MF_LBM.ipynb

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      "authorship_tag": "ABX9TyP5gjD0KGx2izeulQwdXW9x",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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        "<a href=\"https://colab.research.google.com/github/sushirito/Methylmercury/blob/main/MF_LBM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#My params\n",
        "custom_simulation_control_content = f\"\"\"\n",
        "#*********************************************************************************#\n",
        "#*          CONTROL FILE FOR MULTIPHASE FLOW SIMULATION IN MF-LBM                *#\n",
        "#*********************************************************************************#\n",
        "\n",
        "#=========================== simulation_status ====================================\n",
        "# value stored on job_status.txt\n",
        "# new_simulation                - new simulation\n",
        "# continue_simulation           - continue previous simulation\n",
        "# simulation_done               - previous simulation is finished. stop\n",
        "# simulation_failed             - previous simulation failed. stop\n",
        "# simulation_reached_max_step   - previous simulation reached maximum step. stop\n",
        "# else                          - wrong status. stop\n",
        "\n",
        "#=========================== simulation setup =====================================\n",
        "#  Initial fluid distribution option:\n",
        "#  1 - fluid1 (nonwetting phase) at inlet: drainage\n",
        "#  2 - fluid2 (wetting phase) at inlet: imbibition\n",
        "#  3 - a fluid1 (nonwetting phase) drop attached to y=1: contact angle measurement\n",
        "#  4 - a fluid2 (wetting phase) drop attached to y=1: contact angle measurement\n",
        "#  5 - a fluid1 drop located in the center of the domain: surface tension measurement\n",
        "#  6 - fluid1 and fluid2 randomly distributed in the pore space with desired fluid1\n",
        "#      saturation: steady state relative perm measurement\n",
        "#  0 or else - error!\n",
        "initial_fluid_distribution_option 1  # 1 - fluid1 (nonwetting phase) at inlet: drainage\n",
        "\n",
        "# Benchmark_cmd:\n",
        "# 0 - regular simulation\n",
        "# 1 - benchmarking simulation (obtain computational performance in MLUPS)\n",
        "benchmark_cmd 0\n",
        "\n",
        "# Necessary modifications for extreme large simulations:\n",
        "# nxglobal*nyglobal*nzglobal may be too large.\n",
        "# parallel I/O for visualization files are needed.\n",
        "# recommended for grid points > 1 billion.\n",
        "# 0 - no; 1 - yes\n",
        "extreme_large_sim_cmd 0\n",
        "\n",
        "# Breakthrough check option:\n",
        "# currently only works for drainage simulation by detecting nonwetting phase\n",
        "# fluid1 near outlet.\n",
        "# 0 - do not check; 1 - check\n",
        "breakthrough_check 1  # currently only works for drainage simulation by detecting nonwetting phase fluid1 near outlet\n",
        "\n",
        "# Steady state option:\n",
        "# 0 - non-steady state simulation (complete based on target injected PV or ntime_max)\n",
        "# 1 - steady state simulation (complete based on capillary pressure)\n",
        "# 2 - steady state simulation (complete based on phase field)\n",
        "# 3 - steady state simulation (complete based on saturation)\n",
        "steady_state_option 3\n",
        "# Convergence_criteria:\n",
        "# used in steady state simulation.\n",
        "# default is 1d-6, use 1d-4 or 1d-3 instead for steady-state-option 3 to reduce simulation\n",
        "# time and deal with fluctuation\n",
        "convergence_criteria 1e-4\n",
        "\n",
        "# output field data precision (simulation is always double precision):\n",
        "# 0 - single precision; 1 - double precision\n",
        "output_fieldData_precision_cmd 0\n",
        "\n",
        "# Modify_geometry_cmd:\n",
        "# 0 do not modify geometry; 1 modify geometry in the code (hard coding, Misc.cpp)\n",
        "modify_geometry_cmd 0\n",
        "\n",
        "# Use external geometry cmd:\n",
        "# 0 - no; 1 - yes\n",
        "external_geometry_read_cmd 1\n",
        "\n",
        "# choose type size of dimension variables saved in the geometry binary file:\n",
        "# 4 - 4 bytes (int, uint, ..etc.); 8 - 8 bytes (long long, unint64, ...etc.)\n",
        "# (0) would be used in relatively small geometries (nxGlobal*nyGlobal*nzGlobal < 384^3), while (1) would be used in relatively large geometries (nxGlobal*nyGlobal*nzGlobal >= 384^3)\n",
        "geometry_dims_type_size 4\n",
        "\n",
        "# Geometry preprocess cmd:\n",
        "# 0 - process the geometry boundary info during the simulation (not recommended for\n",
        "# large simulations);\n",
        "# 1 - load external pre-processed geometry boundary info data\n",
        "geometry_preprocess_cmd 0\n",
        "\n",
        "# Place a porous plate along the flow direction:\n",
        "# 0 - no; 1 - block fluid1; 2 - block fluid2\n",
        "# example 1: injecting fluid2 (wetting) during imbibition cycle and block fluid1 from\n",
        "# exiting the inlet.\n",
        "# example 2: injecting fluid1 (nonwetting) during drainage cycle and block fluid1 from\n",
        "# exiting the outlet.\n",
        "# This option should only be used when you fully understand how it works.\n",
        "porous_plate_cmd 1  # This option should only be used when you fully understand how it works\n",
        "\n",
        "# Location of the porous plate along flow direction (Z):\n",
        "# placing the porous plate a couple of lattices before or after rock sample is recommended.\n",
        "# example 1 (near outlet): Z_porous_plate = inlet_buffer_layers + nxsample + 2\n",
        "# example 2 (near inlet): Z_porous_plate = inlet_buffer_layers + 1 - 2\n",
        "# only effective when porous_plate_cmd = 1\n",
        "# This option should only be used when you fully understand how it works.\n",
        "Z_porous_plate 252  # placing the porous plate a couple of lattices before or after rock sample is recommended\n",
        "\n",
        "# Change inlet fluid phase for Z < initial_interface_position:\n",
        "# 0 - do nothing\n",
        "# 1 - 100% fluid1\n",
        "# 2 - 100% fluid2\n",
        "# Use case:\n",
        "# After the completion of the drainage simulation, one can use current fluid distribution\n",
        "# as the starting point to perform imbibition simulation. To reduce the simulation time, one\n",
        "# can eliminate unnecessary fluid displacement in buffer zone and numerical artifacts from\n",
        "# open boundary conditions by changing the inlet fluid to pure fluid2 (wetting phase).\n",
        "# This option should only be used when you fully understand how it works.\n",
        "change_inlet_fluid_phase_cmd 0  # To reduce the simulation time, one can eliminate unnecessary fluid displacement in buffer zone\n",
        "\n",
        "#=================================  geometry ======================================\n",
        "# Lattice dimensions nxGlobal, nyGlobal, nzGlobal, must be integral multiple of\n",
        "# npx, npy and npz, respectively\n",
        "#lattice_dimensions 240,240,260\n",
        "nxGlobal\t240\n",
        "\n",
        "nyGlobal\t240\n",
        "\n",
        "nzGlobal\t260\n",
        "\n",
        "# inlet/outlet zone excluded in the bulk properties calculation\n",
        "# 1<=k<=n_exclude_inlet and nzglobal-n_exclude_outlet+1<=k<=nzglobal\n",
        "# does not affect simulation\n",
        "#excluded_layers 10,10\n",
        "n_exclude_inlet\t\t10\n",
        "\n",
        "n_exclude_outlet\t10\n",
        "\n",
        "# domain_wall_status at xmin and xmax, status: 1 - wall; 0 - no wall\n",
        "#domain_wall_status_x 1,1\n",
        "domain_wall_status_x_min\t1\n",
        "\n",
        "domain_wall_status_x_max\t1\n",
        "\n",
        "# domain_wall_status at ymin and ymax, status: 1 - wall; 0 - no wall\n",
        "#domain_wall_status_y 1,1\n",
        "domain_wall_status_y_min\t1\n",
        "\n",
        "domain_wall_status_y_max\t1\n",
        "\n",
        "# default flow direction, domain_wall_status at ymin and ymax, status: 1 - wall; 0 - no wall\n",
        "#domain_wall_status_z 0,0\n",
        "domain_wall_status_z_min\t0\n",
        "\n",
        "domain_wall_status_z_max\t0\n",
        "\n",
        "# periodic BC indicator, 1 periodic, 0 non-periodic\n",
        "# currently, x direction can not apply periodic BC\n",
        "#periodic_indicator 0,0,0\n",
        "iper\t0\n",
        "\n",
        "jper\t0\n",
        "\n",
        "kper\t0\n",
        "\n",
        "#==================================== CUDA information ========================================\n",
        "# Number of threads in a thread block, in each direction\n",
        "# Preferred to be a multiple of 32 (warp size)\n",
        "block_Threads_X\t\t128\n",
        "\n",
        "block_Threads_Y\t\t1\n",
        "\n",
        "block_Threads_Z\t\t1\n",
        "\n",
        "#===================================== MPI information ========================================\n",
        "# mpi process numbers along each axis: npx, npy, npz\n",
        "# x direction domain decomposition disabled: npx = 1\n",
        "#MPI_process_num 1,1,1\n",
        "npx\t1\n",
        "\n",
        "npy\t1\n",
        "\n",
        "npz\t1\n",
        "\n",
        "# Number of halo nodes used in overlapping computation and communication.\n",
        "# x direction domain decomposition disabled and must be 0\n",
        "# This option should only be modified when you fully understand how it works.\n",
        "#MPI_async_layers_num  0,4,4\n",
        "ix_async\t0\n",
        "\n",
        "iy_async\t4\n",
        "\n",
        "iz_async\t4\n",
        "\n",
        "#=============================== fluid property ===================================\n",
        "# fluid viscosity\n",
        "fluid1_viscosity 0.00152  # Dynamic viscosity of mercury at room temperature\n",
        "\n",
        "fluid2_viscosity 0.0010016  # Dynamic viscosity of water at 20°C (updated from previous value)\n",
        "\n",
        "# surface tension\n",
        "surface_tension 0.415  # Interfacial tension between mercury and water at 20°C in N/m (updated from previous value)\n",
        "\n",
        "# Contact angle (measured through fluid 2):\n",
        "# It should always be less than 90 degrees due to the particular numerical scheme used in this\n",
        "# code to form desired contact angle. Therefore, fluid 1 is always the nonwetting phase, while\n",
        "# fluid 2 is always the wetting phase.\n",
        "# Imbibition simulation can be performed by injecting fluid 2 instead of fluid 1\n",
        "theta 45  # This represents the supplementary angle to the actual contact angle of ~135° for mercury-water-rock system\n",
        "\n",
        "# beta in RK recolor scheme - control the sharpness of the interface (from 0.9 to 0.95 is recommended)\n",
        "RK_beta 0.95\n",
        "\n",
        "#============================= injection conditions ===============================\n",
        "# inlet_BC selection (overridden by z (flow) direction periodic BC): 1-velocity; 2-pressure\n",
        "inlet_BC 2\n",
        "\n",
        "# outlet_BC selection (overridden by z (flow) direction periodic BC): 1-convective; 2-pressure\n",
        "outlet_BC 2\n",
        "\n",
