From 01c5faaa2edeaf7442e710f84f94ad858b0d4efb Mon Sep 17 00:00:00 2001 From: Michael Zingale Date: Thu, 5 Sep 2024 09:28:18 -0400 Subject: [PATCH] remove the static compresssible compare page and add the KH comparison to the new notebook this makes the comparison reproducible. --- docs/source/compressible-rt-compare.ipynb | 203 +++++++++++++++++++++- docs/source/compressible_compare.rst | 119 ------------- docs/source/index.rst | 1 - pyro/compressible/problems/_kh.defaults | 6 +- pyro/compressible/problems/inputs.kh | 5 +- pyro/compressible/problems/kh.py | 17 +- 6 files changed, 212 insertions(+), 139 deletions(-) delete mode 100644 docs/source/compressible_compare.rst diff --git a/docs/source/compressible-rt-compare.ipynb b/docs/source/compressible-rt-compare.ipynb index dee17e95e..d8f056337 100644 --- a/docs/source/compressible-rt-compare.ipynb +++ b/docs/source/compressible-rt-compare.ipynb @@ -13,8 +13,7 @@ "id": "c6fb6238-510a-451a-ae84-8ab8e132dc5d", "metadata": {}, "source": [ - "Here we'll run the same problem (the single-mode Rayleigh-Taylor instability)\n", - "with 3 different compressible solvers." + "Here we'll compare how different compressible solvers perform when run with the same problem setup." ] }, { @@ -32,7 +31,15 @@ "id": "0509854d-e847-4b3a-a753-6f811161b3de", "metadata": {}, "source": [ - "## First run" + "## Rayleigh-Taylor" + ] + }, + { + "cell_type": "markdown", + "id": "8f4c763d-08fd-42f8-9fbb-5b3803cce8cc", + "metadata": {}, + "source": [ + "The `rt` setup initializes a single-mode Rayleigh-Taylor instability." ] }, { @@ -171,7 +178,7 @@ "id": "754feb65-82ae-41ba-bae8-01ed70c0c110", "metadata": {}, "source": [ - "## Comparisons" + "### Comparisons" ] }, { @@ -216,7 +223,7 @@ "metadata": {}, "outputs": [], "source": [ - "params = {\"mesh.nx\": 32, \"mesh.ny\": 96}" + "params = {\"mesh.nx\": 32, \"mesh.ny\": 96, \"io.do_io\": 0}" ] }, { @@ -269,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "9afb841c-f08f-4554-8fcb-06939d0fa131", "metadata": {}, "outputs": [], @@ -329,6 +336,190 @@ "source": [ "We see that the 4th order solver has more structure at fine scales (this becomes even more apparently at higher resolutions)." ] + }, + { + "cell_type": "markdown", + "id": "d951c141-925a-4a2e-b675-993ddde46196", + "metadata": {}, + "source": [ + "## Kelvin-Helmholtz" + ] + }, + { + "cell_type": "markdown", + "id": "da4075a1-972f-43a7-bbae-35ae3c6859a7", + "metadata": {}, + "source": [ + "The Kelvin-Helmholtz instability arises from a shear layer.\n", + "[McNally et al. 2012](https://iopscience.iop.org/article/10.1088/0067-0049/201/2/18) describe a version of KH that is\n", + "boosted, to understand how numerical dissipation affects the growth of the instability. The initial conditions\n", + "set up a heavier fluid moving in the negative x-direction sandwiched between regions of lighter fluid moving in the positive x-direction. A bulk\n", + "velocity can be applied in the vertical direction." + ] + }, + { + "cell_type": "markdown", + "id": "55e4e13d-c0f5-4649-a367-d4d37f1ec29d", + "metadata": {}, + "source": [ + "Again, we'll start by looking at the defaults." