{ "cells": [ { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000001", "metadata": {}, "source": [ "# LES Intercomparison Study for Neutral Boundary Layers: SGS Model Coefficients" ] }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000002", "metadata": {}, "source": [ "*Last updated: May 2026*\n" ] }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000003", "metadata": {}, "source": [ "For case setup and physical parameters, see the [Description](NBL_A94_Description.ipynb) notebook.\n", "\n", "Vertical profiles of the SGS coefficients and scale-dependence parameters $\\beta_1$ (momentum) are compared across the four grid resolutions ($64^3$, $128^3$, $256^3$, $384^3$) for a user-selected SGS model and precision." ] }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000004", "metadata": {}, "source": [ "## Setup\n", "\n", "The next cells load Python packages, locate the simulation outputs, and define the grid and averaging window used throughout the notebook." ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000005", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:51.873596Z", "start_time": "2026-06-12T07:08:51.857795Z" } }, "source": [ "import os\n", "import re\n", "import glob\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000006", "metadata": {}, "source": [ "### Output directories" ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000007", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:51.905690Z", "start_time": "2026-06-12T07:08:51.878383Z" } }, "source": [ "from pathlib import Path\n", "\n", "# Base directory (jaxalfa/)\n", "def find_repo_root(start=None):\n", " path = Path(start or ('__file__' in globals() and __file__) or Path.cwd()).resolve()\n", " for candidate in (path, *path.parents):\n", " if (candidate / 'examples').is_dir() and (candidate / 'docs').is_dir():\n", " return candidate\n", " raise FileNotFoundError('Could not locate jaxalfa repository root')\n", "\n", "BaseDir = find_repo_root()\n", "\n", "def read_config(run_dir):\n", " cfg = {}\n", " exec((run_dir / 'Config.py').read_text(), cfg)\n", " return cfg\n", "\n", "\n", "optSGS = 1 # LASDD-SM: 1, LASDD-WL: 2, LAD-SM: 3, LAD-WL: 4\n", "optPrecision = 'DP' # 'DP' or 'SP'\n", "\n", "sgs_names = {1: 'LASDD-SM', 2: 'LASDD-WL', 3: 'LAD-SM', 4: 'LAD-WL'}\n", "\n", "run_styles = {\n", " '64x64x64': {'color': 'red', 'linestyle': '-'},\n", " '128x128x128': {'color': 'blue', 'linestyle': '-'},\n", " '256x256x256': {'color': 'green', 'linestyle': '-'},\n", " '384x384x384': {'color': 'black', 'linestyle': '-'},\n", "}\n", "\n", "_sgs = {1: 'LASDD_SM', 2: 'LASDD_WL', 3: 'LAD_SM', 4: 'LAD_WL'}\n", "_run = lambda res: f'{res}_{_sgs[optSGS]}_{optPrecision}'\n", "\n", "OutputDir1 = BaseDir / f'examples/NBL_A94/runs/{_run(\"64x64x64\")}/output'\n", "OutputDir2 = BaseDir / f'examples/NBL_A94/runs/{_run(\"128x128x128\")}/output'\n", "OutputDir3 = BaseDir / f'examples/NBL_A94/runs/{_run(\"256x256x256\")}/output'\n", "OutputDir4 = BaseDir / f'examples/NBL_A94/runs/{_run(\"384x384x384\")}/output'" ], "outputs": [], "execution_count": 2 