        "# Injecting fluid saturation:\n",
        "# 1: pure fluid1 injection (drainage)\n",
        "# 0: pure fluid2 injection (imbibition)\n",
        "# 0<sa<1: mixed injection\n",
        "saturation_injection 1.0  # pure fluid1 injection (drainage)\n",
        "\n",
        "# Option to stop simulation based on reaching target fluid injection:\n",
        "# It is enabled by modifying ntime_max based on injection rate.\n",
        "# The value is in PV (pore-volume).\n",
        "# It is not very precise considering LBM compressibility error.\n",
        "# A negative value will disable target_inject_pore_volume – simulation continues until existing ntime_max\n",
        "target_inject_pore_volume -1.0\n",
        "\n",
        "# Initial interface position along z direction or initial drop radius for drop test\n",
        "initial_interface_position 8.0\n",
        "\n",
        "# Capillary number, based on viscosity of fluid1\n",
        "capillary_number 100e-6  # Dimensionless number representing the ratio of viscous forces to surface tension forces\n",
        "\n",
        "# initial value of body force or pressure gradient along z direction\n",
        "body_force_0 1e-4  # Represents gravity or other body forces in the system\n",
        "\n",
        "# override loading inlet density value from the checkpoint file and use a new value based on the pressure gradient\n",
        "rho_in_new\t1\n",
        "\n",
        "# use the BC density value for the outlet density\n",
        "rho_out_BC\t0\n",
        "\n",
        "# Target fluid1 saturation:\n",
        "# used in steady state relative permeability measurement\n",
        "# randomly distribute fluid1 and fluid2 with target fluid1 saturation\n",
        "target_fluid1_saturation 0.4\n",
        "\n",
        "#================================== timers ========================================\n",
        "# timer: max iterations, will be modified if target_inject_pore_volume > 0\n",
        "max_time_step 200000\n",
        "\n",
        "# timer: max iterations for benchmarking\n",
        "max_time_step_benchmark 100\n",
        "\n",
        "# timer: when to output detailed visualization data - overridden by d_vol_detail if d_vol_detail>0\n",
        "ntime_visual 10000\n",
        "\n",
        "# timer: when to output animation (phase info only) data - overridden by d_vol_animation if d_vol_animation>0\n",
        "ntime_animation 1000\n",
        "\n",
        "# timer: when to output bulk property data - overridden by d_vol_monitor if d_vol_monitor>0\n",
        "monitor_timer 1000\n",
        "\n",
        "# timer: when to output profile data along flow direction (Z)\n",
        "# monitor_profile_timer = monitor_profile_timer_ratio * monitor_timer\n",
        "monitor_profile_timer_ratio 5\n",
        "\n",
        "# timer: gaps used to record computation time\n",
        "# it is also used to check the checkpoint data saving status\n",
        "computation_time_timer 10000\n",
        "\n",
        "# timer: when to display time steps\n",
        "display_steps_timer 10000\n",
        "\n",
        "# timer: interval to save checkpoint data based on wall clock time, in hours\n",
        "checkpoint_save_timer 1.0\n",
        "\n",
        "# timer: time interval used to save secondary checkpoint data based on wall clock time, in hours\n",
        "# use a large value to reduce the storage size\n",
        "checkpoint_2rd_save_timer 5.5\n",
        "\n",
        "# timer: simulation duration in hours\n",
        "# save checkpoint data and exit program after simulation_duration_timer\n",
        "simulation_duration_timer 168\n",
        "\n",
        "# timer: when to output animation data - based on injected pore volume\n",
        "# only effective for constant velocity BC, disabled if it is a negative value\n",
        "d_vol_animation 0.05\n",
        "\n",
        "# timer: when to output full field data - based on injected pore volume\n",
        "# only effective for constant velocity BC, disabled if it is a negative value\n",
        "d_vol_detail 0.2\n",
        "\n",
        "# timer: when to output bulk property data - based on injected pore volume\n",
        "# only effective for constant velocity BC, disabled if it is a negative value\n",
        "d_vol_monitor 0.01\n",
        "\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "GkK0veBHiWop"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Original Params\n",
        "custom_simulation_control_content=f'''\n",
        "#*********************************************************************************#\n",
        "#*          CONTROL FILE FOR MULTIPHASE FLOW SIMULATION IN MF-LBM                *#\n",
        "#*********************************************************************************#\n",
        "\n",
        "#=========================== simulation_status ====================================\n",
        "# value stored on job_status.txt\n",
        "# new_simulation                - new simulation\n",
        "# continue_simulation           - continue previous simulation\n",
        "# simulation_done               - previous simulation is finished. stop\n",
        "# simulation_failed             - previous simulation failed. stop\n",
        "# simulation_reached_max_step   - previous simulation reached maximum step. stop\n",
        "# else                          - wrong status. stop\n",
        "\n",
        "#=========================== simulation setup =====================================\n",
        "#  Initial fluid distribution option:\n",
        "#  1 - fluid1 (nonwetting phase) at inlet: drainage\n",
        "#  2 - fluid2 (wetting phase) at inlet: imbibition\n",
        "#  3 - a fluid1 (nonwetting phase) drop attached to y=1: contact angle measurement\n",
        "#  4 - a fluid2 (wetting phase) drop attached to y=1: contact angle measurement\n",
        "#  5 - a fluid1 drop located in the center of the domain: surface tension measurement\n",
        "#  6 - fluid1 and fluid2 raondomely distributed in the pore space with desired fluid1\n",
        "#      saturation: steady state relative perm measurement\n",
        "#  0 or else - error!\n",
        "initial_fluid_distribution_option 2\n",
        "\n",
        "# Benchmark_cmd:\n",
        "# 0 - regular simulation\n",
        "# 1 - benchmarking simulation (obtain computational performance in MLUPS)\n",
        "benchmark_cmd 0\n",
        "\n",
        "# Necesssary modifications for extreme large simulations:\n",
        "# nxglobal*nyglobal*nzglobal may be too large.\n",
        "# parallel I/O for visualization files are needed.\n",
        "# recommended for grid points > 1 billion.\n",
        "# 0 - no; 1 - yes\n",
        "extreme_large_sim_cmd 0\n",
        "\n",
        "# Breakthrough check option:\n",
        "# currently only works for drainage simulation by detecting nonwetting phase\n",
        "# fluid1 near outlet.\n",
        "# 0 - do not check; 1 - check\n",
        "breakthrough_check 0\n",
        "\n",
        "# Steady state option:\n",
        "# 0 - non-steady state simulation (complete based on target injected PV or ntime_max)\n",
        "# 1 - steady state simulation (complete based on capillary pressure)\n",
        "# 2 - steady state simulation (complete based on phase field)\n",
        "# 3 - steady state simulation (complete based on saturation)\n",
        "steady_state_option 3\n",
        "\n",
        "# Convergence_criteria:\n",
        "# used in steady state simulation.\n",
        "# default is 1d-6, use 1d-4 or 1d-3 instead for steady-state-option 3 to reduce simulation\n",
        "# time and deal with fluctuation\n",
        "convergence_criteria 1e-4\n",
        "\n",
        "# output field data precision (simulation is always double precision):\n",
        "# 0 - single precision; 1 - double precision\n",
        "output_fieldData_precision_cmd 0\n",
        "\n",
        "# Modify_geometry_cmd:\n",
        "# 0 do not modify geometry; 1 modify geometry in the code (hard coding, Misc.cpp)\n",
        "modify_geometry_cmd 0\n",
        "\n",
        "# Use external geometry cmd:\n",
        "# 0 - no; 1 - yes\n",
        "external_geometry_read_cmd 1\n",
        "\n",
        "# choose type size of dimension variables saved in the geometry binary file:\n",
        "# 4 - 4 bytes (int, uint, ..etc.); 8 - 8 bytes (long long, unint64, ...etc.)\n",
        "# (0) would be used in relatively small geometries (nxGlobal*nyGlobal*nzGlobal < 384^3), while (1) would be used in relatively large geometries (nxGlobal*nyGlobal*nzGlobal >= 384^3)\n",
        "geometry_dims_type_size 4\n",
        "\n",
        "# Geometry preprocess cmd:\n",
        "# 0 - process the geometry boundary infor during the simulation (not recommended for\n",
        "# large simulations);\n",
        "# 1 - load external pre-processed geometry boundary info data\n",
        "geometry_preprocess_cmd 0\n",
        "\n",
        "# Place a porous plate along the flow direction:\n",
        "# 0 - no; 1 - block fluid1; 2 - block fluid2\n",
        "# example 1: injecting fluid2 (wetting) during imbibition cycle and block fluid1 from\n",
        "# exiting the inlet.\n",
        "# example 2: injecting fluid1 (nonwetting) during drainage cycle and block fluid1 from\n",
        "# exiting the outlet.\n",
        "# This option should only be used when you fully understand how it works.\n",
        "porous_plate_cmd 2\n",
        "\n",
        "# Location of the porous plate along flow direction (Z):\n",
        "# placing the porous plate a couple of lattices before or after rock sample is recommended.\n",
        "# example 1 (near outlet): Z_porous_plate = inlet_buffer_layers + nxsample + 2\n",
        "# example 2 (near inlet): Z_porous_plate = inlet_buffer_layers + 1 - 2\n",
        "# only effective when porous_plate_cmd = 1\n",
        "# This option should only be used when you fully understand how it works.\n",
        "Z_porous_plate 252\n",
        "\n",
        "# Change inlet fluid phase for Z < initial_interface_position:\n",
        "# 0 - do nothing\n",
        "# 1 - 100% fluid1\n",
        "# 2 - 100% fluid2\n",
        "# Use case:\n",
        "# After the completion of the drainage simulation, one can use current fluid distribution\n",
        "# as the starting point to perform imbibition simulation. To reduce the simulation time, one\n",
        "# can eliminate unecessary fluid displacement in buffer zone and numerical artifacts from\n",
        "# open boundary conditions by change the inlet fluid to pure fluid2 (wetting phase).\n",
        "# This option should only be used when you fully understand how it works.\n",
        "change_inlet_fluid_phase_cmd 0\n",
        "\n",
        "#=================================  geometry ======================================\n",
        "# Lattice dimensions nxGlobal, nyGlobal, nzGlobal, must be integral multiple of\n",
        "# npx, npy and npz, respectively\n",
        "#lattice_dimensions 240,240,260\n",
        "nxGlobal\t240\n",
        "nyGlobal\t240\n",
        "nzGlobal\t260\n",
        "\n",
        "# inlet/outlet zone excluded in the bulk properties calculation\n",
        "# 1<=k<=n_exclude_inlet and nzglobal-n_exclude_outlet+1<=k<=nzglobal\n",
        "# does not affect simulation\n",
        "#excluded_layers 10,10\n",
        "n_exclude_inlet\t\t10\n",
        "n_exclude_outlet\t10\n",
        "\n",
        "# domain_wall_status at xmin and xmax, status: 1 - wall; 0 - no wall\n",
        "#domain_wall_status_x 1,1\n",
        "domain_wall_status_x_min\t1\n",