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "81172d3e-41b5-425b-8bfe-998866abd982", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mwarning, key: io.tplot not defined\u001b[0m\n", + "Solver = compressible\n", + "Problem = kh\n", + "Simulation time = 0.0\n", + "Simulation step number = 0\n", + "\n", + "Runtime Parameters\n", + "------------------\n", + "compressible.cvisc = 0.1\n", + "compressible.delta = 0.33\n", + "compressible.grav = 0.0\n", + "compressible.limiter = 2\n", + "compressible.riemann = HLLC\n", + "compressible.use_flattening = 1\n", + "compressible.z0 = 0.75\n", + "compressible.z1 = 0.85\n", + "driver.cfl = 0.8\n", + "driver.fix_dt = -1.0\n", + "driver.init_tstep_factor = 0.01\n", + "driver.max_dt_change = 2.0\n", + "driver.max_steps = 5000\n", + "driver.tmax = 2.0\n", + "driver.verbose = 0\n", + "eos.gamma = 1.4\n", + "io.basename = kh_\n", + "io.do_io = 1\n", + "io.dt_out = 0.1\n", + "io.n_out = 10000\n", + "kh.bulk_velocity = 0.0\n", + "kh.rho_1 = 1\n", + "kh.rho_2 = 2\n", + "kh.u_1 = -0.5\n", + "kh.u_2 = 0.5\n", + "mesh.grid_type = Cartesian2d\n", + "mesh.nx = 64\n", + "mesh.ny = 64\n", + "mesh.xlboundary = periodic\n", + "mesh.xmax = 1.0\n", + "mesh.xmin = 0.0\n", + "mesh.xrboundary = periodic\n", + "mesh.ylboundary = periodic\n", + "mesh.ymax = 1.0\n", + "mesh.ymin = 0.0\n", + "mesh.yrboundary = periodic\n", + "particles.do_particles = 0\n", + "particles.n_particles = 100\n", + "particles.particle_generator = grid\n", + "vis.dovis = 0\n", + "vis.store_images = 0\n", + "\n" + ] + } + ], + "source": [ + "p = Pyro(\"compressible\")\n", + "p.initialize_problem(\"kh\")\n", + "print(p)" + ] + }, + { + "cell_type": "markdown", + "id": "b9f7913c-1c5a-4fb4-8620-d492699d1f38", + "metadata": {}, + "source": [ + "Now let's run the different solvers with a bulk velocity of 3" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "723b7336-ae07-4aa1-91d0-b5509870bc9b", + "metadata": {}, + "outputs": [], + "source": [ + "runs = []\n", + "solvers = [\"compressible\", \"compressible_rk\", \"compressible_fv4\"]\n", + "params = {\"mesh.nx\": 96, \"mesh.ny\": 96,\n", + " \"kh.bulk_velocity\": 3.0, \"io.do_io\": 0}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4d74bb04-c81c-4b96-88f4-6906c8a79407", + "metadata": {}, + "outputs": [], + "source": [ + "for s in solvers:\n", + " p = Pyro(s)\n", + " p.initialize_problem(problem_name=\"kh\", inputs_dict=params)\n", + " p.run_sim()\n", + " runs.append(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7bec5d57-f7f3-4863-9581-10c9ed4013fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(7, 5))\n", + "grid = ImageGrid(fig, 111, nrows_ncols=(1, len(runs)), axes_pad=0.1,\n", + " share_all=True, cbar_mode=\"single\", cbar_location=\"right\")\n", + "\n", + "for ax, s, p in zip(grid, solvers, runs):\n", + " rho = p.get_var(\"density\")\n", + " g = p.get_grid()\n", + " im = ax.imshow(rho.v().T,\n", + " extent=[g.xmin, g.xmax, g.ymin, g.ymax],\n", + " origin=\"lower\", vmin=0.9, vmax=2.1)\n", + " ax.set_title(s, fontsize=\"small\")\n", + "grid.cbar_axes[0].colorbar(im)" + ] + }, + { + "cell_type": "markdown", + "id": "25e4e12b-5848-4668-938c-305e309164d1", + "metadata": {}, + "source": [ + "Here we see that for the second-order solvers, the growth of the instability at the top interface is very diffusive. It is better in the unsplit CTU version than in the Runge-Kutta solver.\n", + "\n", + "The 4th order compressible solver looks the best, with well-defined rolls on both the top and bottom interface. This solver does even better at higher resolution--when the resolution is coarse, the averaging of the initial conditions from cell-centers to cell-averages in the 4th order solver seems to smear the perturbation out more." + ] } ], "metadata": { diff --git a/docs/source/compressible_compare.rst b/docs/source/compressible_compare.rst deleted file mode 100644 index da90024b2..000000000 --- a/docs/source/compressible_compare.rst +++ /dev/null @@ -1,119 +0,0 @@ -Compressible solver comparisons -=============================== - -We run various problems run with the different compressible solvers in pyro (standard Riemann, Runge-Kutta, fourth order). - - -Kelvin-Helmholtz -^^^^^^^^^^^^^^^^ -The McNally Kelvin-Helmholtz problem sets up a heavier fluid moving in the negative x-direction sandwiched between regions of lighter fluid moving in the positive x-direction. - -The image below shows the KH problem initialized with McNally's test. It ran on a 128 x 128 grid, with gamma = 1.7, and ran until t = 2.0. This is run with: - -.. prompt:: bash - - pyro_sim.py compressible kh inputs.kh kh.vbulk=0 - pyro_sim.py compressible_rk kh inputs.kh kh.vbulk=0 - pyro_sim.py compressible_fv4 kh inputs.kh kh.vbulk=0 - pyro_sim.py compressible_sdc kh inputs.kh kh.vbulk=0 - -.. image:: ./solver_comparisons/kh.png - :align: center - - -We vary the velocity in the positive y-direction (vbulk) to see how effective the solvers are at preserving the initial shape. - - -Sedov -^^^^^ -The Sedov problem ran on a 128 x 128 grid, with gamma = 1.4, and until t = 0.1, which can be run as: - -.. prompt:: bash - - pyro_sim.py compressible sedov inputs.sedov - pyro_sim.py compressible_rk sedov inputs.sedov - pyro_sim.py compressible_fv4 sedov inputs.sedov - pyro_sim.py compressible_sdc sedov inputs.sedov - -.. image:: ./solver_comparisons/sedov.png - :align: center - -.. image:: ./solver_comparisons/sedov_rk.png - :align: center - -.. image:: ./solver_comparisons/sedov_fv4.png - :align: center - -.. image:: ./solver_comparisons/sedov_sdc.png - :align: center - -Quad -^^^^ -The quad problem ran on a 256 x 256 grid until t = 0.8, which can be run as: - -.. prompt:: bash - - pyro_sim.py compressible quad inputs.quad - pyro_sim.py compressible_rk quad inputs.quad - pyro_sim.py compressible_fv4 quad inputs.quad - pyro_sim.py compressible_sdc quad inputs.quad - -.. image:: ./solver_comparisons/quad.png - :align: center - -.. image:: ./solver_comparisons/quad_rk.png - :align: center - -.. image:: ./solver_comparisons/quad_fv4.png - :align: center - -.. image:: ./solver_comparisons/quad_sdc.png - :align: center - - -Bubble -^^^^^^ -The bubble problem ran on a 128 x 256 grid until t = 3.0, which can be run as: - -.. prompt:: bash - - pyro_sim.py compressible bubble inputs.bubble - pyro_sim.py compressible_rk bubble inputs.bubble - pyro_sim.py compressible_fv4 bubble inputs.bubble - pyro_sim.py compressible_sdc bubble inputs.bubble - -.. image:: ./solver_comparisons/bubble.png - :align: center - -.. image:: ./solver_comparisons/bubble_rk.png - :align: center - -.. image:: ./solver_comparisons/bubble_fv4.png - :align: center - -.. image:: ./solver_comparisons/bubble_sdc.png - :align: center - - -Rayleigh-Taylor -^^^^^^^^^^^^^^^ -The Rayleigh-Taylor problem ran on a 64 x 192 grid until t = 3.0, which can be run as: - -.. prompt:: bash - - pyro_sim.py compressible rt inputs.rt - pyro_sim.py compressible_rk rt inputs.rt - pyro_sim.py compressible_fv4 rt inputs.rt - pyro_sim.py compressible_sdc rt inputs.rt - -.. image:: ./solver_comparisons/rt.png - :align: center - -.. image:: ./solver_comparisons/rt_rk.png - :align: center - -.. image:: ./solver_comparisons/rt_fv4.png - :align: center - -.. image:: ./solver_comparisons/rt_sdc.png - :align: center diff --git a/docs/source/index.rst b/docs/source/index.rst index 38f391b62..261764cb5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -59,7 +59,6 @@ new ideas. advection_basics burgers_basics compressible_basics - compressible_compare diffusion_basics incompressible_basics incompressible_viscous_basics diff --git a/pyro/compressible/problems/_kh.defaults b/pyro/compressible/problems/_kh.defaults index d2709ebe0..affec1de0 100644 --- a/pyro/compressible/problems/_kh.defaults +++ b/pyro/compressible/problems/_kh.defaults @@ -1,8 +1,10 @@ [kh] rho_1 = 1 -v_1 = -1.0 +u_1 = -1.0 rho_2 = 2 -v_2 = 1.0 +u_2 = 1.0 + +bulk_velocity = 0.0 diff --git a/pyro/compressible/problems/inputs.kh b/pyro/compressible/problems/inputs.kh index 92a3fc3f6..45c0bcd9d 100644 --- a/pyro/compressible/problems/inputs.kh +++ b/pyro/compressible/problems/inputs.kh @@ -10,7 +10,6 @@ cvisc = 0.1 [io] basename = kh_ -tplot = 0.01 [eos] @@ -32,10 +31,10 @@ yrboundary = periodic [kh] rho_1 = 1 -v_1 = -0.5 +u_1 = -0.5 rho_2 = 2 -v_2 = 0.5 +u_2 = 0.5 [vis] diff --git a/pyro/compressible/problems/kh.py b/pyro/compressible/problems/kh.py index 775914a8e..a5b75dfb0 100644 --- a/pyro/compressible/problems/kh.py +++ b/pyro/compressible/problems/kh.py @@ -31,9 +31,10 @@ def init_data(my_data, rp): ymom[:, :] = 0.0 rho_1 = rp.get_param("kh.rho_1") - v_1 = rp.get_param("kh.v_1") + u_1 = rp.get_param("kh.u_1") rho_2 = rp.get_param("kh.rho_2") - v_2 = rp.get_param("kh.v_2") + u_2 = rp.get_param("kh.u_2") + bulk_velocity = rp.get_param("kh.bulk_velocity") gamma = rp.get_param("eos.gamma") @@ -41,7 +42,7 @@ def init_data(my_data, rp): dy = 0.025 w0 = 0.01 - vm = 0.5*(v_1 - v_2) + vm = 0.5*(u_1 - u_2) rhom = 0.5*(rho_1 - rho_2) idx1 = myg.y2d < 0.25 @@ -53,23 +54,23 @@ def init_data(my_data, rp): # lower quarter dens[idx1] = rho_1 - rhom*np.exp((myg.y2d[idx1] - 0.25)/dy) - xmom[idx1] = v_1 - vm*np.exp((myg.y2d[idx1] - 0.25)/dy) + xmom[idx1] = u_1 - vm*np.exp((myg.y2d[idx1] - 0.25)/dy) # second quarter dens[idx2] = rho_2 + rhom*np.exp((0.25 - myg.y2d[idx2])/dy) - xmom[idx2] = v_2 + vm*np.exp((0.25 - myg.y2d[idx2])/dy) + xmom[idx2] = u_2 + vm*np.exp((0.25 - myg.y2d[idx2])/dy) # third quarter dens[idx3] = rho_2 + rhom*np.exp((myg.y2d[idx3] - 0.75)/dy) - xmom[idx3] = v_2 + vm*np.exp((myg.y2d[idx3] - 0.75)/dy) + xmom[idx3] = u_2 + vm*np.exp((myg.y2d[idx3] - 0.75)/dy) # fourth quarter dens[idx4] = rho_1 - rhom*np.exp((0.75 - myg.y2d[idx4])/dy) - xmom[idx4] = v_1 - vm*np.exp((0.75 - myg.y2d[idx4])/dy) + xmom[idx4] = u_1 - vm*np.exp((0.75 - myg.y2d[idx4])/dy) # upper half xmom[:, :] *= dens - ymom[:, :] = dens * w0 * np.sin(4*np.pi*myg.x2d) + ymom[:, :] = dens * (bulk_velocity + w0 * np.sin(4*np.pi*myg.x2d)) p = 2.5 ener[:, :] = p/(gamma - 1.0) + 0.5*(xmom[:, :]**2 + ymom[:, :]**2)/dens[:, :]