CFD_from_scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CFD from scratch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Governing equations\n",
    "* Pressure\n",
    "* Finite volume formulation\n",
    "* Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Governing Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reynolds transport theorem (RTT)\n",
    "\n",
    "* The RTT connects a Lagrangian system with an Eulerian control volume. \n",
    "    * By Lagrangian system, we mean a system of a given specified mass, or a *marked mass*.\n",
    "* Conservation laws are written in terms of Lagrangian systems, but we solve CFD problems on Eulerian domains. The RTT connects these.\n",
    "\n",
    "<img src=\"rtt.png\" width=\"350\"/>\n",
    "\n",
    "* The RTT is written as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\underbrace{\\frac{dB_{sys}}{dt}}_{\\text{Lagrangian system}} = \\underbrace{\\frac{d}{dt}\\int_V\\rho\\beta dV + \\int_A\\rho\\beta\\vec{v}\\cdot\\vec{n}dA}_{\\text{Eulerian control volume}}.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* $B_{sys}$ is some extensive quantity, like mass, momentum, or energy.\n",
    "* $\\beta = B_{sys}/m$ is intensive.\n",
    "* $\\rho\\beta$ will be mass, momentum, energy, etc. per unit volume.\n",
    "* $\\rho\\beta\\vec{v}$ is $B$-flux, like mass flux, momentum flux, energy flux.\n",
    "* $\\vec{n}$ is a unit normal vector pointing *out* of the surface of a control volume. \n",
    "* In an area dA, $\\rho\\beta\\vec{v}\\cdot\\vec{n}dA$ will be rate of B flowing out of a control volume through that area dA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mass\n",
    "\n",
    "* $B_{sys} = m$\n",
    "* $\\beta = B_{sys}/m = 1$.\n",
    "* Lagrangian conservation law: mass is conserved, or the rate of change of a given mass is zero:\n",
    "$$\\frac{dm}{dt} = 0.$$\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho dV + \\int_A\\rho\\vec{v}\\cdot\\vec{n}dA = 0.$$\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum\n",
    "* $B_{sys} = m\\vec{v}$\n",
    "* $\\beta = B_{sys}/m = \\vec{v}$\n",
    "* Lagrangian conservation law: the rate of change of momentum of a fixed mass (system) is the sum of the external forces on the mass (system).\n",
    "    * We have surface forces $\\vec{F}$, and body forces (denoted with external field $\\vec{g}$, nominally gravitational acceleration):\n",
    "$$\\frac{dm\\vec{v}}{dt} = \\int_A \\vec{F}dA + \\int_V\\vec{g}\\rho dV.$$\n",
    "    * Consider viscous and pressure forces, so that $\\vec{F} = -\\boldsymbol{\\tau}\\cdot\\vec{n} - P\\boldsymbol{\\delta}\\cdot{\\vec{n}}$, where $\\boldsymbol{\\tau}$ is the viscous stress tensor, and $\\boldsymbol{\\delta}$ is the unit tensor. \n",
    "        * (The negative sign is because $\\vec{n}$ points *out* of the suface, and we want the force on or into the surface.) \n",
    "    * This gives\n",
    "$$\\frac{dm\\vec{v}}{dt} = -\\int_A\\boldsymbol{\\tau}\\cdot\\vec{n}dA -\\int_AP\\boldsymbol{\\delta}\\cdot\\vec{n}dA+ \\int_V\\vec{g}\\rho dV.$$\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho\\vec{v}dV + \\int_A\\rho\\vec{v}\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\boldsymbol{\\tau}\\cdot\\vec{n}dA -\\int_AP\\boldsymbol{\\delta}\\cdot\\vec{n}dA+ \\int_V\\vec{g}\\rho dV.$$\n",
    "</font>\n",
    "* Here, $\\vec{v}\\vec{v}$ is a tensor, and can be written as $\\vec{v}\\otimes\\vec{v}$, or $v_iv_j$ in index notation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy\n",
    "* $B_{sys} = E$ (where $E = mu + \\frac{1}{2}m\\vec{v}\\cdot\\vec{v}$ is internal + kinetic energy).\n",
    "* $\\beta = E/m = e$.\n",
    "* Lagrangian conservation law: the rate of change of energy of a given mass is the sum of the heat transfered to the mass and the work performed on the mass:\n",
    "$$\\frac{dE}{dt} = -\\int_A\\vec{q}\\cdot\\vec{n}dA + \\int_A\\vec{F}\\cdot\\vec{v}dA + \\int_V\\rho\\vec{g}\\cdot\\vec{v}dV.$$\n",
    "    * Here, $\\vec{q}$ is the heat flux vector. As before, $\\vec{F} = -\\boldsymbol{\\tau}\\cdot\\vec{n} - P\\boldsymbol{\\delta}\\cdot{\\vec{n}}$. \n",
    "    * The symmetry of $\\boldsymbol{\\tau}$ and $\\boldsymbol{\\delta}$ let us write $\\boldsymbol{\\tau}\\cdot\\vec{n}\\cdot\\vec{v} = \\boldsymbol{\\tau}\\cdot\\vec{v}\\cdot\\vec{n}$ and $P\\boldsymbol{\\delta}\\cdot\\vec{n}\\cdot\\vec{v} = P\\boldsymbol{\\delta}\\cdot\\vec{v}\\cdot\\vec{n}$.\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho edV + \\int_A\\rho e\\vec{v}\\cdot\\vec{n}dA = \n",
    "-\\int_A\\vec{q}\\cdot\\vec{n}dA - \\int_A(\\boldsymbol{\\tau}\\cdot\\vec{v})\\cdot\\vec{n}dA - \\int_A(P\\boldsymbol{\\delta}\\cdot\\vec{v})\\cdot\\vec{n}dA+ \\int_V\\rho\\vec{g}\\cdot\\vec{v}dV.$$\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equation of state and constitutive relations\n",
    "* The above (blue) equations for mass, momentum and energy are three equations in $\\rho$, $\\vec{v}$ and $e$.\n",
    "* An equation of state is used to relate these quantities to pressure $P$.\n",
    "    * For, example, the ideal gas law:\n",
    "$$P = \\frac{\\rho R T}{M}$$\n",
    "    * Here, $R$ is the universal gas constant, $M$ is the mean molecular weight, and $T$ is temperature.\n",
    "* Thermodynamic relations will relate $T$ to $e$, $\\vec{v}$, and $P$. For ideal gases, $T$ is a function of internal energy $e-\\frac{1}{2}\\vec{v}\\cdot\\vec{v}$.\n",
    "* Consitutive relations are also required to write $\\vec{q}$, and $\\boldsymbol{\\tau}$ in terms of $\\rho$, $\\vec{v}$, $e$, $P$."