        "domain_wall_status_x_max\t1\n",
        "\n",
        "# domain_wall_status at ymin and ymax, status: 1 - wall; 0 - no wall\n",
        "#domain_wall_status_y 1,1\n",
        "domain_wall_status_y_min\t1\n",
        "domain_wall_status_y_max\t1\n",
        "\n",
        "# default flow direction, domain_wall_status at ymin and ymax, status: 1 - wall; 0 - no wall\n",
        "#domain_wall_status_z 0,0\n",
        "domain_wall_status_z_min\t0\n",
        "domain_wall_status_z_max\t0\n",
        "\n",
        "# periodic BC indicator, 1 periodic, 0 non-periodic\n",
        "# currently, x direction can not apply periodic BC\n",
        "#periodic_indicator 0,0,0\n",
        "iper\t0\n",
        "jper\t0\n",
        "kper\t0\n",
        "\n",
        "#==================================== CUDA information ========================================\n",
        "# Number of threads in a thread block, in each direction\n",
        "# Preferred to be a multiple of 32 (warp size)\n",
        "block_Threads_X\t\t128\n",
        "block_Threads_Y\t\t1\n",
        "block_Threads_Z\t\t1\n",
        "\n",
        "#===================================== MPI information ========================================\n",
        "# mpi process numbers along each axis: npx, npy, npz\n",
        "# x direction domain decomposition disabled: npx = 1\n",
        "#MPI_process_num 1,1,1\n",
        "npx\t1\n",
        "npy\t1\n",
        "npz\t1\n",
        "\n",
        "# Number of halo nodes used in overlaping computation and communication.\n",
        "# x direction domain decomposition disabled and must be 0\n",
        "# This option should only be modified when you fully understand how it works.\n",
        "#MPI_async_layers_num  0,4,4\n",
        "ix_async\t0\n",
        "iy_async\t4\n",
        "iz_async\t4\n",
        "\n",
        "#=============================== fluid property ===================================\n",
        "# fluid viscosity\n",
        "fluid1_viscosity 0.04\n",
        "fluid2_viscosity 0.4\n",
        "\n",
        "# surface tension\n",
        "surface_tension 0.03\n",
        "\n",
        "# Contact angle (measured through fluid 2):\n",
        "# It should always be less than 90 degree due to the particular numerical scheme used in this\n",
        "# code to form desired contact angle. Therefore, fluid 1 is always the nonwetting phase, while\n",
        "# fluid 2 is always the wetting phase.\n",
        "# Imbibition simulation can be performed by injecting fluid 2 instead of fluid 1\n",
        "theta 65\n",
        "\n",
        "# beta in RK recolor scheme - control the sharpness of the interface (from 0.9 to 0,95 is recommended)\n",
        "RK_beta 0.95\n",
        "\n",
        "#============================= injection conditions ===============================\n",
        "# inlet_BC selection (overridden by z (flow) direction periodic BC): 1-velocity; 2-pressure\n",
        "inlet_BC 2\n",
        "# outlet_BC selection (overridden by z (flow) direction periodic BC): 1-convective; 2-pressure\n",
        "outlet_BC 2\n",
        "\n",
        "# Injecting fluid saturation:\n",
        "# 1: pure fluid1 injection (drainage)\n",
        "# 0: pure fluid2 injection (imbibition)\n",
        "# 0<sa<1: mixed injection\n",
        "saturation_injection 0.0\n",
        "\n",
        "# Option to stop simulation based on reaching target fluid injection:\n",
        "# It is enabled by modify ntime_max based on injection rate.\n",
        "# The value is in PV (pore-volume).\n",
        "# It is not very precise considering LBM compressibility error.\n",
        "# A negative value will disable target_inject_pore_volume – simulation continues until existing ntime_max\n",
        "target_inject_pore_volume -1.0\n",
        "\n",
        "# Initial interface position along z direction or initial drop radius for drop test\n",
        "initial_interface_position 8.0\n",
        "\n",
        "# Capillary number, based on viscosity of fluid1\n",
        "capillary_number 100e-6\n",
        "\n",
        "# initial value of body force or pressure gradient along z direction\n",
        "body_force_0 1e-4\n",
        "\n",
        "# override loading inlet density value from the checkpoint file and use a new value based on the pressure gradient\n",
        "rho_in_new\t1\n",
        "\n",
        "# use the BC density value for the outlet density\n",
        "rho_out_BC\t0\n",
        "\n",
        "# Target fluid1 saturation:\n",
        "# used in steady state relative permeability measurement\n",
        "# randomly distribute fluid1 and fluid2 with target fluid1 saturation\n",
        "target_fluid1_saturation 0.4\n",
        "\n",
        "#================================== timers ========================================\n",
        "# timer: max iterations, will be modified if target_inject_pore_volume > 0\n",
        "max_time_step 500000\n",
        "\n",
        "# timer: max iterations for benchmarking\n",
        "max_time_step_benchmark 100\n",
        "\n",
        "# timer: when to output detailed visualization data - overridden by d_vol_detail if d_vol_detail>0\n",
        "ntime_visual 5000000\n",
        "\n",
        "# timer: when to output animation (phase info only) data - overridden by d_vol_animation if d_vol_animation>0\n",
        "ntime_animation 10000\n",
        "\n",
        "# timer: when to output bulk property data - overridden by d_vol_monitor if d_vol_monitor>0\n",
        "monitor_timer 10000\n",
        "\n",
        "# timer: when to output profile data along flow direction (Z)\n",
        "# monitor_profile_timer = monitor_profile_timer_ratio * monitor_timer\n",
        "monitor_profile_timer_ratio 5\n",
        "\n",
        "# timer: gaps used to record computation time\n",
        "# it is also used to check the checkpoint data saving status\n",
        "computation_time_timer 10000\n",
        "\n",
        "# timer: when to display time steps\n",
        "display_steps_timer 10000\n",
        "\n",
        "# timer: interval to save checkpoint data based on wall clock time, in hours\n",
        "checkpoint_save_timer 1.0\n",
        "\n",
        "# timer: time interval used to save secondary checkpoint data based on wall clock time, in hours\n",
        "# use a large value to reduce the storage size\n",
        "checkpoint_2rd_save_timer 5.5\n",
        "\n",
        "# timer: simulation duration in hours\n",
        "# save checkpoint data and exit program after simulation_duration_timer\n",
        "simulation_duration_timer 168\n",
        "\n",
        "\n",
        "# timer: when to output animation data - based on injected pore volume\n",
        "# only effective for constant velocity BC, disabled if it is a negative value\n",
        "d_vol_animation -0.05\n",
        "\n",
        "# timer: when to output full field data - based on injected pore volume\n",
        "# only effective for constant velocity BC, disabled if it is a negative value\n",
        "d_vol_detail -1.0\n",
        "\n",
        "# timer: when to output bulk property data - based on injected pore volume\n",
        "# only effective for constant velocity BC, disabled if it is a negative value\n",
        "d_vol_monitor -0.01\n",
        "'''"
      ],
      "metadata": {
        "id": "A2L9f12ciZ5w"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install dependencies\n",
        "!apt-get update\n",
        "!apt-get install -y git build-essential nvidia-cuda-toolkit\n",
        "\n",
        "# Verify CUDA installation\n",
        "!nvcc --version\n",
        "\n",
        "# Clone the repository\n",
        "!git clone https://github.com/Mahmoud-Sedahmed/MF-LBM-CUDA.git\n",
        "%cd MF-LBM-CUDA\n",
        "\n",
        "# Ensure the input directory exists\n",
        "import os\n",
        "if not os.path.exists('input'):\n",
        "    os.makedirs('input')\n",
        "\n",
        "# Write the f-string content to simulation_control.txt in the correct directory\n",
        "with open('input/simulation_control.txt', 'w') as file:\n",
        "    file.write(custom_simulation_control_content)\n",
        "\n",
        "# Verify that the file has been overwritten (for debugging purposes)\n",
        "with open('input/simulation_control.txt', 'r') as file:\n",
        "    print(file.read())\n",
        "\n",
        "# Edit Makefile for CUDA paths and compute capability\n",
        "!sed -i 's|COMPUTE_CAPABILITY := .*|COMPUTE_CAPABILITY := 80|' Makefile\n",
        "!sed -i 's|CUDA_LIBRARY_LOCATION := .*|CUDA_LIBRARY_LOCATION := /usr/local/cuda/lib64|' Makefile\n",
        "!sed -i 's|CUDA_LNK_PATH := .*|CUDA_LNK_PATH := /usr/local/cuda/lib64|' Makefile\n",
        "!sed -i 's|BUILD := .*|BUILD := release|' Makefile\n",
        "\n",
        "# Set solver precision to single precision\n",
        "!sed -i 's|#define PRECISION (.*)|#define PRECISION (SINGLE_PRECISION)|' includes/solver_precision.h\n",
        "\n",
        "# Compile the program\n",
        "!make\n",
        "\n",
        "# Prepare geometry file\n",
        "!cp input/geometry/bentheimer_240_240_240.dat Geometry_File_Path.txt\n",
        "\n",
        "# Run the simulation\n",
        "!./MF_LBM_CUDA\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIt6RCWTcMU8",
        "outputId": "9a66cc5a-2ad6-43a9-b1ec-6e52c6d4af0d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r0% [Working]\r            \rHit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease\n",
            "Hit:2 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\n",
            "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Hit:4 http://security.ubuntu.com/ubuntu jammy-security InRelease\n",
            "Hit:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n",
            "Hit:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
            "Ign:7 https://r2u.stat.illinois.edu/ubuntu jammy InRelease\n",
            "Hit:8 https://r2u.stat.illinois.edu/ubuntu jammy Release\n",
            "Hit:10 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Hit:11 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:12 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Reading package lists... Done\n",
            "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.9ubuntu3).\n",
            "nvidia-cuda-toolkit is already the newest version (11.5.1-1ubuntu1).\n",
            "git is already the newest version (1:2.34.1-1ubuntu1.11).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 56 not upgraded.\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n",
            "Cloning into 'MF-LBM-CUDA'...\n",
            "remote: Enumerating objects: 101, done.\u001b[K\n",
            "remote: Counting objects: 100% (101/101), done.\u001b[K\n",
            "remote: Compressing objects: 100% (70/70), done.\u001b[K\n",
            "remote: Total 101 (delta 39), reused 89 (delta 27), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (101/101), 1.34 MiB | 5.79 MiB/s, done.\n",
            "Resolving deltas: 100% (39/39), done.\n",
            "/content/MF-LBM-CUDA/MF-LBM-CUDA/MF-LBM-CUDA/MF-LBM-CUDA/MF-LBM-CUDA/MF-LBM-CUDA/MF-LBM-CUDA/MF-LBM-CUDA\n",
            "\n",
            "#*********************************************************************************#\n",
            "#*          CONTROL FILE FOR MULTIPHASE FLOW SIMULATION IN MF-LBM                *#\n",
            "#*********************************************************************************#\n",
            "\n",
            "#=========================== simulation_status ====================================\n",
