere on Stack Overflow but can't find the URL now; sorry!
    def null(A, eps=1e-15):
        """Find the null space of a matrix using singular value decomposition."""
        u, s, vh = np.linalg.svd(A)
        null_space = np.compress(s <= eps, vh, axis=0)
        return null_space.T
    M = null(K, eps=1e-5)
    coeffs = (M.T/M[0])[0]
    for letter, coeff in zip("ABCDE", coeffs):
        print(letter, "=", np.round(coeff, decimals=5))
    A = 1.0
    B = 0.0
    C = 0.1
    D = -0.0
    E = 0.0

    This method tells us that our data fits the equation


    which is what we expected!

    Would this method work with experimental data?

    So far, we’ve been using analytical partial derivatives from the exact solution. However, imagine we had various temperature probes on a physical model of a heat conducting system, which were sampled in time. We can sample the analytical solution at specified points, but add some Gaussian noise to approximate a sensor in the real world. Can we still unveil the heat equation with this added noise? To compute the derivatives, we’ll use finite differences, which would imply we may need to put multiple probes close to each other at a given location to resolve the spatial derivatives, but for now we will assume we can specify our spatial and temporal resolution.

    # Create a helper function compute derivatives with the finite difference method
    def diff(dept_var, indept_var, index=None, n_deriv=1):
        """Compute the derivative of the dependent variable w.r.t. the independent at the
        specified array index. Uses NumPy's `gradient` function, which uses second order
        central differences if possible, and can use second order forward or backward
        differences. Input values must be evenly spaced.
        dept_var : array of floats
        indept_var : array of floats
        index : int
            Index at which to return the numerical derivative
        n_deriv : int
            Order of the derivative (not the numerical scheme)
        # Rename input variables
        u = dept_var.copy()
        x = indept_var.copy()
        dx = x[1] - x[0]
        for n in range(n_deriv):
            dudx = np.gradient(u, dx, edge_order=2)
            u = dudx.copy()
        if index is not None:
            return dudx[index]
            return dudx
    # Test this with a sine
    x = np.linspace(0, 6.28, num=1000)
    u = np.sin(x)
    dudx = diff(u, x)
    d2udx2 = diff(u, x, n_deriv=2)
    assert_almost_equal(dudx, np.cos(x), decimal=5)
    assert_almost_equal(d2udx2, -u, decimal=2)
    def detect_coeffs(noise_amplitude=0.0):
        """Detect coefficients from analytical solution."""
        u, x, t = analytical_soln(nx=500, nt=500)
        # Add Gaussian noise to u
        u += np.random.randn(*u.shape) * noise_amplitude
        nterms = 5
        ni, nj = u.shape
        K = np.zeros((5, 5))
        for n in range(nterms):
            i = int(np.random.rand()*(ni - 1))
            j = int(np.random.rand()*(nj - 1))
            u_t = diff(u[:, j], t, index=i)
            u_x = diff(u[i, :], x, index=j)
            u_xx = diff(u[i, :], x, index=j, n_deriv=2)
            uu_x = u[i, j] * u_x
            K[n, 0] = u_t
            K[n, 1] = -u_x
            K[n, 2] = -u_xx
            K[n, 3] = -uu_x
            K[n, 4] = -1.0
        M = null(K, eps=1e-3)
        coeffs = (M.T/M[0])[0]
        for letter, coeff in zip("ABCDE", coeffs):
            print(letter, "=", np.round(coeff, decimals=3))
    for noise_level in np.logspace(-10, -6, num=5):
        print("Coefficients for noise amplitude:", noise_level)
        except ValueError:
    Coefficients for noise amplitude: 1e-10
    A = 1.0
    B = -0.0
    C = 0.1
    D = 0.0
    E = -0.0
    Coefficients for noise amplitude: 1e-09
    A = 1.0
    B = 0.0
    C = 0.1
    D = -0.0
    E = -0.0
    Coefficients for noise amplitude: 1e-08
    A = 1.0
    B = 0.0
    C = 0.1
    D = -0.001
    E = 0.0
    Coefficients for noise amplitude: 1e-07
    Coefficients for noise amplitude: 1e-06

    We see that the method breaks down for noise with amplitude on the order of $1 \times 10^{-6}$, which is not great considering the amplitude of the initial condition is $O(1)$, but not too bad for a first start. Some filtering or more stable finite difference schemes might be necessary to apply this technique to real experimental data.

