%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                                                                              %
% SU2 configuration file                                                       %
% Case description: Tyrrell 026 Front Wing in Wind Tunnel - h17mm___________  %
% Author: Matheus Ribeiro Vidal______________________________________________  %
% Institution: Universidade de Brasilia______________________________________  %
% Date: 13/06/2023                                                             %
% File Version 7.3.1 "Blackbird"                                               %
%                                                                              %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% ------------- DIRECT, ADJOINT, AND LINEARIZED PROBLEM DEFINITION ------------%
%
% Solver type (EULER, NAVIER_STOKES, RANS,
%              INC_EULER, INC_NAVIER_STOKES, INC_RANS,
%              NEMO_EULER, NEMO_NAVIER_STOKES,
%              FEM_EULER, FEM_NAVIER_STOKES, FEM_RANS, FEM_LES,
%              HEAT_EQUATION_FVM, ELASTICITY)
SOLVER= INC_RANS
%
% Specify turbulence model (NONE, SA, SA_NEG, SST, SA_E, SA_COMP, SA_E_COMP, SST_SUST)
KIND_TURB_MODEL= SST
%
% Mathematical problem (DIRECT, CONTINUOUS_ADJOINT, DISCRETE_ADJOINT)
% Defaults to DISCRETE_ADJOINT for the SU2_*_AD codes, and to DIRECT otherwise.
MATH_PROBLEM= DIRECT
%
% System of measurements (SI, US)
% International system of units (SI): ( meters, kilograms, Kelvins,
%                                       Newtons = kg m/s^2, Pascals = N/m^2,
%                                       Density = kg/m^3, Speed = m/s,
%                                       Equiv. Area = m^2 )
SYSTEM_MEASUREMENTS= SI
%
%
% ------------------------------- SOLVER CONTROL ------------------------------%
%
% Number of iterations for single-zone problems
ITER= 1
%
% Maximum number of inner iterations
INNER_ITER= 15000
%
% Maximum number of outer iterations (only for multizone problems)
OUTER_ITER= 1
%
% Maximum number of time iterations
TIME_ITER= 1
%
% Convergence field
CONV_FIELD= PRESSURE
%
% Min value of the residual (log10 of the residual)
CONV_RESIDUAL_MINVAL= -8
%
% Start convergence criteria at iteration number
CONV_STARTITER= 10
%
% Number of elements to apply the criteria
CONV_CAUCHY_ELEMS= 100
%
% Epsilon to control the series convergence
CONV_CAUCHY_EPS= 1E-10
%
% Iteration number to begin unsteady restarts
RESTART_ITER= 0
%
%% Time convergence monitoring
WINDOW_CAUCHY_CRIT = YES
%
% List of time convergence fields
CONV_WINDOW_FIELD = (TAVG_DRAG, TAVG_LIFT)
%
% Time Convergence Monitoring starts at Iteration WINDOW_START_ITER + CONV_WINDOW_STARTITER
CONV_WINDOW_STARTITER = 0
%
% Epsilon to control the series convergence
CONV_WINDOW_CAUCHY_EPS = 1E-3
%
% Number of elements to apply the criteria
CONV_WINDOW_CAUCHY_ELEMS = 10
%
% ------------------------- TIME-DEPENDENT SIMULATION -------------------------------%
%
% Time domain simulation
TIME_DOMAIN= NO
%
% Unsteady simulation (NO, TIME_STEPPING, DUAL_TIME_STEPPING-1ST_ORDER,
%                      DUAL_TIME_STEPPING-2ND_ORDER, HARMONIC_BALANCE)
TIME_MARCHING= NO
%
% Time Step for dual time stepping simulations (s) -- Only used when UNST_CFL_NUMBER = 0.0
% For the DG-FEM solver it is used as a synchronization time when UNST_CFL_NUMBER != 0.0
TIME_STEP= 0.00001
%
% Total Physical Time for dual time stepping simulations (s)
MAX_TIME= 2.0
%
% Unsteady Courant-Friedrichs-Lewy number of the finest grid
UNST_CFL_NUMBER= 1.0
%
%%  Windowed output time averaging
% Time iteration to start the windowed time average in a direct run
WINDOW_START_ITER = 500
%
% Window used for reverse sweep and direct run. Options (SQUARE, HANN, HANN_SQUARE, BUMP) Square is default.
WINDOW_FUNCTION = SQUARE
%
% ------------------------------- DES Parameters ------------------------------%
%
% Specify Hybrid RANS/LES model (SA_DES, SA_DDES, SA_ZDES, SA_EDDES)
HYBRID_RANSLES= SA_DDES
%
% DES Constant (0.65)
DES_CONST= 0.65
%
% ---------------- INCOMPRESSIBLE FLOW CONDITION DEFINITION -------------------%
%
% Density model within the incompressible flow solver.
% Options are CONSTANT (default), BOUSSINESQ, or VARIABLE. If VARIABLE,
% an appropriate fluid model must be selected.
INC_DENSITY_MODEL= CONSTANT
%
% Solve the energy equation in the incompressible flow solver
INC_ENERGY_EQUATION = NO
%
% Initial density for incompressible flows
% (1.2886 kg/m^3 by default (air), 998.2 Kg/m^3 (water))
INC_DENSITY_INIT= 1.2886
%
% Initial velocity for incompressible flows (1.0,0,0 m/s by default)
INC_VELOCITY_INIT= ( 30.0, 0.0, 0.0 )
%
% Initial temperature for incompressible flows that include the
% energy equation (288.15 K by default). Value is ignored if
% INC_ENERGY_EQUATION is false.
INC_TEMPERATURE_INIT= 288.15
%
% Non-dimensionalization scheme for incompressible flows. Options are
% INITIAL_VALUES (default), REFERENCE_VALUES, or DIMENSIONAL.
% INC_*_REF values are ignored unless REFERENCE_VALUES is chosen.
INC_NONDIM= INITIAL_VALUES
%
% Reference density for incompressible flows (1.0 kg/m^3 by default)
INC_DENSITY_REF= 1.0
%
% Reference velocity for incompressible flows (1.0 m/s by default)
INC_VELOCITY_REF= 1.0
%
% Reference temperature for incompressible flows that include the
% energy equation (1.0 K by default)
INC_TEMPERATURE_REF = 1.0
%
% List of inlet types for incompressible flows. List length must
% match number of inlet markers. Options: VELOCITY_INLET, PRESSURE_INLET.
INC_INLET_TYPE= VELOCITY_INLET
%
% Damping coefficient for iterative updates at pressure inlets. (0.1 by default)
INC_INLET_DAMPING= 0.1
%
% List of outlet types for incompressible flows. List length must
% match number of outlet markers. Options: PRESSURE_OUTLET, MASS_FLOW_OUTLET
INC_OUTLET_TYPE= PRESSURE_OUTLET
%
% Damping coefficient for iterative updates at mass flow outlets. (0.1 by default)
INC_OUTLET_DAMPING= 0.1
%
% Epsilon^2 multipier in Beta calculation for incompressible preconditioner.
BETA_FACTOR= 4.1
%
% ---------------------- REFERENCE VALUE DEFINITION ---------------------------%
%
% Reference origin for moment computation (m or in) % Atualmente em um quarto de Asa
REF_ORIGIN_MOMENT_X = 0.00
REF_ORIGIN_MOMENT_Y = 0.00
REF_ORIGIN_MOMENT_Z = 0.00
%
% Reference length for moment non-dimensional coefficients (m or in)
REF_LENGTH= 0.380
%
% Reference area for non-dimensional force coefficients (0 implies automatic
% calculation) (m^2 or in^2)
REF_AREA= 0.405
%
% Aircraft semi-span (0 implies automatic calculation) (m or in)
SEMI_SPAN= 0.0
%
% --------------------------- VISCOSITY MODEL ---------------------------------%
%
% Viscosity model (SUTHERLAND, CONSTANT_VISCOSITY, POLYNOMIAL_VISCOSITY).
VISCOSITY_MODEL= CONSTANT_VISCOSITY