            "# value stored on job_status.txt                                      \n",
            "# new_simulation                - new simulation                                  \n",
            "# continue_simulation           - continue previous simulation               \n",
            "# simulation_done               - previous simulation is finished. stop              \n",
            "# simulation_failed             - previous simulation failed. stop    \n",
            "# simulation_reached_max_step   - previous simulation reached maximum step. stop    \n",
            "# else                          - wrong status. stop                                       \n",
            "\n",
            "#=========================== simulation setup =====================================\n",
            "#  Initial fluid distribution option: \n",
            "#  1 - fluid1 (nonwetting phase) at inlet: drainage \n",
            "#  2 - fluid2 (wetting phase) at inlet: imbibition \n",
            "#  3 - a fluid1 (nonwetting phase) drop attached to y=1: contact angle measurement\n",
            "#  4 - a fluid2 (wetting phase) drop attached to y=1: contact angle measurement\n",
            "#  5 - a fluid1 drop located in the center of the domain: surface tension measurement\n",
            "#  6 - fluid1 and fluid2 randomly distributed in the pore space with desired fluid1\n",
            "#      saturation: steady state relative perm measurement\n",
            "#  0 or else - error!                  \n",
            "initial_fluid_distribution_option 1  # 1 - fluid1 (nonwetting phase) at inlet: drainage \n",
            "\n",
            "# Benchmark_cmd: \n",
            "# 0 - regular simulation\n",
            "# 1 - benchmarking simulation (obtain computational performance in MLUPS)\n",
            "benchmark_cmd 0\n",
            "\n",
            "# Necessary modifications for extreme large simulations:\n",
            "# nxglobal*nyglobal*nzglobal may be too large.\n",
            "# parallel I/O for visualization files are needed.\n",
            "# recommended for grid points > 1 billion. \n",
            "# 0 - no; 1 - yes \n",
            "extreme_large_sim_cmd 0 \n",
            "\n",
            "# Breakthrough check option:\n",
            "# currently only works for drainage simulation by detecting nonwetting phase \n",
            "# fluid1 near outlet. \n",
            "# 0 - do not check; 1 - check\n",
            "breakthrough_check 1  # currently only works for drainage simulation by detecting nonwetting phase fluid1 near outlet\n",
            "\n",
            "# Steady state option: \n",
            "# 0 - non-steady state simulation (complete based on target injected PV or ntime_max)\n",
            "# 1 - steady state simulation (complete based on capillary pressure)\n",
            "# 2 - steady state simulation (complete based on phase field)\n",
            "# 3 - steady state simulation (complete based on saturation) \n",
            "steady_state_option 3  \n",
            "# Convergence_criteria:\n",
            "# used in steady state simulation. \n",
            "# default is 1d-6, use 1d-4 or 1d-3 instead for steady-state-option 3 to reduce simulation\n",
            "# time and deal with fluctuation\n",
            "convergence_criteria 1e-4\n",
            "\n",
            "# output field data precision (simulation is always double precision): \n",
            "# 0 - single precision; 1 - double precision\n",
            "output_fieldData_precision_cmd 0\n",
            "\n",
            "# Modify_geometry_cmd: \n",
            "# 0 do not modify geometry; 1 modify geometry in the code (hard coding, Misc.cpp) \n",
            "modify_geometry_cmd 0\n",
            "\n",
            "# Use external geometry cmd:  \n",
            "# 0 - no; 1 - yes \n",
            "external_geometry_read_cmd 1\n",
            "\n",
            "# choose type size of dimension variables saved in the geometry binary file:  \n",
            "# 4 - 4 bytes (int, uint, ..etc.); 8 - 8 bytes (long long, unint64, ...etc.)\n",
            "# (0) would be used in relatively small geometries (nxGlobal*nyGlobal*nzGlobal < 384^3), while (1) would be used in relatively large geometries (nxGlobal*nyGlobal*nzGlobal >= 384^3)\n",
            "geometry_dims_type_size 4\n",
            "\n",
            "# Geometry preprocess cmd: \n",
            "# 0 - process the geometry boundary info during the simulation (not recommended for \n",
            "# large simulations);\n",
            "# 1 - load external pre-processed geometry boundary info data\n",
            "geometry_preprocess_cmd 0\n",
            "\n",
            "# Place a porous plate along the flow direction: \n",
            "# 0 - no; 1 - block fluid1; 2 - block fluid2\t\t\t\t\t\t\t\t\t\t\t\t\n",
            "# example 1: injecting fluid2 (wetting) during imbibition cycle and block fluid1 from\t\t\n",
            "# exiting the inlet.\n",
            "# example 2: injecting fluid1 (nonwetting) during drainage cycle and block fluid1 from\t\t\n",
            "# exiting the outlet.\n",
            "# This option should only be used when you fully understand how it works.\n",
            "porous_plate_cmd 1  # This option should only be used when you fully understand how it works\n",
            "\n",
            "# Location of the porous plate along flow direction (Z):\n",
            "# placing the porous plate a couple of lattices before or after rock sample is recommended.\n",
            "# example 1 (near outlet): Z_porous_plate = inlet_buffer_layers + nxsample + 2\n",
            "# example 2 (near inlet): Z_porous_plate = inlet_buffer_layers + 1 - 2\n",
            "# only effective when porous_plate_cmd = 1\n",
            "# This option should only be used when you fully understand how it works.\n",
            "Z_porous_plate 252  # placing the porous plate a couple of lattices before or after rock sample is recommended\n",
            "\n",
            "# Change inlet fluid phase for Z < initial_interface_position:\n",
            "# 0 - do nothing\n",
            "# 1 - 100% fluid1\n",
            "# 2 - 100% fluid2\n",
            "# Use case: \n",
            "# After the completion of the drainage simulation, one can use current fluid distribution\n",
            "# as the starting point to perform imbibition simulation. To reduce the simulation time, one\n",
            "# can eliminate unnecessary fluid displacement in buffer zone and numerical artifacts from \n",
            "# open boundary conditions by changing the inlet fluid to pure fluid2 (wetting phase). \n",
            "# This option should only be used when you fully understand how it works.\n",
            "change_inlet_fluid_phase_cmd 0  # To reduce the simulation time, one can eliminate unnecessary fluid displacement in buffer zone\n",
            "\n",
            "#=================================  geometry ======================================\n",
            "# Lattice dimensions nxGlobal, nyGlobal, nzGlobal, must be integral multiple of \n",
            "# npx, npy and npz, respectively \n",
            "#lattice_dimensions 240,240,260\n",
            "nxGlobal\t240\n",
            "\n",
            "nyGlobal\t240\n",
            "\n",
            "nzGlobal\t260\n",
            "\n",
            "# inlet/outlet zone excluded in the bulk properties calculation \n",
            "# 1<=k<=n_exclude_inlet and nzglobal-n_exclude_outlet+1<=k<=nzglobal\n",
            "# does not affect simulation\n",
            "#excluded_layers 10,10\n",
            "n_exclude_inlet\t\t10\n",
            "\n",
            "n_exclude_outlet\t10\n",
            "\n",
            "# domain_wall_status at xmin and xmax, status: 1 - wall; 0 - no wall\n",
            "#domain_wall_status_x 1,1\n",
            "domain_wall_status_x_min\t1\n",
            "\n",
            "domain_wall_status_x_max\t1\n",
            "\n",
            "# domain_wall_status at ymin and ymax, status: 1 - wall; 0 - no wall\n",
            "#domain_wall_status_y 1,1\n",
            "domain_wall_status_y_min\t1\n",
            "\n",
            "domain_wall_status_y_max\t1\n",
            "\n",
            "# default flow direction, domain_wall_status at ymin and ymax, status: 1 - wall; 0 - no wall\n",
            "#domain_wall_status_z 0,0\n",
            "domain_wall_status_z_min\t0\n",
            "\n",
            "domain_wall_status_z_max\t0\n",
            "\n",
            "# periodic BC indicator, 1 periodic, 0 non-periodic\n",
            "# currently, x direction can not apply periodic BC\n",
            "#periodic_indicator 0,0,0\n",
            "iper\t0\n",
            "\n",
            "jper\t0\n",
            "\n",
            "kper\t0\n",
            "\n",
            "#==================================== CUDA information ========================================\n",
            "# Number of threads in a thread block, in each direction\n",
            "# Preferred to be a multiple of 32 (warp size)\n",
            "block_Threads_X\t\t128\n",
            "\n",
            "block_Threads_Y\t\t1\n",
            "\n",
            "block_Threads_Z\t\t1\n",
            "\n",
            "#===================================== MPI information ========================================\n",
            "# mpi process numbers along each axis: npx, npy, npz\n",
            "# x direction domain decomposition disabled: npx = 1\n",
            "#MPI_process_num 1,1,1\n",
            "npx\t1\n",
            "\n",
            "npy\t1\n",
            "\n",
            "npz\t1\n",
            "\n",
            "# Number of halo nodes used in overlapping computation and communication. \n",
            "# x direction domain decomposition disabled and must be 0\n",
            "# This option should only be modified when you fully understand how it works.\n",
            "#MPI_async_layers_num  0,4,4\n",
            "ix_async\t0\n",
            "\n",
            "iy_async\t4\n",
            "\n",
            "iz_async\t4\n",
            "\n",
            "#=============================== fluid property ===================================\n",
            "# fluid viscosity\n",
            "fluid1_viscosity 0.00152  # Dynamic viscosity of mercury at room temperature\n",
            "\n",
            "fluid2_viscosity 0.0010016  # Dynamic viscosity of water at 20°C (updated from previous value)\n",
            "\n",
            "# surface tension\n",
            "surface_tension 0.415  # Interfacial tension between mercury and water at 20°C in N/m (updated from previous value)\n",
            "\n",
            "# Contact angle (measured through fluid 2):\n",
            "# It should always be less than 90 degrees due to the particular numerical scheme used in this\n",
            "# code to form desired contact angle. Therefore, fluid 1 is always the nonwetting phase, while \n",
            "# fluid 2 is always the wetting phase.\n",
            "# Imbibition simulation can be performed by injecting fluid 2 instead of fluid 1\n",
            "theta 45  # This represents the supplementary angle to the actual contact angle of ~135° for mercury-water-rock system\n",
            "\n",
            "# beta in RK recolor scheme - control the sharpness of the interface (from 0.9 to 0.95 is recommended)\n",
            "RK_beta 0.95\n",
            "\n",
            "#============================= injection conditions ===============================\n",
            "# inlet_BC selection (overridden by z (flow) direction periodic BC): 1-velocity; 2-pressure \n",
            "inlet_BC 2\n",
            "\n",