   ]
  },
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   "metadata": {},
   "source": [
    "### Differential form\n",
    "\n",
    "* The above equations are in integral form, which is convenient for a Finite Volume solution. (It is also convenient for derivation.)\n",
    "* We can find the differential form as follows.\n",
    "    * If the control volume is fixed in time, we can move $d/dt$ inside the volume integral.\n",
    "    * Replace integrals over the surface area with volume integrals by applying the Gauss Divergence Theorem:\n",
    "    $$\\int_A\\vec{v}\\cdot\\vec{n}dA = \\int_V\\nabla\\cdot\\vec{v},$$\n",
    "    where $\\vec{v}$ is some vector (not necessarily velocity).\n",
    "        * Hence, in the equation for mass we have $\\int_A\\rho\\vec{v}\\cdot\\vec{n}dA = \\int_V\\nabla\\cdot(\\rho\\vec{v})dV$.\n",
    "    * Combine all the volume integrals into $\\int_V(\\text{all terms})dV = 0$. Since the volume integrated over is arbitrary, this equation can only be true if the integrand $(\\text{all terms})$ itself is 0. This gives the final result. (Also, $\\nabla\\cdot(P\\boldsymbol{\\delta}) = \\nabla P$.)\n",
    "    \n",
    "\\begin{align}\n",
    "\\text{mass:} \\phantom{xxxx}& \\frac{d\\rho}{dt} + \\nabla\\cdot(\\rho\\vec{v}) = 0\\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d\\rho\\vec{v}}{dt} + \\nabla\\cdot(\\rho\\vec{v}\\vec{v}) = -\\underbrace{\\nabla\\cdot\\boldsymbol{\\tau}}_{\\text{viscous forces}} - \\underbrace{\\nabla P}_{\\text{pressure forces}} + \\underbrace{\\rho\\vec{g}}_{\\text{gravitational forces}} \\\\\n",
    "\\text{energy:}\\phantom{xxxx} & \\frac{d\\rho e}{dt} + \\nabla\\cdot(\\rho e\\vec{v}) = -\\underbrace{\\nabla\\cdot\\vec{q}}_{\\text{heat flux}} - \\underbrace{\\nabla\\cdot(\\boldsymbol{\\tau}\\cdot\\vec{v})}_{\\text{viscous heating}} -\\underbrace{\\nabla\\cdot(P\\vec{v})}_{\\text{PV work}} + \\underbrace{\\rho\\vec{g}\\cdot\\vec{v}}_{\\text{field work}}\n",
    "\\end{align}\n",
    "\n",
    "* The terms on the left hand side (LHS) of the equation are the accumulation and in/out transport through the control volume.\n",
    "* The terms on the right hand side (RHS) are as noted. In the energy equation, field work will convert potential energy to kinetic energy (which is part of $e$)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pressure and compressibility\n",
    "* The governing equations, along with equation of state and constitutive, define the system. \n",
    "    * Oh, and boundary conditions too, don't forget those.\n",
    "* These are the compressible flow equations. \n",
    "* Pressure waves will be part of the solution.\n",
    "* Those waves travel at the sound speed (347 m/s in at at 1 atm, 300 K). \n",
    "* If we are at low speed relative to the acoustic waves (low Mach), then the flow is largely unimpacted by these waves and we call the flow incompressible.\n",
    "    * From the Bernoulli equation, ignoring gravity, we have\n",
    "    $$\\Delta P = \\frac{\\rho\\Delta v^2}{2}.$$\n",
    "    * Take pressures relative to stagnation, so $\\Delta v=v$ (most flows have $v=0$ somewhere). Using $c^2=\\gamma P/\\rho$, where $\\gamma = c_p/c_v$, and $Ma = v/c$, we have\n",
    "    $$\\frac{\\Delta P}{P} = \\frac{\\gamma}{2}Ma^2.$$\n",
    "    * For v=100 m/s, $\\Delta P/P_{atm}$ is 6%. (100 m/s is 233 mph and gives Ma=0.288 for air at 1 atm.)\n",
    "    * For v=347 m/s, $\\Delta P/P_{atm}$ is 70%. (347 m/s is 776 mph and gives Ma=1 for air at 1 atm.)\n",
    "* Flows are often considered incompressible for $Ma<0.3$.\n",
    "* Flows can have large density changes and still be considered incompressible. Heat release and chemical mixing in gases can cause large density variations, but at low speed, the changes in velocity themselves only slightly compress the fluid."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Low-Mach flows\n",
    "* At low speeds, acoustic waves do not alter the thermodynamic state or affect the velocity fields. \n",
    "* But for unsteady, explicit flow solvers, we are required to take timesteps based on the smallest timescales. Here, those correspond to acoustic waves, or rather, $v+c$. \n",
    "* This is a numerically *stiff* problem: we have to take timesteps for stability that are much smaller than we need for accuracy (for capturing the relevant physics, of which acoustics don't contribute.)\n",
    "    * The ratio of the number of timesteps needed for stability to the ideal number for accuracy is $(v+c)/v = 1+1/Ma$. \n",
    "        * This is based on a CFL condition: accurate timestep sizes will be smaller than the convective time across a given grid cell: $\\Delta t < \\alpha\\Delta x/v$ for accuracy, but $\\Delta t < \\alpha\\Delta x/(v+c)$ for stability, where $\\alpha$ is a constant (the CFL) determined by the method used. \n",
    "    * At Ma=0.1 (34.7 m/s or 77.6 mph in air at 1 atm, 300 K), we would need 11 times more steps for a stable solution than we need for an acurate solution. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incompressible flow\n",
    "* The numerical stiffness for low-Mach flows can be explicitly removed from the equations by assuming the flow is incompressible and using a so-called pressure projection method.\n",
    "* The density is assumed constant, and the energy equation is no longer needed.\n",
    "* The resulting equations, ignoring gravity, and taking $\\hat{\\tau}=\\tau/\\rho$, and $\\hat{P} = P/\\rho$ are\n",
    "<font color=\"blue\">\n",
    "\\begin{align}\n",
    "\\text{mass:}\\phantom{xxxx} & \\int_A\\vec{v}\\cdot\\vec{n}dA = 0 \\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d}{dt}\\int_V\\vec{v}dV + \\int_A\\vec{v}\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}\\cdot\\vec{n}dA -\\int_A\\hat{P}\\boldsymbol{\\delta}\\cdot\\vec{n}dA.\n",
    "\\end{align}\n",
    "</font>\n",
    "* These are two equations in $\\vec{v}$ and $P$. \n",
    "* The momentum equation gives the equation for $\\vec{v}$, but there is no explicit pressure equation. The mass equation (continuity) provides a constraint on the velocities.\n",
    "* Pressure can be thought of as a scalar field that constrains velocity components given by the momentum equation to satisfy continuity.\n",
    "* A pressure equation is derived by effectively taking the divergence of the momentum equation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pressure equation\n",
