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https://github.com/danpacho/capstone_1


https://github.com/danpacho/capstone_1

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README 

        
# Capstone design(1)



## 1. Project goal



Optimize **vent hole(outlet) pattern** for **reducing the [cooling drag](https://www.sciencedirect.com/topics/engineering/cooling-drag)** of `UAV`'s motor efficiency.



## 2. Project structure overview



### 2.1 Surrogate modeling

![first pipeline](./assets/1.png)

1. Extract arbitrary outlet data from CFD simulation(ANSYS Fluent)

2. Upscale the input data for high-dimensional surrogate modeling

3. PCA(Principal Component Analysis) for dimension reduction(for train quality)

4. Train the surrogate model with the input data and output data

  - `Input data`: Vent-hole matrix

  - `Output data1`: Drag

  - `Output data2`: Motor surface temperature(avg)

  - `Output data3`: Motor surface temperature(max)



> **Note**: `Drag(Cd)` and `surface temperature(Tavg, mean)` are conflicting objectives. So, we decided to optimize this multi-objective problem using Genetic-algorithm.



### 2.2 Generate vent hole pattern sets

![second pipeline](./assets/2.png)



1. Pattern type selection - defined by transformation group

    - `Grid`

    - `Circular`

    - `Corn`

2. Shape definition - define by closed area function(user-defined) `F(x, y) <= 0`

3. Chromosome(pattern) generation - generate vent hole pattern sets

4. Generate pattern matrix - Model's input data

  Example of generated patterns:

  ![generated patterns](./assets/pattern.png)



### 2.3 Genetic algorithm

![third pipeline](./assets/3.png)



1. Initialize population - can be customized

    - `Shape` gene distribution

    - `Pattern` gene distribution

    - population size, ...etc

2. Evaluate fitness - can be customized

    - `Fitness1`: Drag

    - `Fitness2`: Motor surface temperature(avg)

    - `Fitness3`: Motor surface temperature(max)

3. Selection - can be customized

    - `Tournament selection`

    - `Roulette wheel selection`

    - `Stochastic universal sampling`

    - `Elitism`

4. Crossover - can be customized

    - `Single-point crossover`

    - `Two-point crossover`

    - `Uniform crossover`



![fourth pipeline](./assets/4.png)



5. Mutation - can be customized

    - `Gaussian mutation`(stochastic approach)

    - Random mutation(probabilistic approach)

6. Termination - can be customized  

    - `Max generation`

    - `Population diversity`

    - `Extraordinary fitness value`

    - `...etc`



## 3. Project running



1. Clone this repository

    ```bash

    git clone [repo link]

    ```



2. Install virtual environment



    vscode based(recommended):

    - `ctrl + shift + p` -> `Python: Create environment`.

    - select `./venv` directory.

    - select python interpreter version, `3.11.0` or higher recommended.



3. Install required packages

    ```bash

    pip3 install -r packages.txt

    ```



4. Run the main script

    1. goto `experimental_template.ipynb` file(make sure you have installed jupyter notebook)

    2. adjust genetic algorithm hyperparameters

    3. run the script



        ```python

        suite = GAPipeline[VentHole](

            suite_name="exp/tournament/config", 

            # 🔼 Name of experiment, and also the directory name store/{suite_name}



            suite_max_count=50, 

            # 🔼 Maximum generation



            suite_min_population=20, 

            # 🔼 Minimum population size



            suite_min_chromosome=40, 

            # 🔼 Minimum unique chromosome size



            crossover_behavior=UniformCrossover(), 

            # 🔼 Crossover behavior



            selector_behavior=TournamentSelectionFilter(tournament_size=4), 

            # 🔼 Selection behavior



            fitness_calculator=VentFitnessCalculator( 

                # 🔼 Fitness model

                model_trainer_tuple=(

                    gpr_model_trainer,

                    gpr_model_trainer,

                    gpr_model_trainer,

                ),

                criteria_weight_list=CRITERIA_WEIGHT,

                drag_criterion=DRAG_CRITERION,

                drag_std_criterion=DRAG_STD_CRITERION,

                avg_temp_criterion=AVG_TEMP_CRITERION,

                avg_temp_std_criterion=AVG_TEMP_STD_CRITERION,

                max_temp_criterion=MAX_TEMP_CRITERION,

                max_temp_std_criterion=MAX_TEMP_STD_CRITERION,

            ),

            immediate_exit_condition=lambda x: x[0] >= 0.725 and x[1] >= 0.725, 

            # 🔼 Exit condition, extra ordinary fitness value



            mutation_probability=0.01,  # 1% 

            # 🔼 Mutation probability



            population_initializer=VentInitializer( 

                population_size=150, 

                # 🔼 Initial population size



                grid_scale=GRID_SCALE,

                grid_resolution=GRID_RESOLUTION,

                pattern_bound=GRID_BOUND,



                pattern_gene_pool=[ 

                    # 🔼 Pattern gene pool, you can adjust the gene pool

                    circular_params,

                    corn_params,

                    grid_params,

                ],



                shape_gene_pool=[ 

                    # 🔼 Shape gene pool, you can adjust the gene pool

                    circle_params,

                ],

            ),

        )

        ```

    4. Check the results

      

        Result example:

        ![result example](./assets/result.png)



      - Gene, fitness data: check `store/{suite_name}` directory

        > **Note**:

        > 

        > All the results are recorded at `store` directory by `json` format data.

        > You can visualize or analyze the results.