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000008", "metadata": {}, "source": [ "### Case configuration" ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000009", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:51.935436Z", "start_time": "2026-06-12T07:08:51.915631Z" } }, "source": [ "cfg_1 = read_config(OutputDir1.parent)\n", "cfg_2 = read_config(OutputDir2.parent)\n", "cfg_3 = read_config(OutputDir3.parent)\n", "cfg_4 = read_config(OutputDir4.parent)\n", "\n", "nz_1 = int(cfg_1['nz'])\n", "nz_2 = int(cfg_2['nz'])\n", "nz_3 = int(cfg_3['nz'])\n", "nz_4 = int(cfg_4['nz'])\n", "\n", "l_z = float(cfg_1['l_z'])\n", "z_damping = float(cfg_1.get('z_damping', np.nan))\n", "OutputInterval_sec = float(cfg_1.get('OutputInterval_sec', 60.0))\n", "\n", "# Averaging window \u2014 NBL quasi-steady state (last 10 h of an 83 h run)\n", "T_start = 73 * 3600 # s\n", "T_end = 83 * 3600 # s" ], "outputs": [], "execution_count": 3 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000010", "metadata": {}, "source": [ "### Derived grid and averaging indices" ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000011", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:51.975420Z", "start_time": "2026-06-12T07:08:51.949524Z" } }, "source": [ "# Half levels \u2014 SGS coefficients live at UVP nodes\n", "z_1 = np.array([(k + 0.5) * l_z / (nz_1 - 1) for k in range(nz_1)])\n", "z_2 = np.array([(k + 0.5) * l_z / (nz_2 - 1) for k in range(nz_2)])\n", "z_3 = np.array([(k + 0.5) * l_z / (nz_3 - 1) for k in range(nz_3)])\n", "z_4 = np.array([(k + 0.5) * l_z / (nz_4 - 1) for k in range(nz_4)])\n", "\n", "# File indices for the averaging window\n", "T_start_index = int(T_start / OutputInterval_sec) - 1\n", "T_end_index = int(T_end / OutputInterval_sec) - 1\n", "\n", "print(f'Averaging window: file indices {T_start_index} \u2013 {T_end_index}')" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Averaging window: file indices 4379 \u2013 4979\n" ] } ], "execution_count": 4 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000012", "metadata": {}, "source": [ "### SGS coefficient loader" ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000013", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:51.998535Z", "start_time": "2026-06-12T07:08:51.982770Z" } }, "source": [ "def LoadSGSAverage(stat_files, T_start_index, T_end_index, nz_expected):\n", " \"\"\"Return time-averaged SGS coefficient profiles over the given window.\n", "\n", " Returns\n", " -------\n", " Cs2_1, Cs2_2 : ndarray (nz)\n", " Smagorinsky coefficient squared: (PlanarMean(Cs))^2 and PlanarMean(Cs^2).\n", " Cs2PrRatio : ndarray (nz)\n", " PlanarMean(Cs^2 / Pr_T) profile.\n", " Beta1, Beta2 : ndarray (nz)\n", " Scale-dependence parameters for momentum and scalar.\n", " \"\"\"\n", " if len(stat_files) == 0:\n", " print(f'No statistics files available; plotting NaN placeholders for nz={nz_expected}.')\n", " nan = np.full(nz_expected, np.nan)\n", " return nan, nan.copy(), nan.copy(), nan.copy(), nan.copy()\n", "\n", " sl = slice(T_start_index, min(T_end_index + 1, len(stat_files)))\n", " if sl.start >= len(stat_files):\n", " print(f'Averaging window starts after available files; plotting NaN placeholders for nz={nz_expected}.')