% Molecular Viscosity that would be constant (1.716E-5 by default)
MU_CONSTANT= 1.716E-5
%
% Sutherland Viscosity Ref (1.716E-5 default value for AIR SI)
MU_REF= 1.716E-5
%
% Sutherland Temperature Ref (273.15 K default value for AIR SI)
MU_T_REF= 273.15
%
% Sutherland constant (110.4 default value for AIR SI)
SUTHERLAND_CONSTANT= 110.4
%
% Temperature polynomial coefficients (up to quartic) for viscosity.
% Format -> Mu(T) : b0 + b1*T + b2*T^2 + b3*T^3 + b4*T^4
MU_POLYCOEFFS= (0.0, 0.0, 0.0, 0.0, 0.0)
%
% ----------------------- DYNAMIC MESH DEFINITION -----------------------------%
%
% Type of dynamic surface movement (NONE, DEFORMING, MOVING_WALL,
% AEROELASTIC, AEROELASTIC_RIGID_MOTION EXTERNAL, EXTERNAL_ROTATION)
SURFACE_MOVEMENT= MOVING_WALL
%
% Moving wall boundary marker(s) (NONE = no marker, ignored for RIGID_MOTION)
MARKER_MOVING= ( ground )
%
% Coordinates of the motion origin
SURFACE_MOTION_ORIGIN= -4 0.0 0.0
%
% Translational velocity (m/s or ft/s) in the x, y, & z directions
SURFACE_TRANSLATION_RATE = 30.0 0.0 0.0
%
%
% Move Motion Origin for marker moving (1 or 0)
MOVE_MOTION_ORIGIN = 0
%
% -------------------- BOUNDARY CONDITION DEFINITION --------------------------%
%
% Euler wall boundary marker(s) (NONE = no marker)
% Implementation identical to MARKER_SYM.
MARKER_EULER= ( wall )
%
% Navier-Stokes (no-slip), constant heat flux wall  marker(s) (NONE = no marker)
% Format: ( marker name, constant heat flux (J/m^2), ... )
MARKER_HEATFLUX= ( wing, 0, ground, 0)
%
% Far-field boundary marker(s) (NONE = no marker)
MARKER_FAR= ( inlet, outlet )
%
% Inlet boundary type (TOTAL_CONDITIONS, MASS_FLOW)
%
%INLET_TYPE= TOTAL_CONDITIONS
%
% Inlet boundary marker(s) with the following formats (NONE = no marker)
% Total Conditions: (inlet marker, total temp, total pressure, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
% Mass Flow: (inlet marker, density, velocity magnitude, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
% Inc. Velocity: (inlet marker, temperature, velocity magnitude, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
% Inc. Pressure: (inlet marker, temperature, total pressure, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
%MARKER_INLET= ( INLET, 288.15, 101325.0, 1.0, 0.0, 0.0)
%
% Outlet boundary marker(s) (NONE = no marker)
% Compressible: ( outlet marker, back pressure (static thermodynamic), ... )
% Inc. Pressure: ( outlet marker, back pressure (static gauge in Pa), ... )
% Inc. Mass Flow: ( outlet marker, mass flow target (kg/s), ... )
%MARKER_OUTLET= ( OUTLET )
%
% ------------------------ WALL FUNCTION DEFINITION --------------------------%
%
% The von Karman constant, the constant below only affects the standard wall function model 
WALLMODEL_KAPPA= 0.41
%
% The wall function model constant B 
WALLMODEL_B= 5.5
%
% The y+ value below which the wall function is switched off and we resolve the wall 
WALLMODEL_MINYPLUS= 5.0
%
% [Expert] Max Newton iterations used for the standard wall function
WALLMODEL_MAXITER= 200
%
% [Expert] relaxation factor for the Newton iterations of the standard wall function 
WALLMODEL_RELFAC= 0.5

% ------------------------ SURFACES IDENTIFICATION ----------------------------%
%
% Marker(s) of the surface in the surface flow solution file
MARKER_PLOTTING = ( wing, wall, ground )
%
% Marker(s) of the surface where the non-dimensional coefficients are evaluated.
MARKER_MONITORING = ( wing )
%
% Viscous wall markers for which wall functions must be applied. (NONE = no marker)
% Format: ( marker name, wall function type -NO_WALL_FUNCTION, STANDARD_WALL_FUNCTION,
%           ADAPTIVE_WALL_FUNCTION, SCALABLE_WALL_FUNCTION, EQUILIBRIUM_WALL_MODEL,
%           NONEQUILIBRIUM_WALL_MODEL-, ... )
MARKER_WALL_FUNCTIONS= ( wing, NO_WALL_FUNCTION )
%
% Marker(s) of the surface where custom thermal BCs are defined.
MARKER_PYTHON_CUSTOM = ( NONE )
%
% Marker(s) of the surface that is going to be analyzed in detail (massflow, average pressure, distortion, etc)
MARKER_ANALYZE = ( wing )
%
% Method to compute the average value in MARKER_ANALYZE (AREA, MASSFLUX).
MARKER_ANALYZE_AVERAGE = AREA

% ------------- COMMON PARAMETERS DEFINING THE NUMERICAL METHOD ---------------%
%
% Numerical method for spatial gradients (GREEN_GAUSS, WEIGHTED_LEAST_SQUARES)
NUM_METHOD_GRAD= GREEN_GAUSS

% Numerical method for spatial gradients to be used for MUSCL reconstruction
% Options are (GREEN_GAUSS, WEIGHTED_LEAST_SQUARES, LEAST_SQUARES). Default value is
% NONE and the method specified in NUM_METHOD_GRAD is used.
NUM_METHOD_GRAD_RECON = NONE
%
% CFL number (initial value for the adaptive CFL number)
CFL_NUMBER= 1.0
%
% Adaptive CFL number (NO, YES)
CFL_ADAPT= NO
%
% Parameters of the adaptive CFL number (factor-down, factor-up, CFL min value,
%                                        CFL max value, acceptable linear solver convergence)
% Local CFL increases by factor-up until max if the solution rate of change is not limited,
% and acceptable linear convergence is achieved. It is reduced if rate is limited, or if there
% is not enough linear convergence, or if the nonlinear residuals are stagnant and oscillatory.
% It is reset back to min when linear solvers diverge, or if nonlinear residuals increase too much.
CFL_ADAPT_PARAM= ( 0.1, 2.0, 10.0, 1e10, 0.001 )
%
% Maximum Delta Time in local time stepping simulations
MAX_DELTA_TIME= 1E6
%
% Runge-Kutta alpha coefficients
RK_ALPHA_COEFF= ( 0.66667, 0.66667, 1.000000 )
%
% Objective function in gradient evaluation  (DRAG, LIFT, SIDEFORCE, MOMENT_X,
%                                             MOMENT_Y, MOMENT_Z, EFFICIENCY, BUFFET,
%                                             EQUIVALENT_AREA, NEARFIELD_PRESSURE,
%                                             FORCE_X, FORCE_Y, FORCE_Z, THRUST,
%                                             TORQUE, TOTAL_HEATFLUX, CUSTOM_OBJFUNC
%                                             MAXIMUM_HEATFLUX, INVERSE_DESIGN_PRESSURE,
%                                             INVERSE_DESIGN_HEATFLUX, SURFACE_TOTAL_PRESSURE,
%                                             SURFACE_MASSFLOW, SURFACE_STATIC_PRESSURE, SURFACE_MACH)
% For a weighted sum of objectives: separate by commas, add OBJECTIVE_WEIGHT and MARKER_MONITORING in matching order.
OBJECTIVE_FUNCTION= DRAG
%
% List of weighting values when using more than one OBJECTIVE_FUNCTION. Separate by commas and match with MARKER_MONITORING.
OBJECTIVE_WEIGHT = 1.0
%
% Expression used when "OBJECTIVE_FUNCTION= CUSTOM_OBJFUNC", any history/screen output can be used together with common
% math functions (sqrt, cos, exp, etc.). This can be used for constraint aggregation (as below) or to compute something
% SU2 does not, see TestCases/user_defined_functions/.
CUSTOM_OBJFUNC= 'DRAG + 10 * pow(fmax(0.4-LIFT, 0), 2)'

% ----------- SLOPE LIMITER AND DISSIPATION SENSOR DEFINITION -----------------%
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the flow equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_FLOW= YES
%
% Slope limiter (NONE, VENKATAKRISHNAN, VENKATAKRISHNAN_WANG,
%                BARTH_JESPERSEN, VAN_ALBADA_EDGE)
SLOPE_LIMITER_FLOW= VENKATAKRISHNAN
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the turbulence equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_TURB= NO
%
% Slope limiter (NONE, VENKATAKRISHNAN, VENKATAKRISHNAN_WANG,
%                BARTH_JESPERSEN, VAN_ALBADA_EDGE)
SLOPE_LIMITER_TURB= VENKATAKRISHNAN
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the adjoint flow equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_ADJFLOW= YES
%
% Slope limiter (NONE, VENKATAKRISHNAN, BARTH_JESPERSEN, VAN_ALBADA_EDGE,
%                SHARP_EDGES, WALL_DISTANCE)
SLOPE_LIMITER_ADJFLOW= VENKATAKRISHNAN
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the turbulence adjoint equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_ADJTURB= NO
%
% Slope limiter (NONE, VENKATAKRISHNAN, BARTH_JESPERSEN, VAN_ALBADA_EDGE)
SLOPE_LIMITER_ADJTURB= VENKATAKRISHNAN
%
% Coefficient for the Venkats limiter (upwind scheme). A larger values decrease
%             the extent of limiting, values approaching zero cause
%             lower-order approximation to the solution (0.05 by default)
VENKAT_LIMITER_COEFF= 0.05
%
% Reference coefficient for detecting sharp edges (3.0 by default).
REF_SHARP_EDGES = 3.0
%
% Coefficient for the adjoint sharp edges limiter (3.0 by default).
ADJ_SHARP_LIMITER_COEFF= 3.0
%
% Remove sharp edges from the sensitivity evaluation (NO, YES)
SENS_REMOVE_SHARP = NO
%
% Freeze the value of the limiter after a number of iterations
LIMITER_ITER= 999999
%
% 1st order artificial dissipation coefficients for
%     the Lax–Friedrichs method ( 0.15 by default )
LAX_SENSOR_COEFF= 0.15
%
% 2nd and 4th order artificial dissipation coefficients for
%     the JST method ( 0.5, 0.02 by default )
JST_SENSOR_COEFF= ( 0.5, 0.02 )
%
% 1st order artificial dissipation coefficients for
%     the adjoint Lax–Friedrichs method ( 0.15 by default )
ADJ_LAX_SENSOR_COEFF= 0.15
%
% 2nd, and 4th order artificial dissipation coefficients for
%     the adjoint JST method ( 0.5, 0.02 by default )
ADJ_JST_SENSOR_COEFF= ( 0.5, 0.02 )