    "* Consider the differential form of the mass and momentum equations, where we drop the vector arrows for simplicity:\n",
    "\\begin{align}\n",
    "\\text{mass:}\\phantom{xxxx} & \\nabla\\cdot v = 0 \\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{dv}{dt} = -\\nabla\\cdot vv - \\nabla\\cdot\\hat{\\boldsymbol{\\tau}} - \\nabla\\hat{P}\n",
    "\\end{align}\n",
    "* Apply a simple Explicit Euler integration over one timestep $h=\\Delta t$ to the momentum equation:\n",
    "$$v^{n+1} = \\underbrace{v^n + h\\left(-\\nabla\\cdot vv-\\nabla\\cdot\\hat{\\boldsymbol{\\tau}}\\right)^n}_{H^n}-h\\nabla\\hat{P}^{n+1}.$$\n",
    "    * Note that $\\hat{P}$ is taken at step $n+1$ (keep reading).\n",
    "* We use $\\nabla\\cdot v=0$ applied to this $v^{n+1}$ equation to get an equation for $\\hat{P}^{n+1}$:\n",
    "<font color=\"blue\">\n",
    "$$\\nabla^2\\hat{P}^{n+1} = \\frac{1}{h}\\nabla\\cdot H^n.$$\n",
    "</font>\n",
    "* **To advance a given step, we solve this equation for $\\hat{P}$, and then use this in the momentum advancement equation for $v^{n+1}$ above:**\n",
    "<font color=\"blue\">\n",
    "$$v^{n+1} = H^n - h\\nabla\\hat{P}^{n+1}.$$\n",
    "</font>\n",
    "* This velocity satisfies continuity by construction.\n",
    "\n",
    "**In practice, the pressure equation is determined from the discretized version of the momentum and continuity equations, discussed later.**\n",
    "\n",
    "#### Boundary conditions for the pressure equation\n",
    "* The pressure equation requires boundary conditions. These are best determined from the momentum equations evaluated on the boundary. \n",
    "* There appears to be ongoing discussion of the boundary condition treatment for the pressure equation. See for example \n",
    "    * [Vremen 2014](https://linkinghub.elsevier.com/retrieve/pii/S002199911400062X)\n",
    "    * [Rempfer 2003](https://www.annualreviews.org/doi/abs/10.1146/annurev.fluid.35.030602.113908?journalCode=fluid)\n",
    "    * [Sani 2006](https://onlinelibrary.wiley.com/doi/10.1002/fld.1062)\n",
    "        * Along with [some comentantary by Rempfer](https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.1750)\n",
    "        * And a [reply by Sani](https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.1762)\n",
    "* In my opinion, the pressure treatment is not so complicated. The simplicity of inserting the velocities into the continuity equation, with boundary velocities taken along for the ride, is pretty straightforward. Pressure boundary conditions are effectively built-in. See below.        \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Solution-Finite Volume Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To solve the above equations, we'll use a finite volume method. \n",
    "* Assume a 2-dimensional rectangular domain. \n",
    "    * We divide the domain into grid cells of size $\\Delta x$ by $\\Delta y$. \n",
    "    * $\\Delta x\\Delta y$ is the volume of a grid cell.\n",
    "    * Cell face areas are $\\Delta x$ and $\\Delta y$.\n",
    "<img src=\"fig_1.png\" width=\"250\"/>\n",
    "    * cell faces are labeled as e, w, n, s for east, west, north, and south, respectively.\n",
    "    * The x-direction (horizontal) is indexed with *i* and the y direction (vertical) is indexed with $j$.\n",
    "* We assume all properties are uniform inside a given cell, and we assume all properties are uniform along a given face.\n",
    "    * With these two assumptions, we integrate the mass and momentum equations over a given cell."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum components\n",
    "\\begin{align}\n",
    "\\text{u-mom:}\\phantom{xxxx}&\\frac{d}{dt}\\int_VudV + \\int_Au\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}_x\\cdot\\vec{n}dA -\\int_A\\hat{P}\\vec{i}\\cdot\\vec{n}dA, \\\\\n",
    "\\text{v-mom:}\\phantom{xxxx}&\\frac{d}{dt}\\int_VvdV + \\int_Av\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}_y\\cdot\\vec{n}dA -\\int_A\\hat{P}\\vec{j}\\cdot\\vec{n}dA.\n",
    "\\end{align}\n",
    "Here, $\\vec{i}$ and $\\vec{j}$ are the unit coordinate vectors in the $x$ and $y$ directions, respectively."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Integrate the u-mom equation over a given grid cell.\n",
    "* The first term is\n",
    "$$\\frac{d}{dt}\\int_VudV \\rightarrow \\frac{d}{dt}u\\int_VdV = \\frac{duV}{dt} = \\frac{du\\Delta x\\Delta y}{dt} = \\Delta x\\Delta y\\frac{du}{dt}.$$\n",
    "* The area integrals are illustrated considering the second term, where $\\vec{n}=\\pm\\vec{i}$ or $\\vec{n}=\\pm\\vec{j}$:\n",
    "\n",
    "\\begin{align}\n",
    "\\int_Au\\vec{v}\\cdot\\vec{n}dA &= \n",
    "\\int_{A_e}u\\vec{v}\\cdot\\vec{i}dA - \n",
    "\\int_{A_w}u\\vec{v}\\cdot\\vec{i}dA + \n",
    "\\int_{A_n}u\\vec{v}\\cdot\\vec{j}dA - \n",
    "\\int_{A_s}u\\vec{v}\\cdot\\vec{j}dA \\\\\n",
    "&= u_eu_e\\Delta y - u_wu_w\\Delta y + u_nv_n\\Delta x - u_sv_s\\Delta x.\n",
    "\\end{align}\n",
    "\n",
    "* Considering all terms in the u-mom equation and dividing through by $\\Delta x\\Delta y$ gives\n",
    "\n",
    "$$\\frac{du}{dt} = -\\frac{u_eu_e - u_wu_w}{\\Delta x} - \\frac{u_nv_n - u_sv_s}{\\Delta y}\n",
    "- \\frac{\\hat{\\tau}_{xx,e}-\\hat{\\tau}_{xx,w}}{\\Delta x} \n",
    "- \\frac{\\hat{\\tau}_{xy,n}-\\hat{\\tau}_{xy,s}}{\\Delta y}\n",
    "- \\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x}$$\n",
    "\n",
    "* The quantities at faces interpolated between values at neighboring cells.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkerboarding\n",
    "* Consider in particular the face pressures:\n",
    "$$\\hat{P}_e = \\frac{\\hat{P}_{i+1,j}+\\hat{P}_{i,j}}{2},$$\n",
    "$$\\hat{P}_w = \\frac{\\hat{P}_{i,j}+\\hat{P}_{i-1,j}}{2}.$$\n",
    "* Inserting these into the u-mom equation gives:\n",
    "$$-\\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x} = -\\frac{\\hat{P}_{i+1,j}+\\hat{P}_{i,j}-\\hat{P}_{i,j}-\\hat{P}_{i-1,j}}{2\\Delta x} = -\\frac{\\hat{P}_{i+1,j}-\\hat{P}_{i-1,j}}{2\\Delta x}.$$\n",
    "* Note that for the given *i,j* cell, $u_{i,j}$ depends on the two neighboring pressures, but not on $\\hat{P}_{i,j}$.\n",
    "* This effectively decouples $u$ from $\\hat{P}$.\n",
    "* Consider a one-dimensional grid with the following pressure values\n",
