Hello,
Here are few general steps that you can follow. But before that , configure your ANSYS Workbench project with parameters and outputs and ensure the ANSYS toolbox is installed in MATLAB for communication.
Define Genetic Algorithm:
Create an objective function in MATLAB that:
- Updates ANSYS parameters.
- Runs the simulation.
- Retrieves results and calculates fitness based on desired vs. actual results.
- Use MATLAB's ga function to set up the genetic algorithm with appropriate bounds and options.
nVars = 5; % Number of parameters to optimize
lb = [0, 0, 0, 0, 0]; % Lower bounds
ub = [10, 10, 10, 10, 10]; % Upper bounds
options = optimoptions('ga', 'Display', 'iter', 'PopulationSize', 50, 'MaxGenerations', 100);
[optimalParams, fval] = ga(@objectiveFunction, nVars, [], [], [], [], lb, ub, [], options);
To automate the interaction, write MATLAB functions to update parameters, run simulations, and retrieve results from ANSYS using the toolbox or scripting.
Execute and Analyze:
- Run the genetic algorithm and monitor its progress.
- Analyze and validate the optimized parameters and results.
- Visualize the comparison between desired and actual outcomes.
This process allows MATLAB to iteratively adjust ANSYS parameters to minimize errors effectively.