% ------------------------ LINEAR SOLVER DEFINITION ---------------------------%
%
% Linear solver or smoother for implicit formulations:
% BCGSTAB, FGMRES, RESTARTED_FGMRES, CONJUGATE_GRADIENT (self-adjoint problems only), SMOOTHER.
LINEAR_SOLVER= FGMRES
%
% Same for discrete adjoint (smoothers not supported), replaces LINEAR_SOLVER in SU2_*_AD codes.
DISCADJ_LIN_SOLVER= FGMRES
%
% Preconditioner of the Krylov linear solver or type of smoother (ILU, LU_SGS, LINELET, JACOBI)
LINEAR_SOLVER_PREC= ILU
%
% Same for discrete adjoint (JACOBI or ILU), replaces LINEAR_SOLVER_PREC in SU2_*_AD codes.
DISCADJ_LIN_PREC= ILU
%
% Linear solver ILU preconditioner fill-in level (0 by default)
LINEAR_SOLVER_ILU_FILL_IN= 0
%
% Minimum error of the linear solver for implicit formulations
LINEAR_SOLVER_ERROR= 1E-6
%
% Max number of iterations of the linear solver for the implicit formulation
LINEAR_SOLVER_ITER= 5
%
% Restart frequency for RESTARTED_FGMRES
LINEAR_SOLVER_RESTART_FREQUENCY= 10
%
% Relaxation factor for smoother-type solvers (LINEAR_SOLVER= SMOOTHER)
LINEAR_SOLVER_SMOOTHER_RELAXATION= 1.0
%
% -------------------- FLOW NUMERICAL METHOD DEFINITION -----------------------%
%
% Convective numerical method (JST, JST_KE, JST_MAT, LAX-FRIEDRICH, CUSP, ROE, AUSM,
%                              AUSMPLUSUP, AUSMPLUSUP2, AUSMPWPLUS, HLLC, TURKEL_PREC,
%                              SW, MSW, FDS, SLAU, SLAU2, L2ROE, LMROE)
CONV_NUM_METHOD_FLOW= JST
%
% Roe Low Dissipation function for Hybrid RANS/LES simulations (FD, NTS, NTS_DUCROS)
ROE_LOW_DISSIPATION= FD
%
% Post-reconstruction correction for low Mach number flows (NO, YES)
LOW_MACH_CORR= NO
%
% Roe-Turkel preconditioning for low Mach number flows (NO, YES)
LOW_MACH_PREC= NO
%
% Use numerically computed Jacobians for AUSM+up(2) and SLAU(2)
% Slower per iteration but potentialy more stable and capable of higher CFL
USE_ACCURATE_FLUX_JACOBIANS= NO
%
% Use the vectorized version of the selected numerical method (available for JST family and Roe).
% SU2 should be compiled for an AVX or AVX512 architecture for best performance.
USE_VECTORIZATION= NO
%
% Entropy fix coefficient (0.0 implies no entropy fixing, 1.0 implies scalar
%                          artificial dissipation)
ENTROPY_FIX_COEFF= 0.0
%
% Higher values than 1 (3 to 4) make the global Jacobian of central schemes (compressible flow
% only) more diagonal dominant (but mathematically incorrect) so that higher CFL can be used.
CENTRAL_JACOBIAN_FIX_FACTOR= 4.0
%
% Time discretization (RUNGE-KUTTA_EXPLICIT, EULER_IMPLICIT, EULER_EXPLICIT)
TIME_DISCRE_FLOW= EULER_IMPLICIT
%
% Use a Newton-Krylov method on the flow equations, see TestCases/rans/oneram6/turb_ONERAM6_nk.cfg
% For multizone discrete adjoint it will use FGMRES on inner iterations with restart frequency
% equal to "QUASI_NEWTON_NUM_SAMPLES".
NEWTON_KRYLOV= NO
%
%
% -------------------- TURBULENT NUMERICAL METHOD DEFINITION ------------------%
%
% Convective numerical method (SCALAR_UPWIND)
CONV_NUM_METHOD_TURB= SCALAR_UPWIND
%
% Time discretization (EULER_IMPLICIT, EULER_EXPLICIT)
TIME_DISCRE_TURB= EULER_IMPLICIT
%
% Reduction factor of the CFL coefficient in the turbulence problem
CFL_REDUCTION_TURB= 1.0
%
% --------------------- HYBRID PARALLEL (MPI+OpenMP) OPTIONS ---------------------%
%
% An advanced performance parameter for FVM solvers, a large-ish value should be best
% when relatively few threads per MPI rank are in use (~4). However, maximum parallelism
% is obtained with EDGE_COLORING_GROUP_SIZE=1, consider using this value only if SU2
% warns about low coloring efficiency during preprocessing (performance is usually worse).
% Setting the option to 0 disables coloring and a different strategy is used instead,
% that strategy is automatically used when the coloring efficiency is less than 0.875.
% The optimum value/strategy is case-dependent.
EDGE_COLORING_GROUP_SIZE= 512
%
% Independent "threads per MPI rank" setting for LU-SGS and ILU preconditioners.
% For problems where time is spend mostly in the solution of linear systems (e.g. elasticity,
% very high CFL central schemes), AND, if the memory bandwidth of the machine is saturated
% (4 or more cores per memory channel) better performance (via a reduction in linear iterations)
% may be possible by using a smaller value than that defined by the system or in the call to
% SU2_CFD (via the -t/--threads option).
% The default (0) means "same number of threads as for all else".
LINEAR_SOLVER_PREC_THREADS= 0
%
% ----------------------- PARTITIONING OPTIONS (ParMETIS) ------------------------ %
%
% Load balancing tolerance, lower values will make ParMETIS work harder to evenly
% distribute the work-estimate metric across all MPI ranks, at the expense of more
% edge cuts (i.e. increased communication cost).
PARMETIS_TOLERANCE= 0.02
%
% The work-estimate metric is a weighted function of the work-per-edge (e.g. spatial
% discretization, linear system solution) and of the work-per-point (e.g. source terms,
% temporal discretization) the former usually accounts for >90% of the total.
% These weights are INTEGERS (for compatibility with ParMETIS) thus not [0, 1].
% To balance memory usage (instead of computation) the point weight needs to be
% increased (especially for explicit time integration methods).
PARMETIS_EDGE_WEIGHT= 1
PARMETIS_POINT_WEIGHT= 0
%
%
% ------------------------- SCREEN/HISTORY VOLUME OUTPUT --------------------------%
%
% Screen output fields (use 'SU2_CFD -d <config_file>' to view list of available fields)
SCREEN_OUTPUT= (INNER_ITER, PRESSURE, RMS_VELOCITY, SIDEFORCE, DRAG, LIFT)
%
% History output groups (use 'SU2_CFD -d <config_file>' to view list of available fields)
HISTORY_OUTPUT= (ITER, RMS_PRESSURE, RMS_VELOCITY, SIDEFORCE, DRAG, LIFT)
%
% Volume output fields/groups (use 'SU2_CFD -d <config_file>' to view list of available fields)
VOLUME_OUTPUT= (COORDINATES, SOLUTION, PRIMITIVE)
%
% Writing frequency for screen output
SCREEN_WRT_FREQ_INNER= 1
%
SCREEN_WRT_FREQ_OUTER= 1
%
SCREEN_WRT_FREQ_TIME= 1
%
% Writing frequency for history output
HISTORY_WRT_FREQ_INNER= 1
%
HISTORY_WRT_FREQ_OUTER= 1
%
HISTORY_WRT_FREQ_TIME= 1
%
% list of writing frequencies corresponding to the list in OUTPUT_FILES 
OUTPUT_WRT_FREQ= 250, 250, 300
%
% Output the performance summary to the console at the end of SU2_CFD
WRT_PERFORMANCE= NO
%
% Overwrite or append iteration number to the restart files when saving 
WRT_RESTART_OVERWRITE= YES
%
% Overwrite or append iteration number to the surface files when saving 
WRT_SURFACE_OVERWRITE= YES
%
% Overwrite or append iteration number to the volume files when saving 
WRT_VOLUME_OVERWRITE= YES
%
% ------------------------- INPUT/OUTPUT FILE INFORMATION --------------------------%
%
% Mesh input file
MESH_FILENAME= mesh_h17.su2
%
% Mesh input file format (SU2, CGNS)
MESH_FORMAT= SU2
%
% Mesh output file
MESH_OUT_FILENAME= mesh_h17.su2
%
% Restart flow input file
SOLUTION_FILENAME= solution_flow.dat
%
% Restart adjoint input file
SOLUTION_ADJ_FILENAME= solution_adj.dat
%
% Output tabular file format (TECPLOT, CSV)
TABULAR_FORMAT= CSV
%
% Files to output
% Possible formats : (TECPLOT_ASCII, TECPLOT, SURFACE_TECPLOT_ASCII,
%  SURFACE_TECPLOT, CSV, SURFACE_CSV, PARAVIEW_ASCII, PARAVIEW_LEGACY, SURFACE_PARAVIEW_ASCII,
%  SURFACE_PARAVIEW_LEGACY, PARAVIEW, SURFACE_PARAVIEW, RESTART_ASCII, RESTART, CGNS, SURFACE_CGNS, STL_ASCII, STL_BINARY)
% default : (RESTART, PARAVIEW, SURFACE_PARAVIEW)
OUTPUT_FILES= (RESTART, PARAVIEW, SURFACE_PARAVIEW)
%
% Output file convergence history (w/o extension)
CONV_FILENAME= history
%
% Output file with the forces breakdown
BREAKDOWN_FILENAME= forces_breakdown.dat
%
% Output file restart flow
RESTART_FILENAME= restart_flow.dat
%
% Output file flow (w/o extension) variables
VOLUME_FILENAME= flow
%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                                                                              %
% SU2 configuration file                                                       %
% Case description: Tyrrell 026 Front Wing in Wind Tunnel____________________  %
% Author: Matheus Ribeiro Vidal______________________________________________  %
% Institution: Universidade de Brasilia______________________________________  %
% Date: 28/01/2023                                                             %
% File Version 7.3.1 "Blackbird"                                               %
%                                                                              %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% ------------- DIRECT, ADJOINT, AND LINEARIZED PROBLEM DEFINITION ------------%
%
% Solver type (EULER, NAVIER_STOKES, RANS,
%              INC_EULER, INC_NAVIER_STOKES, INC_RANS,
%              NEMO_EULER, NEMO_NAVIER_STOKES,
%              FEM_EULER, FEM_NAVIER_STOKES, FEM_RANS, FEM_LES,
%              HEAT_EQUATION_FVM, ELASTICITY)
SOLVER= INC_RANS
%
% Specify turbulence model (NONE, SA, SA_NEG, SST, SA_E, SA_COMP, SA_E_COMP, SST_SUST)
KIND_TURB_MODEL= SST
%
% Mathematical problem (DIRECT, CONTINUOUS_ADJOINT, DISCRETE_ADJOINT)
% Defaults to DISCRETE_ADJOINT for the SU2_*_AD codes, and to DIRECT otherwise.
MATH_PROBLEM= DIRECT
%
% System of measurements (SI, US)
% International system of units (SI): ( meters, kilograms, Kelvins,
%                                       Newtons = kg m/s^2, Pascals = N/m^2,
%                                       Density = kg/m^3, Speed = m/s,
%                                       Equiv. Area = m^2 )
SYSTEM_MEASUREMENTS= SI
%
%
% ------------------------------- SOLVER CONTROL ------------------------------%
%
% Number of iterations for single-zone problems
ITER= 1
%
% Maximum number of inner iterations
INNER_ITER= 15000
%
% Maximum number of outer iterations (only for multizone problems)
OUTER_ITER= 1
%
% Maximum number of time iterations
TIME_ITER= 1
%
% Convergence field
CONV_FIELD= PRESSURE
%
% Min value of the residual (log10 of the residual)
CONV_RESIDUAL_MINVAL= -8
%
% Start convergence criteria at iteration number
CONV_STARTITER= 10
%
% Number of elements to apply the criteria
CONV_CAUCHY_ELEMS= 100
%
% Epsilon to control the series convergence
CONV_CAUCHY_EPS= 1E-10
%
% Iteration number to begin unsteady restarts
RESTART_ITER= 0
%
%% Time convergence monitoring
WINDOW_CAUCHY_CRIT = YES
%
% List of time convergence fields
CONV_WINDOW_FIELD = (TAVG_DRAG, TAVG_LIFT)
%
% Time Convergence Monitoring starts at Iteration WINDOW_START_ITER + CONV_WINDOW_STARTITER
CONV_WINDOW_STARTITER = 0
%
% Epsilon to control the series convergence
CONV_WINDOW_CAUCHY_EPS = 1E-3
%
% Number of elements to apply the criteria
CONV_WINDOW_CAUCHY_ELEMS = 10
%
% ------------------------- TIME-DEPENDENT SIMULATION -------------------------------%
%
% Time domain simulation
TIME_DOMAIN= NO
%
% Unsteady simulation (NO, TIME_STEPPING, DUAL_TIME_STEPPING-1ST_ORDER,
%                      DUAL_TIME_STEPPING-2ND_ORDER, HARMONIC_BALANCE)
TIME_MARCHING= NO
%
% Time Step for dual time stepping simulations (s) -- Only used when UNST_CFL_NUMBER = 0.0
% For the DG-FEM solver it is used as a synchronization time when UNST_CFL_NUMBER != 0.0
TIME_STEP= 0.00001
%
% Total Physical Time for dual time stepping simulations (s)
MAX_TIME= 2.0
%
% Unsteady Courant-Friedrichs-Lewy number of the finest grid
UNST_CFL_NUMBER= 1.0
%
%%  Windowed output time averaging
% Time iteration to start the windowed time average in a direct run
WINDOW_START_ITER = 500
%
% Window used for reverse sweep and direct run. Options (SQUARE, HANN, HANN_SQUARE, BUMP) Square is default.
WINDOW_FUNCTION = SQUARE
%
% ------------------------------- DES Parameters ------------------------------%
%
% Specify Hybrid RANS/LES model (SA_DES, SA_DDES, SA_ZDES, SA_EDDES)
HYBRID_RANSLES= SA_DDES
%
% DES Constant (0.65)
DES_CONST= 0.65
%
% ---------------- INCOMPRESSIBLE FLOW CONDITION DEFINITION -------------------%
%
% Density model within the incompressible flow solver.
% Options are CONSTANT (default), BOUSSINESQ, or VARIABLE. If VARIABLE,
% an appropriate fluid model must be selected.
INC_DENSITY_MODEL= CONSTANT
%
% Solve the energy equation in the incompressible flow solver
INC_ENERGY_EQUATION = NO
%
% Initial density for incompressible flows
% (1.2886 kg/m^3 by default (air), 998.2 Kg/m^3 (water))
INC_DENSITY_INIT= 1.2886
%
% Initial velocity for incompressible flows (1.0,0,0 m/s by default)
INC_VELOCITY_INIT= ( 30.0, 0.0, 0.0 )
%
% Initial temperature for incompressible flows that include the
% energy equation (288.15 K by default). Value is ignored if
% INC_ENERGY_EQUATION is false.
INC_TEMPERATURE_INIT= 288.15
%
% Non-dimensionalization scheme for incompressible flows. Options are
% INITIAL_VALUES (default), REFERENCE_VALUES, or DIMENSIONAL.
% INC_*_REF values are ignored unless REFERENCE_VALUES is chosen.
INC_NONDIM= INITIAL_VALUES
%
% Reference density for incompressible flows (1.0 kg/m^3 by default)
INC_DENSITY_REF= 1.0
%
% Reference velocity for incompressible flows (1.0 m/s by default)
INC_VELOCITY_REF= 1.0
%
% Reference temperature for incompressible flows that include the
% energy equation (1.0 K by default)
INC_TEMPERATURE_REF = 1.0
%
% List of inlet types for incompressible flows. List length must
% match number of inlet markers. Options: VELOCITY_INLET, PRESSURE_INLET.
INC_INLET_TYPE= VELOCITY_INLET
%
% Damping coefficient for iterative updates at pressure inlets. (0.1 by default)
INC_INLET_DAMPING= 0.1
%
% List of outlet types for incompressible flows. List length must
% match number of outlet markers. Options: PRESSURE_OUTLET, MASS_FLOW_OUTLET
INC_OUTLET_TYPE= PRESSURE_OUTLET
%
% Damping coefficient for iterative updates at mass flow outlets. (0.1 by default)
INC_OUTLET_DAMPING= 0.1
%
% Epsilon^2 multipier in Beta calculation for incompressible preconditioner.
BETA_FACTOR= 4.1
%
% ---------------------- REFERENCE VALUE DEFINITION ---------------------------%
%
% Reference origin for moment computation (m or in) % Atualmente em um quarto de Asa
REF_ORIGIN_MOMENT_X = 0.00
REF_ORIGIN_MOMENT_Y = 0.00
REF_ORIGIN_MOMENT_Z = 0.00
%
% Reference length for moment non-dimensional coefficients (m or in)
REF_LENGTH= 0.380
%
% Reference area for non-dimensional force coefficients (0 implies automatic
% calculation) (m^2 or in^2)
REF_AREA= 0.405
%
% Aircraft semi-span (0 implies automatic calculation) (m or in)
SEMI_SPAN= 0.0
%
% --------------------------- VISCOSITY MODEL ---------------------------------%
%
% Viscosity model (SUTHERLAND, CONSTANT_VISCOSITY, POLYNOMIAL_VISCOSITY).
VISCOSITY_MODEL= CONSTANT_VISCOSITY