            "# outlet_BC selection (overridden by z (flow) direction periodic BC): 1-convective; 2-pressure \n",
            "outlet_BC 2\n",
            "\n",
            "# Injecting fluid saturation:\n",
            "# 1: pure fluid1 injection (drainage)\n",
            "# 0: pure fluid2 injection (imbibition) \n",
            "# 0<sa<1: mixed injection \n",
            "saturation_injection 1.0  # pure fluid1 injection (drainage)\n",
            "\n",
            "# Option to stop simulation based on reaching target fluid injection:\n",
            "# It is enabled by modifying ntime_max based on injection rate.\n",
            "# The value is in PV (pore-volume). \n",
            "# It is not very precise considering LBM compressibility error.\n",
            "# A negative value will disable target_inject_pore_volume – simulation continues until existing ntime_max\n",
            "target_inject_pore_volume -1.0\n",
            "\n",
            "# Initial interface position along z direction or initial drop radius for drop test \n",
            "initial_interface_position 8.0\n",
            "\n",
            "# Capillary number, based on viscosity of fluid1\n",
            "capillary_number 100e-6  # Dimensionless number representing the ratio of viscous forces to surface tension forces\n",
            "\n",
            "# initial value of body force or pressure gradient along z direction\n",
            "body_force_0 1e-4  # Represents gravity or other body forces in the system\n",
            "\n",
            "# override loading inlet density value from the checkpoint file and use a new value based on the pressure gradient \n",
            "rho_in_new\t1\n",
            "\n",
            "# use the BC density value for the outlet density\n",
            "rho_out_BC\t0\n",
            "\n",
            "# Target fluid1 saturation: \n",
            "# used in steady state relative permeability measurement\n",
            "# randomly distribute fluid1 and fluid2 with target fluid1 saturation\n",
            "target_fluid1_saturation 0.4\n",
            "\n",
            "#================================== timers ========================================\n",
            "# timer: max iterations, will be modified if target_inject_pore_volume > 0\n",
            "max_time_step 200000\n",
            "\n",
            "# timer: max iterations for benchmarking \n",
            "max_time_step_benchmark 100\n",
            "\n",
            "# timer: when to output detailed visualization data - overridden by d_vol_detail if d_vol_detail>0  \n",
            "ntime_visual 10000\n",
            "\n",
            "# timer: when to output animation (phase info only) data - overridden by d_vol_animation if d_vol_animation>0 \n",
            "ntime_animation 1000\n",
            "\n",
            "# timer: when to output bulk property data - overridden by d_vol_monitor if d_vol_monitor>0 \n",
            "monitor_timer 1000\n",
            "\n",
            "# timer: when to output profile data along flow direction (Z)\n",
            "# monitor_profile_timer = monitor_profile_timer_ratio * monitor_timer \n",
            "monitor_profile_timer_ratio 5\n",
            "\n",
            "# timer: gaps used to record computation time\n",
            "# it is also used to check the checkpoint data saving status\n",
            "computation_time_timer 10000\n",
            "\n",
            "# timer: when to display time steps\n",
            "display_steps_timer 10000\n",
            "\n",
            "# timer: interval to save checkpoint data based on wall clock time, in hours\n",
            "checkpoint_save_timer 1.0\n",
            "\n",
            "# timer: time interval used to save secondary checkpoint data based on wall clock time, in hours\n",
            "# use a large value to reduce the storage size\n",
            "checkpoint_2rd_save_timer 5.5\n",
            "\n",
            "# timer: simulation duration in hours\n",
            "# save checkpoint data and exit program after simulation_duration_timer\n",
            "simulation_duration_timer 168\n",
            "\n",
            "# timer: when to output animation data - based on injected pore volume \n",
            "# only effective for constant velocity BC, disabled if it is a negative value\n",
            "d_vol_animation 0.05\n",
            "\n",
            "# timer: when to output full field data - based on injected pore volume \n",
            "# only effective for constant velocity BC, disabled if it is a negative value\n",
            "d_vol_detail 0.2\n",
            "\n",
            "# timer: when to output bulk property data - based on injected pore volume \n",
            "# only effective for constant velocity BC, disabled if it is a negative value\n",
            "d_vol_monitor 0.01\n",
            "\n",
            "\n",
            "\n",
            "#-------------------------------------------------\n",
            "# Building (MF_LBM_CUDA) Started! \n",
            "# Author: M. Sedahmed \n",
            "#-------------------------------------------------\n",
            "nvcc -I includes -I /usr/local/cuda/lib64  -arch=sm_80 -std=c++17 -rdc=true -O3 -lineinfo -c src/Init_multiphase_GPU.cu -o obj/Init_multiphase_GPU.o -lcudart\n",
            "nvcc -I includes -I /usr/local/cuda/lib64  -arch=sm_80 -std=c++17 -rdc=true -O3 -lineinfo -c src/main_iteration_GPU.cu -o obj/main_iteration_GPU.o -lcudart\n",
            "nvcc -arch=sm_80 -dlink obj/Init_multiphase_GPU.o -o obj/Init_multiphase_GPU_link.o -lcudadevrt -lcudart\n",
            "nvcc -arch=sm_80 -dlink obj/main_iteration_GPU.o -o obj/main_iteration_GPU_link.o -lcudadevrt -lcudart\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/Geometry_preprocessing.cpp -o obj/Geometry_preprocessing.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/Init_multiphase.cpp -o obj/Init_multiphase.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/IO_multiphase.cpp -o obj/IO_multiphase.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/main.cpp -o obj/main.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/Misc.cpp -o obj/Misc.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/Monitor.cpp -o obj/Monitor.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/Phase_gradient.cpp -o obj/Phase_gradient.o\t\n",
            "g++  -I includes -I /usr/local/cuda/lib64  -std=c++17 -O3 -Wall   -c src/utils.cpp -o obj/utils.o\t\n",
            "g++ obj/Init_multiphase_GPU.o obj/main_iteration_GPU.o obj/Init_multiphase_GPU_link.o obj/main_iteration_GPU_link.o obj/Geometry_preprocessing.o obj/Init_multiphase.o obj/IO_multiphase.o obj/main.o obj/Misc.o obj/Monitor.o obj/Phase_gradient.o obj/utils.o -o MF_LBM_CUDA -L/usr/local/cuda/lib64 -lm -lcudart\n",
            "#-------------------------------------------------\n",
            "# Building (MF_LBM_CUDA) finished! \n",
            "#-------------------------------------------------\n",
            "==============================================================================\n",
            "MF-LBM solver - CUDA/C++ version\n",
            "Author: M. Sedahmed\n",
            "Github repository: \n",
            "Original MF-LBM solver - Fortran/MPI/OpenMP/OpenACC version by Authors: Dr. Yu Chen, Dr. Qinjun Kang\n",
            "Github repository: (https://github.com/lanl/MF-LBM)\n",
            "==============================================================================\n",
            " \n",
            "Solver precision: Single precision\n",
            " \n",
            "***************************** Initialization **********************************\n",
            "Checking simulation status ... \n",
            "Simulation status is: New Simulation!\n",
            " \n",
            "****************** Reading in parameters from control file *******************\n",
            ".........\n",
            "initial_fluid_distribution_option: 1\n",
            "---------------------------\n",
            "breakthrough_check:    1\n",
            "---------------------------\n",
            "steady_state_option:   3\n",
            "---------------------------\n",
            "convergence_criteria:  0.0001\n",
            "---------------------------\n",
            "benchmark_cmd: 0\n",
            "---------------------------\n",
            "output_fieldData_precision_cmd:    0\n",
            "---------------------------\n",
            "extreme_large_sim_cmd: 0\n",
            "---------------------------\n",
            "modify_geometry_cmd:   0\n",
            "---------------------------\n",
            "external_geometry_read_cmd:   1\n",
            "---------------------------\n",
            "geometry_dims_type_size:   4\n",
            "---------------------------\n",
            "geometry_preprocess_cmd:   0\n",
            "---------------------------\n",
            "porous_plate_cmd:   1\n",
            "---------------------------\n",
            "change_inlet_fluid_phase_cmd:   0\n",
            "---------------------------\n",
            "nxGlobal:   240\n",
            "nyGlobal:   240\n",
            "nzGlobal:   260\n",
            "Inlet_excluded_layers:   10\n",
            "Outlet_excluded_layers:   10\n",
            "Apply nonslip boundary condition at x = 1:   1\n",
            "Apply nonslip boundary condition at x = nxGlobal:   1\n",
            "Apply nonslip boundary condition at y = 1:   1\n",
            "Apply nonslip boundary condition at y = nyGlobal:   1\n",
            "Apply nonslip boundary condition at z = 1:   0\n",
            "Apply nonslip boundary condition at z = nzGlobal:   0\n",
            "X_periodic_option =   0\n",
            "Y_periodic_option =   0\n",
            "Z_periodic_option =   0\n",
            "---------------------------\n",
            "fluid1_viscosity:   0.00152\n",
            "fluid2_viscosity:   0.0010016\n",
            "surface_tension:   0.415\n",
            "Contact_angle:   45\n",
            "RK_beta:   0.95\n",
            "---------------------------\n",
            "inlet_BC =   2\n",
            "outlet_BC =   2\n",
            "target_inject_pore_volume =   -1\n",
            "capillary_number =   0.0001\n",
            "Injecting_fluid_saturation =   1\n",
            "initial_interface_position =   8\n",
            "Body_force_Z0 =   0.0001\n",
            "target_fluid1_saturation =   0.4\n",
            "Z_porous_plate =   252\n",
            "---------------------------\n",
            "Max_iterations =   200000\n",
            "Max_iterations_benchmark =   100\n",
            "Full_VTK_timer =   10000\n",
            "Animation_VTK_timer =   1000\n",
            "Monitor_timer =   1000\n",
            "Monitor_profile_timer_ratio =   5\n",
            "Computation_time_timer =   10000\n",
            "Display_time_steps_timer =   10000\n",
            "---------------------------\n",
            "Checkpoint_save_timer (wall clock time, hours) =   1\n",
            "Checkpoint_2rd_save_timer (wall clock time, hours) =   5.5\n",
            "Simulation_duration (wall clock time, hours) =   168\n",
            "---------------------------\n",
            "Animation_VTK_by_injected_vol =   0.05\n",
            "Full_VTK_by_injected_vol =   0.2\n",
            "Monitor_by_injected_vol =   0.01\n",
            "---------------------------\n",
            "new_inlet_density: 1\n",
            "---------------------------\n",
            "CUDA block_Threads_X: 128\n",
            "CUDA block_Threads_Y: 1\n",
            "CUDA block_Threads_Z: 1\n",
            "---------------------------\n",
            "monitor_profile_timer =     5000\n",
