Inverse Design of Wave Energy Converter Using Artificial Intelligence

By Christopher Snook
Slide 1: Title slide for Inverse Design of Wave Energy Converter Using Artificial Intelligence

Slide-1

Presented by Christopher Snook

REU Mentor: Dr. Yufei Tang

Slide 2: Methods of Design comparing Traditional Design vs Inverse Design approaches

Slide-2

Methods of Design

This slide compares two design methodologies:

Traditional Design

Process: Design Specifications → Implementation & Testing → Final Design

Good For:

  • Areas with few prior designs
  • Simple systems with describable relationships

Inverse Design

Process: Design Specifications → ML Algorithm → Final Design

Uses Prior Design Data

Good For:

  • Systems with complex relationships between parameters
  • Efficient optimization
Slide 3: Machine Learning Algorithms comparing Decision Tree and Random Forest approaches

Slide-3

Machine Learning Algorithms

Decision Tree

  • Path of decisions from input to output
  • Good for inverse design

Random Forest

  • Combines multiple decision trees
  • Generally more accurate than a decision tree

Challenge: We need a decision tree to implement inverse design but want the accuracy of a random forest

Image reference: Decision Tree vs. Random Forest diagram from Wikimedia Commons

Slide 4: Model Manipulation workflow showing 4-step process from initial data to decision tree

Slide-4

Model Manipulation

The model manipulation process consists of 4 steps:

Step 1 - Initial Data: Original dataset from simulation

Step 2 - Random Forest: High accuracy ML model

Step 3 - New Data: Large quantity (1000x size)

Step 4 - Decision Tree: Accurate and usable for inverse design

Slide 5: WEC-SIM Initial Data Generation showing RM3 wave energy converter specifications

Slide-5

WEC-SIM Initial Data Generation

WEC-SIM is software developed by National Renewable Energy Lab and Sandia National Lab.

WEC device specification

Image shows three illustrations of wave energy converter components

Relevant numerical methods

Multi-body dynamics

Hydrodynamics

PTO & mooring

WEC performance, motions, and loads

Graphs showing performance data and a colored power matrix table

Reference: WEC-Sim theory documentation available at wec-sim.github.io

Data

  • RM3 wave energy converter
  • Design variables:
    • Damping coefficient
    • Mass of float
    • Mass of base
  • Power as output
  • 10000 data points generated
Slide 6: What the Model Does diagram showing forward design training and inverse design process

Slide-6

What the Model Does

Forward Design & Training

Design Specifications → Power Output of Design

Inverse Design

Desired Power Output → Design Specification

Slide 7: Results showing efficiency and accuracy metrics of the ML models

Slide-7

Results

Accuracy

  • R² of DT trained on original data: 0.9999827285388839
  • R² of RF trained on original data: 0.9999785551484541

Efficiency

  • Initial data generation: ~7s/sample
  • Model training and analysis: 3s total
Slide 8: Conclusions summarizing project outcomes and future directions

Slide-8

Conclusions

Was this practical?

  • Inverse design worked
  • Actual results were limited due to data

What next?

  • More Data
  • More complex systems
  • Optimize ML algorithm
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Additional Information
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