% Molecular Viscosity that would be constant (1.716E-5 by default)
MU_CONSTANT= 1.716E-5
%
% Sutherland Viscosity Ref (1.716E-5 default value for AIR SI)
MU_REF= 1.716E-5
%
% Sutherland Temperature Ref (273.15 K default value for AIR SI)
MU_T_REF= 273.15
%
% Sutherland constant (110.4 default value for AIR SI)
SUTHERLAND_CONSTANT= 110.4
%
% Temperature polynomial coefficients (up to quartic) for viscosity.
% Format -> Mu(T) : b0 + b1*T + b2*T^2 + b3*T^3 + b4*T^4
MU_POLYCOEFFS= (0.0, 0.0, 0.0, 0.0, 0.0)
%
% ----------------------- DYNAMIC MESH DEFINITION -----------------------------%
%
% Type of dynamic surface movement (NONE, DEFORMING, MOVING_WALL,
% AEROELASTIC, AEROELASTIC_RIGID_MOTION EXTERNAL, EXTERNAL_ROTATION)
SURFACE_MOVEMENT= NONE
%
% Moving wall boundary marker(s) (NONE = no marker, ignored for RIGID_MOTION)
% MARKER_MOVING= ( NONE )
%
% Coordinates of the motion origin
% SURFACE_MOTION_ORIGIN= -4 0.0 0.0
%
% Translational velocity (m/s or ft/s) in the x, y, & z directions
% SURFACE_TRANSLATION_RATE = 30.0 0.0 0.0
%
%
% Move Motion Origin for marker moving (1 or 0)
% MOVE_MOTION_ORIGIN = 0
%
% -------------------- BOUNDARY CONDITION DEFINITION --------------------------%
%
% Euler wall boundary marker(s) (NONE = no marker)
% Implementation identical to MARKER_SYM.
MARKER_EULER= ( NONE )
%
% Navier-Stokes (no-slip), constant heat flux wall  marker(s) (NONE = no marker)
% Format: ( marker name, constant heat flux (J/m^2), ... )
MARKER_HEATFLUX= ( wing, 0)
%
% Far-field boundary marker(s) (NONE = no marker)
MARKER_FAR= ( inlet, outlet )
%
% Inlet boundary type (TOTAL_CONDITIONS, MASS_FLOW)
%
%INLET_TYPE= TOTAL_CONDITIONS
%
% Inlet boundary marker(s) with the following formats (NONE = no marker)
% Total Conditions: (inlet marker, total temp, total pressure, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
% Mass Flow: (inlet marker, density, velocity magnitude, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
% Inc. Velocity: (inlet marker, temperature, velocity magnitude, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
% Inc. Pressure: (inlet marker, temperature, total pressure, flow_direction_x,
%           flow_direction_y, flow_direction_z, ... ) where flow_direction is
%           a unit vector.
%MARKER_INLET= ( INLET, 288.15, 101325.0, 1.0, 0.0, 0.0)
%
% Outlet boundary marker(s) (NONE = no marker)
% Compressible: ( outlet marker, back pressure (static thermodynamic), ... )
% Inc. Pressure: ( outlet marker, back pressure (static gauge in Pa), ... )
% Inc. Mass Flow: ( outlet marker, mass flow target (kg/s), ... )
%MARKER_OUTLET= ( OUTLET )
%
% ------------------------ WALL FUNCTION DEFINITION --------------------------%
%
% The von Karman constant, the constant below only affects the standard wall function model 
WALLMODEL_KAPPA= 0.41
%
% The wall function model constant B 
WALLMODEL_B= 5.5
%
% The y+ value below which the wall function is switched off and we resolve the wall 
WALLMODEL_MINYPLUS= 5.0
%
% [Expert] Max Newton iterations used for the standard wall function
WALLMODEL_MAXITER= 200
%
% [Expert] relaxation factor for the Newton iterations of the standard wall function 
WALLMODEL_RELFAC= 0.5