    Conclusions and future work

    A simple algorithm was developed to detect the governing PDE from a given analytical solution. The algorithm uses “reverse” finite differences to sample the solution at random points, assembles a linear system of equations, and solves these using singular value decomposition to find the constant, homogeneous coefficients associated with terms of interest. Using an analytical solution to the heat equation, the algorithm successfully identified that the system’s evolution was only affected by diffusion, whereas additional terms for convection and first order wave propagation were shown to be insignificant.

    When subjected to Gaussian noise (to simulate experimental data), the algorithm failed for noise amplitudes roughly 6 orders of magnitude smaller than the amplitude of the initial condition. This shows an important weakness for looking at noisy data or higher derivatives, and may necessitate filtering or more robust linear solution techniques.

    The example presented here is admittedly trivial. However, it could be used to develop new theories or models for more complex systems, e.g., turbulent flow. For example, we may take the Reynolds-averaged Navier–Stokes equations: and attempt to solve for the Reynolds stresses (the last term on the RHS, a.k.a., the “closure problem”) in terms of many arbitrary combinations of the mean velocity and/or pressure, and their partial derivatives—where numerical values for all could be computed from a direct numerical simulation (DNS) of the exact Navier–Stokes equations.

    Most likely, resulting equations would not be theoretically correct, and the dimensions of their coefficients may not be able to be formulated in terms of physical parameters, e.g., viscosity. However, the equations would still satisfy the exact equations within some tolerance, and could prove to be useful models that would not have been derived via conventional analytical or phenomenological means.

  • December 28, 2015

    Diesel Explorer: Exhaust header design and fabrication

    Note: This work was performed Fall 2008.

    Fitting the B3.3 in the Explorer was tough. Even with a body lift, the turbo was way too high. To move it down, the intake and exhaust had to be modified. Cutting down and welding the intake was easy enough, but the exhaust was a bit trickier. It wasn’t possible to simply flip the OEM exhaust manifold over, so a custom exhaust header was in order.

    Intake height reduced.
    Intake height reduced.

    Rather than use ad hoc methods as in previous exhaust projects, I wanted to design this from scratch, using 3-D CAD, and fabricate to the design—a process that takes more time up front, but is certainly worth it in my opinion.

    Design was easy enough. Measurements were taken with the engine mocked up such that the relative positions of the head and turbine flanges could be identified. These were easy to fix in the appropriate coordinate system in the CAD assembly. Next, lofted solids were created to transition from the rectangular exhaust ports to the circular tubing. Since the bent sections would be made from tube elbows, these were brought into the assembly as necessary. Combined with straight sections, tubes were run into each other and eventually two tubes met at the turbine flange. Swept cuts were then made to cope all the pieces in the assembly.

    SolidWorks CAD assembly.
    SolidWorks CAD assembly.

    Next, since the fabrication was to be done by hand with a cutoff wheel, patterns for coping needed to be made. SolidWorks sheet metal tools were used to unwrap the cut pieces, from which flat pattern drawings were made, then wrapped around the tubes for marking. This was not quite at easy as it sounds, as the original parts were not sheet metal, and could not be easily converted, so they had to be re-modeled as such.

    Coping template for the header's longest straight tube.
    Coping template for the header's longest straight tube.

    After jigging the flanges up properly, creating the round-rectangle transitions, and coping the tubing by hand, the header was TIG welded. Finally, the pyrometer fitting was installed and the header was ready to run, allowing for optimal hood and firewall clearance in the Explorer.

    Header all welded up.
    Header all welded up.
    Header installed.
    Header installed.