    "\n",
    "```\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "```\n",
    "\n",
    "* For any given cell, pressure term will cancel since (8-8)=0 and (2-2)=0. Hence, this pressure field will not affect the velocity. \n",
    "* This is clearly unphysical since there is an alternative pressure gradient that should drive velocity.\n",
    "* In two dimensions, we could have something like:\n",
    "\n",
    "```\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  6  |  4  |  6  |  4  |  6  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  6  |  4  |  6  |  4  |  6  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Staggered grid\n",
    "* The decoupling of velocity and pressure can cause instability. Numerical error/noise can be preserved and amplified.\n",
    "* To remedy this, a staggered grid is used.\n",
    "    * A u-grid is formed by shifting all grid cells left by half a cell.\n",
    "        * u-velocity components are stored at the cell centers of this shifted u-grid.\n",
    "        * The u-momentum equation is integrated over the u-cells.\n",
    "    * A v-grid is formed by shifting all grid cells down by half a cell\n",
    "        * v-velocity components are stored at the cell centers of this shifted v-grid.\n",
    "        * The v-momentum equation is integrated over the u-cells.\n",
    "    * Pressure is stored on the regular grid. \n",
    "        * The continuity equation is integrated over the P-cells.\n",
    "    \n",
    "<img src=\"fig_2.png\" width=\"600\"/>\n",
    "\n",
    "Note the overlap of the cells:\n",
    "<img src=\"fig_3.png\" width=\"100\"/>\n",
    "* In the u-momentum equation, $\\hat{P}_e$ and $\\hat{P}_w$ are needed. Now, rather than interpolate from neighboring cells, $\\hat{P}_e$ is simply $\\hat{P}_{i,j}$, and $\\hat{P}_w$ is just $\\hat{P}_{i-1,j}$, where indexing on $\\hat{P}$ is with respect to the P-grid.\n",
    "* Similarly, in the v-momentum equation, $\\hat{P}_n$, and $\\hat{P}_s$ appear, and these are available directly, without interpolation. \n",
    "* This precludes any checkerboarding since momentum components are now driven by pressure differences in adjacent cells."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### u-mom equation\n",
    "\n",
    "* The FV equation for u above is still correct, but is now applied to the u-grid, and all face quantities are with respect to a given u-cell.\n",
    "* We will still interpolate values to faces, as needed with $u$, $v$, and $P$ cell values refering to the respective u-grid, v-grid, and P-grid.\n",
    "* Note the definition of $\\hat{\\tau}$ for incompressible Newtonian fluids below. Don't confuse $\\nu$ (kinematic viscosity) with $v$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{du}{dt} = \\underbrace{-\\frac{u_eu_e - u_wu_w}{\\Delta x} - \\frac{u_nv_n - u_sv_s}{\\Delta y}\n",
    "- \\frac{\\hat{\\tau}_{xx,e}-\\hat{\\tau}_{xx,w}}{\\Delta x} \n",
    "- \\frac{\\hat{\\tau}_{xy,n}-\\hat{\\tau}_{xy,s}}{\\Delta y}}_{H^u}\n",
    "- \\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x}.$$\n",
    "\n",
    "<font color=\"blue\">\n",
    "$$\\frac{du}{dt} = H^u - \\frac{\\hat{P}_e - \\hat{P}_w}{\\Delta x}.$$\n",
    "</font>\n",
    "\n",
    "\n",
    "<font color=\"blue\">\n",
    "\\begin{align}\n",
    "&u_e = \\frac{u_{i+1,j}+u_{i,j}}{2}, \\\\\n",
    "&u_w = \\frac{u_{i,j}+u_{i-1,j}}{2}, \\\\\n",
    "&u_n = \\frac{u_{i,j+1}+u_{i,j}}{2}, \\\\\n",
    "&u_s = \\frac{u_{i,j}+u_{i,j-1}}{2}, \\\\\n",
    "&v_n = \\frac{v_{i,j+1}+v_{i-1,j+1}}{2}, \\\\\n",
    "&v_s = \\frac{v_{i,j}+v_{i-1,j}}{2}, \\\\\n",
    "&\\hat{\\tau}_{xx,e} = -2\\nu\\left(\\frac{\\partial u}{\\partial x}\\right)_e = -2\\nu\\left(\\frac{u_{i+1,j}-u_{i,j}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{\\tau}_{xx,w} = -2\\nu\\left(\\frac{\\partial u}{\\partial x}\\right)_w = -2\\nu\\left(\\frac{u_{i,j}-u_{i-1,j}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{\\tau}_{xy,n} = -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_n \n",
    "                     -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_n =  \n",
    "-\\nu\\left(\\frac{u_{i,j+1}-u_{i,j}}{\\Delta y}\\right)\n",
    "-\\nu\\left(\\frac{v_{i,j+1}-v_{i-1,j+1}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{\\tau}_{xy,s} = -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_s \n",
    "                     -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_s =  \n",
    "-\\nu\\left(\\frac{u_{i,j}-u_{i,j-1}}{\\Delta y}\\right)\n",
    "-\\nu\\left(\\frac{v_{i,j}-v_{i-1,j}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{P}_e = \\hat{P}_{i,j}, \\\\\n",
    "&\\hat{P}_w = \\hat{P}_{i-1,j}.\n",
    "\\end{align}\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### v-mom equation\n",
    "\n",
    "* We can follow the same procedure for the v-mom equation as for the u-mom equation, but applied to the v-grid. When interpolating face values, we again write quantities with respect to their associated grids.\n",
    "* There is a direct symmetry, however, and we can use the u-mom eqution with the following replacements:\n",
    "    * $u\\rightarrow v$\n",
    "    * $e\\rightarrow n$\n",
    "    * $w\\rightarrow s$\n",
    "    * $n\\rightarrow e$\n",
    "    * $s\\rightarrow w$\n",
    "    * $x\\rightarrow y$\n",
    "    * $y\\rightarrow x$\n",
    "    * subscripts: $u_{i+1,j}\\rightarrow v_{i,j+1}$, for example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{dv}{dt} = \\underbrace{-\\frac{v_nv_n - v_sv_s}{\\Delta y} - \\frac{v_eu_e - v_wu_w}{\\Delta x}\n",
    "- \\frac{\\hat{\\tau}_{yy,n}-\\hat{\\tau}_{yy,s}}{\\Delta y} \n",
    "- \\frac{\\hat{\\tau}_{yx,e}-\\hat{\\tau}_{yx,w}}{\\Delta x}}_{H^v}\n",
    "- \\frac{\\hat{P}_n-\\hat{P}_s}{\\Delta y}.$$\n",
    "\n",
    "<font color=\"red\">\n",
    "$$\\frac{dv}{dt} = H^v - \\frac{\\hat{P}_n - \\hat{P}_s}{\\Delta y}.$$\n",
    "</font>\n",
    "\n",
    "\n",
    "<font color=\"red\">\n",
    "\\begin{align}\n",
    "&v_n = \\frac{v_{i,j+1}+v_{i,j}}{2}, \\\\\n",
    "&v_s = \\frac{v_{i,j}+v_{i,j-1}}{2}, \\\\\n",
    "&v_e = \\frac{v_{i+1,j}+v_{i,j}}{2}, \\\\\n",
    "&v_w = \\frac{v_{i,j}+v_{i-1,j}}{2}, \\\\\n",
    "&u_e = \\frac{u_{i+1,j}+u_{i+1,j-1}}{2}, \\\\\n",
    "&u_w = \\frac{u_{i,j}+u_{i,j-1}}{2}, \\\\\n",