            "************** End reading in parameters from control file *******************\n",
            "************ Start checking correctness of input parameters ******************\n",
            "Everything looks good!\n",
            "************** End checking correctness of input parameters ******************\n",
            "\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Creating directories if not exist\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "\n",
            "**************************** Processing geometry *****************************\n",
            ".........\n",
            "This simulation uses external geometry data!\n",
            "Porous media sample size: nx = 240, ny = 240, nz = 260\n",
            "Inlet effective open area =    56644\n",
            "------ Start processing boundary nodes info ------\n",
            "Total number of solid boundary nodes: 1056542\n",
            "Total number of fluid boundary nodes: 966378\n",
            "------ End processing boundary nodes info --------\n",
            "Total number of pore nodes = 3670813\n",
            "Inlet open cross sectional area = 56644\n",
            "Total number of effective pore nodes (excluding inlet/outlet) = 2537933\n",
            "Full domain porosity = 0.24925\n",
            "Effective domain porosity  (excluding inlet/outlet) = 0.186687\n",
            "************************** End Processing geometry ***************************\n",
            "\n",
            "******************** Processing fluid and flow info **********************\n",
            "Fluid 1 relaxation time = 0.50456\n",
            "Fluid 2 relaxation time = 0.503005\n",
            "Inlet boundary nodes order parameter = 1\n",
            "Inlet density (pressure inlet BC) = 1.00867\n",
            "******************** End processing fluid and flow info **********************\n",
            "************************** Initialization ends ********************************\n",
            "***************************** Initialization - GPU **********************************\n",
            "***************************** GPU Specifications **********************************\n",
            "0: NVIDIA A100-SXM4-40GB: 8.0\n",
            "  Global memory:   40513 mb\n",
            "  Shared memory:   48 kb\n",
            "  Constant memory: 64 kb\n",
            "  Block registers: 65536\n",
            "  Number of SMs: 108\n",
            "  Clock frequencey: 1410 MHz\n",
            "  Warp size:         32\n",
            "  Threads per block: 1024\n",
            "  Max block dimensions: [ 1024, 1024, 64 ]\n",
            "  Max grid dimensions:  [ 2147483647, 65535, 65535 ]\n",
            "***************************** GPU Specifications **********************************\n",
            "Total amount of global memory needed on GPU (GB) = 2.8864\n",
            "************************** Initialization ends - GPU ********************************\n",
            "Initial saturation: 0.123447\n",
            "************************** Entering main loop *********************************\n",
            "Copying results back to the host side ....  Complete \n",
            "Simulation failed due to NAN of saturation or capillary number\n",
            "************************** Exiting main iteration *********************************\n",
            "Code speed:\t2099.99 MLUPS\n",
            "Simulation failed after\t1000 iterations!\n",
            "\n",
            "Code Finished \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install vtk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HBDoEBdJhLg",
        "outputId": "823df227-f190-4f42-b0b0-bc1510f092ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting vtk\n",
            "  Downloading vtk-9.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.2 kB)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from vtk) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->vtk) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=2.0.0->vtk) (1.16.0)\n",
            "Downloading vtk-9.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (92.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.1/92.1 MB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: vtk\n",
            "Successfully installed vtk-9.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize results\n",
        "import vtk\n",
        "print(\"VTK version:\", vtk.vtkVersion.GetVTKVersion())\n",
        "from vtkmodules.util.numpy_support import vtk_to_numpy\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def visualize_vtk(file_path):\n",
        "    reader = vtk.vtkStructuredPointsReader()\n",
        "    reader.SetFileName(file_path)\n",
        "    reader.Update()\n",
        "\n",
        "    output = reader.GetOutput()\n",
        "    dims = output.GetDimensions()\n",
        "\n",
        "    scalar_range = output.GetScalarRange()\n",
        "    scalar_data = vtk_to_numpy(output.GetPointData().GetScalars()).reshape(dims, order='F')\n",
        "\n",
        "    slice_index = dims[2] // 2\n",
        "    plt.imshow(scalar_data[:, :, slice_index], cmap='jet', vmin=scalar_range[0], vmax=scalar_range[1])\n",
        "    plt.colorbar()\n",
        "    plt.title('Flow Visualization')\n",
        "    plt.show()\n",
        "\n",
        "# Visualize the VTK output\n",
        "vtk_file_path = \"results/out3.field_data/flow_data_000001.vtk\"\n",
        "visualize_vtk(vtk_file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "ONDv3w3TJm5T",
        "outputId": "8d2a4e3c-c832-4090-87ea-b5ce9b99df2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VTK version: 9.3.1\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'NoneType' object has no attribute 'GetDataType'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-0800b0c9dc77>\u001b[0m in \u001b[0;36m<cell line: 27>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# Visualize the VTK output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mvtk_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"results/out3.field_data/flow_data_000001.vtk\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mvisualize_vtk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvtk_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-6-0800b0c9dc77>\u001b[0m in \u001b[0;36mvisualize_vtk\u001b[0;34m(file_path)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mscalar_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetScalarRange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mscalar_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvtk_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetPointData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetScalars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mslice_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/vtkmodules/util/numpy_support.py\u001b[0m in \u001b[0;36mvtk_to_numpy\u001b[0;34m(vtk_array)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \"\"\"\n\u001b[0;32m--> 215\u001b[0;31m     \u001b[0mtyp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvtk_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetDataType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mget_vtk_to_numpy_typemap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m            \u001b[0;34m\"Unsupported array type %s\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'GetDataType'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyvista"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2nH2BrFQBKa",
        "outputId": "4fc28637-6486-498f-bc5c-8a72bd0107ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyvista\n",
            "  Downloading pyvista-0.44.1-py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: matplotlib>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from pyvista) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pyvista) (1.26.4)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from pyvista) (9.4.0)\n",
            "Requirement already satisfied: pooch in /usr/local/lib/python3.10/dist-packages (from pyvista) (1.8.2)\n",
            "Requirement already satisfied: scooby>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from pyvista) (0.10.0)\n",
            "Requirement already satisfied: vtk in /usr/local/lib/python3.10/dist-packages (from pyvista) (9.3.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from pyvista) (4.12.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (2.8.2)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch->pyvista) (4.2.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch->pyvista) (2.31.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.1->pyvista) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (2024.7.4)\n",
            "Downloading pyvista-0.44.1-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pyvista\n",
            "Successfully installed pyvista-0.44.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyvista as pv\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000000001.vtk\")  # Replace with your actual file path\n",
        "\n",
        "# Load the walls to overlay\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter\n",
        "plotter = pv.Plotter()\n",
        "\n",
        "# Add the first phase\n",
        "phase1 = phase_data.threshold(above_value=0.5)  # Assume phase1 is above 0.5 threshold\n",
        "plotter.add_mesh(phase1, color='orange', opacity=0.8)\n",
        "\n",
        "# Add the second phase\n",
        "phase2 = phase_data.threshold(below_value=0.5)  # Assume phase2 is below 0.5 threshold\n",
        "plotter.add_mesh(phase2, color='cyan', opacity=0.8)\n",
        "\n",
        "# Overlay the porous structure\n",
        "plotter.add_mesh(walls_data, color='tan', opacity=0.3)\n",
        "\n",
        "# Customize the plot\n",
        "plotter.show_grid()\n",
        "plotter.background_color = 'white'\n",
        "plotter.add_axes()\n",
        "\n",
        "# Show the plot\n",
        "plotter.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "OFIpBvHxP-fg",
        "outputId": "34eb635b-a4e3-4086-9914-e33b1111bc92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyvista/plotting/plotter.py:159: UserWarning: \n",
            "This system does not appear to be running an xserver.\n",
            "PyVista will likely segfault when rendering.\n",
            "\n",
            "Try starting a virtual frame buffer with xvfb, or using\n",
            "  ``pyvista.start_xvfb()``\n",
            "\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "DataSetFilters.threshold() got an unexpected keyword argument 'above_value'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-a8e6e9e6c2fa>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Add the first phase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mphase1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mphase_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabove_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Assume phase1 is above 0.5 threshold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_mesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphase1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'orange'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: DataSetFilters.threshold() got an unexpected keyword argument 'above_value'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyvista as pv\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000500001.vtk\")\n",
        "\n",
        "# Inspect the scalar data to determine the range\n",
        "print(\"Scalar names in the dataset:\", phase_data.array_names)\n",
        "phi_array = phase_data.get_array(\"phi\")\n",
        "phi_min, phi_max = phi_array.min(), phi_array.max()\n",
        "print(\"Scalar range for 'phi':\", phi_min, phi_max)\n",
        "\n",
        "# Determine a threshold based on the scalar range or known values\n",
        "# Since phi represents phase distribution, determine threshold around the midpoint or specific saturation value\n",
        "threshold_value = (phi_max - phi_min) * 0.5 + phi_min  # This will place the threshold in the middle of the range\n",
        "\n",
        "# Load the porous structure\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter\n",
        "plotter = pv.Plotter()\n",
        "\n",
        "# Extract and visualize the first fluid phase\n",
        "phase1 = phase_data.threshold(value=threshold_value, scalars='phi', invert=False)\n",
        "plotter.add_mesh(phase1, color='orange', opacity=0.7, show_scalar_bar=False)\n",
        "\n",
        "# Extract and visualize the second fluid phase\n",
        "phase2 = phase_data.threshold(value=threshold_value, scalars='phi', invert=True)\n",
        "plotter.add_mesh(phase2, color='cyan', opacity=0.7, show_scalar_bar=False)\n",
        "\n",
        "# Overlay the porous structure\n",
        "plotter.add_mesh(walls_data, color='tan', opacity=0.3)\n",
        "\n",
        "# Customize plot settings\n",
        "plotter.show_grid()\n",
        "plotter.background_color = 'white'\n",
        "plotter.add_axes()\n",
        "\n",
        "# Display the plot\n",
        "plotter.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Z2ygBQPQ1Ky",
        "outputId": "0470fe7b-7a78-4556-e7cb-c25a513a0560"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scalar names in the dataset: ['phi']\n",
            "Scalar range for 'phi': -1.0034077 1.0009024\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyvista/plotting/plotter.py:159: UserWarning: \n",
            "This system does not appear to be running an xserver.\n",
            "PyVista will likely segfault when rendering.\n",
            "\n",
            "Try starting a virtual frame buffer with xvfb, or using\n",
            "  ``pyvista.start_xvfb()``\n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install necessary packages\n",
        "!pip install pyvista vtk pyvistaqt\n",
        "\n",
        "# Start a virtual framebuffer\n",
        "import pyvista as pv\n",
        "pv.start_xvfb()\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000500001.vtk\")\n",
        "\n",
        "# Inspect the scalar data to determine the range\n",
        "print(\"Scalar names in the dataset:\", phase_data.array_names)\n",
        "phi_array = phase_data.get_array(\"phi\")\n",
        "phi_min, phi_max = phi_array.min(), phi_array.max()\n",
        "print(\"Scalar range for 'phi':\", phi_min, phi_max)\n",
        "\n",
        "# Determine a threshold based on the scalar range\n",
        "threshold_value = (phi_max - phi_min) * 0.5 + phi_min  # Midpoint of the scalar range\n",
        "\n",
        "# Load the porous structure\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter\n",
        "plotter = pv.Plotter(off_screen=True)  # Use off-screen rendering to prevent crashes\n",
        "\n",
        "# Extract and visualize the first fluid phase\n",
        "phase1 = phase_data.threshold(value=threshold_value, scalars='phi', invert=False)\n",
        "plotter.add_mesh(phase1, color='orange', opacity=0.7, show_scalar_bar=True)\n",
        "\n",
        "# Extract and visualize the second fluid phase\n",
        "phase2 = phase_data.threshold(value=threshold_value, scalars='phi', invert=True)\n",
        "plotter.add_mesh(phase2, color='cyan', opacity=0.7, show_scalar_bar=True)\n",
        "\n",
        "# Overlay the porous structure\n",
        "plotter.add_mesh(walls_data, color='tan', opacity=0.3)\n",
        "\n",
        "# Customize plot settings\n",
        "plotter.show_grid()\n",
        "plotter.background_color = 'white'\n",
        "plotter.add_axes()\n",
        "\n",
        "# Save the plot as a screenshot\n",
        "plotter.show(screenshot='output_image.png')  # This will save the plot as an image\n",
        "\n",
        "# Display the saved image using matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "img = mpimg.imread('output_image.png')\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-CV8qV80Rssi",
        "outputId": "3ca5b95f-052f-4fd9-c272-246d216efb7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyvista in /usr/local/lib/python3.10/dist-packages (0.44.1)\n",
            "Requirement already satisfied: vtk in /usr/local/lib/python3.10/dist-packages (9.3.1)\n",
            "Collecting pyvistaqt\n",
            "  Downloading pyvistaqt-0.11.1-py3-none-any.whl.metadata (4.5 kB)\n",
            "Requirement already satisfied: matplotlib>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from pyvista) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pyvista) (1.26.4)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from pyvista) (9.4.0)\n",
            "Requirement already satisfied: pooch in /usr/local/lib/python3.10/dist-packages (from pyvista) (1.8.2)\n",
            "Requirement already satisfied: scooby>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from pyvista) (0.10.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from pyvista) (4.12.2)\n",
            "Collecting QtPy>=1.9.0 (from pyvistaqt)\n",
            "  Downloading QtPy-2.4.1-py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.1->pyvista) (2.8.2)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch->pyvista) (4.2.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch->pyvista) (2.31.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.1->pyvista) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch->pyvista) (2024.7.4)\n",
            "Downloading pyvistaqt-0.11.1-py3-none-any.whl (131 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.2/131.2 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading QtPy-2.4.1-py3-none-any.whl (93 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.5/93.5 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: QtPy, pyvistaqt\n",
            "Successfully installed QtPy-2.4.1 pyvistaqt-0.11.1\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "Please install Xvfb with:\n\nDebian\n$ sudo apt install libgl1-mesa-glx xvfb\n\nCentOS / RHL\n$ sudo yum install libgl1-mesa-glx xvfb\n\n",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-b18c31b30268>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Start a virtual framebuffer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyvista\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_xvfb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Load the phase distribution data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyvista/plotting/utilities/xvfb.py\u001b[0m in \u001b[0;36mstart_xvfb\u001b[0;34m(wait, window_size)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'which Xvfb > /dev/null'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXVFB_INSTALL_NOTES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;31m# use current default window size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Please install Xvfb with:\n\nDebian\n$ sudo apt install libgl1-mesa-glx xvfb\n\nCentOS / RHL\n$ sudo yum install libgl1-mesa-glx xvfb\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get update\n",
        "!apt-get install -y xvfb\n",
        "!pip install pyvista vtk pyvistaqt\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohyeOBiKR3Ki",
        "outputId": "a531845b-7255-40cc-d35b-02bcc793928a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r0% [Working]\r            \rGet:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n",
            "\r0% [Connecting to archive.ubuntu.com (185.125.190.81)] [1 InRelease 5,481 B/129 kB 4%] [Waiting for \r                                                                                                    \rHit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease\n",
            "Hit:3 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\n",
            "Ign:4 https://r2u.stat.illinois.edu/ubuntu jammy InRelease\n",
            "Hit:5 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Hit:6 https://r2u.stat.illinois.edu/ubuntu jammy Release\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n",
            "Hit:9 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Hit:10 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:11 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Hit:12 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
            "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2,396 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,422 kB]\n",
            "Fetched 4,074 kB in 2s (2,221 kB/s)\n",
            "Reading package lists... Done\n",
            "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libxfont2 x11-xkb-utils xfonts-base xfonts-encodings xfonts-utils xserver-common\n",
            "The following NEW packages will be installed:\n",
            "  libxfont2 x11-xkb-utils xfonts-base xfonts-encodings xfonts-utils xserver-common xvfb\n",
            "0 upgraded, 7 newly installed, 0 to remove and 49 not upgraded.\n",
            "Need to get 7,727 kB of archives.\n",
            "After this operation, 11.7 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxfont2 amd64 1:2.0.5-1build1 [94.5 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 x11-xkb-utils amd64 7.7+5build4 [172 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 xfonts-encodings all 1:1.0.5-0ubuntu2 [578 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 xfonts-utils amd64 1:7.7+6build2 [94.6 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 xfonts-base all 1:1.0.5 [5,896 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 xserver-common all 2:21.1.4-2ubuntu1.7~22.04.11 [28.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 xvfb amd64 2:21.1.4-2ubuntu1.7~22.04.11 [863 kB]\n",
            "Fetched 7,727 kB in 1s (11.7 MB/s)\n",
            "Selecting previously unselected package libxfont2:amd64.\n",
            "(Reading database ... 128575 files and directories currently installed.)\n",
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            "Setting up x11-xkb-utils (7.7+5build4) ...\n",
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            "Setting up xfonts-utils (1:7.7+6build2) ...\n",
            "Setting up xfonts-base (1:1.0.5) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
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            "\n",
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            "\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.system('Xvfb :99 -screen 0 1024x768x24 &')  # Start Xvfb\n",
        "os.environ['DISPLAY'] = ':99.0'  # Set DISPLAY environment variable\n"
      ],
      "metadata": {
        "id": "ds8kn-S9R5CW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pyvista as pv\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Start PyVista's virtual framebuffer for off-screen rendering\n",
        "pv.start_xvfb()\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000500001.vtk\")\n",
        "\n",
        "# Inspect the scalar data to determine the range\n",
        "print(\"Scalar names in the dataset:\", phase_data.array_names)\n",
        "phi_array = phase_data.get_array(\"phi\")\n",
        "phi_min, phi_max = phi_array.min(), phi_array.max()\n",
        "print(\"Scalar range for 'phi':\", phi_min, phi_max)\n",
        "\n",