\n", " nan = np.full(nz_expected, np.nan)\n", " return nan, nan.copy(), nan.copy(), nan.copy(), nan.copy()\n", "\n", " Cs2_1_list = []; Cs2_2_list = []; Cs2PR_list = []\n", " B1_list = []; B2_list = []\n", "\n", " for f in stat_files[sl]:\n", " with np.load(f) as d:\n", " Cs2_1_list.append(d['Cs2_1'])\n", " Cs2_2_list.append(d['Cs2_2'])\n", " Cs2PR_list.append(d['Cs2PrRatio'])\n", " B1_list.append(d['Beta1'])\n", " B2_list.append(d['Beta2'])\n", "\n", " return (\n", " np.mean(Cs2_1_list, axis=0),\n", " np.mean(Cs2_2_list, axis=0),\n", " np.mean(Cs2PR_list, axis=0),\n", " np.mean(B1_list, axis=0),\n", " np.mean(B2_list, axis=0),\n", " )\n" ], "outputs": [], "execution_count": 5 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000014", "metadata": {}, "source": [ "### Available statistics files" ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000015", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:52.098380Z", "start_time": "2026-06-12T07:08:52.004344Z" } }, "source": [ "def get_stat_files(output_dir):\n", " files = sorted(\n", " glob.glob(str(output_dir / 'ALFA_Statistics_Iteration_*.npz')),\n", " key=lambda x: int(re.search(r'Iteration_(\\d+)', x).group(1))\n", " )\n", " return files\n", "\n", "StatFiles1 = get_stat_files(OutputDir1)\n", "StatFiles2 = get_stat_files(OutputDir2)\n", "StatFiles3 = get_stat_files(OutputDir3)\n", "StatFiles4 = get_stat_files(OutputDir4)\n", "\n", "print(f'64^3 : {len(StatFiles1)} files')\n", "print(f'128^3 : {len(StatFiles2)} files')\n", "print(f'256^3 : {len(StatFiles3)} files')\n", "print(f'384^3 : {len(StatFiles4)} files')" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "64^3 : 5000 files\n", "128^3 : 5000 files\n", "256^3 : 5000 files\n", "384^3 : 0 files\n" ] } ], "execution_count": 6 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000016", "metadata": {}, "source": [ "### Temporally averaged profiles" ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000017", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:53.251348Z", "start_time": "2026-06-12T07:08:52.100575Z" } }, "source": [ "(Cs2_1_avg_1, Cs2_2_avg_1, Cs2PR_avg_1, B1_avg_1, B2_avg_1) = \\\n", " LoadSGSAverage(StatFiles1, T_start_index, T_end_index, nz_1)\n", "\n", "(Cs2_1_avg_2, Cs2_2_avg_2, Cs2PR_avg_2, B1_avg_2, B2_avg_2) = \\\n", " LoadSGSAverage(StatFiles2, T_start_index, T_end_index, nz_2)\n", "\n", "(Cs2_1_avg_3, Cs2_2_avg_3, Cs2PR_avg_3, B1_avg_3, B2_avg_3) = \\\n", " LoadSGSAverage(StatFiles3, T_start_index, T_end_index, nz_3)\n", "\n", "(Cs2_1_avg_4, Cs2_2_avg_4, Cs2PR_avg_4, B1_avg_4, B2_avg_4) = \\\n", " LoadSGSAverage(StatFiles4, T_start_index, T_end_index, nz_4)\n", "\n", "# SGS coefficient: two averaging conventions\n", "# Method 1: C = PlanarMean(C), then squared for storage \u2192 take sqrt\n", "# Method 2: C = sqrt(PlanarMean(C^2))\n", "Cs_m1_1 = np.sqrt(np.abs(Cs2_1_avg_1)); Cs_m2_1 = np.sqrt(np.abs(Cs2_2_avg_1))\n", "Cs_m1_2 = np.sqrt(np.abs(Cs2_1_avg_2)); Cs_m2_2 = np.sqrt(np.abs(Cs2_2_avg_2))\n", "Cs_m1_3 = np.sqrt(np.abs(Cs2_1_avg_3)); Cs_m2_3 = np.sqrt(np.abs(Cs2_2_avg_3))\n", "Cs_m1_4 = np.sqrt(np.abs(Cs2_1_avg_4)); Cs_m2_4 = np.sqrt(np.abs(Cs2_2_avg_4))\n", "\n", "# PrSGS = Cs2_2 / Cs2PrRatio (NaN where Cs2PrRatio \u2248 0)\n", "_tol = 1e-10\n", "PrSGS_1 = np.where(Cs2PR_avg_1 > _tol, Cs2_2_avg_1 / Cs2PR_avg_1, np.nan)\n", "PrSGS_2 = np.where(Cs2PR_avg_2 > _tol, Cs2_2_avg_2 / Cs2PR_avg_2, np.nan)\n", "PrSGS_3 = np.where(Cs2PR_avg_3 > _tol, Cs2_2_avg_3 / Cs2PR_avg_3, np.nan)\n", "PrSGS_4 = np.where(Cs2PR_avg_4 > _tol, Cs2_2_avg_4 / Cs2PR_avg_4, np.nan)\n", "\n", "print(f'Averaging over {T_end_index - T_start_index + 1} files '\n", " f'({T_start/3600:.1f}\u20131{T_end/3600:.1f} h)')" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No statistics files available; plotting NaN placeholders for nz=384.\n", "Averaging over 601 files (73.0\u2013183.0 h)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/fm/088xfr2x1vs0yr7dmrdgjfrh0000gn/T/ipykernel_14282/2150077097.py:23: RuntimeWarning: divide by zero encountered in divide\n", " PrSGS_1 = np.where(Cs2PR_avg_1 > _tol, Cs2_2_avg_1 / Cs2PR_avg_1, np.nan)\n", "/var/folders/fm/088xfr2x1vs0yr7dmrdgjfrh0000gn/T/ipykernel_14282/2150077097.py:23: RuntimeWarning: invalid value encountered in divide\n", " PrSGS_1 = np.where(Cs2PR_avg_1 > _tol, Cs2_2_avg_1 / Cs2PR_avg_1, np.nan)\n", "/var/folders/fm/088xfr2x1vs0yr7dmrdgjfrh0000gn/T/ipykernel_14282/2150077097.py:24: RuntimeWarning: divide by zero encountered in divide\n", " PrSGS_2 = np.where(Cs2PR_avg_2 > _tol, Cs2_2_avg_2 / Cs2PR_avg_2, np.nan)\n", "/var/folders/fm/088xfr2x1vs0yr7dmrdgjfrh0000gn/T/ipykernel_14282/2150077097.py:24: RuntimeWarning: invalid value encountered in divide\n", " PrSGS_2 = np.where(Cs2PR_avg_2 > _tol, Cs2_2_avg_2 / Cs2PR_avg_2, np.nan)\n", "/var/folders/fm/088xfr2x1vs0yr7dmrdgjfrh0000gn/T/ipykernel_14282/2150077097.py:25: RuntimeWarning: divide by zero encountered in divide\n", " PrSGS_3 = np.where(Cs2PR_avg_3 > _tol, Cs2_2_avg_3 / Cs2PR_avg_3, np.nan)\n", "/var/folders/fm/088xfr2x1vs0yr7dmrdgjfrh0000gn/T/ipykernel_14282/2150077097.py:25: RuntimeWarning: invalid value encountered in divide\n", " PrSGS_3 = np.where(Cs2PR_avg_3 > _tol, Cs2_2_avg_3 / Cs2PR_avg_3, np.nan)\n" ] } ], "execution_count": 7 }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000018", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:53.315910Z", "start_time": "2026-06-12T07:08:53.304135Z" } }, "source": [ "plt.rcParams.update({\n", " \"text.usetex\": True,\n", " \"font.size\": 14,\n", " \"axes.labelsize\": 16,\n", " \"xtick.labelsize\": 12,\n", " \"ytick.labelsize\": 12\n", "})" ], "outputs": [], "execution_count": 8 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000019", "metadata": {}, "source": [ "## SGS Model Coefficient\n", "\n", "For SM variants (LASDD-SM, LAD-SM) the coefficient is the Smagorinsky coefficient $C_s$; for WL variants (LASDD-WL, LAD-WL) it is the Wong-Lilly SGS coefficient $C$. Two planar-averaging conventions are shown:\n", "- **Method 1** (left): $C = \\langle C \\rangle_{xy}$ \u2014 planar mean of the pointwise field.