Cohort Level: Cohort - III

Career Goal: I either plan to work after college or pursue a master degree in either Aerodynamics or Aerospace.

Expected Graduation Date: June 1, 2022

Degree: B.E Mechanical Engineering

Research Title: On the Resilience of the Power Infrastructure of Coastal Areas with Wind Tunnel Testing and Power Outage Forecast Models

Research Synopsis: Tropical storm Maria was a Category 5 Hurricane that was formed in the North Atlantic and crossed the Caribbean in September of 2017. It was a learning lesson for coastal communities that destroyed nearly all the power transmission lines in the Island of Puerto Rico leaving more than 3 million people without power for months.

The specific aim of the research is to analyze the effects that catastrophic hurricanes such as Maria could have on Puerto Rico’s power infrastructure by identifying the transmission towers that were destroyed or damaged by the Category 5 wind speeds. By using satellite data and image recognition, it is possible to identify where and how many transmission towers failed during the hurricane. Previous studies and research teams have used Caffe and Google Earth Engine for similar purposes for image recognition. However, we implemented a machine learning code that builds its own criteria to locate the transmission towers. In addition, we reproduced similar hurricane-like scenarios through wind tunnel testing and the use of 3D models of transmission towers to assess at what point the tower fails while undergoing mechanical stress due to extreme wind conditions. This case study of Hurricane Maria and Puerto Rico will help us to improve the coastal resilience of islanded communities who are increasingly exposed to tropical storms and hurricanes.

Tropical storm Maria was a Category 5 Hurricane that was formed in the North Atlantic and crossed the Caribbean in September of 2017. It was a learning lesson for coastal communities that destroyed nearly all the power transmission lines in the Island of Puerto Rico leaving more than 3 million people without power for months.

The specific aim of the research is to analyze the effects that catastrophic hurricanes such as Maria could have on Puerto Rico’s power infrastructure by identifying the transmission towers that were destroyed or damaged by the Category 5 wind speeds. By using satellite data and image recognition, it is possible to identify where and how many transmission towers failed during the hurricane. Previous studies and research teams have used Caffe and Google Earth Engine for similar purposes for image recognition. However, we implemented a machine learning code that builds its own criteria to locate the transmission towers. In addition, we reproduced similar hurricane-like scenarios through wind tunnel testing and the use of 3D models of transmission towers to assess at what point the tower fails while undergoing mechanical stress due to extreme wind conditions.

This case study of Hurricane Maria and Puerto Rico will help us to improve the coastal resilience of islanded communities who are increasingly exposed to tropical storms and hurricanes.