README

Multi-Objective Multidisciplinary Optimization of Wave Energy Converter Array Layout and Controls
Authors: Kapil Khanal, Nate DeGoede, Olivia Vitale, Maha Haji,


- Multidisciplinary formulation to optimize the configuration of the array of Wave energy converter is presented.
- Optimal controls for each Wave energy converter within the layout.
- Regression model for interpretation of the trade-off between objectives for optimal designs prompts additional analysis.
- Post optimality global sensitivity analysis of a set of optimal designs reveals that the expected variance in optimal LCOE (minimized) is very sensitive to the variance in wave frequency $(\omega)$ and interest rate.



PROJECT STRUCTURE

├── __init__.py
├─data <- All relevant data.
├──── experiments <- Single objective experiments.
│   ├─ grid.pdf,line.pdf,random.pdf
│──── mesh_convergence <- data from mesh convergence
│   ├─ convergence.csv
│──── paretos <- dominant designs objective and dvs
│   ├─ FinaldomDesign.csv , FinaldomObjective
│──── sensitivities
│   ├─ total.csv,first.csv,second.csv
├── notebooks <- nominal run --not required
├── src <- Main source code for MDO
│   ├── modules <- Each python file is a disciplinary modules.
│     ├── distances.py <- To find maximum and minimum spacing in array.
│     ├── dynamics_contrrols.py <- Calculate motion and power of the WEC.
│     ├── econ.py <- Calculates Capex / Opex and LCOE model.
│     ├── hydro_terms.py <- Get all the hydro terms. (A,B,F,C,M)
│     ├── model_nWECs.py <- Utility functions to call the multidisciplinary analysis (MDA)
│     ├── wec_array_initialization.py <- Utility function to get array of bodies and their neighbors if required.
│   ├── optimization_interfaces
│     ├── multi_objective_opt.py <- Calls the optimizer for multi Objectives [J1,J2]
│     ├── single_objective_opt.py <- Calls the optimizer for single Objective at a time.
│   ├── post_pro
│     ├── plots
│       ├── *.pdf <- contains all the figures.
│     ├── mesh_convergence.py <- Performs mesh convergence for the design 
│     ├── pareto_analysis.py <- Ad-hoc regression model for pareto design analysis
│     ├── plot_design.py <- plots any designs
│     ├── plot_pareto.py <- plots the pareto designs. 
│     ├── plot_qfactor.py <- plots the qfactor for pareto designs
│     ├── plot_sensitivity.py <- plots the sensitivity of the design to the parameters
│     ├── q.py <- Calculates thee q-factor
│     ├── sensitivity.py <- Calculates the sensitivity upto second order.
│     ├── wave_field.py <- plots the wave_field for optimal designs 
├─ requirement.txt <- All the packages.
├─ environment.yml <- All the packages.




 Note: Run code from src folder.