% ------------------------ SURFACES IDENTIFICATION ----------------------------%
%
% Marker(s) of the surface in the surface flow solution file
MARKER_PLOTTING = ( wing )
%
% Marker(s) of the surface where the non-dimensional coefficients are evaluated.
MARKER_MONITORING = ( wing )
%
% Viscous wall markers for which wall functions must be applied. (NONE = no marker)
% Format: ( marker name, wall function type -NO_WALL_FUNCTION, STANDARD_WALL_FUNCTION,
%           ADAPTIVE_WALL_FUNCTION, SCALABLE_WALL_FUNCTION, EQUILIBRIUM_WALL_MODEL,
%           NONEQUILIBRIUM_WALL_MODEL-, ... )
MARKER_WALL_FUNCTIONS= ( wing, NO_WALL_FUNCTION )
%
% Marker(s) of the surface where custom thermal BCs are defined.
MARKER_PYTHON_CUSTOM = ( NONE )
%
% Marker(s) of the surface that is going to be analyzed in detail (massflow, average pressure, distortion, etc)
MARKER_ANALYZE = ( wing )
%
% Method to compute the average value in MARKER_ANALYZE (AREA, MASSFLUX).
MARKER_ANALYZE_AVERAGE = AREA

% ------------- COMMON PARAMETERS DEFINING THE NUMERICAL METHOD ---------------%
%
% Numerical method for spatial gradients (GREEN_GAUSS, WEIGHTED_LEAST_SQUARES)
NUM_METHOD_GRAD= GREEN_GAUSS

% Numerical method for spatial gradients to be used for MUSCL reconstruction
% Options are (GREEN_GAUSS, WEIGHTED_LEAST_SQUARES, LEAST_SQUARES). Default value is
% NONE and the method specified in NUM_METHOD_GRAD is used.
NUM_METHOD_GRAD_RECON = NONE
%
% CFL number (initial value for the adaptive CFL number)
CFL_NUMBER= 1.0
%
% Adaptive CFL number (NO, YES)
CFL_ADAPT= NO
%
% Parameters of the adaptive CFL number (factor-down, factor-up, CFL min value,
%                                        CFL max value, acceptable linear solver convergence)
% Local CFL increases by factor-up until max if the solution rate of change is not limited,
% and acceptable linear convergence is achieved. It is reduced if rate is limited, or if there
% is not enough linear convergence, or if the nonlinear residuals are stagnant and oscillatory.
% It is reset back to min when linear solvers diverge, or if nonlinear residuals increase too much.
CFL_ADAPT_PARAM= ( 0.1, 2.0, 10.0, 1e10, 0.001 )
%
% Maximum Delta Time in local time stepping simulations
MAX_DELTA_TIME= 1E6
%
% Runge-Kutta alpha coefficients
RK_ALPHA_COEFF= ( 0.66667, 0.66667, 1.000000 )
%
% Objective function in gradient evaluation  (DRAG, LIFT, SIDEFORCE, MOMENT_X,
%                                             MOMENT_Y, MOMENT_Z, EFFICIENCY, BUFFET,
%                                             EQUIVALENT_AREA, NEARFIELD_PRESSURE,
%                                             FORCE_X, FORCE_Y, FORCE_Z, THRUST,
%                                             TORQUE, TOTAL_HEATFLUX, CUSTOM_OBJFUNC
%                                             MAXIMUM_HEATFLUX, INVERSE_DESIGN_PRESSURE,
%                                             INVERSE_DESIGN_HEATFLUX, SURFACE_TOTAL_PRESSURE,
%                                             SURFACE_MASSFLOW, SURFACE_STATIC_PRESSURE, SURFACE_MACH)
% For a weighted sum of objectives: separate by commas, add OBJECTIVE_WEIGHT and MARKER_MONITORING in matching order.
OBJECTIVE_FUNCTION= DRAG
%
% List of weighting values when using more than one OBJECTIVE_FUNCTION. Separate by commas and match with MARKER_MONITORING.
OBJECTIVE_WEIGHT = 1.0
%
% Expression used when "OBJECTIVE_FUNCTION= CUSTOM_OBJFUNC", any history/screen output can be used together with common
% math functions (sqrt, cos, exp, etc.). This can be used for constraint aggregation (as below) or to compute something
% SU2 does not, see TestCases/user_defined_functions/.
CUSTOM_OBJFUNC= 'DRAG + 10 * pow(fmax(0.4-LIFT, 0), 2)'

% ----------- SLOPE LIMITER AND DISSIPATION SENSOR DEFINITION -----------------%
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the flow equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_FLOW= YES
%
% Slope limiter (NONE, VENKATAKRISHNAN, VENKATAKRISHNAN_WANG,
%                BARTH_JESPERSEN, VAN_ALBADA_EDGE)
SLOPE_LIMITER_FLOW= VENKATAKRISHNAN
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the turbulence equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_TURB= NO
%
% Slope limiter (NONE, VENKATAKRISHNAN, VENKATAKRISHNAN_WANG,
%                BARTH_JESPERSEN, VAN_ALBADA_EDGE)
SLOPE_LIMITER_TURB= VENKATAKRISHNAN
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the adjoint flow equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_ADJFLOW= YES
%
% Slope limiter (NONE, VENKATAKRISHNAN, BARTH_JESPERSEN, VAN_ALBADA_EDGE,
%                SHARP_EDGES, WALL_DISTANCE)
SLOPE_LIMITER_ADJFLOW= VENKATAKRISHNAN
%
% Monotonic Upwind Scheme for Conservation Laws (TVD) in the turbulence adjoint equations.
%           Required for 2nd order upwind schemes (NO, YES)
MUSCL_ADJTURB= NO
%
% Slope limiter (NONE, VENKATAKRISHNAN, BARTH_JESPERSEN, VAN_ALBADA_EDGE)
SLOPE_LIMITER_ADJTURB= VENKATAKRISHNAN
%
% Coefficient for the Venkats limiter (upwind scheme). A larger values decrease
%             the extent of limiting, values approaching zero cause
%             lower-order approximation to the solution (0.05 by default)
VENKAT_LIMITER_COEFF= 0.05
%
% Reference coefficient for detecting sharp edges (3.0 by default).
REF_SHARP_EDGES = 3.0
%
% Coefficient for the adjoint sharp edges limiter (3.0 by default).
ADJ_SHARP_LIMITER_COEFF= 3.0
%
% Remove sharp edges from the sensitivity evaluation (NO, YES)
SENS_REMOVE_SHARP = NO
%
% Freeze the value of the limiter after a number of iterations
LIMITER_ITER= 999999
%
% 1st order artificial dissipation coefficients for
%     the Lax–Friedrichs method ( 0.15 by default )
LAX_SENSOR_COEFF= 0.15
%
% 2nd and 4th order artificial dissipation coefficients for
%     the JST method ( 0.5, 0.02 by default )
JST_SENSOR_COEFF= ( 0.5, 0.02 )
%
% 1st order artificial dissipation coefficients for
%     the adjoint Lax–Friedrichs method ( 0.15 by default )
ADJ_LAX_SENSOR_COEFF= 0.15
%
% 2nd, and 4th order artificial dissipation coefficients for
%     the adjoint JST method ( 0.5, 0.02 by default )
ADJ_JST_SENSOR_COEFF= ( 0.5, 0.02 )