    "&\\hat{\\tau}_{yy,n} = -2\\nu\\left(\\frac{\\partial v}{\\partial y}\\right)_n = -2\\nu\\left(\\frac{v_{i,j+1}-v_{i,j}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{\\tau}_{yy,s} = -2\\nu\\left(\\frac{\\partial v}{\\partial y}\\right)_s = -2\\nu\\left(\\frac{v_{i,j}-v_{i,j-1}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{\\tau}_{yx,e} = -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_e \n",
    "                     -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_e =  \n",
    "-\\nu\\left(\\frac{v_{i+1,j}-v_{i,j}}{\\Delta x}\\right)\n",
    "-\\nu\\left(\\frac{u_{i+1,j}-u_{i+1,j-1}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{\\tau}_{yx,w} = -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_w \n",
    "                     -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_w =  \n",
    "-\\nu\\left(\\frac{v_{i,j}-v_{i-1,j}}{\\Delta x}\\right)\n",
    "-\\nu\\left(\\frac{u_{i,j}-u_{i,j-1}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{P}_n = \\hat{P}_{i,j}, \\\\\n",
    "&\\hat{P}_s = \\hat{P}_{i,j-1}.\n",
    "\\end{align}\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuity\n",
    "* The mass balance is given by\n",
    "$$\\int_A\\vec{v}\\cdot\\vec{n}dA = 0.$$\n",
    "* Integrate this over the P-cell to give\n",
    "$$(u_e-u_w)\\Delta y + (v_n-v_s)\\Delta x = 0.$$\n",
    "\\begin{align}\n",
    "&u_e = u_{i+1,j}, \\\\\n",
    "&u_w = u_{i,j}, \\\\\n",
    "&v_n = v_{i,j+1}, \\\\\n",
    "&v_s = v_{i,j}. \\\\\n",
    "\\end{align}\n",
    "Hence,\n",
    "$$(u_{i+1,j}-u_{i,j})\\Delta y + (v_{i,j+1}-v_{i,j})\\Delta x = 0.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete pressure equation\n",
    "* Apply a simple Explicit Euler step of size $\\Delta t=h$ to the $u$ and $v$ momentum equations for *i,j* pairs appearing in the above continuity equation:\n",
    "\n",
    "\\begin{align}\n",
    "u_{i+1,j}^{n+1} &= u_{i+1,j}^n + hH_{i+1,j}^u - \\frac{h}{\\Delta x}(\\hat{P}_{i+1,j}-\\hat{P}_{i,j}), \\\\\n",
    "u_{i,j}^{n+1}   &= u_{i,j}^n + hH_{i,j}^u - \\frac{h}{\\Delta x}(\\hat{P}_{i,j}-\\hat{P}_{i-1,j}), \\\\\n",
    "v_{i,j+1}^{n+1} &= v_{i,j+1}^n + hH_{i,j+1}^v - \\frac{h}{\\Delta y}(\\hat{P}_{i,j+1}-\\hat{P}_{i,j}), \\\\\n",
    "v_{i,j}^{n+1}   &= v_{i,j}^n + hH_{i,j}^v - \\frac{h}{\\Delta y}(\\hat{P}_{i,j}-\\hat{P}_{i,j-1}).\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We insert these into the continuity equation, and put terms involving $\\hat{P}$ on the left hand side, and all other terms on the right hand side. \n",
    "$$\n",
    "\\hat{P}_{i,j-1}\\underbrace{\\left[-\\frac{h\\Delta x}{\\Delta y}\\right]}_{v_s} + \n",
    "\\hat{P}_{i-1,j}\\underbrace{\\left[-\\frac{h\\Delta y}{\\Delta x}\\right]}_{u_w} + \n",
    "\\hat{P}_{i,j}\\underbrace{  \\left[\\frac{2h\\Delta y}{\\Delta x} + \\frac{2h\\Delta x}{\\Delta y}\\right]}_{(u_e,u_w) + (v_n,v_s)} + \n",
    "\\hat{P}_{i+1,j}\\underbrace{\\left[-\\frac{h\\Delta y}{\\Delta x}\\right]}_{u_e} + \n",
    "\\hat{P}_{i,j+1}\\underbrace{\\left[-\\frac{h\\Delta x}{\\Delta y}\\right]}_{v_n}  = $$\n",
    "$$\\underbrace{\\Delta x(v_{i,j}^n+hH_{i,j}^v)}_{v_s}\n",
    "+\\underbrace{\\Delta y(u_{i,j}^n+hH_{i,j}^u)}_{u_w}\n",
    "-\\underbrace{\\Delta y(u_{i+1,j}^n+hH_{i+1,j}^u)}_{u_e}\n",
    "-\\underbrace{\\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)}_{v_n}\n",
    "$$\n",
    " * The terms are labeled to show which velocity components they arise from. This is convenient for dealing with boundary cells.\n",
    "* This is a linear system of equations in the form $A\\hat{P}=b$, where $A$ is a matrix and $\\hat{P}$ and $b$ are vectors.\n",
    "    * $A$ is the matrix of coefficients of $\\hat{P}$.\n",
    "        * There is a row in $A$ for each *i,j* cell in the grid.\n",
    "        * More on this below...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the Cartesian grid shown, there are nine types of cells:\n",
    "    * **Corner** cells for the top-right, top-left, bottom-right, and bottom-left.\n",
    "    * **Edge** cells for the top, bottom, left, and right.\n",
    "    * **Interior** cells.\n",
    "    \n",
    "<img src=\"fig_4.png\" width=\"250\"/>\n",
    "\n",
    "* We have a P-equation for each type of cell. \n",
    "    * The equations differ in which velocity making up the equation is on a boundary face. \n",
    "    * For a cell with a velocity on a face, like and ET cell, the coefficient of $\\hat{P}_{i,j+1}$ is zero, the second term of the coefficient of $\\hat{P}_{i,j}$ is decreased (factor 2 $\\rightarrow 1$) and the corresponding term on the right hand side of the P-equation is replaced with the known face velocity: $-\\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)\\rightarrow -\\Delta xv_n$, where $v_n$ here is the value on the top boundary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$\\hat{P}$ coefficients for each cell type.**\n",
    "\n",
    "|Type| $P_{i,j-1}$ | $P_{i-1,j}$ | $P_{i,j}$ | $P_{i+1,j}$ | $P_{i,j+1}$ |\n",
    "|-------|-------|-------|-------|-------|-------|\n",
    "|I | $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{2h\\Delta y}{\\Delta x}+\\frac{2h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|CBL | $0$ | $0$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|EB |$0$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{2h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|CBR|$0$| $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|EL | $-\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{2h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|ER| $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{2h\\Delta x}{\\Delta y}$ | $0$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|CTL | $-\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $0$ |\n",
    "|ET | $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{2h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $0$ |\n",
    "|CTR | $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $0$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$b$ vector for each cell type.**\n",
    "\n",
    "| Type | $b_{v_s}$ | $+b_{u_w}$ | $+b_{u_e}$ | $+b_{v_n}$ |\n",
    "|------|----------|----------|----------|----------|\n",
    "| I    | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| CBL  | $\\Delta xv_s$| $+\\Delta yu_w$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| EB   | $\\Delta xv_s$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| CBR  | $\\Delta xv_s$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta yu_e$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| EL   | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta yu_w$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| ER   | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta yu_e$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| CTL  | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta yu_w$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta xv_n$ |\n",
    "| ET   | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta xv_n$ |\n",
    "| CTR  | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta yu_e$ | $- \\Delta xv_n$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation\n",
    "### 2-D lid-driven cavity\n",
    "* This is a box (cavity) with sliding walls. \n",
    "* Boundary conditions are simply zero normal flow through the domain boundaries, and constant tangential wall velocities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boilerplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import diags\n",
    "from scipy.sparse.linalg import spsolve\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "IJ = np.ix_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_user_specifications():\n",
    "    global nx, ny, Lx, Ly, ν, ubc_t, ubc_b, vbc_r, vbc_l, n_τ_run, cfl\n",
    "    \n",
    "    nx = 40         # number of grid points in x direction (P-grid)\n",
    "    ny = 60         # number of grid points in the y direction (P-grid)\n",
    "    Lx = 1.0        # domain length in x\n",
    "    Ly = 1.5        # domain length in y\n",
    "    ν  = 1.0        # kinematic viscosity\n",
    "    \n",
    "    ubc_t = 10.0      # u on top boundary\n",
    "    ubc_b = 0.0       # u on bottom boundary\n",
    "    vbc_r = 10.0      # v on right boundary\n",
    "    vbc_l = 0.0       # v on left boundary\n",
    "    \n",
    "    n_τ_run = 2      # number of box timescales to run for\n",
    "    cfl  = 0.05       # timestep size factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid and time parameters\n",
    "\n",
    "**Indexing:** \n",
    "* We use an x-y Cartesian grid. \n",
    "* For an array ```u(nx,ny)```, ```u[i,j]``` refers to x-index ```i``` and y-index ```j```.\n",
    "* As a matrix, we normally think of ```u[i,j]``` as row ```i``` and column ```j``` and visualize ```i``` varying vertically (rows change top-to-bottom) and ```j``` varying horizontally (columns change left-to-right).\n",
    "* However, in visualizing our grid, x is horizontal (increasing left-to-right), and y is vertical (increasing bottom to top).\n",
    "* This difference in the arrays versus the visual grid is only important in the tiling of the 2-D grid to a 1-D array for the pressure solve (see below), and in plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_grid_time_vars():\n",
    "    global x, y, Δx, Δy, tend, Δt, nsteps, u, v, P\n",
    "    \n",
    "    #x  = np.linspace(0,Lx,nx)     # x-grid\n",
    "    #y  = np.linspace(0,Ly,ny)     # y-grid\n",
    "    x  = np.linspace(Lx/nx/2,Lx-Lx/nx/2,nx)     # x-grid\n",
    "    y  = np.linspace(Ly/ny/2,Ly-Ly/ny/2,ny)     # x-grid\n",
    "    Δx = x[1] - x[0]              # x-grid spacing\n",
    "    Δy = y[1] - y[0]              # y-grid spacing\n",
    "    \n",
    "    τ_box  = (Lx+Ly)/2 / np.max(np.abs([ubc_t, ubc_b, vbc_r, vbc_l]))      # box timescale\n",
    "    tend   = τ_box * n_τ_run                                               # simulation run time\n",
    "    Δt     = cfl*np.min([Δx,Δy])/np.max(np.abs([ubc_t,ubc_b,vbc_r,vbc_l])) # timestep size\n",
    "    nsteps = int(tend/Δt)                                                  # number of timesteps\n",
    "    Δt     = tend/nsteps                                                   # timestep size\n",
    "    \n",
    "    #-------------------- set solution variables\n",
    "    \n",
    "    u = np.zeros((nx+1,ny))\n",
    "    v = np.zeros((nx,ny+1))               \n",
    "    P = np.zeros((nx,ny))                # P = P/ρ (P_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### u-Mom function (Hu)\n",
    "* Array slices are used to avoid loops for speed and simplicity\n",
    "* Note the use of the IJ = np.ix_ for certain array slices.\n",
    "* Solving on the u-grid:\n",
    "\n",
    "<img src=\"ugrid.png\" width=\"200\"/>\n",
    "\n",
    "\n",
    "* The u array and u-grid face quantities including $H^u$ are size ```(nx+1, ny)```\n",
    "* Values of u on the left and right boundaries are part of the matrix and initialized, but don't change.\n",
    "* Interior u-cells are solved.\n",
    "    * python: ```i=1:nx```, ```j=0:ny```, or $i\\in[1,n_x)$ $j\\in[0,n_y)$.\n",
    "    * including the boundaries in the matrix facilitates the indexing by minimizing special cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Hu():\n",
    "    \"\"\"\n",
    "    ue, uw, un, us, vn, vs are values on u-cell faces\n",
    "    These arrays include ALL u-cells (nx+1,ny) for convenience,\n",
    "    but only interior u-cells (and corresponding face values) are set.\n",
    "    \"\"\"\n",
    "    \n",
    "    ue   = np.zeros((nx+1,ny))   \n",
    "    uw   = np.zeros((nx+1,ny))\n",
    "    un   = np.zeros((nx+1,ny))\n",
    "    us   = np.zeros((nx+1,ny))\n",
    "    vn   = np.zeros((nx+1,ny))\n",
    "    vs   = np.zeros((nx+1,ny))\n",
    "    τxxe = np.zeros((nx+1,ny))\n",
    "    τxxw = np.zeros((nx+1,ny))\n",
    "    τxyn = np.zeros((nx+1,ny))\n",
    "    τxys = np.zeros((nx+1,ny))\n",
    "    Hu   = np.zeros((nx+1,ny))\n",
    "    \n",
    "    i = np.arange(1,nx)              # u-cell centers in domain interior\n",
    "    \n",
    "    ue[i,:] = (u[i+1,:] + u[i,:])/2\n",
    "    uw[i,:] = (u[i,:]   + u[i-1,:])/2\n",
    "    \n",
    "    j = np.arange(0,ny-1)\n",
    "    un[IJ(i,j)] = (u[IJ(i,j+1)] + u[IJ(i,j)])/2\n",
    "    un[i,ny-1] = ubc_t\n",
    "    j = np.arange(1,ny)\n",
    "    us[IJ(i,j)] = (u[IJ(i,j)] + u[IJ(i,j-1)])/2\n",
    "    us[i,0] = ubc_b\n",
    "    \n",
    "    j = np.arange(0,ny)\n",
    "    vn[IJ(i,j)] = (v[IJ(i-1,j+1)]+v[IJ(i,j+1)])/2\n",
    "    vs[IJ(i,j)] = (v[IJ(i-1,j)]  +v[IJ(i,j)])  /2\n",
    "    \n",
    "    τxxe[i,:] = -2*ν*(u[i+1,:] - u[i,:])  /Δx\n",
    "    τxxw[i,:] = -2*ν*(u[i,:]   - u[i-1,:])/Δx\n",