        "# Determine a threshold based on the scalar range\n",
        "threshold_value = (phi_max - phi_min) * 0.5 + phi_min  # Midpoint of the scalar range\n",
        "\n",
        "# Load the porous structure\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter for off-screen rendering\n",
        "plotter = pv.Plotter(off_screen=True)\n",
        "\n",
        "# Extract and visualize the first fluid phase\n",
        "phase1 = phase_data.threshold(value=threshold_value, scalars='phi', invert=False)\n",
        "plotter.add_mesh(phase1, color='orange', opacity=0.7, show_scalar_bar=True)\n",
        "\n",
        "# Extract and visualize the second fluid phase\n",
        "phase2 = phase_data.threshold(value=threshold_value, scalars='phi', invert=True)\n",
        "plotter.add_mesh(phase2, color='cyan', opacity=0.7, show_scalar_bar=True)\n",
        "\n",
        "# Overlay the porous structure\n",
        "plotter.add_mesh(walls_data, color='tan', opacity=0.3)\n",
        "\n",
        "# Customize plot settings\n",
        "plotter.show_grid()\n",
        "plotter.background_color = 'white'\n",
        "plotter.add_axes()\n",
        "\n",
        "# Save the plot as a screenshot\n",
        "plotter.show(screenshot='output_image.png')  # Save plot as an image\n",
        "\n",
        "# Display the saved image using matplotlib\n",
        "img = mpimg.imread('output_image.png')\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "W6HCO6KQR7ac",
        "outputId": "8e6102a5-540b-4a1e-d922-fb8dcb442d20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scalar names in the dataset: ['phi']\n",
            "Scalar range for 'phi': -1.0034077 1.0009024\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pyvista as pv\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Ensure Xvfb is running\n",
        "os.system('Xvfb :99 -screen 0 1024x768x24 &')\n",
        "os.environ['DISPLAY'] = ':99.0'\n",
        "\n",
        "# Start PyVista's virtual framebuffer\n",
        "pv.start_xvfb()\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000500001.vtk\")\n",
        "\n",
        "# Inspect the scalar data to determine the range\n",
        "print(\"Scalar names in the dataset:\", phase_data.array_names)\n",
        "phi_array = phase_data.get_array(\"phi\")\n",
        "phi_min, phi_max = phi_array.min(), phi_array.max()\n",
        "print(\"Scalar range for 'phi':\", phi_min, phi_max)\n",
        "\n",
        "# Determine a threshold based on the scalar range\n",
        "threshold_value = 0.0  # Threshold for separating phases\n",
        "\n",
        "# Load the porous structure\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter for off-screen rendering\n",
        "plotter = pv.Plotter(off_screen=True)\n",
        "\n",
        "# Extract and visualize the first fluid phase\n",
        "phase1 = phase_data.threshold(value=threshold_value, scalars='phi', invert=False)\n",
        "phase1_poly = phase1.extract_geometry()  # Convert to PolyData for smoothing\n",
        "phase1_smoothed = phase1_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(phase1_smoothed, color='orange', opacity=0.8, show_scalar_bar=False, specular=0.3)\n",
        "\n",
        "# Extract and visualize the second fluid phase\n",
        "phase2 = phase_data.threshold(value=threshold_value, scalars='phi', invert=True)\n",
        "phase2_poly = phase2.extract_geometry()  # Convert to PolyData for smoothing\n",
        "phase2_smoothed = phase2_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(phase2_smoothed, color='cyan', opacity=0.8, show_scalar_bar=False, specular=0.3)\n",
        "\n",
        "# Overlay the porous structure\n",
        "walls_poly = walls_data.extract_geometry()  # Convert to PolyData for smoothing\n",
        "walls_smoothed = walls_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(walls_smoothed, color='gray', opacity=0.2)\n",
        "\n",
        "# Customize plot settings\n",
        "plotter.show_grid()  # Enable the grid with default settings\n",
        "plotter.background_color = 'white'\n",
        "plotter.add_axes(interactive=False)  # Disable interactive axes\n",
        "\n",
        "# Set a better camera angle and lighting\n",
        "plotter.view_isometric()\n",
        "plotter.enable_eye_dome_lighting()  # Enhance depth perception with lighting\n",
        "\n",
        "# Save the plot as a screenshot\n",
        "plotter.show(screenshot='output_image.png')  # Save plot as an image\n",
        "\n",
        "# Display the saved image using matplotlib\n",
        "img = mpimg.imread('output_image.png')\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "pk2GWzl5SfZH",
        "outputId": "dfd6f2ff-4fe5-4520-8ab8-00fba515cefc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scalar names in the dataset: ['phi']\n",
            "Scalar range for 'phi': -1.0034077 1.0009024\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pyvista as pv\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Ensure Xvfb is running\n",
        "os.system('Xvfb :99 -screen 0 1024x768x24 &')\n",
        "os.environ['DISPLAY'] = ':99.0'\n",
        "\n",
        "# Start PyVista's virtual framebuffer\n",
        "pv.start_xvfb()\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000500001.vtk\")\n",
        "\n",
        "# Inspect the scalar data to determine the range\n",
        "print(\"Scalar names in the dataset:\", phase_data.array_names)\n",
        "phi_array = phase_data.get_array(\"phi\")\n",
        "phi_min, phi_max = phi_array.min(), phi_array.max()\n",
        "print(\"Scalar range for 'phi':\", phi_min, phi_max)\n",
        "\n",
        "# Determine a threshold based on the scalar range\n",
        "threshold_value = 0.0  # Threshold for separating phases\n",
        "\n",
        "# Load the porous structure\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter for off-screen rendering\n",
        "plotter = pv.Plotter(off_screen=True, window_size=[1920, 1080])\n",
        "\n",
        "# Extract and visualize the first fluid phase\n",
        "phase1 = phase_data.threshold(value=threshold_value, scalars='phi', invert=False)\n",
        "phase1_poly = phase1.extract_geometry()  # Convert to PolyData for smoothing\n",
        "phase1_smoothed = phase1_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(phase1_smoothed, color='orange', opacity=0.8, show_scalar_bar=False, specular=0.3)\n",
        "\n",
        "# Extract and visualize the second fluid phase\n",
        "phase2 = phase_data.threshold(value=threshold_value, scalars='phi', invert=True)\n",
        "phase2_poly = phase2.extract_geometry()  # Convert to PolyData for smoothing\n",
        "phase2_smoothed = phase2_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(phase2_smoothed, color='cyan', opacity=0.8, show_scalar_bar=False, specular=0.3)\n",
        "\n",
        "# Overlay the porous structure\n",
        "walls_poly = walls_data.extract_geometry()  # Convert to PolyData for smoothing\n",
        "walls_smoothed = walls_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(walls_smoothed, color='gray', opacity=0.15)\n",
        "\n",
        "# Customize plot settings\n",
        "plotter.background_color = None  # Set transparent background\n",
        "plotter.show_grid()  # Disable the grid for a cleaner look\n",
        "plotter.add_axes(interactive=False)  # Disable interactive axes\n",
        "\n",
        "# Adjust the camera position for better angle\n",
        "plotter.camera_position = [(300, 300, 300), (120, 120, 130), (0, 0, 1)]\n",
        "plotter.view_vector((1, -1, 1))  # Adjust view vector for better perspective\n",
        "\n",
        "# Enable enhanced lighting\n",
        "plotter.enable_eye_dome_lighting()  # Enhance depth perception with lighting\n",
        "\n",
        "# Save the plot as a screenshot\n",
        "plotter.show(screenshot='output_image.png')  # Save plot as an image\n",
        "\n",
        "# Display the saved image using matplotlib\n",
        "img = mpimg.imread('output_image.png')\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "DR3G7kLoTgJE",
        "outputId": "36d75be7-042e-4887-c6c9-e66342cb3f18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scalar names in the dataset: ['phi']\n",
            "Scalar range for 'phi': -1.0034077 1.0009024\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pyvista as pv\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Ensure Xvfb is running\n",
        "os.system('Xvfb :99 -screen 0 1024x768x24 &')\n",
        "os.environ['DISPLAY'] = ':99.0'\n",
        "\n",
        "# Start PyVista's virtual framebuffer\n",
        "pv.start_xvfb()\n",
        "\n",
        "# Load the phase distribution data\n",
        "phase_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/phase_distribution/small_0000500001.vtk\")\n",
        "\n",
        "# Inspect the scalar data to determine the range\n",
        "print(\"Scalar names in the dataset:\", phase_data.array_names)\n",
        "phi_array = phase_data.get_array(\"phi\")\n",
        "phi_min, phi_max = phi_array.min(), phi_array.max()\n",
        "print(\"Scalar range for 'phi':\", phi_min, phi_max)\n",
        "\n",
        "# Determine a threshold based on the scalar range\n",
        "threshold_value = 0.0  # Threshold for separating phases\n",
        "\n",
        "# Load the porous structure\n",
        "walls_data = pv.read(\"/content/MF-LBM-CUDA/results/out3.field_data/walls_bin.vtk\")\n",
        "\n",
        "# Create a plotter for off-screen rendering\n",
        "plotter = pv.Plotter(off_screen=True, window_size=[1920, 1080])\n",
        "\n",
        "# Extract and visualize the second fluid phase (cyan)\n",
        "phase2 = phase_data.threshold(value=threshold_value, scalars='phi', invert=False)\n",
        "phase2_poly = phase2.extract_geometry()  # Convert to PolyData for smoothing\n",
        "phase2_smoothed = phase2_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(phase2_smoothed, color='cyan', opacity=0.8, show_scalar_bar=False, specular=0.3)\n",
        "\n",
        "# Overlay the porous structure\n",
        "walls_poly = walls_data.extract_geometry()  # Convert to PolyData for smoothing\n",
        "walls_smoothed = walls_poly.smooth(n_iter=50, relaxation_factor=0.1)\n",
        "plotter.add_mesh(walls_smoothed, color='gray', opacity=0.15)\n",
        "\n",
        "# Customize plot settings\n",
        "plotter.background_color = 'white'  # Set the background color to white\n",
        "plotter.show_grid()  # Disable the grid for a cleaner look\n",
        "plotter.add_axes(interactive=False)  # Disable interactive axes\n",
        "\n",
        "# Adjust the camera position for better angle\n",
        "plotter.camera_position = [(300, 300, 300), (120, 120, 130), (0, 0, 1)]\n",
        "plotter.view_vector((1, -1, 1))  # Adjust view vector for better perspective\n",
        "\n",
        "# Enable enhanced lighting\n",
        "plotter.enable_eye_dome_lighting()  # Enhance depth perception with lighting\n",
        "\n",
        "# Save the plot as a screenshot\n",
        "plotter.show(screenshot='output_image.png')  # Save plot as an image\n",
        "\n",
        "# Display the saved image using matplotlib\n",
        "img = mpimg.imread('output_image.png')\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "SL9hnDjtUJyK",
        "outputId": "0592985d-e6dd-45c5-b8a4-7b0df334cdee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scalar names in the dataset: ['phi']\n",
            "Scalar range for 'phi': -1.0034077 1.0009024\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}