% ------------------------ LINEAR SOLVER DEFINITION ---------------------------%
%
% Linear solver or smoother for implicit formulations:
% BCGSTAB, FGMRES, RESTARTED_FGMRES, CONJUGATE_GRADIENT (self-adjoint problems only), SMOOTHER.
LINEAR_SOLVER= FGMRES
%
% Same for discrete adjoint (smoothers not supported), replaces LINEAR_SOLVER in SU2_*_AD codes.
DISCADJ_LIN_SOLVER= FGMRES
%
% Preconditioner of the Krylov linear solver or type of smoother (ILU, LU_SGS, LINELET, JACOBI)
LINEAR_SOLVER_PREC= ILU
%
% Same for discrete adjoint (JACOBI or ILU), replaces LINEAR_SOLVER_PREC in SU2_*_AD codes.
DISCADJ_LIN_PREC= ILU
%
% Linear solver ILU preconditioner fill-in level (0 by default)
LINEAR_SOLVER_ILU_FILL_IN= 0
%
% Minimum error of the linear solver for implicit formulations
LINEAR_SOLVER_ERROR= 1E-6
%
% Max number of iterations of the linear solver for the implicit formulation
LINEAR_SOLVER_ITER= 5
%
% Restart frequency for RESTARTED_FGMRES
LINEAR_SOLVER_RESTART_FREQUENCY= 10
%
% Relaxation factor for smoother-type solvers (LINEAR_SOLVER= SMOOTHER)
LINEAR_SOLVER_SMOOTHER_RELAXATION= 1.0
%
% -------------------- FLOW NUMERICAL METHOD DEFINITION -----------------------%
%
% Convective numerical method (JST, JST_KE, JST_MAT, LAX-FRIEDRICH, CUSP, ROE, AUSM,
%                              AUSMPLUSUP, AUSMPLUSUP2, AUSMPWPLUS, HLLC, TURKEL_PREC,
%                              SW, MSW, FDS, SLAU, SLAU2, L2ROE, LMROE)
CONV_NUM_METHOD_FLOW= JST
%
% Roe Low Dissipation function for Hybrid RANS/LES simulations (FD, NTS, NTS_DUCROS)
ROE_LOW_DISSIPATION= FD
%
% Post-reconstruction correction for low Mach number flows (NO, YES)
LOW_MACH_CORR= NO
%
% Roe-Turkel preconditioning for low Mach number flows (NO, YES)
LOW_MACH_PREC= NO
%
% Use numerically computed Jacobians for AUSM+up(2) and SLAU(2)
% Slower per iteration but potentialy more stable and capable of higher CFL
USE_ACCURATE_FLUX_JACOBIANS= NO
%
% Use the vectorized version of the selected numerical method (available for JST family and Roe).
% SU2 should be compiled for an AVX or AVX512 architecture for best performance.
USE_VECTORIZATION= NO
%
% Entropy fix coefficient (0.0 implies no entropy fixing, 1.0 implies scalar
%                          artificial dissipation)
ENTROPY_FIX_COEFF= 0.0
%
% Higher values than 1 (3 to 4) make the global Jacobian of central schemes (compressible flow
% only) more diagonal dominant (but mathematically incorrect) so that higher CFL can be used.
CENTRAL_JACOBIAN_FIX_FACTOR= 4.0
%
% Time discretization (RUNGE-KUTTA_EXPLICIT, EULER_IMPLICIT, EULER_EXPLICIT)
TIME_DISCRE_FLOW= EULER_IMPLICIT
%
% Use a Newton-Krylov method on the flow equations, see TestCases/rans/oneram6/turb_ONERAM6_nk.cfg
% For multizone discrete adjoint it will use FGMRES on inner iterations with restart frequency
% equal to "QUASI_NEWTON_NUM_SAMPLES".
NEWTON_KRYLOV= NO
%
%
% -------------------- TURBULENT NUMERICAL METHOD DEFINITION ------------------%
%
% Convective numerical method (SCALAR_UPWIND)
CONV_NUM_METHOD_TURB= SCALAR_UPWIND
%
% Time discretization (EULER_IMPLICIT, EULER_EXPLICIT)
TIME_DISCRE_TURB= EULER_IMPLICIT
%
% Reduction factor of the CFL coefficient in the turbulence problem
CFL_REDUCTION_TURB= 1.0
%
% --------------------- HYBRID PARALLEL (MPI+OpenMP) OPTIONS ---------------------%
%
% An advanced performance parameter for FVM solvers, a large-ish value should be best
% when relatively few threads per MPI rank are in use (~4). However, maximum parallelism
% is obtained with EDGE_COLORING_GROUP_SIZE=1, consider using this value only if SU2
% warns about low coloring efficiency during preprocessing (performance is usually worse).
% Setting the option to 0 disables coloring and a different strategy is used instead,
% that strategy is automatically used when the coloring efficiency is less than 0.875.
% The optimum value/strategy is case-dependent.
EDGE_COLORING_GROUP_SIZE= 512
%
% Independent "threads per MPI rank" setting for LU-SGS and ILU preconditioners.
% For problems where time is spend mostly in the solution of linear systems (e.g. elasticity,
% very high CFL central schemes), AND, if the memory bandwidth of the machine is saturated
% (4 or more cores per memory channel) better performance (via a reduction in linear iterations)
% may be possible by using a smaller value than that defined by the system or in the call to
% SU2_CFD (via the -t/--threads option).
% The default (0) means "same number of threads as for all else".
LINEAR_SOLVER_PREC_THREADS= 0
%
% ----------------------- PARTITIONING OPTIONS (ParMETIS) ------------------------ %
%
% Load balancing tolerance, lower values will make ParMETIS work harder to evenly
% distribute the work-estimate metric across all MPI ranks, at the expense of more
% edge cuts (i.e. increased communication cost).
PARMETIS_TOLERANCE= 0.02
%
% The work-estimate metric is a weighted function of the work-per-edge (e.g. spatial
% discretization, linear system solution) and of the work-per-point (e.g. source terms,
% temporal discretization) the former usually accounts for >90% of the total.
% These weights are INTEGERS (for compatibility with ParMETIS) thus not [0, 1].
% To balance memory usage (instead of computation) the point weight needs to be
% increased (especially for explicit time integration methods).
PARMETIS_EDGE_WEIGHT= 1
PARMETIS_POINT_WEIGHT= 0
%
%
% ------------------------- SCREEN/HISTORY VOLUME OUTPUT --------------------------%
%
% Screen output fields (use 'SU2_CFD -d <config_file>' to view list of available fields)
SCREEN_OUTPUT= (INNER_ITER, PRESSURE, RMS_VELOCITY, SIDEFORCE, DRAG, LIFT)
%
% History output groups (use 'SU2_CFD -d <config_file>' to view list of available fields)
HISTORY_OUTPUT= (ITER, RMS_PRESSURE, RMS_VELOCITY, SIDEFORCE, DRAG, LIFT)
%
% Volume output fields/groups (use 'SU2_CFD -d <config_file>' to view list of available fields)
VOLUME_OUTPUT= (COORDINATES, SOLUTION, PRIMITIVE)
%
% Writing frequency for screen output
SCREEN_WRT_FREQ_INNER= 1
%
SCREEN_WRT_FREQ_OUTER= 1
%
SCREEN_WRT_FREQ_TIME= 1
%
% Writing frequency for history output
HISTORY_WRT_FREQ_INNER= 1
%
HISTORY_WRT_FREQ_OUTER= 1
%
HISTORY_WRT_FREQ_TIME= 1
%
% list of writing frequencies corresponding to the list in OUTPUT_FILES 
OUTPUT_WRT_FREQ= 250, 250, 300
%
% Output the performance summary to the console at the end of SU2_CFD
WRT_PERFORMANCE= NO
%
% Overwrite or append iteration number to the restart files when saving 
WRT_RESTART_OVERWRITE= YES
%
% Overwrite or append iteration number to the surface files when saving 
WRT_SURFACE_OVERWRITE= YES
%
% Overwrite or append iteration number to the volume files when saving 
WRT_VOLUME_OVERWRITE= YES
%
% ------------------------- INPUT/OUTPUT FILE INFORMATION --------------------------%
%
% Mesh input file
MESH_FILENAME= hinf.su2
%
% Mesh input file format (SU2, CGNS)
MESH_FORMAT= SU2
%
% Mesh output file
MESH_OUT_FILENAME= hinf.su2
%
% Restart flow input file
SOLUTION_FILENAME= solution_flow.dat
%
% Restart adjoint input file
SOLUTION_ADJ_FILENAME= solution_adj.dat
%
% Output tabular file format (TECPLOT, CSV)
TABULAR_FORMAT= CSV
%
% Files to output
% Possible formats : (TECPLOT_ASCII, TECPLOT, SURFACE_TECPLOT_ASCII,
%  SURFACE_TECPLOT, CSV, SURFACE_CSV, PARAVIEW_ASCII, PARAVIEW_LEGACY, SURFACE_PARAVIEW_ASCII,
%  SURFACE_PARAVIEW_LEGACY, PARAVIEW, SURFACE_PARAVIEW, RESTART_ASCII, RESTART, CGNS, SURFACE_CGNS, STL_ASCII, STL_BINARY)
% default : (RESTART, PARAVIEW, SURFACE_PARAVIEW)
OUTPUT_FILES= (RESTART, PARAVIEW, SURFACE_PARAVIEW)
%
% Output file convergence history (w/o extension)
CONV_FILENAME= history
%
% Output file with the forces breakdown
BREAKDOWN_FILENAME= forces_breakdown.dat
%
% Output file restart flow
RESTART_FILENAME= restart_flow.dat
%
% Output file flow (w/o extension) variables
VOLUME_FILENAME= flow
%