\n", "- **Method 2** (right): $C = \\sqrt{\\langle C^2 \\rangle_{xy}}$ \u2014 root of the planar mean of $C^2$.\n", "\n", "By Jensen's inequality, Method 2 $\\geq$ Method 1." ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000020", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:54.135725Z", "start_time": "2026-06-12T07:08:53.323336Z" } }, "source": [ "fig, axs = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True)\n", "\n", "_coeff_sym = r'C_s' if optSGS in [1, 3] else r'C'\n", "_coeff_name = 'Smagorinsky' if optSGS in [1, 3] else 'Wong-Lilly SGS'\n", "\n", "for lbl, Cs_m1, Cs_m2, z in [\n", " ('64x64x64', Cs_m1_1, Cs_m2_1, z_1),\n", " ('128x128x128', Cs_m1_2, Cs_m2_2, z_2),\n", " ('256x256x256', Cs_m1_3, Cs_m2_3, z_3),\n", " ('384x384x384', Cs_m1_4, Cs_m2_4, z_4),\n", "]:\n", " style = run_styles[lbl]\n", " axs[0].plot(Cs_m1, z, color=style['color'], linestyle=style['linestyle'], linewidth=2, label=lbl)\n", " axs[1].plot(Cs_m2, z, color=style['color'], linestyle=style['linestyle'], linewidth=2, label=lbl)\n", "\n", "axs[0].set_xlabel(rf\"${_coeff_sym} = \\langle {_coeff_sym} \\rangle_{{xy}}$\")\n", "axs[0].set_ylabel(r\"$z$ (m)\")\n", "axs[0].set_title(\"Method 1\")\n", "\n", "axs[1].set_xlabel(rf\"${_coeff_sym} = \\sqrt{{\\langle {_coeff_sym}^2 \\rangle_{{xy}}}}$\")\n", "axs[1].set_ylabel(r\"$z$ (m)\")\n", "axs[1].set_title(\"Method 2\")\n", "\n", "for ax in axs:\n", " ax.set_ylim(0, z_damping)\n", " ax.grid()\n", " ax.legend(frameon=False)\n", "\n", "fig.suptitle(f\"SGS Model Coefficient (73--83 h average): {_coeff_name} ({sgs_names[optSGS]} model) ({optPrecision})\", fontsize=18)\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 9 }, { "cell_type": "markdown", "id": "nb94sgsc-0003-4003-8003-000000000023", "metadata": {}, "source": [ "## Scale-dependence Parameter\n", "\n", "The scale-dependence parameter $\\beta_1$ characterises how the dynamic momentum coefficient varies across filter scales. Values near unity indicate weak scale dependence; departures signal the model adapting to local turbulence structure." ] }, { "cell_type": "code", "id": "nb94sgsc-0003-4003-8003-000000000024", "metadata": { "ExecuteTime": { "end_time": "2026-06-12T07:08:54.682824Z", "start_time": "2026-06-12T07:08:54.176259Z" } }, "source": [ "fig, ax = plt.subplots(figsize=(5, 6), constrained_layout=True)\n", "\n", "for lbl, B1, z in [\n", " ('64x64x64', B1_avg_1, z_1),\n", " ('128x128x128', B1_avg_2, z_2),\n", " ('256x256x256', B1_avg_3, z_3),\n", " ('384x384x384', B1_avg_4, z_4),\n", "]:\n", " style = run_styles[lbl]\n", " ax.plot(B1, z, color=style['color'], linestyle=style['linestyle'], linewidth=2, label=lbl)\n", "\n", "ax.set_xlabel(r\"$\\beta_1$ (momentum)\")\n", "ax.set_ylabel(r\"$z$ (m)\")\n", "ax.set_ylim(0, z_damping)\n", "ax.grid()\n", "ax.legend(frameon=False)\n", "\n", "fig.suptitle(f\"Momentum Scale-dependence Parameter (73--83 h average): {sgs_names[optSGS]} model ({optPrecision})\", fontsize=18)\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 10 } ], "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": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }