run_5_compute_all.m

function run_5_compute_all()
% Compute many inductor inductor designs (properties, thermal, losses, etc.).
%
%    Load the ANN/regression obtained with the FEM/ANN workflow.
%    Sweep different inductor designs.
%    Compute the inductor properties, thermal, losses, etc.
%
%    Use the ANN/regression are used for predicting:
%        - the thermal model (hotspot and average temperatures)
%        - the magnetic model (inductance, current density, flux density, and magnetic field)
%
%    The complete code is running:
%        - parallel: on different MATLAB workers
%        - vectorized: many designs at the same time
%
%    Warning: The code use functions that are internally (multithreaded).
%             Therefore, the speedup achieved with parallel MATLAB workers can be very low.
%
%    This function requires a running ANN Python Server (if this regression method is used):
%        - run 'run_ann_server.py' with Python
%        - use 'start_python_ann_server.bat' on MS Windows
%        - use 'start_python_ann_server.sh' on Linux
%
%    (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod

init_toolbox();

% run model with ANN/regression
run_sub('ann')

% run model with analytical approximation
run_sub('approx')

end

function run_sub(eval_type)
% Compute many inductor inductor designs (with different evaluation methods).
%
%    Parameters:
%        eval_type (str): type of the evaluation ('ann', or approx')

% path of the file containing the exported data from the FEM/ANN
file_export = 'data/export.mat';

% path of the file to be written with the computed designs
file_compute_all = ['data/compute_all_' eval_type '.mat'];

% get the design parameters for the inductors
[sweep, n_split, fct, eval_ann, data_compute] = get_design_data_compute_all(eval_type);

% compute the inductor designs
master_compute_all(file_compute_all, file_export, sweep, n_split, fct, eval_ann, data_compute)

end