    "    \n",
    "    j = np.arange(0,ny-1)\n",
    "    τxyn[IJ(i,j)] = -ν*(u[IJ(i,j+1)]-u[IJ(i,j)])/Δy - ν*(v[IJ(i,j+1)]-v[IJ(i-1,j+1)])/Δx\n",
    "    τxyn[i,ny-1]  = -ν*(ubc_t-u[i,ny-1])/(Δy/2)     - ν*(v[i,ny]-v[i-1,ny])/Δx \n",
    "    \n",
    "    j = np.arange(1,ny)\n",
    "    τxys[IJ(i,j)] = -ν*(u[IJ(i,j)]-u[IJ(i,j-1)])/Δy - ν*(v[IJ(i,j)]-v[IJ(i-1,j)])/Δx\n",
    "    τxys[i,0]     = -ν*(u[i,0]-ubc_b)/(Δy/2)        - ν*(v[i,0]-v[i-1,0])/Δx\n",
    "    \n",
    "    #τxys[:,0] = 0.0 # doldb\n",
    "    \n",
    "    Hu[i,:] = -((ue[i,:]*ue[i,:] - uw[i,:]*uw[i,:])/Δx + (un[i,:]*vn[i,:] - us[i,:]*vs[i,:])/Δy) \\\n",
    "              -((τxxe[i,:] - τxxw[i,:])/Δx + (τxyn[i,:] - τxys[i,:])/Δy)\n",
    "    \n",
    "    return Hu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### v-Mom function (Hv)\n",
    "* Array slices are used to avoid loops for speed and simplicity\n",
    "* Note the use of the IJ = np.ix_ for certain array slices.\n",
    "* Solving on the v-grid:\n",
    "\n",
    "<img src=\"vgrid.png\" width=\"200\"/>\n",
    "\n",
    "\n",
    "* The v array and v-grid face quantities including $H^v$ are size ```(nx, ny+1)```\n",
    "* Values of v on the left and right boundaries are part of the matrix and initialized, but don't change.\n",
    "* Interior v-cells are solved.\n",
    "    * python: ```i=0:nx```, ```j=1:ny```, or $i\\in[0,n_x)$ $j\\in[1,n_y)$.\n",
    "    * including the boundaries in the matrix facilitates the indexing by minimizing special cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Hv():\n",
    "    \"\"\"\n",
    "    vn, vs, ve, vw, ue, uw are values on v-cell faces\n",
    "    These arrays include ALL v-cells (nx,ny+1) for convenience,\n",
    "    but only interior v-cells (and corresponding face values) are set.\n",
    "    \"\"\"\n",
    "    \n",
    "    vn   = np.zeros((nx,ny+1))   \n",
    "    vs   = np.zeros((nx,ny+1))\n",
    "    ve   = np.zeros((nx,ny+1))\n",
    "    vw   = np.zeros((nx,ny+1))\n",
    "    ue   = np.zeros((nx,ny+1))\n",
    "    uw   = np.zeros((nx,ny+1))\n",
    "    τyyn = np.zeros((nx,ny+1))\n",
    "    τyys = np.zeros((nx,ny+1))\n",
    "    τyxe = np.zeros((nx,ny+1))\n",
    "    τyxw = np.zeros((nx,ny+1))\n",
    "    Hv   = np.zeros((nx,ny+1))\n",
    "    \n",
    "    j = np.arange(1,ny)              # v-cell centers in domain interior\n",
    "    \n",
    "    vn[:,j] = (v[:,j+1] + v[:,j])/2\n",
    "    vs[:,j] = (v[:,j]   + v[:,j-1])/2\n",
    "    \n",
    "    i = np.arange(0,nx-1)\n",
    "    ve[IJ(i,j)] = (v[IJ(i+1,j)] + v[IJ(i,j)])/2\n",
    "    ve[nx-1,j] = vbc_r\n",
    "    i = np.arange(1,nx)\n",
    "    vw[IJ(i,j)] = (v[IJ(i,j)] + v[IJ(i-1,j)])/2\n",
    "    vw[0,j] = vbc_l\n",
    "    \n",
    "    i = np.arange(0,nx)\n",
    "    ue[IJ(i,j)] = (u[IJ(i+1,j-1)] + u[IJ(i+1,j)])/2\n",
    "    uw[IJ(i,j)] = (u[IJ(i,j-1)]   + u[IJ(i,j)])  /2\n",
    "    \n",
    "    τyyn[:,j] = -2*ν*(v[:,j+1] - v[:,j])  /Δy\n",
    "    τyys[:,j] = -2*ν*(v[:,j]   - v[:,j-1])/Δy\n",
    "    \n",
    "    i = np.arange(0,nx-1)\n",
    "    τyxe[IJ(i,j)] = -ν*(v[IJ(i+1,j)]-v[IJ(i,j)])/Δx - ν*(u[IJ(i+1,j)]-u[IJ(i+1,j-1)])/Δy\n",
    "    τyxe[nx-1,j]  = -ν*(vbc_r-v[nx-1,j])/(Δx/2)     - ν*(u[nx,j]-u[nx,j-1])/Δy \n",
    "    \n",
    "    i = np.arange(1,nx)\n",
    "    τyxw[IJ(i,j)] = -ν*(v[IJ(i,j)]-v[IJ(i-1,j)])/Δx - ν*(u[IJ(i,j)]-u[IJ(i,j-1)])/Δy\n",
    "    τyxw[0,j]     = -ν*(v[0,j]-vbc_l)/(Δx/2)        - ν*(u[0,j]-u[0,j-1])/Δy\n",
    "    \n",
    "    #τyxw[0,:]  = 0.0 # doldb\n",
    "    #τyxe[-1,:] = 0.0 # doldb\n",
    "    \n",
    "    Hv[:,j] = -((vn[:,j]*vn[:,j] - vs[:,j]*vs[:,j])/Δy + (ve[:,j]*ue[:,j] - vw[:,j]*uw[:,j])/Δx) \\\n",
    "              -((τyyn[:,j] - τyys[:,j])/Δy + (τyxe[:,j] - τyxw[:,j])/Δx)\n",
    "    \n",
    "    return Hv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pressure solver\n",
    "\n",
    "* Solving $\\hat{P}$ on the P-grid:\n",
    "\n",
    "<img src=\"Pgrid2.png\" width=\"200\"/>\n",
    "\n",
    "* We have an algebraic equation for each cell. The collection of equations will be an $A\\hat{P}=b$ system.\n",
    "* We need to create $A$ and $b$, where $A$ is the matrix of coefficients of $\\hat{P}$ and $b$ is a vector.\n",
    "* The equations were shown previously.\n",
    "    * We had 9 equation types: (1) interior, (4) sides, and (4) corners.\n",
    "    * The code below parallels the tables shown with the equations.\n",
    "* Each cell has a $\\hat{P}$ coefficient for itself and four neighbors. \n",
    "    * This gives an ```(nx,ny)``` array for the base point and each neighbor.\n",
    "* Similarly, we have an ```(nx,ny)``` array for $b$.\n",
    "* Once the $\\hat{P}$ coefficient and $b$ arrays are set, we need to reorder them from 2-D arrays to a 1-D array.\n",
    "    * We tile the points as indicated in the grid above: that is, counting cells in the x-direction from low to high y.\n",
    "    * This is done with numpy's reshape function.\n",
    "        * Since we are numbering along x, and x varies along columns in our arrays, we are using a column-major ordering, as in Fortran (as opposed to row-major ordering as in C and here in Python). Specify ```order='F'``` in the reshape functions.\n",
    "* For incompressible flows, pressure is only determined up to an additive constant: only pressure differences matter. This is evident in pressure only appearing in a gradient; adding any constant to $\\hat{P}$ doesn't change the term.\n",
    "    * To keep pressure from drifting, at the end of the pressure solve, we subtract the mean pressure so that the average is always pinned at zero.\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_P(h):\n",
    "    \"\"\"\n",
    "    Set up and solve the AP=b system, where A is a matrix, P (=Phat) and b are vectors.\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    nP = nx*ny     # total grid points solved (all P-grid cells)\n",
    "    \n",
    "    b    = np.zeros((nx,ny))           # set below\n",
    "    cP   = np.zeros((nx,ny))           # coefficient of P_i,j; set below\n",
    "    cPjm = np.full((nx,ny),-h*Δx/Δy)   # coefficient of P_i,j-1; initialized here, specialized below\n",
    "    cPim = np.full((nx,ny),-h*Δy/Δx)   # coefficient of P_i-1,j; initialized here, specialized below\n",