import os
import csv
import numpy as np
import matplotlib.pyplot as plt


# Important folder PATHs
print('Aonde esse script ta rodando?')
print('1. PC do Matheus')
print('2. Cluster Amadea')
print("")
machine = input()
print("")
print("###################################################################")
if machine == '1':
    base_folder = r'C:\Users\mathe\Documents\Meus Arquivos\UnB\Projeto de Pesquisa\Cluster Mirror\pg2'
    history_plotter_fig_plain_path = r'C:\Users\mathe\Documents\Meus Arquivos\UnB\Projeto de Pesquisa\Cluster Mirror\pg2\pg_files\results\History Plotter Figs\Plain'
    history_plotter_fig_full_path = r'C:\Users\mathe\Documents\Meus Arquivos\UnB\Projeto de Pesquisa\Cluster Mirror\pg2\pg_files\results\History Plotter Figs\Full'
    simulating_folder_name = r'Simulating'
    results_file_path = r'C:\Users\mathe\Documents\Meus Arquivos\UnB\Projeto de Pesquisa\Cluster Mirror\pg2\pg_files\results'
else:
    base_folder = r'/home/matheus/pg2'
    history_plotter_fig_plain_path = r'/home/matheus/pg2/pg_files/results/History Plotter Figs/Plain'
    history_plotter_fig_full_path = r'/home/matheus/pg2/pg_files/results/History Plotter Figs/Full'
    simulating_folder_name = r'Simulating'
    results_file_path = r'/home/matheus/pg2/pg_files/results'

folders_to_check = [17, 20, 22, 25, 27, 30, 32, 35, 37, 40, 45, 50, 60, 70, 80, 85, 90, 95, 100, 110, 120, 130, 150, 225, 'inf']

# Separate integers and strings
integers = []
strings = []
for item in folders_to_check:
    if isinstance(item, int):
        integers.append(item)
    else:
        strings.append(item)

# Sort the integer list in ascending order
sorted_integers = sorted(integers)

# Sort the string list
sorted_strings = sorted(strings)

# Concatenate the sorted integer list with the sorted string list
sorted_folders = sorted_strings + sorted_integers

print('')


for case_number in sorted_folders:
    folder_name = f'h{case_number}'
    mesh_filename = f'mesh_h{case_number}.su2'
    output_filename = 'output.dat'
    folder_path = os.path.join(base_folder, folder_name)
    output_filepath = os.path.join(folder_path, output_filename)
    history_file = os.path.join(folder_path, 'history.csv')
    mesh_file = os.path.join(folder_path,mesh_filename)

    print(folder_name)
    print("")
    print(f"Checking folder: {folder_path}")

    if os.path.exists(history_file):

        target_string = "grid points before partitioning"

        with open(output_filepath, "r") as file:
            lines = file.readlines()
            print(" ")
            print(f"Checking for mesh file: {mesh_filename}")
            if os.path.exists(mesh_file):
                print(f"Mesh file: {mesh_filename} found in folder")
            else:
                print('Mesh file not found')
            for lines_in_output, line in enumerate(lines):
                if target_string in line:
                    if lines_in_output + 1 < len(lines):
                        next_line = lines[lines_in_output + 1]
                        volume_mesh_elements = int(next_line.split(" ")[0])
                        print(f'Quantidade de volumes na malha: {volume_mesh_elements}')
                    else:
                        print("Error checking for the number of volume mesh elements")
                    break
            else:
                print("Number of volume mesh elements not found in the file.")

        print(" ")
        print(f"Checking file: {history_file}")
        data_history = np.loadtxt(history_file, comments='"', delimiter=',')

        # Case name differentiation
        case_h = str(case_number)
        casename = f'h{case_number}'

        # Get data from history file
        iter_data = data_history[:, 2]
        rms_data = data_history[:, 3]
        cd_data = data_history[:, 4]
        csf_data = data_history[:, 5]
        cl_data = data_history[:, 6]

        # Determine the number of values for mean calculation
        value_pool = len(iter_data) // 5

        # Calculate mean values from the last fifth part of the values
        cd_mean = np.mean(cd_data[-value_pool:])
        cl_mean = np.mean(cl_data[-value_pool:])
        csf_mean = np.mean(csf_data[-value_pool:])

        # Calculate the standard deviation
        cd_std = np.std(cd_data[-value_pool:])
        cl_std = np.std(cl_data[-value_pool:])
        csf_std = np.std(csf_data[-value_pool:])

        # Determine the number of iterations made and to make
        iter_made = np.max(iter_data)+1
        iter_max = 15000

        # Determine the progress percentage made
        sim_prog = (iter_made/iter_max) * 100
        if sim_prog > 100:
            sim_prog = 100

        # Defining the static loading bar function
        def print_loading_bar(progress):
            bar_width = 45
            filled_width = int(bar_width * progress / 100)
            remaining_width = bar_width - filled_width
            loading_bar = '[' + '█' * filled_width + ' ' * remaining_width + ']'
            print(loading_bar)

        # Print Log
        print('Iterations history file: history.csv found in folder')
        print(f'{iter_made:.0f} iterations made')
        print(f'{sim_prog:.2f}% done with the simulation based on expected {iter_max:.0f} iterations')
        print_loading_bar(sim_prog)
        print("")

        print('Média:')
        print(f'CD: {cd_mean:.5f}')
        print(f'CL: {cl_mean:.5f}')
        print(f'CSF: {csf_mean:.5f}')
        print("")

        print('Desvio Padrão:')
        print(f'CD: {cd_std:.5f}')
        print(f'CL: {cl_std:.5f}')
        print(f'CSF: {csf_std:.5f}')
        print("")

        # Calculate the standard deviation as a percentage of the mean
        cd_std_percent = np.abs((cd_std / cd_mean) * 100)
        cl_std_percent = np.abs((cl_std / cl_mean) * 100)
        csf_std_percent = np.abs((csf_std / csf_mean) * 100)

        print('Desvio Padrão em formato percentual absoluto em relação à média:')
        print(f'CD: {cd_std_percent:.2f}%')
        print(f'CL: {cl_std_percent:.2f}%')
        print(f'CSF: {csf_std_percent:.2f}%')
        print("")
        print("###################################################################")
        print("")

        # Find the index of the data point where the mean is calculated
        mean_index = len(iter_data) - value_pool

        #################################################
        ######### Plotting values #######################
        #################################################

        # Plotting CD and CL
        fig, ax1 = plt.subplots()

        # Plotting CD data
        color_cd = 'tab:red'
        ax1.set_xlabel('Iterações')
        ax1.set_ylabel('CD', color=color_cd)
        ax1.plot(iter_data, cd_data, color=color_cd)
        ax1.tick_params(axis='y', labelcolor=color_cd)

        # Centering the CD axis on zero
        cd_max = np.max(np.abs(cd_data))
        ax1.set_ylim(-cd_max, cd_max)

        # Creating a second y-axis for CL data
        ax2 = ax1.twinx()

        # Plotting CL data
        color_cl = 'tab:blue'
        ax2.set_ylabel('CL', color=color_cl)
        ax2.plot(iter_data, cl_data, color=color_cl)
        ax2.tick_params(axis='y', labelcolor=color_cl)

        # Centering the CL axis on zero
        cl_max = np.max(np.abs(cl_data))
        ax2.set_ylim(-cl_max, cl_max)

        # Creating a third y-axis for CSF data
        ax3 = ax1.twinx()

        # Plotting CSF data
        color_csf = 'tab:green'
        ax3.spines['right'].set_position(('outward', 60))  # Adjust position of the third y-axis
        ax3.set_ylabel('CSF', color=color_csf)
        ax3.plot(iter_data, csf_data, color=color_csf)
        ax3.tick_params(axis='y', labelcolor=color_csf)

        # Centering the CSF axis on zero
        csf_max = np.max(np.abs(csf_data)*30)
        ax3.set_ylim(-csf_max, csf_max)

        # Adding a legend
        lines = [plt.Line2D([], [], color=color_cd, label='CD'),
                plt.Line2D([], [], color=color_cl, label='CL'),
                plt.Line2D([], [], color=color_csf, label='CSF')]
        plt.legend(handles=lines, loc='upper left')