    "    cPip = np.full((nx,ny),-h*Δy/Δx)   # coefficient of P_i+1,j; initialized here, specialized below\n",
    "    cPjp = np.full((nx,ny),-h*Δx/Δy)   # coefficient of P_i,j+1; initialized here, specialized below\n",
    "    \n",
    "    #-------------------- \n",
    "    \n",
    "    # Interior\n",
    "    i = np.arange(1,nx-1); j = np.arange(1,ny-1)\n",
    "    b[IJ(i,j)]    = -Δy*(u[IJ(i+1,j)]+h*Hu[IJ(i+1,j)]) + Δy*(u[IJ(i,j)]+h*Hu[IJ(i,j)]) - Δx*(v[IJ(i,j+1)]+h*Hv[IJ(i,j+1)]) + Δx*(v[IJ(i,j)]+h*Hv[IJ(i,j)])\n",
    "    cP[IJ(i,j)]   =  2*h*Δy/Δx + 2*h*Δx/Δy\n",
    "    \n",
    "    # Corner bottom left\n",
    "    i = 0; j = 0\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*u[i,j]                     - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*v[i,j]\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPjm[i,j] =  0.0\n",
    "    cPim[i,j] =  0.0\n",
    "    \n",
    "    # Side bottom\n",
    "    i = np.arange(1,nx-1); j = 0\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*(u[i,j]+h*Hu[i,j])         - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*v[i,j]\n",
    "    cP[i,j]   =  2*h*Δy/Δx + h*Δx/Δy\n",
    "    cPjm[i,j] =  0.0\n",
    "    \n",
    "    # Corner bottom right\n",
    "    i = nx-1; j = 0\n",
    "    b[i,j]    = -Δy*u[i+1,j]                          + Δy*(u[i,j]+h*Hu[i,j])         - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*v[i,j]\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPjm[i,j] =  0.0\n",
    "    cPip[i,j] =  0.0\n",
    "    \n",
    "    # Side left\n",
    "    i = 0; j = np.arange(1,ny-1)\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*u[i,j]                     - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + 2*h*Δx/Δy\n",
    "    cPim[i,j] =  0.0\n",
    "    \n",
    "    # Side right\n",
    "    i = nx-1; j = np.arange(1,ny-1)\n",
    "    b[i,j]    = -Δy*u[i+1,j]                          + Δy*(u[i,j]+h*Hu[i,j])         - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + 2*h*Δx/Δy\n",
    "    cPip[i,j] =  0.0\n",
    "    \n",
    "    # Corner top left\n",
    "    i = 0; j = ny-1\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*u[i,j]                     - Δx*v[i,j+1]                        + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPim[i,j] =  0.0\n",
    "    cPjp[i,j] =  0.0\n",
    "    \n",
    "    # Side top\n",
    "    i = np.arange(1,nx-1); j = ny-1\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*(u[i,j]+h*Hu[i,j])         - Δx*v[i,j+1]                        + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  2*h*Δy/Δx + h*Δx/Δy\n",
    "    cPjp[i,j] =  0.0\n",
    "    \n",
    "    # Corner top right\n",
    "    i = nx-1; j = ny-1\n",
    "    b[i,j]    = -Δy*u[i+1,j]                          + Δy*(u[i,j]+h*Hu[i,j])         - Δx*v[i,j+1]                        + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPip[i,j] =  0.0\n",
    "    cPjp[i,j] =  0.0\n",
    "    \n",
    "    #---------------------------------\n",
    "    \n",
    "    b    = np.reshape(b,    nP, order='F')\n",
    "    \n",
    "    cP   = np.reshape(cP,   nP, order='F')\n",
    "    cPjm = np.reshape(cPjm, nP, order='F')\n",
    "    cPim = np.reshape(cPim, nP, order='F')\n",
    "    cPip = np.reshape(cPip, nP, order='F')\n",
    "    cPjp = np.reshape(cPjp, nP, order='F')\n",
    "    \n",
    "    A = diags([cPjm[nx:], cPim[1:], cP, cPip[:-1], cPjp[:-nx]], [-nx, -1, 0, 1, nx], format='csr')\n",
    "    \n",
    "    #---------------------------------\n",
    "    \n",
    "    P = spsolve(A,b)\n",
    "    \n",
    "    P -= np.average(P)\n",
    "    \n",
    "    P = np.reshape(P, (nx,ny),order='F')\n",
    "    \n",
    "    return P\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Driver and integrator\n",
    "\n",
    "* Solve the problem with a simple Explicit Euler integration.\n",
    "    * Higher order integrators are possible, with minor adjustments to the pressure solve.\n",
    "* To monitor the approach to steady state, record the average kinetic energy on the domain at each timestep.\n",
    "* Velocities are interpolated to the P-grid for convenient plotting, and evaluating kinetic energy.\n",
    "* The velocity magnitude is stored for convenient plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.5 s, sys: 1.18 s, total: 14.7 s\n",
      "Wall time: 14.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "set_user_specifications()\n",
    "set_grid_time_vars()\n",
    "\n",
    "ke = np.zeros(nsteps+1)\n",
    "times = np.linspace(0,tend,nsteps+1)\n",
    "\n",
    "for k in range(nsteps):\n",
    "    \n",
    "    Hu = get_Hu()\n",
    "    Hv = get_Hv()\n",
    "    \n",
    "    P = solve_P(Δt)\n",
    "    \n",
    "    i = np.arange(1,nx)\n",
    "    u[i,:] = u[i,:] + Δt*Hu[i,:] - Δt*(P[i,:]-P[i-1,:])/Δx\n",
    "    \n",
    "    j = np.arange(1,ny)\n",
    "    v[:,j] = v[:,j] + Δt*Hv[:,j] - Δt*(P[:,j]-P[:,j-1])/Δy\n",
    "    \n",
    "    #-----------------------------\n",
    "    \n",
    "    U = (u[:-1,:] + u[1:,:])/2\n",
    "    V = (v[:,:-1] + v[:,1:])/2\n",
    "    velmag = np.sqrt(U*U + V*V)\n",
    "    ke[k+1] = 0.5*(np.average(velmag**2))\n",
    "    \n",
    "    \n",
    "#----------- interpolate velocities to the P-grid\n",
    "\n",
    "U = (u[:-1,:] + u[1:,:])/2    # u-velocity on the P-grid\n",
    "V = (v[:,:-1] + v[:,1:])/2    # v-velocity on the P-grid\n",
    "velmag = np.sqrt(U*U + V*V)   # velocity magnitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,Y = np.meshgrid(x,y)\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,10))\n",
    "\n",
    "ax1.set_aspect('equal', adjustable='box')\n",
    "ax1.streamplot(x,y,U.T,V.T, density=2.5, linewidth=1, arrowsize=1.001, color=velmag.T)\n",
    "ax1.set_title(r'$|\\vec{v}|$')\n",
    "ax1.set_xlim([0,Lx])\n",
    "ax1.set_ylim([0,Ly])\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([]);\n",
    "\n",
    "ax2.set_aspect('equal', adjustable='box')\n",
    "ax2.contourf(X,Y,P.T,40)\n",
    "ax2.set_title(r'$P/\\rho$')\n",
    "ax2.set_xlim([0,Lx])\n",
    "ax2.set_ylim([0,Ly])\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([]);\n",
    "\n",
    "plt.figure(figsize=(3.5,3))\n",
    "plt.plot(times,ke)\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('KE');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}