        # Displaying the mean values in a box
        mean_box_text = f'CD Médio: {cd_mean:.5f}\nCL Médio: {cl_mean:.5f}\nCSF Médio: {csf_mean:.5f}'
        plt.text(0.98, 0.95, mean_box_text, transform=ax1.transAxes,
                verticalalignment='top', horizontalalignment='right',
                bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.3'))

        # Adding a vertical line at the mean index
        plt.axvline(x=iter_data[mean_index], color='grey', linestyle='-.')
        plt.axvline(x=0, color='black', linestyle='-')

        # Setting the title and subtitle with enlarged font size
        plt.title('Evolução de CD, CL e CSF ao longo das iterações', fontsize=15)
        plt.suptitle(f'Caso {casename}', y=0.95, fontsize=30, fontweight='bold')

        # Show plot grid with vertical lines and bolden zero line
        plt.grid(True, axis='both', which='major', linestyle='--', linewidth=0.5)
        ax1.axhline(0, color='k', linewidth=1.5)
        ax2.axhline(0, color='k', linewidth=1.5)
        ax3.axhline(0, color='k', linewidth=1.5)

        # Adjusting layout
        plt.tight_layout(rect=[0, 0, 1.1, 1])

        # Set the figure size
        fig.set_size_inches(12, 5)

        # Save the plot as a PNG file with casename in the file name
        filename = f"convergence_CL_CD_CSF_{casename}.png"
        #history_plotter_fig_plain_path = r'C:\Users\mathe\Documents\Meus Arquivos\UnB\Projeto de Pesquisa\Cluster Mirror\pg2\pg_files\results\History Plotter Figs\Plain'
        #simulating_folder_name = r'Simulating'
        if sim_prog >= 100:
            save_fig_param = os.path.join(history_plotter_fig_plain_path, filename)
            plt.savefig(save_fig_param, dpi=300)
        else:
            save_fig_param = os.path.join(history_plotter_fig_plain_path, simulating_folder_name, filename)
            plt.savefig(save_fig_param, dpi=300)


        # Displaying the plot
        #plt.show()
        plt.close()


        #################################################
        ######### Full Technincal Plot ##################
        #################################################

        # Plotting CD and CL
        fig, ax1 = plt.subplots()

        # Plotting CD data
        color_cd = 'tab:red'
        ax1.set_xlabel('Iterações')
        ax1.set_ylabel('CD', color=color_cd)
        ax1.plot(iter_data, cd_data, color=color_cd)
        ax1.tick_params(axis='y', labelcolor=color_cd)

        # Centering the CD axis on zero
        cd_max = np.max(np.abs(cd_data))
        ax1.set_ylim(-cd_max, cd_max)

        # Creating a second y-axis for CL data
        ax2 = ax1.twinx()

        # Plotting CL data
        color_cl = 'tab:blue'
        ax2.set_ylabel('CL', color=color_cl)
        ax2.plot(iter_data, cl_data, color=color_cl)
        ax2.tick_params(axis='y', labelcolor=color_cl)

        # Centering the CL axis on zero
        cl_max = np.max(np.abs(cl_data))
        ax2.set_ylim(-cl_max, cl_max)

        # Creating a third y-axis for CSF data
        ax3 = ax1.twinx()

        # Plotting CSF data
        color_csf = 'tab:green'
        ax3.spines['right'].set_position(('outward', 60))  # Adjust position of the third y-axis
        ax3.set_ylabel('CSF', color=color_csf)
        ax3.plot(iter_data, csf_data, color=color_csf)
        ax3.tick_params(axis='y', labelcolor=color_csf)

        # Centering the CSF axis on zero
        csf_max = np.max(np.abs(csf_data)*30)
        ax3.set_ylim(-csf_max, csf_max)

        # Adding a legend
        lines = [plt.Line2D([], [], color=color_cd, label='CD'),
                plt.Line2D([], [], color=color_cl, label='CL'),
                plt.Line2D([], [], color=color_csf, label='CSF')]
        plt.legend(handles=lines, loc='upper left')

        # Displaying the mean values in a box
        mean_box_text = f'CD Médio: {cd_mean:.5f} ± {cd_std_percent:.2f}%\nCL Médio: {cl_mean:.5f} ± {cl_std_percent:.2f}%\nCSF Médio: {csf_mean:.5f} ± {csf_std_percent:.2f}%'
        plt.text(0.98, 0.95, mean_box_text, transform=ax1.transAxes,
                verticalalignment='top', horizontalalignment='right',
                bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.3'))

        # Adding a vertical line at the mean index
        plt.axvline(x=iter_data[mean_index], color='grey', linestyle='-.')
        plt.axvline(x=0, color='black', linestyle='-')

        # Setting the title and subtitle with enlarged font size
        if sim_prog >= 100:
            plt.title(f'Evolução de CD, CL e CSF ao longo das iterações', fontsize=15)
        else:
            plt.title(f'Evolução de CD, CL e CSF ao longo de {sim_prog:.2f}% das iterações planejadas', fontsize=15)
        plt.suptitle(f'Caso {casename}', y=0.95, fontsize=30, fontweight='bold')

        # Show plot grid with vertical lines and bolden zero line
        plt.grid(True, axis='both', which='major', linestyle='--', linewidth=0.5)
        ax1.axhline(0, color='k', linewidth=1.5)
        ax2.axhline(0, color='k', linewidth=1.5)
        ax3.axhline(0, color='k', linewidth=1.5)

        # Adjusting layout
        plt.tight_layout(rect=[0, 0, 1.1, 1])

        # Set the figure size
        fig.set_size_inches(12, 5)

        # Save the plot as a PNG file with casename in the file name
        filename = f"convergence_full_CL_CD_CSF_{casename}.png"
        if sim_prog >= 100:
            save_fig_param = os.path.join(history_plotter_fig_full_path, filename)
            plt.savefig(save_fig_param, dpi=300)
        else:
            save_fig_param = os.path.join(history_plotter_fig_full_path, simulating_folder_name, filename)
            plt.savefig(save_fig_param, dpi=300)


        # Displaying the plot
        #plt.show()
        plt.close()


        #################################################
        ###### Update the csv results file ##############
        #################################################

        def update_csv_file(filename, h_input, cl_simulation, cd_simulation , q, s, g):
            rows = []
            with open(filename, 'r') as file:
                reader = csv.reader(file)
                rows = list(reader)

            header = rows[0]
            
            cl_simulation_index = header.index('*CL - Simulation*')
            cd_simulation_index = header.index('*CD - Simulation*')
            cl_zerihan_index = header.index('*CL - Zerihan*')
            delta_cl_index = header.index('*Delta CL*')
            cd_zerihan_index = header.index('*CD - Zerihan*')
            delta_cd_index = header.index('*Delta CD*')
            delta_fy_kgf_index = header.index('*Delta Fy (Kgf)*')
            delta_fx_kgf_index = header.index('*Delta Fx (Kgf)*')
            delta_fy_pct_index = header.index('*Delta Fy (%)*')
            delta_fx_pct_index = header.index('*Delta Fx (%)*')

            for row in rows[1:]:
                h_value = float(row[0])
                if h_value == float(h_input):
                    row[cl_simulation_index] = f'{cl_simulation:.5f}'
                    row[cd_simulation_index] = f'{cd_simulation:.5f}'
                    delta_cl = np.abs(cl_simulation) - np.abs(float(row[cl_zerihan_index]))
                    row[delta_cl_index] = f'{delta_cl:.5f}'
                    delta_cd = np.abs(cd_simulation) - np.abs(float(row[cd_zerihan_index]))
                    row[delta_cd_index] = f'{delta_cd:.5f}'
                    delta_fy_kgf = delta_cl * q * s / g
                    row[delta_fy_kgf_index] = f'{delta_fy_kgf:.5f}'
                    delta_fx_kgf = delta_cd * q * s / g
                    row[delta_fx_kgf_index] = f'{delta_fx_kgf:.5f}'
                    delta_fy_pct = (np.abs(delta_cl))/(np.max([np.abs(cl_simulation),np.abs(float(row[cl_zerihan_index]))]))
                    row[delta_fy_pct_index] = f'{delta_fy_pct:.5f}'
                    delta_fx_pct = (np.abs(delta_cd))/(np.max([np.abs(cd_simulation),np.abs(float(row[cd_zerihan_index]))]))
                    row[delta_fx_pct_index] = f'{delta_fx_pct:.5f}'

            with open(filename, 'w', newline='') as file:
                writer = csv.writer(file)
                writer.writerows(rows)

        # Updating the results .csv file:
        results_filename = 'all_results.csv'
        results_file = os.path.join(results_file_path, results_filename)
        cl_simulation = cl_mean
        cd_simulation = cd_mean
        u_sim = 30 # m/s
        rho = 1.2886 # kg/m^3
        q = ((u_sim ** 2) * rho) / 2  # (u^2 * rho) / 2
        s = 0.405 # m^2
        g = 9.80665 # m/s^2

        update_csv_file(results_file, case_h, cl_simulation, cd_simulation, q, s, g)




    else:
        if os.path.exists(mesh_file):
            print(f"No history file found in folder {folder_name}.")
            print("")
            print('This probably means the simulation hasn\'t started yet, check the queue')
            print("")
            print("###################################################################")
            print("")
        else:
            print(f"History file not found.")
            print("")
            print('This probably means the mesh file hast\'t been uploaded and the simulation hasn\'t started yet')
            print("")
            print("###################################################################")
            print("")

if machine == 1:
    end = input('Aperte enter pra sair')
else:
    end = 'end program'